# Deep analytics - Technologies for humanity, AI & Security

Sumit Chakraborty, Kusumita Chakraborty, Suryashis Chakraborty

<figure><img src="image_1.png" /></figure>

## To cite this version:

Sumit Chakraborty, Kusumita Chakraborty, Suryashis Chakraborty. Deep analytics - Technologies for humanity, AI & Security. 2020. [hal-03265284](https://hal.science/hal-03265284)

HAL Id: hal-03265284

https://hal.science/hal-03265284

Submitted on 19 Jun 2021

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

Public Domain
# Deep Analytics:

## Technologies for Humanity, AI & Security
1st Edition, 2021

By

Sumit Chakraborty

Suryashis Chakraborty

Kusumita Chakraborty

<figure><img src="image_2.png" /></figure>

Natural disaster security: Artificial rainfall, Bushfire

Food & home security: smart agriculture, nanohousing

Education security: Learning policy, methodologies

Health security: cancer, biomedical technology, epidemic

Energy & utilities security: Solar power

Logistics Security: Electrical vehicles, DAS, RTFD

Financial security: Fault attacks, Sybil attack

Social security: social networking, digital governance, HRS

Digital technologies security: E-Governance
Preface

Deep analytics does not only mean statistics or data mining or big data analytics, it
is a complex multi-dimensional analysis through ‘7-S’ model based on rational,
logical and analytical reasoning from different perspectives such as scope, system,
structure, security, strategy staff-resources and skill-style-support. This book
presents an analytical model through a consistent and systematic approach and
highlights its utility and application for reasoning the complexity of a set of
emerging technology innovations today: (a) Technology for humanity, (b) Deep
analytics - ‘7-S’ model, (c) Solar computing and self-healing mechanism, (d)
Adaptive security for SCADA & Industrial Control System, (e) Secure Multi-party
Quantum Computing, (f) Secure adaptive filter in adversarial environment, (g)
Solar power electronics & Nanotechnology, (h) Electrical and hybrid vehicles :
smart batteries, (i) RailTech security: Driver advice system & real-time fault
diagnostics, (j) Cancer prediction and prevention: deep learning, (k) Biomedical
technology for cancer care, (l) Natural disaster : epidemic and pandemic outbreak
control, (m) Artificial rainfall, laser and cloud physics, (n) Real-time moving target
search for astronomical hazards, (o) smart agriculture and nanhousing technology
for smart cities and smart villages and (p) Emerging digital technologies for social,
financial and education security. All the characters, sessions, plot and storyline of
the technologies for humanity summit mentioned in this book are imaginative.
Kusumita Chakraborty has contributed on education and social security and
Suryashis Chakraborty has contributed on social and financial security.

The reality is that every stakeholder is impacted by the challenges and opportunities
of innovation ecosystems today. The concept of technology for humanity and deep
analytics is still relatively new; it has now emerged as a powerful tool for business
analytics and a real world theme in the modern global economy. The target
audience of this book includes academic and research community, corporate
leaders, policy makers, administrators and governments, entrepreneurs, investors,
engineers, producers and directors interested in production of documentary films,
news and TV serials. We are excited to share the ideas of deep analytics with you.
We hope that you will find them really value adding and useful and will share with
your communities. It is a rational and interesting option to teach deep analytics in
various academic programmes of various Business Management programmes (e.g.
Technology Management, Human Resources Management, Information
Technology, Information Systems, Management Information Systems (MIS),
Strategic Management and Analytics for BBA, MBA, PGDM, PGDBM) and also
Electrical and Electronics Engineering (e.g. B.Tech, M.Tech, B.E.E., M.E., Ph.D.). It
is also interesting to produce TV serials, webseries and movies and organize global
summit on technologies for humanities based on the plot of this book. This e-book is
the electronic version of 1st edition; Price: ($250 per copy). This book contains
information obtained from authentic sources; sincere efforts have been made to
publish reliable data and information. Thanks and regards.

Sumit Chakraborty, Fellow (IIM Cacutta), BEE (Jadavpur University), India.
Suryashis Chakraborty, BBA.

Kusumita Chakraborty, BA (Honours, Education), MA, Ph.D., CU, India
Business Analytics Research Lab, India. 1.1.2021
Content

<table><thead><tr><td>Serial no.</td><td>SESSIONS – TECHNOLOGIES for HUMANITY &amp; GLOBAL SECURITY SUMMIT</td></tr></thead><tbody><tr><td>1.</td><td>SESSION 1: DEEP ANALYTICS - TECHNOLOGIES for HUMANITY and GLOBAL SECURITY</td></tr><tr><td>2.</td><td>SESSION 2: TECHNOLOGIES for SECURITY AGAINST NATURAL DISASTERS - ARTIFICIAL RAINFALL, ASTRONOMICAL HAZARDS EARTHQUAKE &amp; BUSHFIRE</td></tr><tr><td>3.</td><td>SESSION 3: FOOD &amp; HOME SECURITY - SMART AGRICULTURE TECHNOLOGIES, NANOHOUSING, SMART VILLAGES &amp; SMART CITIES</td></tr><tr><td>4.</td><td>SESSION 4: EDUCATION SECURITY - EMERGING TECHNOLOGIES, POLICY, METHODOLOGIES &amp; MATERIALS</td></tr><tr><td>5.</td><td>SESSION 5: HEALTH SECURITY - CANCER CARE, BIOMEDICAL TECHNOLOGY and ARTIFICIAL IMMUNE SYSTEM for EPIDEMIC CONTROL</td></tr><tr><td>6.</td><td>SESSION 6: ENERGY &amp; UTILITIES SECURITY for HUMANITY - SOLAR POWER ELECTRONICS, NANO SOLAR CELL</td></tr><tr><td>7.</td><td>SESSION 7: LOGISTICS SECURITY - ELECTRICAL &amp; HYBRID VEHICLES, SMART BATTERIES, RAILTECH SECURITY &amp; DRIVER ADVICE SYSTEM</td></tr><tr><td>8.</td><td>SESSION 8: TECHNOLOGIES for HUMANITY - FINANCIAL SECURITY &amp; PROOF OF WORKS</td></tr><tr><td>9.</td><td>SESSION 9: SOCIAL SECURITY - SOCIAL ENGINEERING, CANCER OF MIND &amp; IME TECHNOLOGIES</td></tr><tr><td>10.</td><td>SESSION 10: EMERGING DIGITAL TECHNOLOGIES for HUMANITY - INFORMATION &amp; COMMUNICATION SECURITY &amp; E-GOVERNANCE</td></tr></tbody></table>
SESSION 1: DEEP ANALYTICS - TECHNOLOGIES for
HUMANITY and GLOBAL SECURITY

Event : Technology for humanity and global security summit

Venue: Deep analytics hall, Technology park : Sanada

Time Schedule : 9 a.m. – 1 p.m., 15.8.2020

Agents : Representatives of various global organizations (nations, childcare, peace, health, bank, economic forum), Technology management experts from science and technology forums, scientists, representatives and ministers from the departments of science and technologies of developed, developing and underdeveloped countries, CEOs of global corporations, business development consultants, representatives from NGOs.

Key focus areas : Deep analytics, 7-S model, Technologies for humanity, Global security, Sustainable development goals, Economic growth, Poverty, Jobs, Environmental protection, Business model innovation, Global welfare.

Keynote speakers: Prof. Nil Bajjio, Prof. Michel Johnson, Prof. Kalyan Som, Prof. David Milla, Dr. Rojer Moore, Dr. M. Schilling, Dr. S.Chakraborty.

1. DEEP ANALYTICS

It is a sunny, windy morning. Seven hundred participants from all over the world have come to the technology park, Sanada to attend Technology for humanity and global security summit'2020. The President of Sanada has inaugurated the summit. It is an open forum; there are ten interactive brainstorming sessions; the participants are raising a set of debtable and intelligent questions on poverty, sustainable development goals, global security policy, business model innovation, economic growth and entrepreneurship. Dr. S.Chakraborty is presenting the basic overview of deep analytics. He is outlining the concept and mechanism of deep analytics to evaluate technology management in terms of seven 'S' elements (scope, system, structure, security, strategy, staff-resources and skill-style-support). He is also defining the significance of various parameters in the context of technology management such as technology security, technology classification, technology association, technology clustering, technology prediction or forecasting, innovation, adoption, diffusion, infusion and dominant design. The other objective of this session is to analyze the emerging concept of technology for humanity and select a set of emerging technologies for the sustainability of human civilization.

Deep analytics is an intelligent, complex, hybrid, multi-phased and multi-
dimensional data analysis system [Figure 1.1]. The basic steps of computation are
data sourcing, data filtering / preprocessing, data ensembling, data analysis and
knowledge discovery from data. The authorized data analysts select an optimal set
of input variables, features and dimensions (e.g. scope, system, structure, security,
strategy, staff-resources, skill-style-support) correctly being free from malicious
attacks (e.g. false data injection, shilling); input data is sourced through
authenticated channels accordingly. The sourced data is filtered, preprocessed (e.g.
bagging, boosting, cross validation) and ensembled. It is rational to adopt an
optimal mix of quantitative (e.g. regression, prediction, sequence, association,
classification and clustering algorithms) and qualitative (e.g. case based reasoning, perception, process mapping, SWOT, CSF and value chain analysis) methods for multi-dimensional analysis. The analysts define intelligent training and testing strategies in terms of selection of correct soft computing tools, network architecture – no. of layers and nodes; training algorithm, learning rate, no. of training rounds, cross validation and stopping criteria. The hidden knowledge is discovered from data in terms of collective, collaborative, machine, security and business intelligence. The analysts audit fairness and correctness of computation and also reliability, consistency, rationality, transparency and accountability of the analytics.

<figure><img src="image_3.png" /><figcaption>Figure 1.1 : Deep Analytics</figcaption></figure>

Deep analysis can process precisely targeted, complex and fast queries on large (e.g. petabytes and exabytes) data sets of real-time and near real-time systems. For example, deep learning is an advanced machine learning technique where artificial
neural networks (e.g. CNN) can learn effectively from large amount of data like human brain learn from experience by performing a task repeatedly and gradually improves the outcome of learning. Deep analytics follows a systematic, streamlined and structured process that can extract, organize and analyze large amounts of data in a form being acceptable, useful and beneficial for an entity (e.g. individual human agent, organization or BI information system). It is basically a specific type of distributed computing across a number of server or nodes to speed up the analysis process. Generally, shallow analytics use the concept of means, standard deviation, variance, probability, proportions, pie charts, bar charts and tabs to analyze small data set. Deep analytics analyze large data sets based on the concepts of data visualization, descriptive and prescriptive statistics, predictive modeling, machine learning, multilevel modeling, data reduction, multivariate analysis, regression analysis, logistic regression analysis, text analysis and data wrangling. Deep analytics is often coupled with business intelligence applications which perform query based search on large data, analyze, extract information from data sets hosted on a complex and distributed architecture and convert that information into specialized data visualization outcome such as reports, charts and graphs. In this summit, deep analytics has been applied for technology management system (TMS).

Technological innovations are practical implementation of creative novel ideas into new products or services or processes. Innovations may be initiated in many forms from various sources such as firms, academic institutions, research laboratories, government and private enterprises and individual agents. There are different types of innovations from the perspectives of scope, strength, weakness, opportunities, threats and demands from the producers, service providers, users, service consumers and regulators.

Innovation funnel is a critical issue in technology management; innovation process is often perceived like a funnel with many potential ideas passing through the wide end of a funnel but very few become successful, profitable, economically and technically feasible products or services through the development process. Deep analytics is an intelligent method and consulting tool that is essential for effective management of top technological innovations today. It is basically an integrated framework which is a perfect combination or fit of seven dimensions. Many technological innovation projects fail due to the inability of the project managers to recognize the importance of the fit and their tendency to concentrate only on a few of these factors and ignore the others. These seven factors must be integrated, coordinated and synchronized for the diffusion of top technological innovations globally.

## Deep Analytics Mechanism [DAM]

**Agents:** Single or a group of data analysts;

**System :** Technology Management System /* Technology for humanity*/

**Moves:**

* Adopt a hybrid approach : quantitative ⊕ qualitative;

* Optional choices :

* Collaborative analytics /* agents : multiple data analysts*/
* Big data

* Predictive modelling

*Objectives:* Evaluate an emerging technology for innovation, adoption and diffusion;

*Constraints:* Availability of authenticated and correct data, time, effort, cost;

*Input:* Technical data (Dt), Business data (Db); /* Entity : An emerging technology for humanity*/

*Procedure:*

* Source data (Dt, Db);

* Filter data;

* Ensemble data;

* Analyze data → select choice

* Choice 1: qualitative analysis (Perception, Case based reasoning, SWOT, TLC);

* Choice 2: quantitative analysis (Prediction, Simulation);

* Choice 3 : Hybrid (quantitative ⊕ qualitative);

* Multi-dimensional analysis → KDD (S<sub>1</sub>, S<sub>2</sub>, S<sub>3</sub>, S<sub>4</sub>, S<sub>5</sub>, S<sub>6</sub>,S<sub>7</sub>); /* S<sub>1</sub>: Technology scope, S<sub>2</sub>: System, S<sub>3</sub> : Structure, S<sub>4</sub>: Technology security, S<sub>5</sub>: Strategy, S<sub>6</sub>: Staff-resources, S<sub>7</sub>: Skill-style-support; KDD: Knowledge discovery from data */

*Revelation principle:*

* Define information disclosure policy → preserve privacy of strategic data.

* Verify authentication, authorization and correct identification in data sourcing.

* Audit fairness, correctness, reliability, consistency and rationality of analytic computation.

*Payment function* : Compare a set of technologies based on cost benefit analysis.

*Output:* Technology intelligence (collective, collaborative, security, machine, business);

Deep analytics is essential to understand the nature of a technological innovation and identify the gaps between as-is and to-be capabilities in a systematic and compelling way. It reasons seven dimensions under three major categories: (a) *Requirements engineering schema:* scope [S<sub>1</sub>]; (b) *Technology schema* : system [S<sub>2</sub>], structure [S<sub>3</sub>], security [S<sub>4</sub>] and (c) *Technology management schema* : strategy [S<sub>5</sub>], staff-resources [S<sub>6</sub>] and skill-style-support [S<sub>7</sub>] [Figure 1.1]. This session analyzes each dimension briefly and reasons a set of cases of top technology innovations today in next sessions [2-10] applying the tool of deep analytics. The basic building blocks of our research methodology include critical reviews of existing works on technology management and case based reasoning. We have reviewed various works on technology management. We have collected the data of the cases from various technical papers and secondary sources. Session 10 concludes this summit.

## 2. SCOPE

Prof. Nil Bajjio is exploring the scope of deep analytics. Technological innovation is basically associated with new product development and new process innovation, act
and initiatives of launching new devices, methods or materials for commercial and practical applications. It is one of the most critical competitive drivers in many industries such as information and communication technologies, high technology manufacturing and life-science. Deep analytics explores miscellaneous issues of top technological innovations today such as dynamics of innovation, innovation strategy and implementation process; the impact of globalization of markets and advanced information and communication technologies, computer sided design, computer aided manufacturing, flexible manufacturing system, economic feasibility, economies of scale and short production run; technology life cycle, technology diffusion; social, environmental and economic effects, negative effects of technological changes; R&D fund allocation strategy; pace, advantages and disadvantages of innovation, critical success factors, causes of failure; cost optimization and differentiation. Technological innovations are essential to create new business models. But, many innovation projects fail to make profit due to various reasons such as scope creep or ill-defined scope analysis.

<figure><img src="image_4.png" /><figcaption>Figure 1.2 : Scope analytics</figcaption></figure>

The first element of deep analytics is scope: How to define the goal of an emerging technology? The scope of a technology innovation project should be explored through various scientific and systematic methods such as sustainable goal setting, process mapping, critical success factors (CSF) analysis, value chain analysis, analysis of business objectives, constraints, requirements engineering, mission, vision and top-down and bottom-up approaches [Figure 1.2]. Process mapping analyzes a set of critical issues: what is as-is process? How to identify gaps of as-is process? How to innovate to-be process? What are the inputs, outputs, mechanism and constraint for each task associated with a business process? How to configure process flow diagram? The basic objective of CSF analysis is to identify a set of critical success factors through business model innovation, application domain and industry analysis.
The scope of a technology innovation project is explored based on CSFs. The basic objective of *value chain analysis* is to find out a set of critical parameters: what is value; it may be product differentiation, cost leadership or improved quality of services? How to define value in a technology innovation? What are the activities associated with primary and secondary value chain? Primary activities add value to a product and service directly such as manufacturing and supply chain management; secondary value chain activities (e.g. HR, Maintenance) support primary value chain activities. *Top bottom approach* analyzes business plans and goals of a firm, defines the basic needs of a system and explores the scope of technology innovation projects. On the other side, bottom up approach analyze as-is system, identifies gaps and explores the basic needs or scope of a project.

The scope of a technological innovation should be explored through *industry analysis* and also external environment and various stakeholders associated with the value chain. In this connection, *Porter's six force model* is useful to assess the bargaining power of the customers and suppliers, role of compliments, threats of new entrants and substitutes and competition. The internal environment should be accessed through SWOT analysis, identification of core competencies and rigidities, dynamic capabilities, potential strength and opportunities of sustainable competitive advantages. The scope should be also explored in terms of strategic intent, vision, mission and goals from different perspectives such as process innovation, organization learning, financial performance and customer satisfaction.

The scope of technological innovations may be analyzed from the perspectives of product or process innovation, radical or incremental, architectural or component and competence enhancing or destroying innovation. Product innovations occur in the outputs of a firm as new products or services. Process innovations try to improve the efficiency of business or manufacturing process such as increase of yield or decrease of rejection rate. Component or modular innovation changes one or more components of a product. Architectural innovation changes the overall design of a system or the way the components of a system interact with each other. Radical innovation is new and different from prior solutions. Incremental innovation makes a slight change of existing product or process.

We have explored a set of innovative concepts such as technology for humanity, cancer genomics, separating chromosomes, DNA computing, large scale cheap solar electricity and photovoltaics technology, solid state batteries, synthetic cells, next generation predictive, collaborative and pervasive analytics, big data analytics, adaptive security and dynamic data protection, secure adaptive filter, secure multi-party quantum computing, smart transformers, applied AI and machine learning, deep learning, assisted transportation, Internet of Things (IoT), cloud computing and cloud streaming, Internet of bodies, Blockchain and distributed ledger technology, homomorphic encryption, crash-proof code, social indexing, gestural interfaces, social credit algorithms, advanced smart material and devices, activity security protection, virtual reality, chatbots, automated voice spam prevention, serverless computing, edge computing, real-time ray tracing, digital twins, tablets and mobile devices in enterprise management, innovative mobile applications and interfaces for a multichannel future, human computer interface, context aware
computing and social media, enterprise app stores and marketplaces, in-memory computing, extreme low energy servers and strategic global sourcing.

The expert panel are defining technology for humanity and debating on its relevance today from the perspectives of sustainable development goals, global security policy and welfare, economic growth, poverty, new job opportunities, business model innovation, environmental pollution, skill development and talent management. They have selected a set of interesting and emerging technologies for the sustainability of human civilization. Some of these technologies are at emergence or birth phase of technology life-cycle: deep analytics, solar computing, adaptive security, secure adaptive filter and secure multi-party quantum computing. The other technologies are growing at moderate rate. Another objective of this session is to explore the concept of technology security, technology transition, technology classification, technology association, technology clustering, technology prediction and innovation, adoption and diffusion of technologies for humanity globally.

### Scope Analytics

**Agents:** System analysts, business analysts, technology management consultants;

**Objects / entities:** sustainable smart cities, smart villages, communities, smart world, smart universe;

**Moves:** Critical success factors analysis, Process mapping, Value chain analysis, Requirements management;

**Global security parameters:** define a set of sustainable development goals.

### Poverty control

* Food security (zero hunger)

* Home security (disaster proof nano-housing schema)

* Garments and consumer goods security

* Education security

* Healthcare security (good health, well being, family planning, population control)

* Financial security (banking, financial services, tax, insurance, retirement planning, stock and derivative trading, economic growth)

* Energy security (clean and affordable renewable energy)

* Utilities security (clean water and sanitation, gas, computing, internet, telecom)

* Communication security (internet, broadcast, satellite communication)

* Logistics security (travel, hospitalities, surface, water, rail, water, EVs and hybrid vehicles)

* Information, media and entertainment security

★ Social security (HR security, decent work, religious and cultural security, gender equality, child security, women's empowerment, peace, justice, partnership, regulatory compliance, strong institutions)

★ Natural disaster security (climate change, flood, drought, storm, cyclone, earthquake, volcano, snowfall, rainfall, fire, bushfire, global warming, heat wave, epidemic, astronomical hazards) (attack of wild animals, insects,
paste); artificial disaster security (defense, war, act of terrorism, bioterrorism)

★ Responsible consumption and production (Enterprise Resource Planning, Supply Chain Management)

★ Industry, innovation and infrastructure (smart cities, smart villages)

★ Life on land (environmental pollution, conservation of resources and forest, population control)

★ Life below water (marine life, water pollution, global warming, oil leakage, nuclear explosion)

Technology for humanity involves integrated strategic planning, forecasting, design, optimization, operation and control of miscellaneous technological products, processes and services for the sustainability of human civilization and to understand the dynamics of technology innovation, hype, priority, capability, maturity, adoption, diffusion, infusion, transfer, life-cycle, dominant design, spillover effects, blind spots and also the value of emerging technologies for our society. How do we define 'Technology for humanity'? In our society, there is very little discussion about what is needed to fundamentally improve our collective quality of life through fundamental rethinking and radical redesign of systems and processes. How do we evolve our societies into something more productive, more rewarding and more in harmony with our natural environment against various threats of disaster? Emerging technologies can not only improve the world in which we live, they can alter who we are as human beings and can shape and improve our quality of life. The next big tech trend is technology for humanity. It is hard to visualize a roadmap from industry, government, academia & R&D communities of what future jobs and the economy might offer to people and what society might look like. By historic measures, future predictions are mostly incorrect. We need a better balance in our thoughts in terms of fairness, correctness and rationality. There is no reason why man and machine cannot work together, with humans at the controls. There is no reason why we cannot make decent investment returns and create meaningful job opportunities through business model innovation and new technologies build communities and protect the environment from pollution. Technology for humanity is definitely about putting the human society back into technology led globalization.

2020 is the year of coming out of the hype of old, traditional, dead and obsolete technologies. Realistically, it involves the critical role of human innovators in shaping a set of emerging technologies to improve the state of humanity. Technology is an enabler, the human society need to aggressively deploy it to address the critical issues of human society globally. These are basically sustainable development goals. Global goals are a universal call to action to end poverty, protect the planet from natural disasters and environmental pollution and ensure that our society enjoy peace and prosperity through business model innovation and creating new jobs opportunities using our human and technological superpowers and imagination. It is humanity and technology working together to solve various problems, assess and mitigate risks properly. The society have to allocate and share resources (e.g. man, machine, material, method and money) rationally and optimally by trading off risk and return intelligently. The society have to learn how to make acceptable risk adjusted returns eliminating hunger and poverty, creating employment diversity at
decent wages and cleaning up the planet. The society can no longer reward behaviors and outcomes that put humanity, communities and the planet in existential jeopardy. There is no point in arguing about a few % better return on capital when half of the world is underwater.

The expert panel are exploring the scope of technologies for humanity based on global security policy and a set of sustainable development goals. What is goal? There are different types of goals such as process goals (with control), performance goals and outcome goals (with least control). Can we define a rational global security policy? What should be the goals for a rational global security policy? How can we define sustainability: is it possible to meet the needs of the present society without compromising the ability of future generations to meet their own needs? What are Sustainable Development Goals (SDGs) or global goals for the sustainability of human civilization: can we protect our planet, the Earth, even this great universe by ensuring peace and prosperity and ending poverty?

Let us analyze the rationality, fairness and correctness of global security policy? What should be the vision of future world: universal respects for human rights and dignity, the rule of law and justice for equality and non-discrimination, end of hunger and improved nutrition through food security and sustainable agriculture, ensuring healthy life-style and promoting well-being for all at all ages, ensuring inclusive and equitable quality education, promotion of lifelong learning opportunities for all, gender equality and women empowerment. This goal setting demands the commitment, self-determination and trust among all nations to take necessary actions against climate change, unemployment, poverty and environmental pollution. It is crucial to maintain global security, peace, cooperation, collaboration and equality to solve economic, social, cultural and humanitarian problems. The basic objective is to define a set of universal goals that meet the urgent economic, political and environmental challenges facing our world.

Sustainable Development Goals (SDGs) are a collection of global goals to achieve a better and more sustainable future for all within a specific timeline: no poverty, zero hunger, good health and well-being, quality education, gender equality, clean water and sanitation, affordable and clean energy, decent work and economic growth, industry, innovation, and infrastructure, reducing inequality, smart cities, villages and communities, responsible consumption and production, climate action, life below water, life on land, peace, justice, strong institutions and partnerships for goals. The goals are broad based and interdependent. Is it possible to innovate a set of emerging technologies for humanity to achieve sustainable development goals and to track and visualize progress towards the goals through a set of performance indicators?

## 3. SYSTEM

Prof. Michel Johnson is analyzing the second element of deep analytics - system [Figure 1.3]. A system is a complex grouping of interrelated parts i.e. machines and agents; it can be decomposed into a set of interacting sub-systems. A system may have single or multiple objectives; it is designed to achieve overall objectives in the best possible way. It is possible to analyze a system from the perspectives of system
state, complexity, model, environment, system dynamics, cause effect analysis, feedback loop, physical and information flows and policy decisions. A system may be open or closed loop. A hard system clearly defines objectives, decision making procedures and quantitative measures of performance. It is hard to define the objectives and qualitative measures of performance and make decisions for a soft system. The state of a system at a specific time is a set of relevant properties of the system. The complexity of a system can be analyzed in terms of number of interacting elements, number of linear and nonlinear dynamic relationships among the elements, number of goals or objectives and number of ways the system interacts with its environment. A model is an abstraction of real system. A model is isolated from its environment through model boundaries. A model may be static or dynamic, linear or non-linear based on functional relationship among various variables in a model.

<figure><img src="image_5.png" /><figcaption>Figure 1.3 : System analytics</figcaption></figure>

A complex system can be analyzed from the perspectives of different branches of engineering and technology such as information and communication technology, electrical and electronics, mechanical, civil, chemical, metallurgical, biotechnology, genetic engineering, pharmacy and others. IT system can be analyzed in terms of computing, communication or networking, data, application and security schema and also application integration (EAI). An electrical system may have various
subsystems such as power system, renewable energy, photonics, system control, power electronics, machines, measurement & instrumentation, illumination and high voltage engineering. A complex system may be associated with various domains of earth science such as space science, water, wind and solar power.

The basic objective of system analytics is to analyze complex, dynamic, non-linear and linear interactions in various types of systems and design new structures and policies to improve the behavior of a system. A system is associated with a problem oriented model; the basic building blocks of system dynamics are cause effects analysis, positive and negative feedback loops and physical and information flows.

The basic functions of system analytics include defining a problem and model boundary, building model, testing and validation of a model, model analysis, evaluation of policy alternatives and recommendation of most viable R&D policy related to technological innovations.

## System Analytics

**Agents**: System analysts, business analysts, technology management consultants;

**Objects / entities**: sustainable smart cities, smart villages, communities, smart world, smart universe;

**Moves**: Requirements engineering, system design, coding, prototype testing, erection, installation, testing, commissioning

**Emerging technologies**: Innovate a set of emerging technologies based on global security parameters and sustainable development goals. /* Refer to scope analytics, section 1 */

<figure><img src="image_6.png" /></figure>

### Poverty control

* **Food and beverage security (zero hunger)**: Automation in agricultural engineering and animal husbandries (dairy, poultry, epiculture, sericulture), biotechnology, genetic engineering (seeds), (chemical (organic fertilizer, paste controllers), electrical (solar water pump), civil, mechanical (solar power enabled tractors), food processing, digital technologies (warehouse management system, ERP, SCM, CPFR);

* **Home security**: disaster proof nano-housing schema, roof-top solar panels, civil, mechanical, metallurgical, virtual reality;

* **Garments and consumer goods security**: chemical (jacket, rain coats), textile, agriculture, process manufacturing, retail;

* **Education security**: digital technology, innovation on education policy (TQM), education methodology, education technology, education materials;

* **Healthcare security (good health, well being, family planning, population control)**: Biomedical technology (laser, surgical robotics), life-science, pharmaceutical, pharmacy, biotechnology, digital technology, artificial intelligence (artificial immune system, soft computing and machine learning, deep learning, case based reasoning), precision medicine, genomics), technology related to R&D
on biological science (biology, botany, zoology, human physiology, microscope), mechatronics;

* Financial security (banking, financial services, tax, insurance, retirement planning, stock and derivative trading, economic growth) : Digital technology, information and communication technology, computers, electrical (solar power), electronics, civil, mechanical;

* Energy security (clean and affordable renewable energy) : solar microgrid, wind power, power plant technology, mechanical, civil, digital technology (AI enabled smart grid);

* Utilities security (clean water and sanitation, gas, computing, internet, telecom): Petrochemical, chemical, electrical (solar power enabled induction cooker), water purifier, wireless communication;

* Communication security: web technology, internet, broadcast communication, satellite communication;

* Logistics security (travel, hospitalities, surface, water, rail, water): Electrical and hybrid vehicles, Automobile technology, mechanical, electrical (electrical cycles and scooters, solar power enabled battery charging), metallurgy, chemical (carbon), water cycles, water scooters, drones, electronics (sensors, global positioning system), digital (Driver advice system);

* Information, media and entertainment security : digital technology (SOC, SOA), cloud computing, quantum computing, secure adaptive filter, solar computing, soft computing, AI, deep analytics, data science, business intelligence, electronics (smart TV, smart phones);

★ Social security (HR security, decent work, religious and cultural security, gender equality, child security, women's empowerment, peace, justice, partnership, regulatory compliance, strong institutions): digital technology (E-Governance, Social networking, e-court, AI enabled legal system, case based reasoning);

★ Natural disaster security (climate change, flood, drought, storm, cyclone, earthquake, volcano, snowfall, rainfall, fire, bushfire, global warming, epidemic, astronomical hazards) (attack of wild animals, insects, paste); artificial disaster security (war, act of terrorism, bioterrorism) : Earth science, artificial rainfall, cloud physics, artificial immune system, real-time moving target search for astronomical hazards, music system;

★ Responsible consumption and production : digital technology (ERP, SCM);

★ Industry, innovation and infrastructure (smart cities, smart villages): civil, mechanical, electrical, electronics, metallurgy;

★ Life on land (environmental pollution, conservation of resources and forest, population control): environmental engineering, sensors, earth science;

★ Life below water (marine life, water pollution, global warming, oil leakage, nuclear explosion): environmental engineering, sensors, earth science, marine technology;
Let us explore requirements engineering of technologies for humanity for the people of our society and the planet, now and into the future. At its heart are the aforesaid sustainable development goals which are an urgent call for action by all developed and developing countries of the world through strategic alliance and global partnership. The fundamental building block of technologies for humanity is business model innovation - how is it possible to create new job opportunities against the threats of environmental pollution (e.g. air, water, soil, sound and light pollution)? Who are the customers and service consumers? Who are the service providers or the selling agents? What do the customers value? What should be the revenue and profit generation streams of an emerging technology? How to deliver value to the customers at appropriate cost? Following table 1.1 outlines a set of global security parameters and related emerging technologies for humanity. The next sessions (2-10) have shown the complexity analysis of these technologies in terms of scope, system, structure, security, strategy, staff-resources and skill-style-support.

## 4. STRUCTURE

Prof. David Milla is analyzing the third element of deep analytics - structure i.e. the backbone of a system associated with a specific technological innovation [Figure 1.4]. What are the basic elements of the system architecture associated with a technology innovation? It has two critical viewpoints: system architecture and organization structure. The first one considers technological aspects of the system architecture in terms of topology, smart grid and various components of industrial control system such as SCADA, Expert system, DCS, PCS, SIS, BAS and EMS. The topology of a system should be analyzed in terms of nodes, connectivity, type of connections such as P2P or multipoint, layers, interfaces between layers and organization of layers.

For example, OSI model is a layered framework for the design of communication networks of information systems. It has seven layers from bottom to top : physical, data link, network, transport, session, presentation and application layers. A data communication system has five basic components such as message, sender, receiver, transmission medium and protocol. On the basis of nodes and links, the physical topology of a communication network can be classified into four categories such as mesh, ring, star and bus. The second viewpoint is organization structure – what type of structure is suitable for specific technological innovation; it may be functional, divisional, matrix or network structure. Is there any link between technology and organization structure? It depends on the characteristics of business model.

Another view of structure should be explored in terms of organization structure, size of a firm, economies of scale in R&D, access to complementary resources such as capital and market, governance mechanisms and organizational learning. There are various types of organization structure such as divisional and networked models. The efficiency and creativity of innovation model is closely associated with different types of structural dimensions such as formalization, standardization, centralization, decentralization and loosely coupled networks within and between
firms. Global firms should consider several critical factors such as knowledge, resources and technological diffusion to conduct R&D activities.

<figure><img src="image_7.png" /><figcaption>Figure 1.4: Structure analytics</figcaption></figure>

How is it possible to represent the structure of a system associated with a technology innovation correctly and transparently? Digital twins may be an interesting solution; it integrates the concept of industrial IoT, AI, machine learning and software analytics to optimize the operation and maintenance of physical assets, systems and manufacturing processes. A digital twin is the digital replica of a living or non-living physical entity (e.g. physical asset, process, agent, place, system, device); it is expected to bridge and support data sharing between the physical and virtual entities. Digital twins can learn from multiple sources such as itself through sensors, historical time series data, experts and other nodes of the networking schema of the system and get updated continuously to represent real-time status, working conditions or positions.

The concept of digital twins are expected to be useful for manufacturing, energy (e.g. HVAC control systems), utilities, healthcare and automotive industries in terms of connectivity, digital traces and product life-cycle management. The concept can be used for 3D modeling to create digital companions of the physical objects i.e. an up-to-date and accurate copy of the properties and states of the objects (e.g. shape, position, gesture, status, motion) based on the data collected by the sensors attached to the system. It may be useful for the maintenance of power generation equipment such as turbines, jet engines and locomotives; monitoring, diagnostics and prognostics to optimize asset performance and utilization through root cause analysis and to overcome the challenges in system development, testing, verification and validation for automotive applications. The physical objects are virtualized and can be represented as digital twin models seamlessly and closely integrated in both physical and cyber spaces. Digital twins should represent the structure of a product innovation intelligently through various phases of the product life-cycle.
Another interesting technology for exploring innovative structure is V-commerce
through virtual (VR), mixed (MR) and augmented reality (AR). A virtual entity may
not exist physically but created by software in a digital environment. VR and AR
are sophisticated, creative and powerful tools to show complex structures and offer
a complete computerized digital experience by integrating AI, computer vision,
graphics and automation in various applications such as manufacturing, retail,
healthcare, entertainment, furniture and interior decoration.

Structure Analytics

Agents: System analysts, business analysts, technology management consultants;

*Objects / entities*: sustainable smart cities, smart villages, communities, smart world, smart universe;

Moves: Design and configure /* refer to scope and system analytics, sections 1 and 2 */

- Organization structure

- Technology forums, innovation research laboratories, technical and management institutes, libraries / digital libraries;

- National level : Government, E-governance model; academy-industry integration, research organizations, ;

- International level : strategic alliance among global organizations alliance (nations, heath, child, peace), joint ventures, enterprise integration;

- National, multinational and global organization;

- System architecture (topology, modules, nodes, connectivity, layers);

***Emerging technologies*** : Innovate a set of emerging technologies based on global security parameters and sustainable development goals. Construct a technology tree.

Level 1: digital technology, information technology, computer science, earth science, environmental engineering, agriculture engineering, genetic engineering, electrical, electronics, telecommunication, sensor, renewable energy, power plant, instrumentation, biomedical, biotechnology, pharmacy, chemical, mechanical, nanotechnology, mechatronics, automobile, civil, construction, architecture, chemical, petroleum, oil and gas, metallurgy;

Level 2 : Identify technology classification and technology association in the
technology tree.

- Information technology

- Computing schema

- Data schema: RDBMS, Datawarehousing, Data mining, Analytics, Data Visualization, Performance scorecard;

- Networking schema

- Application schema

- Security schema

* Electrical technology - Power electronics, Electrical machines, Power system, Renewable energy, Measurements and instrumentation, High voltage engineering, Control system, Illumination;
* Electronics - Telecommunication, Biomedical electronics, Digital electronics, Optoelectronics;

* Mechanical - Materials science, Mechatronics, Robotics, Hydraulic Engineering, Heat Engines, power plant, Automobiles;

* Civil - Construction, Architecture;

* Chemical - Inorganic, Organic;

* Metallurgical

* Healthcare engineering - Pharmacy, Biotechnology, Life science, Biomedical engineering

## 5. SECURITY

Prof. Kalyan Som is analyzing the fourth element of deep analytics - security [Figure 1.5]. What do you mean by technology-security? A system may face various types of threats from both external and internal environments but it should be vigilant and protected through a set of security policies. An emerging technology demands the support of an adaptive security architecture so that the associated system can continuously assess and mitigate risks intelligently. Adaptive security is a critical feature of a technology that monitors the network or grid associated with a system in real time to detect any anomalies, vulnerabilities or malicious traffic congestion and. If a threat is detected, the technology should be able to mitigate the risks through a set of preventive, detective, retrospective and predictive capabilities and measures. Adaptive security analyzes the behaviors and events of a system to protect against and adapt to specific threats before the occurrence of known or unknown types of malicious attacks.

Let us explain the objectives of adaptive security architecture in depth. New threats are getting originated as an outcome of technology innovation and may cause new forms of disruptions with severe impact. Today, it is essential to deploy adaptive security architecture for the emerging technologies. The systems demand continuous monitoring and remediation; traditional ‘prevent and detect’ and incident response mindsets may be not sufficient to prevent a set of malicious attacks. It is required to assess as-is system administration strategies, investment and competencies; identify the gaps and deficiencies and adopt a continuous, contextual and coordinated approach.

How to verify the security intelligence of the system associated with an emerging technology? It is essential to verify security intelligence of a technological innovation collectively through rational threat analytics at five levels: L1, L2, L3, L4 and L5 (Figure 1.5). It is essential to assess risks associated with an emerging technology and mitigate the risks by adopting a set of countermeasures. The basic building blocks of the security element are an adversary model and an intelligent threat analytics. An adversary is a malicious agent who attacks a system or a protocol; the basic objectives are to cause disruption and malfunctioning of a secure system. The security element should be analyzed in terms of the assumptions, goals and capabilities of the adversary. It is also crucial to analyze the adversary model in
terms of environment, location, network, resources, access privileges, equipments,
devices, actions, results, risks, reasons and motivations of attacks and probable
targets (i.e. why the adversary attacks and to obtain what data).

<figure><img src="image_8.png" /><figcaption>Figure 1.5 : Security analytics</figcaption></figure>

Let us consider the security of an information system innovation. At level L1, it is required to verify the efficiency of access control in terms of authentication, authorization, correct identification, privacy, audit, confidentiality, non-repudiation and data integrity. For any secure service, the system should ask the identity and authentication of one or more agents involved in a transaction. The agents of the same trust zone may skip authentication but it is essential for all sensitive communication across different trust boundaries.

After the identification and authentication, the system should address the issue of authorization. The system should be configured in such a way that an unauthorized agent cannot perform any task out of scope. The system should ask the credentials of the requester; validate the credentials and authorize the agents to perform a specific task as per agreed protocol. Each agent should be assigned an explicit set of access rights according to role. Privacy is another important issue; an agent can view only the information according to authorized access rights. A protocol preserves privacy if no agent learns anything more than its output; the only information that should be disclosed about other agent's inputs is what can be derived from the output itself. The agents must commit the confidentiality of data exchange associated with private communication.

Privacy is the primary concern of the revelation principle of an information system;
the issue of secure private communication can be addressed through the concept of
cryptography, digital signature, signcryption and secure multiparty computation.
The fundamental objectives of cryptography are to provide confidentiality, data integrity, authentication and non-repudiation. Cryptography ensures privacy and secrecy of information through encryption methods. Data integrity ensures that data is protected from unauthorized modifications or false data injection attack. The system should provide public verifiability so that anyone can verify the integrity of the data. Redundancy of data is a critical issue which is resulted through replication across the writers.

Traditionally, cryptographic solutions are focused to ensure information security and privacy. But there are other different types of security concerns. At level L2, it is required to verify the efficiency of secure multiparty computation associated with a technological innovation in terms of fairness, robustness, correctness, transparency, accountability, trust and commitment. A protocol ensures correctness if the sending agent broadcasts correct data and each recipient receives the same correct data in time without any change and modification done by any malicious agent. Fairness is associated with the commitment, honesty and rational reasoning on payment function, trust and quality of service. Fairness ensures that something will or will not occur infinitely often under certain conditions. The recipients expect fairness in private communication according to their demand plan, objectives and constraints. The sending agent expects fairness from the recipients in terms of true feedback and commitment on confidentiality of data. As per traditional definition of fairness of secure multi-party computation, either all parties learn the output or none. The system must ensure the accountability and responsibility of the agents in access control, data integrity and non-repudiation. In fact, accountability is also associated with collective intelligence. Transparency is associated with communication protocols, revelation principle and automated system verification procedures. For example, a mechanism should clearly state its goal to define a policy. There exist an inherent tension between transparency and privacy. A fully transparent system allows anyone to view any data without any provision of privacy. On the other side, a fully private system provides no transparency. Privacy can be achieved using cryptographic techniques at increased cost of computation and communication. Is it possible to trade-off privacy vs. transparency? Is it possible to provide public verifiability of its overall state without disclosing information about the state of each entity? Public Verifiability allows anyone to verify the correctness of the state of the system.

Next, it is required to verify the system performance at level L3 in terms of stability, robustness, reliability, consistency, resiliency, liveness, deadlock freeness, reachability, synchronization and safety. The performance of a system and quality of service is expected to be consistent and reliable. Reachability ensures that some particular state or situation can be reached. Safety indicates that under certain conditions, an event never occurs. Safety is a critical requirement of any system whether it may be mechanical, electrical, electronics, information technology, civil, chemical, metallurgical or instrumentation engineering. Liveness ensures that under certain conditions an event will ultimately occur. Deadlock freeness indicates that a system can never be in a state in which no progress is possible; this indicates the correctness of a real-time dynamic system. Another important issue is robustness of
a system. The delivery of the output should be guaranteed and the adversary should not be able to threaten a denial of service attack against a protocol.

At level L4, it is required to assess the risks of various types of malicious attacks by adversaries on a system such as Denial of Service (DoS), false data injection attack, sybil attack, shilling attack, coremelt attack (or network traffic congestion), blackhole, neighbor, node deletion, rushing and jellyfish attacks. At level L5, it is required to assess the risks of various types of corruptions such as agents (e.g. sending agent, receiving agents), system administrator, communication protocol and payment function of a mechanism associated with a technological innovation.

For example, prevention and detection are traditional approaches to the security of a system. In today's world of expanding threats and risks, real-time system monitoring is essential to predict new threats and automate routine responses and practices. The system should not only rely on traditional prevent-and-detect perimeter defense strategies and rule based security but should adopt cloud based solutions and open application programming interfaces also. Advanced analytics is the basic building block of next generation security protection which should be to manage an enormous volume, velocity and variety of data through AI and machine learning techniques. Intelligent analytics are expected to detect anomalous patterns by comparing with the normal profile and the activities of the users, peer groups and other entities such as devices, applications and smart networks and trigger alarms by sensing single or multiple attacks on the system. The security element must overcome the barriers among security, application development and operations teams and be integrated deeply into system architecture.

Next, it is essential to develop effective ways to move towards adaptive security architecture. The mechanism should surface anomalies and adjusts individualized security controls proactively in near real-time to protect the critical data of a system. Adaptive Security with dynamic data protection is expected to offer many benefits over traditional security platforms depending on the size of the system and complexity of networking schema – real time monitoring of events, users and network traffic; autonomous and dynamic resolutions; prioritization and filtering of security breaches; reduction of attack surface and impact or damage of a threat and reduction of resolution time. The emerging technology is expected to adapt to the needs of a system irrespective of the size of network, nature of operation or exposure of threats. It can assess the requirements of security with greater accuracy through a set of intelligent policies and procedures and can ensure better understanding of strength, weakness, opportunities and threats of the security architecture.

## Security Analytics

**Agents:** System analysts, business analysts, technology management consultants;

**Objects / entities:** sustainable smart cities, smart villages, communities, smart world, smart universe;

**Global security parameters:** define a set of sustainable development goals. /* Refer to scope and system analytics, sections 1 and 2 */
**Verification algorithm**: audit security intelligence of emerging technology innovation.

* access control: verify authentication, authorization, correct identification, privacy, audit confidentiality, data integrity and non-repudiation;

* computational intelligence: verify rationality, fairness, correctness, transparency, accountability, trust and commitment;

* system performance of SOA: verify reliability, consistency, scalability, resiliency, liveness, deadlock freeness, reachability, synchronization, safety;

* malicious attacks: verify the risk of Sybil, false data injection, shilling: push and pull, denial of service (DoS), coercion or rubber hose attack, fault injection attack, node replication wormhole, blackhole, neighbor, jellyfish, coremelt attack;

* web security: session hijack, phishing, hacking, cross site request forgery, cross site script, broken authentication, improper error handling;

* multi-party corruption: system analysts, business analysts, system administrators, innovators;

call threat analytics and assess risks of emerging technologies:

* what is corrupted or compromised (agents, computing schema, communication schema, data schema, application schema)? detect type of threat.

* time: what occurred? what is occuring? what will occur? assess probability of occurrence and impact.

* insights: how and why did it occur? do cause-effect analysis on performance, sensitivity, trends, exception and alerts.

* recommend: what is the next best action?

* predict: what is the best or worst that can happen?

**Output**: security intelligence

It is essential to verify security intelligence of emerging technologies for humanity collectively through rational threat analytics. The security intelligence is a multi-dimensional parameter which should be verified at multiple levels. Each emerging technology must be developed and audited by a group of authorized agents such as business analysts, system analysts and system administrators. The scope of BBIS should be correctly identified in terms of a set of corporate functions and performance metrics; relevant data should be sourced through authenticated raters. The system should preserve confidentiality and privacy of the raters and system administrators, nonrepudiation and integrity of data. For business performance measurement, the system should ask the identity and authentication of the raters. After correct identification and authentication, the system should address the issue of authorization. The system should be configured in such a way that an unauthorized agent cannot perform any task out of scope. The system should ask the credentials of the requester; validate the credentials and authorize the agents to perform a specific task. The raters and analysts should be assigned an explicit set of access rights according to role. Privacy is another important issue; the analysts and the raters can view only the information according to authorized access rights.
It is also crucial to verify the computational intelligence of BBIS in terms of fairness, correctness, rationality, transparency, accountability, trust and commitment. The raters are expected to provide fair and correct rating of each performance metric through proper justification, evidence, examples and case studies. The trust and commitment of the raters should be validated by the system administrator. The scorecard may be corrupted by dishonest raters, system administrators, business and system analysts, data visualization and data interpretation schema.

The web enabled scorecard must evaluate the corporate performance and the related financial impact in terms of reliability, consistency, resiliency, liveness and stability. The performance of the system is expected to be consistent and reliable. Reachability ensures that some particular state or situation can be reached. Safety indicates that under certain conditions, an event never occurs. Liveness ensures that under certain conditions an event will ultimately occur. Deadlock freeness indicates that the system should never be in a state in which no progress is possible. The scorecard should be protected from various types of malicious attacks such as false data injection attack, fault attack, denial of service attack, Sybil attack, shilling attack (push and pull attack). The raters should not be able to inject false data to push or pull the performance of an entity incorrectly. The BBIS system is expected to be an interactive system through which the raters can justify the quality of reasoning and decision making capabilities.

The scorecard is expected to be a resilient system. The resiliency measures the ability to and the speed at which the web enabled system can return to normal performance level following a disruption in survey. The system administrator face a set of challenges to solve the problem of resiliency: what are the critical issues to be focused on? what can be done to reduce the probability of a disruption? what can be done to reduce the impact of a disruption? How to improve the resiliency of the service oriented architecture? The critical steps of risk assessment are to identify a set of feasible risk metrics; assess the probability of each risk metric; assess severity of each risk metric and plot each risk metric in project vulnerability map. The critical steps of risk mitigation are to prioritize risks; do causal analysis for each risk metric; develop specific strategies for each cell of vulnerability map and be adaptive and do system monitoring.

Let us try to ask a set of basic questions to define security: what is poverty? what are the causes of poverty? what are the effects of poverty? How can we assess risks of poverty? How can we mitigate the risks of poverty? How can we create new jobs through business model innovation? How can we fight against environmental pollution? What should be rational public policy to ensure social security? Traditionally, poverty means not having enough money to satisfy the basic needs of life such as food, clothing, home, education, healthcare, energy and utilities. Many poor people may have jobs, but do not earn enough money to satisfy the aforesaid basic needs. The World Bank has defined poverty in absolute terms. The bank defines extreme poverty as living on less than US$1.90 per day and moderate poverty as less than $3.10 a day. It was as been estimated that in 2008, 1.4 billion people had consumption levels below US$1.25 a day and 2.7 billion lived on less than $2 a day. Rural poverty is often caused by poor infrastructure that hinders development, mobility and agricultural activities (e.g. access to agricultural inputs
and markets). Without roads, the rural poor people are cut off from technological
development and emerging markets in urban zones. The causes of child poverty
may be poor parents, adult poverty, government policies, lack of education,
unemployment, social services, disabilities and discrimination.

Poverty is not just lack of money; it is not having the capability to realize one's full potential as a human agent. It is a multidimensional problem and each dimension requires a different solution. There is no one gun to shoot all the problems in a simple way: the problems of healthcare or food are different from those of education or energy. Monetary dole may work to reduce desperation to some extent. The poor people should have access to essential resources of living at optimum level. But, there are several other constraints. The rich and super rich people of urban zone are often involved in overconsumption of resources. There are threats of intrusion, infiltration and migration of refugees who may consume resources of a country or a state irrationally through various ways.

Let us consider an example. The refugees of country B are migrated to country I at mass scale. Those refugees try to capture the essential resources of life of the people of country I through strategic alliance and local colony development and try to capture job market aggressively. The local people of country I become the victims of poverty and get unemployed due to the economic stress caused by the intrusion and infiltration of the refugees from country B. The problem may be extended to the migration of people from a poor, underdeveloped and unfertile state to a developed state of a country. Therefore, irrational resource consumption by the rich and super rich classes and intrusion, migration and infiltration of the refugees may be critical causal factors of poverty. Existing works on poor economics and poverty should address these issues critically. What should be the strategy for the intruders or refugees? Country I should help country B for the economic and social development through collaborative planning, emerging technology transfer and effective rehabilitation programmes for the poor refugees. Border security force may not be able to control the intrusion correctly.

The innovation, adoption and diffusion of emerging technologies are expected to
improve the growth rate of economy at macro level and profits and market shares of
the firms at micro level. For developing countries, access to emerging
technology should boost nation's economy by reducing the cost of production,
saving of labour, automation, growth of new business models, intelligent
communication system, optimal capacity utilization, allocation and distribution of
various natural resources. It is hard to fight against climate change such as global
warming Poverty increases health risks due to food insecurity; poor children may
have lower birth weight and life expectancies. It is interesting to explore the scope of
technology for humanity: how to create jobs through business model innovation and
economic growth through industrial revolution. There are other various issues of
human resource management such as minimum wage, pay equity, paid leave and
paid sick days and rational work schedules. Social security is crucial for the poor
people who are unable to satisfy the basic needs of life such as food, clothing,
housing, education, healthcare, energy and utilities. The social development occurs
if a society can apply technological advances and reflect the same to their social life.
Economic development improves the standard of living and quality of life of a
nation from low to high income economy. Science, Technology, Engineering and Mathematics (STEM) is important to ensure social, financial and information security globally today.

Poverty reduction or alleviation is a set of economic and humanitarian measures to ensure social and financial security. Can we explore and innovate a set of emerging technological solutions to ensure physical, social, information and financial security globally such as sustainable energy through solar power, improved agricultural technology and farming methods (e.g. fertilizers, pesticides, high yield seeds, water sewage), access to clean drinking water, conservation of water, increased access to education through Internet, intelligent waste management, social empowerment through advancement of information and communication technologies, improved and safe transportation (e.g. carpool, walk, cycling, bike or taking public transit), mobile healthcare, mobile banking, natural disaster management and adoption of reduce-reuse-recycle for environmental protection (e.g. reusable bags, avoiding plastic, less printing, save water and energy)?

The topmost goal of global security policy is to regulate poverty control i.e. no poverty and end poverty in all its forms everywhere by 2030. Extreme poverty has been cut by more than 50% since 1990. Still, around 1 in 10 people live on less than the target figure of international $1.25 per day. A very low poverty threshold is justified by highlighting the need of those people who are worst off. Poverty is more than the lack of income or resources: people live in poverty if they lack basic services such as healthcare, security, and education and experience hunger, social discrimination, and exclusion from decision making processes. Multidimensional Poverty Index should be used to monitor poverty globally.

Emerging and innovative agriculture technologies should be applied to end hunger through food security, improved nutrition and sustainable agriculture. It is crucial to improve agricultural productivity and incomes of small-scale food producers through sustainable food production systems and improving land and soil quality. Agriculture is the single largest employer for 40% of the global population and largest source of income for poor rural households. Women make up about 43% of the agricultural labor force in developing countries and over 50% in Asia and Africa; but own only 20% of the land. It is possible to ensure food security through advanced agriculture and food processing technologies (e.g. maintaining genetic diversity of seeds, increasing access to land, preventing trade restriction and distortions in world agricultural markets to limit extreme food price volatility, eliminating waste through International Food Waste Coalition, and ending malnutrition and undernutrition of children).

Advanced effective life-science, healthcare, biomedical engineering and biotechnologies should be applied to ensure health security through universal health coverage, access to essential medicines and vaccines, to end the preventable death of newborns and children under 5 and to end epidemics (e.g. AIDS, tuberculosis, malaria, and water borne diseases) and prevention and treatment of substance abuse, deaths and injuries from traffic accidents and from hazardous chemicals and air, water and soil pollution and contamination. Global security policy should focus on good health and well-being for people for all at all ages by increasing life
expectancy and reducing common killers of child and maternal mortality. Another
critical area is cancercare.

Emerging digital technologies (e.g. Internet, TV, Radio) should be used to provide inclusive and equitable quality education and promote lifelong learning opportunities for all. Major progress is expected in access to education, specifically at the primary school level, for both boys and girls. The number of out-of-school children has reduced from 112 million in 1997 to 60 million in 2014. Still, at least 22 million children in 43 countries miss out on pre-primary education; 103 million youth worldwide still lack basic literacy skills and more than 60 percent of those are women. Massive open online courses (MOOCs) are free open education offered through online platforms to a wider audience. Education for Sustainable Development (ESD) is defined as education that encourages changes in knowledge, skills, values and attitudes to enable a more sustainable and equitable society. It aims to empower and equip current and future generations to meet the needs using a balanced and integrated approach to the economic, social and environmental dimensions of sustainable development.

Emerging technologies should be used to ensure energy security in terms of
affordable, reliable and sustainable clean energy for all by increasing the share of
renewable energy (e.g. solar power, wind power) in the global energy mix,
improving energy efficiency and enhancing international cooperation to facilitate
more open access to clean energy technology and more investment in clean energy
infrastructure. Another challenge is to ensure drinking water supply, availability
and sustainable management of water and sanitation for all. 6 out of 10 people lack
safely managed sanitation services and 3 out of 10 lack safely managed water
services globally. Safe drinking water and hygienic toilets can protect people from
diseases due to water pollution.

Manufacturing is a major source of employment in our society. It is crucial to build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation in discrete, batch and continuous production technology, to improve manufacturing value added per capita in developing world and improve productivity and yield. Modern construction, mechanical, electrical and electronics engineering and nano-housing technologies must be applied to develop smart cities and villages and inclusive, safe, resilient, and sustainable human settlements. It is crucial to give access to safe and affordable housing in densely populated slum areas. ERP and SCM systems should be used to monitor responsible consumption and production of essential resources through various means such as eco-friendly production methods and reducing the amount of waste by recycling and reuse in developed and developing countries. We must take precautions and proper actions to combat against climate change, global warming and extreme weather conditions by regulating emissions and promoting developments in renewable energy such as solar and wind power.

Innovative biological technologies, rational and regulated business practice and advanced marine science should be used to protect life below water, to conserve and sustainably use the oceans, seas and marine resources for sustainable development, to mitigate adverse effects of increased ocean acidification, water pollution and
negative impact on marine biodiversity. Oceans cover 71% of the Earth's surface making the planet livable. Rainwater, drinking water and global climate are regulated by ocean temperatures and currents. Over 3 billion people depend on marine life for their livelihood. Oceans absorb 30% of CO2 produced by humans. The oceans contain about 200,000 species. Oceans are the world's largest sources of protein. But, the marine world is facing various types of threats such as marine pollution at shocking levels (e.g. 15 tons of plastic are released into the oceans per minute), 26% increase in acidification, death of 30 percent of marine habitats, death of 1 million sea birds and 100000 marine mammals overexploitation of 30% of world's fish stocks, Marine biodiversity is currently at stake due to extinctions, invasions, hybridizations and reductions in species.

Intelligent technologies and Geographical Information Systems (GIS) should be applied to protect life on land and to preserve biodiversity of forest, desert, and mountain eco-systems as a percentage of total land mass. It is crucial to protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. It is a hard challenge to regulate land loss due to flood and drought and protect endangered species. Emerging digital technologies i.e. information and communication technologies should be applied for peace, justice and strong institutions. It is crucial to promote peaceful and inclusive societies for sustainable development, provide access to justice for all, build effective, accountable and inclusive institutions at all levels and regulate violence, sex trafficking, forced labor, child abuse and various types of crime through real-time, online data tracking system, universal legal identity and birth registration, nationality right, civil rights and easy access to AI enabled justice and social services.

Emerging technologies should be applied to promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. Long term economic growth and infrastructure investment should be prioritized properly. Sustainable tourism may create new jobs. It is also essential to strengthen domestic financial institutions through trade related technical assistance for least developed countries and increase aid for trade support for developing countries is considered essential to economic development. An interesting sustainable development goal is to reduce income inequality within and among countries for shared prosperity through adoption of mobile commerce and digital payment technologies in banking, retail and financial services. It is also crucial to ensure gender equality through women empowerment and employment; it is not only a fundamental human right, but a necessary foundation for a peaceful, prosperous and sustainable world. It is essential to regulate child marriage.

Emerging digital technologies should be also applied to strengthen the means of implementation and revitalize the global partnership for sustainable development. It is essential to share knowledge, expertise, technology and financial support for global cooperation and public-private partnerships using digital technologies. This requires promotion of multidisciplinary and transdisciplinary research across economic, socio-political, and environmental sectors. Commitments should be
transformed into effective actions requiring a correct perception of target populations and must address all vulnerable groups such as children, elderly folks, persons with disabilities, refugees, indigenous peoples, migrants, and internally displaced persons.

## 6. STRATEGY

Dr. M. Schilling is analyzing the fifth element of deep analytics - strategy [Figure 1.6]. This element can be analyzed from different dimensions such as R&D policy, learning curve, SWOT analysis, technology life-cycle analysis and knowledge management strategy. An intelligent R&D policy should be defined in terms of shared vision, goal, strategic alliance, collaborative, collective and business intelligence. Top technological innovations are closely associated with various strategies of organization learning and knowledge management, more specifically creation, storage, transfer and intelligent application of knowledge. It is essential to analyze strength, weakness, opportunities, threats, technological trajectories, technology diffusion and dominant design of top innovations today. Diffusion is the movement of molecules from high density zone to low density zone of a solution. Can an emerging technology diffuse in the same way globally? What is the pressure acting on technology diffusion? Is the external pressure natural or artificial? Another analogy is osmosis where the molecules move from low density zone to high density zone through a barrier? Can the emerging technology spread and move from low to high density zone smoothly like osmosis or reverse osmosis?

Technological innovation is closely associated with R&D policy and organizational learning strategies in new product development and process innovation. There are various strategies of learning such as learning-by-doing and learning-before-doing. Learning by doing is effective in semi-conductor manufacturing and bio-technology sectors which demand low level of theoretical and practical knowledge. On the other side, learning-before-doing is possible through various methods such as prototype testing, computer simulations, pilot production run and laboratory experiments. It is effective in chemical and metallurgical engineering where deep practical and theoretical knowledge can be achieved through laboratory experiments that model future commercial production experience.

Let us explore the role of deep analytics on technological innovation. It is interesting to analyze the impact of different learning strategies and timing of technology transfer on product development performance, process re-engineering and R&D cost of top technological innovations. It is important to compare the effectiveness of various types of learning strategies in terms of cost, quality and time. It is also critical to analyze the relationship between process innovation and learning curve in terms of dynamic cost reduction and improvements in yield. In case of learning-by-doing, it is possible to acquire knowledge of new process development in specific production environment. But, some knowledge may be lost when a new process is transferred to commercial production environment. It is also interesting to analyze the impact of dedicated process development facilities, geographic proximity
between R&D lab and production plant and the duplication of equipment between
development and production facilities on practical implementation, speed and
effectiveness of top technological innovations. It is essential to identify the critical
success factors (e.g. resource allocation, ERP and SCM strategies) that influence the
rate of learning and superior performance.

<figure><img src="image_9.png" /><figcaption>Figure 1.6: Strategy analytics</figcaption></figure>

SWOT Analysis

<figure><img src="image_10.png" /><figcaption>Figure 1.7 : SWOT Analysis</figcaption></figure>

It is rational to evaluate strength, weakness, opportunities and threats of a technological innovation [Figure 1.7]. There may be major and minor strengths and weaknesses. Strength indicates positive aspects, benefits and advantages of a strategic option. Weakness indicates negative aspects, limitations and disadvantages of that option. Opportunities indicate the areas of growth of market and industries from the perspective of profit. Threats are the risks or challenges posed by an unfavorable trend causing deterioration of profit or revenue and losses.

***Technological life-cycle analysis*** : Deep analytics evaluate and explores top technological innovations in terms of technology life-cycle, technology trajectory, S-
curve, technology diffusion and dominant design. No element in this universe exists eternally. Similarly, each technology emerges, grows to some level of maturity and then declines and eventually expires [Figure 1.8]. It is essential to evaluate the status of each technological innovation through TLC analysis. Some technologies may have relatively long technology life-cycle; others never reach a maturity stage. Emergence of new technologies follows a complex nonlinear process. It is hard to understand how the technology life-cycle interacts with other technologies, systems, cultures, enterprise activities and impacts on society. All technologies evolve from their parents at birth or emergence phase; they interact with each other to form complex technological ecologies. The parents add their technological DNA which interacts to form the new development. A new technological development must be nurtured; many technologies perish before they are embedded in their environments. Next phase is growth; if a technology survives its early phases, it adapts and forwards to its intended environment with the emergence of competitors. This is a question of struggle for existence and survival for the fittest. Next phase is a stable maturity state with a set of incremental changes. At some point, all technologies reach a point of unstable maturity i.e. a strategic inflection point. The final stage is decline and phase out or expire; all technologies eventually decline and are phased out or expire at a substantial cost. TLC may have other different types of phases such as acquisition, utilization, and phase-out and disposal; preparation or initiation, implementation and operation; organization, directive, delegation, coordinate, collaborative, and dissolution; acquisition; emergence, diffusion, development and maturity.

<figure><img src="image_11.png" /><figcaption>Figure 1.8 : Technology life-cycle analysis</figcaption></figure>

Let us consider the analysis of the performance of a new technology vs. effort; it is basically S-curve. Initially, it is difficult and costly to improve the performance of a new technology. The performance begins to improve with better understanding of the fundamental principles and system architecture. Finally, the technology approaches its inherent limits with diminishing returns. Next, let us analyze the adoption of a new technology over time which is also an S curve. Initially, a new technology is costly for the adopters due to various uncertainties and risks. Gradually, this new technology is adopted by large segments of the market due to reduced cost and risks. Gradually, the diffusion of new technology slows with the saturation of market or due to the threats imposed by other new technologies.
The rate of improvement of a new technology is often faster than the rate of market demand over time; the market share increases with high performance. Technological change follows a cyclical pattern. The evolution of a technology passes through a phase of turbulence and uncertainty; various stakeholders of a supply chain explore different competing design options of the new technology and a dominant design emerges along with a consensus and convergence of structure. Then, the producers try to improve the efficiency and design of products based on stable benchmark of the industry. The dominant design considers an optimal set of most advanced technological features which meet the demand of the customer, supply and design chain in the best possible way.

*Technology trajectory* is the path that a technology takes through its time and life-cycle from the perspectives of rate of performance improvement, rate of diffusion or rate of adoption in the market. It is really interesting to analyze the impact of various factors and patterns of technology trajectories of top innovations today. How to manage evolution of technological innovation? The nature of innovation shifts markedly after a dominant design emerges. The pace of performance improvement utilizing a particular technological approach is expected to follow S-curve pattern. The evolution of innovation is determined by intersecting trajectories of performance demanded in the market vs. performance supplied by technologies. Technology diffusion indicates how new technologies spread through a population of potential adopters. It is controlled by characteristics of innovation, characteristics of social environment and characteristics of the adopters such as innovators, early adopters, early majority, late majority and laggards.

*Technology forecasting* : From the perspectives of technology management, the basic objectives of deep analytics involve integrated strategic planning, forecasting, design, optimization, operation and control of various technological products, processes and services to understand the dynamics of technology innovation, hype, priority, capability, maturity, adoption, diffusion, infusion, transfer, life-cycle, dominant design, spillover effects, blind spots and also the value of emerging technologies for our society. The basic objective of technology forecasting is to predict the future characteristics of emerging technologies for humanity (e.g. machines, procedures, speed, power, accuracy or precision) through a set of commonly adopted methods such as Delphi method, forecast by analogy, growth curves and extrapolation; may not have to state how these characteristics will be achieved. It is possible to combine two forecast methods (e.g. growth and trend curves) to offset the weaknesses of a method with the strengths of another method and to give the forecaster more insight.

*Dominant design* : Dominant design is a concept of technology management, identifies key technological features that become a de facto standard. The innovators must try to explore dominant design to win the market share. Dominant design may be a new technology, product or a set of key features as the outcome of a set of independent technological innovations. When a new technology emerges, different firms introduce a number of alternative designs based on incremental improvements. The dominant design enforces standardization, results economies of scale and competition starts based on cost, scale, product features and performance. Dominant design may not be better than other designs; simply incorporate a set of
key features that emerge due to technological path-dependence and not necessarily strict customer preferences. Dominant designs are expected to acquire more than 50% of the market share. The process of dominance passes through a few milestones. An innovator conducts R&D to create a new product or service or improve an existing design. The first working prototype of emerging technology sends a signal to competitors to review the feasibility of their research programs. The first commercial product is launched and directed at a small group of customers and force the competitors to review and speed up their research efforts. A clear front runner emerges from the early market. Finally, a particular technological trajectory achieves dominance.

***Technology innovation:*** What is diffusion innovation theory developed by E.M. Rogers (1962)? It explains how, over time, an idea or product gains momentum and diffuses or spreads through a specific population or social system. What is the difference between innovation and diffusion? Diffusion refers to the process by which innovations spread among the members of a social system over time in an organizations, adoption is a decision of implementing innovations based on knowledge, and persuasion of individuals within a given system. An organization seeks to improve efficiency through the adoption of innovative information and communication technologies such as better safety, competitive advantage, fewer errors, greater accuracy, higher quality products, improved communications, increased efficiency and productivity, efficient administration, and improved financial and managerial performance. What is product innovation and diffusion process? The diffusion of innovation is the process by which new products are adopted (or not) by their intended audiences. It allows designers and marketers to examine why it is that some inferior products are successful when some superior products are not. Why is diffusion of innovation important? Diffusion of innovation is responsible for the launch and spread of some of the most important advanced technologies in human history, What is technology absorption? It is the acquisition, development, assimilation and utilization of technological knowledge and capability by a firm from an external source; it occurs between transferring and receiving entities. What is technology innovation management? The basic objective is to train aspiring entrepreneurs on creating wealth at the early stages of a venture or opportunity life cycles; it focuses on the launch and growth of technology companies and growth seeking initiatives of existing companies.

What is the diffusion of innovation curve? The diffusion of innovations curve (innovation adoption curve) of Rogers is useful to understand that trying to quickly and massively convince the mass of a new controversial idea is useless. It makes more sense in these circumstances to start with convincing innovators and early adopters first. What factors influence the rate of adoption and diffusion of innovations? There are specific products and service attributes that affect the diffusion process and can influence consumer acceptance of new products and services; these factors are relative advantage, compatibility, complexity, trialability, and observability. What is technology export? High-technology exports are products with high R&D intensity such as in aerospace, computers, pharmaceuticals, scientific instruments, and electrical machinery. What are the two advantages of technology management? The advantages of
new technology is that it allows companies to automate functions that previously required employees. Tasks like data entry and analytics, bookkeeping, and contact management can be partially or completely automated, which allows businesses to work more efficiently without the risk of human errors. How is technology transferred? Transfer of technology is the process of transferring or disseminating technology from the person or organization that owns or holds it to another person or organization, Horizontal transfer is the movement of technologies from one area to another. The diffusion of an innovation is the spread of a product, process, or idea perceived as new, through communication channels, among the members of a social system over time.

What are the disadvantages of a technology? The machines are becoming more intelligent, advanced and efficient and support innovation. The disadvantages are loss of job opportunities. Can we survive without technology? Yes, for most people, technology is not something we give a second thought but some people can't live without it. For some people, the existence of technology is the difference between silence and laughter, loneliness and interaction and even life and death. How does a technology affect our life? Technology can affect life both positively and negatively. New technology always changes our life very much and takes it to a new level. It is like the new way of thinking or doing the normal things differently, better and much more faster with less hassle and at a much affordable rate.

***Technology diffusion*** : Diffusion is a physical process that refers to the net movement of molecules from a region of high concentration to one of lower concentration. The material that diffuses may be a solid, liquid or gas. A drop of ink coloring diffuses throughout the water in a glass; the smell of perfume diffuses everywhere in a room. What is diffusion mechanism? The diffusion of a particular lattice atom by a vacancy mechanism is inextricably linked to the movements of vacancies, but is something different! Others are indirect interstitial mechanisms. What is the principle of diffusion? It is the movement of a component through space under the influence of a physical stimulus. The most common cause of diffusion is a concentration gradient which tends to adjust the component concentration until it reaches equilibrium. In short, diffusion is the physical flow of material. What is the difference between imbibition and diffusion? Imbibition is a reversible process whereas diffusion is an irreversible process. Imbibition is the absorption of water by general surface whereas diffusion is the movement of solid, liquid or gaseous molecules from the region of higher concentration to lower concentration. Can we explore the concept of technology osmosis?

***Technology diffusion*** is the process by which innovations of emerging technologies are adopted by a population. The rate of diffusion depends on several factors such as nature and quality of innovation, how information about the innovation is communicated and characteristics of the population into which the technology is introduced. Why is technology diffusion important? Technology diffusion plays a major role in most of the countries today. We can increase the trade by removing the diffusion barriers since the countries achieve higher productivity by taking the technology from the diffusion process. What is meant by technology diffusion? Technological diffusion is the process by which innovations (new products, new processes or new methods) spread within and across economies. What are the steps
of diffusion? Diffusion occurs through a five step decision making process. It occurs
through a series of communication channels over a period of time among the
members of a similar social system. Rogers' five stages are awareness, interest,
evaluation, trial, and adoption of technologies. What are the five adopter
categories? Market researchers have classified consumers into five categories on the
basis of their adoption of a product during different stages of that product's life
cycle. The five adopter categories are innovators, early adopters, early majority, late
majority and laggards. What are different diffusion strategies? The goal of
any diffusion strategy is to spread the word about your innovation and encourage
users to adopt it. These strategies may also be modified and used to target
any other user group. What is rate of diffusion in technology management?
Diffusion is the process by which a new idea or new product is accepted by the
market. The rate of diffusion is the speed with which the new idea spreads from one
consumer to the next.

*Technology adoption:* Within the rate of adoption, there is a point at which an innovation reaches critical mass. The categories of adopters are innovators, early adopters, early majority, late majority, and laggards. What is the difference between diffusion and adoption process? Diffusion is a macro process concerned with the spread of a new product from its source to the consuming public. Adoption is a micro process that focuses on the stages through which an individual consumer passes when deciding to accept or reject a new product. What is the late majority? Late majority refers to the second to last segment of a population to adopt an innovative technology. The late majority accounts for roughly 34% of the population and may adopt a new product only after seeing that the majority of the population already has successfully adopted it. What is diffusion and adoption of innovation? There are various strategies of technology adoption : align technology and strategy; communicate for buy-in and engagement; perform a current systems analysis; develop training approach early and integrate technology deployment with change management; create an effective governance structure. Technology diffusion is a measure of how widely technology is spread throughout an organization. *Technology infusion*, on the other hand, is the extent to which technology permeates an area or department. Technology infusion is the process of strategically binding technical needs and potential solutions.

*Technology spillover effects:* A group of firms from emerging economies may enjoy unintentional technological benefits from R&D efforts of leading firms from advanced economies without any additional cost sharing. Successful technology spillovers depend on the absorptive capability of the receiving firms, technological gaps, interactions, information symmetry and knowledge flows between sending and receiving firms and also geographical and cultural proximity of a social process. Technology spillover is ultimately a learning experience for both sending and receiving firms. In economics, a spillover may be an economic event resulting positive or negative spillover effects. Negative spillover effects may occur when a market or economy suffers due to the slowdown in a different economy. For example, odors from a rendering plant or a flower garden are negative or positive spillover effects upon its neighbors. Spillover benefits are the opposite of spillover costs; the benefits that third parties or society receive from the actions of
others. It may be considered as third party effect. For example, the economic benefits of increased trade are the spillover effects anticipated in the formation of strategic alliances (e.g. SAARC, ASEAN).

## Strategy Analytics

Agents: System analysts, business analysts, technology management consultants;

Objects / entities: sustainable smart cities, smart villages, communities, smart world, smart universe;

Strategic moves :

Call deep analytics ‘7-S’ model; explore how to ensure a perfect fit among 7-S elements – scope, system, structure, security, strategy, staff-resources, skill-style-support;

Define a set of sustainable development goals and emerging technologies accordingly. /* Refer to scope and system analytics, sections 1 and 2 */

Business model innovation

* Who are the consumers?

* What should be the offering of products and services?

* What do the consumers value?

* What is the revenue stream ?

* How to deliver values to the consumers at rational cost?

Define R&D policy : shared vision (refer : scope analytics, section 1), sustainable development goals, collaborative intelligence, collective intelligence, business intelligence;

Do learning curve analysis – learning by doing, learning before doing;

Do SWOT analysis – strength, weakness, opportunities, threats.

Explore dominant design.

Do technology life-cycle analysis and technology spillover effects.

Explore technology innovation-adoption-diffusion strategy.

Explore innovation model and knowledge management model for creation, storage, sharing and application of knowledge.

Adopt ‘4E’ approach : Envision, Explore, Exercise, and Extend.

Let us first explain the motivation of the problem on ‘Business Analytics – Technology for Humanity’. We can consider a technology tree for the sustainable goal of human civilization based on security, global economic growth, generation of new job opportunities, elimination of poverty, business model innovation and the regulation of environmental pollution, global warming and climate change. Is it possible to ensure security of the people of the world from different perspectives such as physical safety from natural disasters and acts of terrorism, social security, financial security, food, garment, accommodation, education, healthcare, communication, culture, energy and utilities globally? We have selected a set of emerging technologies for sustainable goals : solar power, electrical and hybrid vehicles, solar computing, deep analytics, Adaptive security for Supervisory Control & Data Acquisition (SCADA) & Industrial Control System (ICS), railtech security & safety, cancer care : deep learning, precision medicine and genomics, biomedical technology for cancer care, artificial rainfall through cloud seeding, real-time
moving target search against astronomical hazards and digital technologies including secure adaptive filter and secure multiparty quantum computing, 5G-6G-7G-8G wireless communication technologies and cloud computing. With the significant advancement of information and communication technology, computing is perceived to be used as the next utility after water, electricity, gas and telecommunication. Session 15 has explored the classification of emerging digital technology through a technology tree. Digital technology is classified into communication and information technologies at level 1 of the technology tree. The scope of emerging communication technology is explored in terms of [cloud computing, cloud streaming, cloud analytics], [Internet of Things (IoT), Industrial IoT, edge computing], next generation wireless and mobile communication, broadcast and satellite communication, RFID and sensor networks at level 2. The scope of emerging information technology is explored in terms of [adaptive security, secure adaptive filter, dynamic data protection, cyber security, crash proof code]; [applied AI and machine learning, soft computing, deep learning, robotics]; [deep analytics, predictive analytics, collaborative analytics], virtual and augmented reality, digital twins, solar computing, pervasive computing, wearable computing, secure multiparty quantum computing and ray tracing.

In today's business environment of increasing globalization, rapid technological advancement and knowledge based economy, human capital should be considered as a strategic asset and a sustainable resource of competitive advantage in improving performance of a research organization. Intelligent analytics should be integrated with human resource information system (HRIS) for efficient, consistent and correct decision making of top management on various strategic issues of research and development of emerging technological innovations. Analytics is positively associated with decision taking on human resource management and can predict future workforce planning rationally. Deep analytics is the backbone of HRIS which uses information technologies to acquire, store, manipulate, analyze, retrieve and distribute strategic data for effective management of various HR functions such as workforce planning, employee benefits, administration, payroll, recruitment; induction, orientation and on-boarding, training and development, skills management, personnel administration, time management, travel management, personnel cost planning; and performance appraisal. Deep analytics is expected to combine people, information, skills, technologies, applications, and business processes to make better strategic and tactical decisions for the innovation, adoption and diffusion of emerging technologies for humanity. Intelligent multidimensional HR analytics should analyze the effectiveness of HR policy, talent acquisition and retention, skill development, incentives, reward and recognition through data visualization (e.g. reports, graphical charts, alerts, dashboards) and performance metrics).

Figure 10.1 shows the classification of emerging digital technology. This is an interesting example of technology association. Level 1 shows the classification of digital technology. Level 2 shows the technology association of various types of communication technologies such as cloud computing, cloud streaming, cloud analytics, IoT and IIoT. It is possible to explore the scope of digital technology
through Business Process Reengineering (BPR) approach (analyze as-is process and related IS, identify gaps and risks of as-is processes and IS and design to-be processes and system); top-down approach, critical success factor (CSF) analysis based on business objectives, constraints and requirements engineering, value chain analysis, bottom-up approach and inside-outside approach. The basic objective of this summit is to explore a set of fundamental questions on technology management.

**Scope:** What is technology swing? What is the scope of a technology innovation? What do we mean by **technology classification**? A **technology tree** shows the classification of technologies at various levels. For instance, it is possible to classify digital technology into communication and information technologies on the basis of computing, data, networking, application and security schema. The concept of technology classification will be explored during session 10. What do we mean by **technology association**: a set of technologies are closely associated with each other, A technology may grow with the advancement of its associated technologies. This book shows the technology association among solar power, electrical and hybrid vehicles and solar computing; another technology association has been found out among precision medicine, genomics, deep learning and biomedical technology for cancer care. We have also observed technology association among web technology, cloud computing, IoT and IIoT; adaptive security, dynamic data protection and secure multi-party computation. Please note that technology association is different from the traditional concept of technology forum. Rather it highlights the link, fit and dependencies among a set of technologies. The growth, maturity, innovation, adoption and diffusion of a technology depends on the advancement of the associated technologies. What do we mean by **technology clustering**? It indicates a cluster of technologies based on similarity. Each cluster of technologies are closely interrelated with each other. Another interesting issue is **technology forecasting**: is it possible to forecast the growth of an emerging technology using rational predictive analytics?

Next critical element is **System**: What are the basic schema and dominant design of a system associated with a technology innovation? What is **structure**; what are the basic elements of the system architecture associated with an emerging technology? How to represent the structure correctly and transparently through multi-dimensional simulated modeling such as digital twins? Another important is security What do you mean by technology security? How to verify the security intelligence of a system associated with a technology innovation? Strategy plays an important role in technology complexity analysis: What are the strategic moves of technology innovation, adoption, diffusion and infusion? What is the outcome of technology life-cycle analysis? How to compare an emerging technology with the existing old technologies through SWOT analysis? What are the technology spillover effects? What are the blind spots and critical success factors? An important demand of an emerging technology is **staff - resources**: how to exercise enterprise resource planning and supply chain management of a technology innovation project in terms of man, machine, material, method and money? What should be the talent management strategy? The final critical factor is **skill-style-support**: what are the skills, leadership style and support demanded by a technological innovation? How to manage technology innovation projects efficiently
through resource and time constrained, stochastic, adaptive multi-objective and multimode project scheduling algorithms? What should be the shared vision, common goals and communication protocols? How to ensure a perfect fit among ‘7-S’ elements? What type of organization structure is essential for various types of technology innovations? How to perform aforesaid analysis using statistical / quantitative tools? What types of tools are useful for technology management :

* Technology forecasting

* Prediction of technology failure

* Prediction of technology success

* Prediction of market share

* Technology clustering

* Technology classification

* Technology association : support and confidence

* Simulation : technology innovation, adoption, diffusion, infusion, transition

The expert panel have adopted two approaches to develop ‘7-S’ model of deep analytics for technological complexity analysis. The first approach is learning-before-doing. We have reviewed relevant literature on technology management and have defined an initial framework of deep analytics. We have reviewed on top emerging technologies today from the technical reports of Gartner, IEEE, MIT and other sources. We have observed that today, technology management demands a rational balanced approach for the benefits and sustainability of humanity globally. There is too much focus on information and communication technology; but less focus on other critical domains such as biomedical and electrical engineering, nanotechnology, earth and space science.

Next, we have selected a set of potential strategic technologies for humanity which have significant impact on our society globally. The most of these technologies are at emergence phase of TLC; the others are passing through growth phase. As a part of learning-by-doing approach; we have analyzed these emerging technologies for humanity using ‘7-S’ model and have backtracked to redefine the initial framework of deep analytics.

Finally, let us consider a set of interesting queries. How to define ‘Sustainable Development Goals’ for the sustainability of human civilization? Should we focus on a set of path breaking technologies based on top most priorities? Is there too much focus on the innovation of digital technology (e.g. ICT) ignoring the basic necessities of life? Can AI and Robotics solve all the problems of our universe? We can not deny the progress of technology today but are we at the point of saturation in innovation or lot of works are still pending? “Miles to go before I sleep; Miles to go before I sleep”. Can we fight against various types of natural disasters effectively such as flood, drought, earthquake, cyclone, storm, volcano and astronomical hazards effectively with existing technologies? Can we manage natural resources such as sunlight, wind, water, soil and others with existing technologies effectively? What should be the critical success factors to deploy technology for humanity effectively throughout the world : democracy, demography, demand, political stability and decisiveness of government? Isn’t the act of terrorism and natural
disaster a real threat to humanity? Shouldn't we fight against terrorism for the sake of humanity? Shouldn't the world be united; how to ensure unity, harmony and peace through sustainable development goals? What should be the vision of global superpower and leadership for global welfare, cooperation and collaboration?

## 7. STAFF-RESOURCES

Dr. Moore is analyzing the sixth element of deep analytics - staff-resources in terms of 5M (man, machine, material, method and money). In today's business environment of increasing globalization, rapid technological advancement and knowledge based economy, human capital should be considered as a strategic asset and a sustainable resource of competitive advantage in improving performance of a research organization. Deep analytics should be integrated with human resource information system (HRIS) for intelligent, efficient, consistent and correct decision making of top management on various strategic issues of research and development of emerging technological innovations through data visualization tools (e.g. reports, graphical charts, dashboards, alerts and performance metrics).

<figure><img src="image_12.png" /><figcaption>Figure 1.9 : Staff-resources analytics</figcaption></figure>

Deep analytics is positively associated with decision taking on human resource management and can predict future workforce planning rationally. Deep analytics is the backbone of HRIS which uses information technologies to acquire, store, manipulate, analyze, retrieve and distribute strategic data for effective management of various HR functions such as workforce planning, employee benefits, administration, payroll, recruitment; induction, orientation and on-boarding, training and development, skills management, personnel administration, time management, travel management, personnel cost planning; and performance appraisal. Workforce planning should be done based on workforce demographic data, headcount development, turnover rates, workforce cost planning and
workforce composition. It is useful for headcount planning, budgeting, recruiting, learning and continuous monitoring of performance. Workforce process analytics analyzes the effectiveness of HR processes such as payroll, employee administration, time management, jobs and organizational structures, Talent management analytics highlights skills of human resources for innovation of emerging technologies and also strategic alignment. Deep analytics is expected to combine people, information, skills, technologies, applications and business processes to make better strategic and tactical decisions for the innovation, adoption and diffusion of emerging technologies for humanity.

‘Man’ analyzes various aspects of human capital management of technological innovations such as talent acquisition and retention strategy, training, payment function, compensation, reward, incentive and performance evaluation. ‘Machine’ analyzes the basic aspects of tools and automated / semi-automated / manual machines; ‘material’ analyzes planning of raw materials, equipments, semi-finished and finished goods. ‘Method’ explores various aspects of process innovation, intelligent mechanism and procedure. Finally, ‘money’ highlights optimal fund allocation for R&D, rational investment analytics, intelligent project analytics and portfolio rationalization.

It is crucial to analyze dynamics of technological innovation in terms of sources of innovation and roles of individuals, firms, organizations, government and collaborative networks; various resources required for effective technological evolution and diffusion such as 5M i.e. man, machine, material, method and money; dominant design factors, effects of timing and mode of entry. Method is basically process innovation through process mapping : analyze as-is process; identify gaps and design to-be process. Innovation demands the commitment of creative people. Creativity is the underlying process for technological innovation which promotes new ideas through intellectual abilities, thinking style, knowledge, personality, motivation, commitment and interaction with environment.

Individual inventors may contribute through their inventive and entrepreneurial traits, skills and knowledge in multiple domains and highly curious argumentative mindset. Some users or customers or clients or private nonprofit organizations may innovate new products or services based on their own needs. Many firms set up excellent R&D lab and also collaborative networks with customers, suppliers, academic institutes, competitors, government laboratories and nonprofit organizations. Many universities define sound research mission and vision and contribute through publication of research papers. Government also plays an active role in R&D either directly or indirectly or through collaboration networks and start-ups (e.g. science parks and incubators).

A complex technological innovation often needs *collaborative intelligence* to manage the gap between demand and supply of a specific set of capabilities, skills and resources. It is possible to control cost, speed and competencies of technological innovations through efficient sharing mechanisms. It is rational to share the cost and risks of new innovations through creation, storage, transfer and application of knowledge among the partners of the innovation ecosystem. There are different modes of collaboration such as strategic alliance, joint ventures, technology licensing, outsourcing and collective research organizations. *Collaborative networks*
are other sources of innovation. Collaboration is facilitated by geographical proximity, regional technology clusters and technology spillovers. Technological spillover results from the spread of knowledge across organizational or regional boundaries; it occurs when the benefits from R&D activities of a firm spill over to other firms. But, it may be hard to control the development of product and process innovation protecting IP of proprietary technologies. The critical success factors of collaborative networks may be the right selection of innovation partners having strategic and resource fit, transparent and flexible monitoring and governance process so that the innovation partners understand their rights and obligations. Technological innovation demands the motivation and commitment of creative people. For example, the evolution of electronics and communication technology has been possible because of the involvement of the creative and efficient engineers and scientists in related domains. Most of top technology innovations are not trivial problems; need useful and novel support of creative, skilled, experienced and knowledgeable talent. Creative talent can look at the problems in unconventional ways; can generate new ideas and articulate shared vision through their intellectual abilities, knowledge, novel thinking style, personality, motivation, confidence, commitment and group dynamics. The impact of knowledge on creativity is double-edged. Lack of knowledge is a major constraint to the original contribution in a technological innovation. But, extensive knowledge may be biased and trapped in existing logic and paradigms. It is difficult to conclude that moderate knowledge is adequate for creativity. A creative person is expected to have confidence in own capabilities, tolerance for ambiguity, interest in solving problems and willingness to overcome obstacles by taking reasonable risks. A cooperative and collaborative environment must recognize and reward creative talent in time. Organizational creativity is associated with several critical factors such as human capital management, talent acquisition and retention policy, complex and tacit knowledge management strategy, organization structure, corporate culture, routines, performance evaluation, compensation, reward and incentive policy, social processes and contextual factors.

**Resource Allocation Analytics :** When the capacity of a firm is more than the total demand of a set of technological innovation projects, the resource manager may like to allocate the required resources such as fund or capital to each project using suitable resource allocation model. However, when the capacity is less than total demand, the resource manager would have to find the combination of projects, which would fit the resource allocation model and give maximum benefit. There are three different types of resource allocation protocols: linear, proportional and selective allocation.

**Linear allocation:** It is an equal sharing of the pain i.e. shortage of capacity of capital among a set of projects. If that pain exceeds the actual demand of a project, then the project becomes passive. The project $P_i$ is allocated $q_i = d_i - (1/n) \max(0, \sum_{j=1}^{n} d*_{ij} - C)$ where n is the number of active projects, C is the capacity of capital of the client.
**Proportional allocation** : The project Pᵢ is allocated qᵢ = min {d*ᵢ, C.d*ᵢ/(∑<sub>i=1</sub><sup>n</sup> d*ᵢ)}.

Here, n is the number of active projects and C is the total capacity of capital of the client. If the demand is more, more capital will be allocated to that project proportionately.

**Selective allocation** : It is basically priority based portfolio rationalization where the capital is allocated as per the priority of a set of technological innovation projects. It is an interesting problem to find the allocation of the projects while maximizing the utility of the client under capacity constraints. This is basically a knapsack problem. Let {(u₁,d*₁), (u₂,d*₂), ..., (uₙ,d*ₙ), C} be an instance of the *knapsack problem* – C is the knapsack capacity i.e. total capacity of capital of the client; (uᵢ,d*ᵢ) are respectively the utility and demand of capital of the project i. The goal is to choose a subset of projects of maximum utility with total demand of capital at most C. According to this resource capacity allocation strategy, all the projects are not treated equally. In case of any shortage of capacity, several projects may become infeasible. The projects are ranked based on utility and priority and the capital is allocated as per the rank of the projects.

The business analysts should consider a financial investment framework for optimal resource allocation and project portfolio rationalization along two dimensions: strategic objective and technology scope [Figure 1.10]. There are four cells: transformation, experiments, process improvements and renewal. Most of the technology innovation projects fall in transformation and experiments cells. The basic objectives of transformation projects are growing need of application integration, end-to-end business process re-engineering and improved support. It demands process change. But, during economic downturn, it may be a costly option. The expected benefits are efficient customer service, greater accuracy and long-term growth. The basic objectives of experiments are to adopt new business models using new technology; the expected benefits are change of organization structure, infrastructure and business process improvements. The basic objective of process improvement is to yield more profit from improved operational performance. The process owner or a functional unit realizes benefits such as short term profitability. The basic objective of renewal is to replace old shared technology with new cost effective powerful technology maintaining the existing infrastructure and keeping it cost effective. The expected benefits are improved maintainability, reduced support and efficient capacity utilization.

**Resource allocation and mobilization** are two critical aspects of project management. It is possible to call different types of logic such as linear, proportional and selective resource allocation (as stated above) subject to shortage of capacity. Each strategic project defines a set of objectives, strategies and demand plans and then the resources are allocated to different projects according to the demand plans. It is basically the principle of management by objectives (MBO) which commits the reservation of different types of financial, non-financial and human resources. The sick projects may need new investment for turnaround and renewal; the star projects may need additional fund for continuous strategic growth; the emerging projects may need capital for appropriate technology management and skill development. The old dead assets should be divested; wastage of energy, utilities,
materials and products should be minimized and existing capacity should be utilized
intelligently.

<figure><img src="image_13.png" /><figcaption>Figure 1.10 : Financial Investment Framework</figcaption></figure>

The resources are generally allocated to different business units through various types of budgeting such as capital budgeting, performance budgeting, zero based budgeting and strategic budgeting. Capital budgeting is decided based on payback period, NPV, IRR and profitability index. Zero based budgeting evaluates the particular demand and need of each project. It involves identification of decisive projects, analysis of each decisive project, ranking of the demand of each project and then allocation of resources. Strategic budgeting asks a set of fundamental questions: What is the goal of a project in terms of performance and results? What are the key activities or tasks to be done to achieve the goal? The management should be cautious of the risk of resource allocation such as limited resource capacity, competition and past commitments.

Staff-resources Analytics

Objects / entities: sustainable smart cities, smart villages, communities, smart world,
smart universe;

Global security parameters: define a set of sustainable development goals. /* refer to
scope and system analytics in sections 1 and 2 */

Do estimation, planning , capacity utilization, allocation and distribution of ‘5M’
resources.

Man (human capital management [scientists, engineers, consultants, business analysts, system analysts, project managers], talent acquisition, talent retention, training, reward and recognition);

Machine (tools, machines, computer hardware, software, internet);

★ Material (raw materials, components, equipments, semi-finished, finished and outsourced goods)

Method (process innovation, product development, R&D);
Money (optimal fund allocation, project portfolio management, investment management, capital budgeting)

8. SKILL-STYLE-SUPPORT

Prof. Nil Bajjio and Prof. Som are analyzing the seventh element of deep analytics is *skill-style-support* [Figure 1.11]. The workforce involved in top technological innovations are expected to develop different types of skills in technical, management and medical science domain such as research and development, knowledge management, new product development, process innovation, team management, design, protection of innovation, project management, supply chain management, sales and marketing, event management, construction, erection, testing, commissioning, product and service maintenance. The *intellectual rights* of technological innovations are protected through patents, trademarks, trade secrets and copyrights. The diffusion of the new technological innovation depends on the skills and capabilities of a group of firms in production, promotion and distribution of the new products and services globally.

The system administrators must have *leadership* skills in smart thinking, communication, coordination and change management. The workforce should develop skills through effective knowledge management programmes. An effective knowledge management system supports creation, storage, sharing and application of knowledge in a transparent, collaborative and innovative way. The life-cycle of a technological innovation also depends on the intelligence of marketing strategies such as branding, promotion, advertising, launching time, pricing mechanism, product quality, profit margin, compatibility and market share. It is important to analyze market segmentation, cost of advertising and promotion, reach, information content and duration of exposure.

The diffusion of top technology innovations needs the *support* of great leadership style; they are not only industry leaders but also political one. The *style* is basically the quality of leadership; the great leaders must have passion, motivation, commitment, support, coordination, integration and excellent communication skill. The leaders must be able to share a rational vision; mission and values related to top technology innovations among all the stakeholders honestly and appropriately in time. It is really challenging for the great leaders to implement top technological innovations physically and practically in the form of commercial products and services. They have to face and tackle threats from traditional industries. Top management must tackle the complexity of system implementation by developing a dedicated project team, a right mix of committed resources and talents like technical and business experts.

What should be the right *organization model* for top technological innovations? A traditional functionally centered organization model may not be suitable for supporting end-to-end business processes. Such process management is more than a way to improve the performance of individual processes; it is a way to operate and manage a business. An enterprise that has institutionalized process management and aligned management systems to support is a process enterprise. It is centered on its customers, managed around its processes and is aligned around a common,
customer oriented goal. The business models of top technological innovations require the support of a process enterprise structure enabled with advanced information and communication technology. The structure should have project, design, production, supply chain management maintenance, human resource management, sales & marketing and finance cells. The structure should be governed by an executive committee comprising of CEO and directors. The process managers should be able to identify core processes in the value chain; communicate throughout the organization about these critical processes; create and deploy measures regarding end-to-end process performance and define process owners with end-to-end authority for process design, resource procurement, process monitoring for redesign and improvement. The structure of process enterprise requires a collaborative and cooperative work culture. Top innovations need proactive, reactive and preventive support for proper technology management.

<figure><img src="image_14.png" /><figcaption>Figure 1.11: Skill-style-support analytics</figcaption></figure>

**Innovation Model**: What should be the innovation model for effective diffusion of emerging technology? Is it possible to adopt K-A-B-C-D-E-T-F model? The model is associated with following stakeholders.

* **Knowledge manager**: The innovators should acquire the basic and fundamental concept through a *differentiated* course work; classify the primary, secondary and tertiary focus areas. Mandatory courses: Innovation, creativity and research methodology; communication. The depth and breadth of the course works should be traded off rationally. It needs proper guidance.

* **Activator**: The activators should initiate the innovation process by identifying a good research problem through scope analysis. Random selection of research problem should be avoided by evaluating the strength, experience and skill of the innovators. The research problem should have potential business intelligence and social benefits.
* **Browser**: The browsers should search for information; investigate throughout the process and find relevant data or information to start innovation. They may review and analyze the existing works through traditional sources of research data (e.g. digital library, books, papers, journals, magazines, industry reports, you tubes) and also through webinars, social networking and attending seminars, workshops and conferences. Random search may result wastage of time; a systematic and planned / guided search process may lead to good results.

* **Creator**: The creators should analyze the gap and think of to-be system; generate new ideas, concepts and possibilities and search for new solutions.

* **Developer**: The developers should transform the ideas of the creation phase into good solutions; turn the ideas into deliverables, products and services. They should collaborate with different research forums, industries and experts during this phase.

* **Executor**: The executors should implement and execute the roadmap of the innovation.

* **Tester**: The testers should do various types of experiments and laboratory works; verify system dynamics and monitor the performance of the deliverables. Advanced research laboratories are required for complicated testing and experiments.

* **Facilitator**: The facilitators should define project plan, corporate governance policy, marketing plan, production plan, investment plan and cost-benefit analysis. They should be able to identify the revenue and profit making stream and fair, rational business intelligence. The government should provide financial assistance to the innovators in patent registration.

## Project Management Skill & Style

Traditional approaches to project management focus on long-term planning and stability to mitigate various risks. But, complex technology innovation project management needs a mix of traditional and agile approaches to cope with uncertainties. The intension driven role develops collaboration. The event driven role integrates planning and review with learning. The other important roles of the project managers are to prevent major disruptions and maintaining forward momentum continuously. They must acknowledge the emergence of a problem and then try to minimize the frequency and negative impact of unexpected events in a dynamic environment. They must be people, information and action oriented.

Traditional project management approach follows four steps such as definition, planning, execution and termination. But, no projects are so linear. Once project execution starts, reality may demand exception management i.e. the adjustment and amendment in the planning or definition phases. Each industry has a different profile of risk. Deep analytics is applicable to both adaptive and linear project management approaches for technology innovation. Many projects fail due to conventional approach which may not adapt to a dynamic business environment. It is very crucial to identify the scope of a project rationally through feasibility study and cost-benefit analysis. It is essential to identify the primary and secondary scopes through portfolio rationalization and analysis of priority, novelty, objectives and
constraints of a set of projects. Perception based emotional and readymade thoughts may affect the correctness of scope analysis. *Scope creep* is a serious concern in project management. It is not a simple task to tackle uncertainties and complexities in time and resource constrained project management for top technological innovations.

*Novelty* indicates how intensely new innovations are crucial aspects of a project. A project should be assessed on the scale of sophistication of technology, which may be low, medium or high. Another critical factor is the complexity of the project in terms of product, service and process. *Pace* indicates the urgency of a project which may be normal, fast, time critical or blitz. Different projects have varying degrees of newness or novelty. A derivative product development project may have low risk and few future concerns. The new version of an existing product needs detailed analysis and market research.

Breakthrough product development projects face high risks. Each project is unique, but not in every respect and may have some common features. The uncertainty in a project is a measure of the mix of new and mature technology and existing knowledge base; it may arise from technological aspects, new service offering or new market segments. High technology projects are subject to time delays, cost overruns and risks of product failure. The complexity base measures three different types of complications within a project such as assembly (low), system (medium) and array (high). High complexity requires efficient coordination and integration among various phases and systems of a project. Pace indicates a sense of urgency and time sensitivity. The failure of time critical projects results from the violation of milestone deadlines and related opportunity loss; blitz projects are crisis projects with extremely urgent timing. There are various strategies for optimal pace management such as contingency plans, alternative solutions in parallel, resilient approach and business cases to manage emergency and to overcome uncertainties and unexpected surprises.

A technology innovation project may be delivered on time and budget through the efforts, skill and professionalism of the project managers. But, it may not meet the needs of the end customers due to uncertainty and misunderstanding. The basic objective of the deep analytics is to figure out the actual structure of a project as compared with the existing capabilities, the gap and the measure of project success in terms of efficiency, impact on the customer, impact on the team, business success and preparation for the future. It is rational to take both short and long term view of a project plan since success may change during the life-cycle of a project with the change of environmental parameters and information. Does anything change from a future point of view? Does a project have sufficient flexibility to adapt to new requirements of a dynamic business environment? Are the incentives aligned properly with customer satisfaction, system performance, deadline and budget requirements? The deep analytics is useful to find the gaps between as-is and to-be requirements of a project, efficient resource planning, uncertainty and risk management. Correct use of deep analytics clearly highlights low-medium-high benefit opportunity and low-medium-high risk difficulty.

The feasibility and opportunities of a technology innovation project are estimated through real option, DEA, net present value (NPV) or internal rate of return (IRR).
But, it is hard to compute discounted cash flows due to inherent risks and uncertainties associated with an innovation of new technology. Data envelopment analysis combines qualitative and quantitative measures; it is basically a multi-criteria decision making approach.

**Project Analytics** : Classical models of resource constrained project scheduling problems are not adequate to solve real world problems due to increased complexities and uncertainties. Intelligent project analytics are essential for complex, fuzzy, stochastic, multi-mode, time and resource constrained project scheduling problems with multiple objectives. This work explores how to apply the concept of intelligent deep analytics for project management. Efficient project management requires coordination and integration among various elements. It is essential to define the scope of a project correctly through feasibility study, priority and cost-benefit analysis. It is a common practice to launch new projects with fake promises before an election. The society should be alert of such corruption.

**Project Performance: KPIs and Data Visualization Strategy:** It is essential for an efficient project manager to understand critical metrics and key performance indicators (KPI) and how to identify, measure, analyze, report and manage for the success of a project. KPIs and metrics are displayed in dashboards, scorecards and reports. Project metric is generic but KPI is specific. KPIs give early warning signs of poor project performance if the problems are not addressed appropriately. The project success is measured in terms of time, cost, performance and customer satisfaction. It is difficult to measure and monitor too many project performance metrics. Therefore, it is essential to consider optimal number of performance metrics and KPIs. It is possible to classify the performance metrics and KPIs into four categories.

*Category 1 [Operation]* : scope creep, project completion stage, flexibility, quality, cost, time, inventory, customer satisfaction; this category is associated with project success and element S₂ and S₃.

*Category 2 [Finance]* : revenue growth rate, cost reduction, profitability, ROI, payback period, NPV; this category is associated with element S₃.

*Category 3 [Human Resources (HR)]* : performance, productivity, capacity utilization, skill; this category is associated with element S₃.

*Category 4 [Security intelligence]* : It is essential to audit fairness and correctness (i.e. accuracy of estimate and measurement) of project plan computation and adjustment as per exceptions based on rationality.

## Skill-style-support Analytics

**Objects / entities:** sustainable smart cities, smart villages, communities, smart world, smart universe;

**Global security parameters:** define a set of sustainable development goals. /* refer to scope and system analytics in sections 1 and 2 */

**Skill:** Knowledge mgmt. Technical, System admin, Management, Legal, Governance, ERP & SCM
Style : Leadership, Shared vision, Communication protocol, Risk assessment & mitigation, Organization learning;

Support : Proactive, preventive and reactive support, system maintenance,

## 9. OPEN AGENDA

The expert panel have found out several open research agendas on aforesaid deep analytics and ‘7-S’ model:

* Should We consider any additional elements for the proposed deep analytics which can streamline the evolution and diffusions of various technological innovations effectively? For instance the deep analytics may be 10-S model instead of 7-S in figure 1.1.

* Is it practically manageable to consider too many elements simultaneously? Should we consider resources instead of staff-resources? Should we decompose sixth element into skill, style and support separately? Should we position the element ‘system’ centrally or ‘strategy’ in figure 1.1?

* There are $ ^7C_2 $ links (such as S1-S7, S2-S7,S3-S7,S4-S7, S5-S7, S6-S7,...) among 7-S elements of the deep analytics; what are the implications of these links on complex technological innovations? There may be other various types of links considering $ ^7C_3, ^7C_4, ^7C_5 $ and $ ^7C_6 $ combinations (S1-S2-S3, S4-S5-S6-S7,...).

* How do these elements of deep analytics impact technological innovation in terms of technology trajectory, spillover, dominant design and organizational learning process?

* How to foster creativity, problem solving capability, learning rate, generalizability, convergence and stopping criteria in organizational learning?

* What are the major constraints and barriers against the innovation, adoption and diffusion of technologies for humanity? There are several open issues of debate on the concept of deep analytics. It is an interesting observation from this book that we are living in 21st century today; but we could not reach the point of saturation of technology management at present; extensive R&D efforts are still pending for the sustainability of human civilization in the coming future.

## FURTHER READING

* P. Attewell. 1992. Technology diffusion and organizational learning: the case of business computing, Organ. Sci., 3(1).

* Basalla, G. 1988. The Evolution of Technology, Cambridge University Press, New York.

* E.M. Rogers. 1995. Diffusion of Innovations, 4th ed., Free Press, New York.
* M.W. Cardullo.1996. Introduction to Managing Technology, Vol. 4, J. A. Brandon, ed., Engineering Management Series, Research Studies Press, Taunton, England.

* D.I. Cleland and W.R. King. 1983. Systems Analysis and Project Management, McGraw-Hill, New York.

* N.W. Hatch and D.C.Mowery. 1996. Process Innovation and Learning by Doing in Semiconductor Manufacturing. Research policy, 25, 1097-1119.

* G. P. Pisano. 1996. Learning-before-doing in the development of new process technology. USA .

* W.B.Hirschmann. 1964. Profit from the learning curve. Harvard Business Review. 42(1)125-139,

* M. Kilbridge, M. 1962. A model for industrial learning. Management Sci. 8.

* G.P. Pisano. 1997. The Development Factory: Unlocking the Potential of Process Innovation. Harvard Business School Press, Boston, Massachusetts.

* M.E.Porter. 1980. Competitive Strategy. Free Press, New York.

* J.D.Teece, G, Pisano and A. Shuen. 1997. Dynamic capabilities and strategic management. Strategic Management. 18(7) 509-533.

* P.Adler and K. Clark. 1991. Behind the learning curve. Management Science 37(3), 267-281.

* K.Ulrich and S. Eppinger. 1995. Product Design and Development. McGraw-
  Hill, New York.

* E.Hippel and M. Tyre. 1995, How the 'learning by doing' is done: problem identification in novel process equipment. Research Policy 24(1), 1-12.

* Richard C. Dorf (Ed.).2000. Technology Management Handbook. Boca Raton: CRC Press.

* R. Adner. 2006. Match Your Innovation Strategy to Your Innovation Ecosystem. Harvard Business Review. April.

* R. Adner. 2002. When are technologies disruptive: a demand-based view of the emergence of competition. Strategic Management Journal 23(8): 667–688.

* R. Adner and D. Levinthal D. 2001. Demand heterogeneity and technology evolution: implication for product and process innovation. Management Science 47(5):611–628.

* R. Adner and P.Zemsky. 2002. Strategy dynamics through a demand-based lens: the evolution of market boundaries, resource rents, and competitive positions. INSEAD working paper series 2003/01/SM.

* R.Adner and P.Zemsky. 2005. Disruptive technology and the emergence of competition. Rand Journal of Economics 36(2): 229–254.

* J.M.Utterback and W. Abernathy. 1975. A dynamic model of process and product innovation. Omega 3(6):639-656.

* B. Wernerfelt. 1984. A resource-based view of the firm. Strategic Management Journal 5(2): 171–180.

* M.A.Schilling. 2017. Strategic management of technological innovation. McGraw-Hill Education.
* M.A.Schilling. 2015. Towards dynamic efficiency: Innovation and its implications for antitrust Antitrust Bulletin.

* M. A. Schilling and C. E. Vasco. Product and Process Technological Change and the Adoption of Modular Organizational Forms. in Winning Strategies in a Deconstructing World, eds. R. Bresser, M. Hitt, R. Nixon, and D. Heuskel (Sussex, England: John Wiley & Sons, 2000), pp. 25–50.

* H. A. Simon. 1973. Technology and Environment. Management Science 19 (1973), pp. 1110–21.

* H. Chesbrough. 2003. Open Innovation: The New Imperative for Creating and Profiting from Technology, Harvard University Press, Boston.

* M.A.Schilling and C.Phelps. 2007. Interfirm Collaboration Networks: The impact of Large-scale Network Structure on Firm Innovation. Management Science 53, pp. 1113–1126.

* M. Boden. 1992. The Creative Mind: Myths and Mechanisms. New York: Basic Books, 1992.

* R. J. Thomas. 1995. New Product Success Stories: Lessons from Leading Innovators, John Wiley & Sons, NY.

* E. Roberts. 2001. Benchmarking Global Strategic Management of Technology. Research Technology Management, March–April, pp. 25–36.

* M. Dodgson. 2000. The Management of Technological Innovation. Oxford University Press, NY.

* A.B. Jaffe. 1986. Technological Opportunity and Spillovers of R&D: Evidence from Firms' Patents, Profits and Market Value. American Economic Review 76, pp. 984–1001.

* J. Sterman. 1983. Economic vulnerability and the energy transition, Energy Systems and Policy 7(4), 259-301.

* J.Sterman and J. Wittenberg. 1989. Path dependence, competition, and succession in the Stewart, I., Does God Play Dice? The Mathematics of Chaos. Cambridge, MA: dynamics of scientific revolution, Organization Science 10(3), 322-341

* J.W.Forrester. 1985. The model versus a modeling process, System Dynamics Review 1(1), 133-134.

* J.W. Forrester. 1968. Principles of systems. MIT Press, Cambridge.

* D. Kim and P. Senge. 1994. Putting systems thinking into practice, System Dynamics Review IO(2-3), 277-290.

* P. Senge. 1990. The Fifth Discipline: The Art and Practice of the Learning Organization. New York Doubleday.

* P. Senge and J. Sterman. 1992. Systems thinking and organizational learning: Acting locally and thinking globally in the organization of the future, European Journal of Operational Research 59(1), 137-150. (eds.) (1994) Modeling for Learning Organizations. Portland, OR: Productivity Press.

* P.K.J. Mohapatra and P. Mandal. 1989. System dynamics paradigm, Decision, 16(no. 4):251-266.
* A. E. Plaza. 1994. Case-based reasoning: foundational issues, methodological variations and system approaches, AI Communication. 7 (1), March, 39–59.

* D.B. Leake (Ed.). 1996. Case-Based Reasoning, MIT Press, Cambridge, MA, 1996.

* T.W.Malone, R.Laubacher and C.Dellarocas. 2010. The Collective Intelligence Genome. MIT Sloan Management Review. Spring, volume 51, no. 3.

* S.L.Epstein. 2015. Wanted : Collaborative Intelligence. Artificial Intelligence 221, 2015, 36 - 45.

* Averbakh. 2010. Nash equilibria in competitive project scheduling. European Journal of Operational Research 205, 552–556.

* W.Herroelen and R.Leus. 2005. Project scheduling under uncertainty: survey and research potentials. European Journal of Operational Research, 165, 289–306.

* A.J.Shenhar. 2001. One size does not fit all projects : exploring classical contingency domains. Management Science. Vol. 47 no. 3, pp. 394 - 414. March.

* H.Kerzner and C. Belack.2010. Managing Complex Projects, John Wiley & Sons and the International Institute for Learning (IIL) Co-publishers.

* H.Kerzner, 2006. Project Management Best Practices; Achieving Global Excellence, Hoboken, NJ:John Wiley & Sons Publishers.

* J.W.Ross and C.M.Beath. 2002. Beyond the business case: new approaches to IT investment. MIT Sloan Management Review. Winter.

* R.H.Waterman, T.J.Peters and J.R. Phillips. 1980. Structure is not organization. Business Horizons. June.

* S. Chakraborty and S.K. Sharma. 2007. Enterprise Resource Planning: An Integrated Strategic Framework. International Journal of Management and Enterprise Development, Volume 4, No. 5.

* B.E. Becker and M.A. Huselid. 2006. Strategic human resource management: Where do we go from here? Journal of Management 32(6), 898–925 (2006).

* A. Mishra and I. Akman, 2010. nformation Technology in Human Resource Management: An Empirical Assessment. Public: Personnel Management 39(3), 271–290.

* N.Kashive. 2011, N.: Managing Today’s Workforce: Human Resource Information System (HRIS), Its challenge and Opportunities. International Journal of Research in Finance & Marketing 1(6), 38–66.

* T.S.Teo, G.S. Lim and S.A.Fedric. 2007. The adoption and diffusion of human resources information systems in Singapore. Asia Pacific Journal of Human Resources 45(1), 44–61.

* M.,G. Martinsons. 1994. Benchmarking human resource information systems in Canada and Hong Kong. Information and Management 26(6), 305–316.

* Sachs, J., Schmidt-Traub, G., Kroll, C., Lafortune, G., Fuller, G. (2019): Sustainable Development Report 2019. New York: Bertelsmann Stiftung and Sustainable Development Solutions Network (SDSN)
* dpicampaigns. "About the Sustainable Development Goals". United Nations Sustainable Development.

* "United Nations Official Document". www.un.org.

* "Transforming our world: the 2030 Agenda for Sustainable Development". United Nations – Sustainable Development knowledge platform. UN2030 agenda (adopted at UN Summit in New York on 25-27 September 2015) by 2030 as per UN Resolution 70/1, 2030 agenda.

* "Global Goals | Policy and advocacy". Sightsavers. 25 September 2017.

* "17Goals – The SDG Tracker: Charts, graphs and data at your fingertips". Retrieved 10 March 2019.

* "The History of Sustainable Development in the United Nations". Rio+20 UN Conference on Sustainable Development. UN. 20–22 June 2012.

* Development, World Commission on Environment and. "Our Common Future, Session 2: Towards Sustainable Development - A/42/427 Annex, Session 2 - UN Documents: Gathering a body of global agreements". www.un-documents.net. 2017.

* "World leaders adopt Sustainable Development Goals". United Nations Development Programme.

* "Extreme poverty is falling: How is poverty changing for higher poverty lines?". Our World in Data.

* Ortiz-Ospina, Esteban; Roser, Max (25 May 2013). "Global Extreme Poverty". Our World in Data.

* Fan, Shenggen and Polman, Paul. 2014. An ambitious development goal: Ending hunger and undernutrition by 2025. In 2013 Global food policy report. Eds. Marble, Andrew and Fritschel, Heidi. Session 2. Pp 15-28. Washington, D.C.: International Food Policy Research Institute (IFPRI).

* WHO and UNICEF (2017) Progress on Drinking Water, Sanitation and Hygiene: 2017 Update and SDG Baselines. Geneva: World Health Organization (WHO) and the United Nations Children's Fund (UNICEF), 2017.

* Staples, D., & Hermes, R. (2012). Marine biodiversity and resource management – what is the link? Aquatic Ecosystem Health & Management, 15(3), 245–252. doi:10.1080/14634988.2012.709429

* Vierros, M. (2017). Global Marine Governance and Oceans Management for the Achievement of SDG 14. UN Chronicle, 54(1/2), 1.

* Metcalfe, K., Collins, T., Abernethy, K. E., Boumba, R., Dengui, J., Miyalou, R., ... Godley, B. J. (2017). Addressing Uncertainty in Marine Resource Management; Combining Community Engagement and Tracking Technology to Characterize Human Behavior. Conservation Letters, 10(4), 459–469. doi:10.1111/conl.12293.

* van Putten, I. E., Plagányi, É. E., Booth, K., Cvitanovic, C., Kelly, R., Punt, A. E., & Richards, S. A. (2018). A framework for incorporating sense of place into the management of marine systems. Ecology & Society, 23(4), 42–65. doi:10.5751/ES-10504-230404
* Hughes, Z. D., Fenichel, E. P., & Gerber, L. R. (2011). The Potential Impact of Labor Choices on the Efficacy of Marine Conservation Strategies. PLoS ONE, 6(8), 1–10. doi:10.1371/journal.pone.0023722

* Finkl, C. W., & Makowski, C. (2010). Increasing sustainability of coastal management by merging monitored marine environments with inventoried shelf resources. International Journal of Environmental Studies, 67(6), 861–870. doi:10.1080/00207230902916786

* Bhargava, A. (2019). "Climate change, demographic pressures and global sustainability", Economics and Human Biology, 33, 149–154.

* Firzli, Nicolas. "Smart Capital and Sustainable Finance in the Sino-American Century", 16 December 2019, Private Debt Investor.

* Lietaer, Bernard (2019). "Towards a sustainable world - 3 paradigms to achieve", available as of Oct.31, 2019 ISBN 978-3-200-06527-7. Discusses "the law of sustainability" presented with Robert E.Ulanowicz and Sally J.Goerner.

* Wilson, Clive (2018). "Designing the Purposeful World - the Sustainable Development Goals as a blueprint for humanity" Routledge.

## Quiz

* What is the technology swing and the scope of technology innovation? How can you define the goal of an emerging technology?

* What is the dominant design of this technology innovation?

* What are the basic elements of the system architecture associated with the technology innovation? How to represent the structure correctly?

* What do you mean by technology_security? How to verify the security intelligence?

* What are the strategic moves of technology diffusion?

* What is the outcome of technology life-cycle analysis?

* How to compare an emerging technology with the existing old technologies through SWOT analysis?

* What are the technology spillover effects?

* What are the blind spots and critical success factors?

* How do you estimate resource allocation for emerging technology management? What should be the talent management strategy? What do you mean by process innovation?

* What are the skills, leadership style and support demanded by a technological innovation?

* How to manage technology innovation projects efficiently?

* What should be the shared vision, common goals and communication protocols?

* How to ensure a perfect fit among ‘7-S’ elements?

* What type of organization structure is essential for various types of technology innovations?
* Can You think of any additional elements for the proposed deep analytics which can streamline the evolution and diffusions of various technological innovations effectively? Is it practically manageable to consider too many elements simultaneously? Is it appropriate to consider resources instead of staff-resources? Is it rational to decompose sixth element into skill, style and support separately? Can You validate to position the element 'system' or 'strategy' centrally in 1.1?

* There are $ ^7C_2 $ links (such as S1-S7, S2-S7,S3-S7,S4-S7, S5-S7, S6-S7,...) among 7-S elements of the deep analytics; what are the implications of these links on complex technological innovation, adoption and diffusions?

* There may be other various types of links considering $ ^7C_3, ^7C_4, ^7C_5 $ and $ ^7C_6 $ combinations (S1-S2-S3, S4-S5-S6-S7,...). Can you explore any interrelationship among these different combinations of elements?

* Please study the following case and analyze 'support' element of deep analytics. It is a real case; the names of the company and customer have been changed to preserve the privacy of the entities.

“
From: A chakraborty
Sent: Thursday, October 24, 2019 5:44 AM
To: customer.connect@oorientelectric.com
CC: akashelectricalitsamiteshrai@gmail.com
Subject: Quality problem of Oorient fan - C-19102400203
To,
The Manager (Customer Care),
Oorient Electric Limited, 240, Hokhla Industrial Estate, Phase - III, New Delhi -
210020, Imdia
Email - customer.connect@oorientelectric.com
CC: Prakash Electricals, P-37, India Exchange Place, Kolkata -1; Email :
akashelectricals@gmail.com; contact - 3986-1593 / 50241593
Subject: Quality problem of Oorient fan - C19102400203
Invoice no.: PI/9446, 30.7.2019
Customer complaint reference: C-19102400203, 24.10.2019
Product / item: 16” Oorient stand 37 Trendzz fan
Respected Sir,

I purchased an Oorient fan of the aforesaid model on 30.7.2019 from Akash Electricals, Koolkata. Last one month, I have been experiencing a problem related to the fan about 10 times. After running sometime, the fan has been getting off automatically. Again, the fan start rotating automatically after sometime.

I don't know whether this is the problem of heating of motor of the fan. Today, I called Customer helpline no. 18001037575 and lodged a complaint. Previously, I informed but your service staff did not come in time (complaint ref.: 419073001645). Request you to take necessary corrective actions on immediate basis. I do not expect such type of quality problem from Oorient product.

Regards.

Amit Chakraborty
## Email 2

Feedback : Quality problem of Oorient fan – C-19102400203
achakraborty 2013@hotmail.com Fri 10/25/2019 3:56 PM

To: customer.connect@oorientelectric.com
Cc: akashelectricals@gmail.com; sec.cad-wb@nic.im secy-ca@nic.im; cimoffice@nic.im; connect@mygov.nic.im
My reference : E-mail dated 24.10.2019

To, The Manager (Customer Care), Oorient Electric Limited,
Email - customer.connect@oorientelectric.com

CC: Akash Electricals, P-37, Imdia Exchange Place, Koolkata -1

CC: Ministry of Consumer Affairs; Govt. of Imdia.
Respected Sir,

Your service technician, Mr. S. Mandal (Mobile no. 9230040544) visited my place around 16-00 today and serviced the Oorient fan against the aforesaid complaint. I have following feedback to You on this service.

1. The service technician replaced the coil of my new fan with another coil he brought. He diagnosed that the coil of the motor of my Trendzz fan is malfunctioning. Is it a good quality of product which is malfunctioning within 3 months of purchase from an authorized Oorient dealer in Koolkata?

2. I don't know the details of the spare parts i.e. the details of the replaced coil: is it a new coil suitable for Oorient stand 37 Trendzz fan? I don't think so. The coil looks old and the color of the paint is missing at some places. I think, the coil is of a table fan, not of stand Trendzz model.

3. The service technician collected the xerox copy of the invoice and warranty card but did not sign the details of this service anywhere on my warranty card or any service document. He has not given me any document stating the details of spare parts replaced.

I do not know what should be the feedback of the aforesaid service – excellent, good or poor. It depends on the product life cycle of the fan. The demand of the consumers is shrinking due to quality problems of products and related service issues in our country today.

<figure><img src="image_15.png" /></figure>

* Define global security policy based on a set of sustainable development goals. What are the goals to protect our planet?

* Define technology for humanity. Select a set of emerging technologies to ensure global security policy.

* Please study the following case study. Discuss the role of technologies to regulate poverty and to achieve social, financial and physical security of human society globally.
## Case Study: Poverty, Intrusion and Enterprise Resource Planning

Let us look at the problem of poverty of a group of neighboring countries: I, B, P, N, S and C. The ministries of finance and economic affairs, home, defense and foreign affairs of country I are brainstorming how to control poverty, create more job opportunities through business model innovation and countermeasures against environmental pollution. The points of discussion are as follows :

* Is poverty really the outcome of luxurious passion and fashion of the economists and public policy makers globally today?

* Is it rational to copy the concept of chemical equilibrium into economic equilibrium to maintain the stability of the global economy?

* Should the government of country I be indifferent to regulate intrusion, migration and infiltration of the refugees from neighbors (e.g. B, P, N) to control poverty and unemployment being provoked by the wise sayings like tolerance, unity in diversity, collaborative intelligence, humanity, actions against promotion of hate crimes and vote bank politics? What are the risks and countermeasures to avoid economic stress in job market and optimal fair resource distribution in country I today? Is it not essential to adopt a strict immigration policy, citizen amendment bill (CAB), national registry of citizens (NRC) and ERP system to curb the problem of intrusion and poverty?

If more resources are allocated and distributed to the poor people of the society by Govt. of I, there will be more intrusion and infiltration from the neighbors to country I and there will be spiraling effects in poverty statistics. The poor people from the neighboring countries will be attracted to the facilities and opportunities adopted by Govt. of country I. The local people of country I are facing the stress in job market and deprived of fair resource allocation and distribution due to the pressure from the refugees / migrants / infiltrators. A large portion of the resources (e.g. space, land, food, energy, utilities) are captured by the refugees, infiltrators and migrants who are involved in setting up their local colony and culture through strategic alliance. Look at the local people of state W of country I. They are forced to migrate to the other states of country I but ill-treated and forced to come back to W. The recent killing of several laborers in state K of country may be a good example of the problem. The migrants are coming to W. They are setting up their own colony, culture and educational system and running parallel government. The migrants and refugees are involved in bad culture ('apasanskriti') and malicious antisocial activities silently through fake broadcast and false data injection attack; the real contributions are big zero; hate crimes (jatibiddesh) are getting originated through a natural process. The resources are getting consumed by the intruders / migrants / refugees in unregulated manner which will surely create problems of resource planning, allocation and distribution in future. It is the time to think scientifically. The local people of I and state W are at war, helpless, victimized and burdened.

So what should be the solution for the burden of intruders/ infiltrators / migrants for poverty control? The conflict between ERP (Enterprise Resource Planning) and emotional economics is inevitable. An interesting solution may be 'Uniform growth and development for all' through collaboration in neighboring countries and proper rehabilitation of the refugees, intruders and infiltrators in the neighboring country /
state through back-propagation mechanism. If more resources are distributed to all the poor people of all neighboring countries (e.g. minimum income) and there is uniform economic development, there will be less intrusion of the refugees towards country I and state W. It may be then easier to tackle the rising problem of poverty and unemployment. Border security force of country I may not be able to tackle the problem of migration and infiltration alone. Country I should help the neighbors following the policy of collaborative intelligence in various domains such as technology management, public policy for poverty control measures and infrastructure development. The ministry of defense, home, foreign and economic affairs of country I are expected to work with governments of neighboring countries closely and jointly. It is a critical issue of fairness, correctness, transparency, accountability and rationality in enterprise resource planning and supply chain management; it is rational to think from the resource based view of a nation, country and state.

The ministers of the aforesaid ministries are also considering several other issues:

- Is the broadcast communication system in country I (e.g. newspapers, TV, radio channels) captured and compromised by the intruders, infiltrators, migrants and refugees from the neighbors?

- Is it rational to focus on traditional old models of Economics for poverty control?

- Are the books on poor economics during hard time really written by the great economists or the same are the outcome of the perception based nonfactual readymade immature confused thoughts of the students / academic community at the college canteens of the academic institutes? Are all data mentioned in the books on poor economics in hard times authentic or readymade?

Is the resource based view of a firm similar to the resource based view of a nation / state / country? What are the negative impacts of unregulated resource consumption by the intruders, refugees, infiltrators and migrants in a country or a state? Will not there be shortage of resources in future in country I? If there is unlimited increase in consumption by the intruders and refugees; there may be increase in demand for a firm in short run but there will be shortage of resources and economic stress in job market for the local people of a country in long run! The local folks may be forced to struggle for existence. Did the great economists think from the perspective of optimal enterprise resource planning (ERP) and supply chain management (SCM) in their old time? Does country I need critical thinking and deep analytics on HR and business model innovation today; the problem of poverty is not so trivial at all; traditional old models may be dead and obsolete in modern times! An apparently popular solution may be a disaster due to biased, perception based nonfactual readymade immature confused thoughts of the so called intellectuals! It is a question of the security of a country: be it social security or defense. The ministries have decided to focus on technology management i.e. proper innovation, adoption and diffusion of a set of path breaking technologies to fight against poverty, environmental pollution and creating new job opportunities through business model innovation.
SESSION 2: TECHNOLOGIES for SECURITY AGAINST
NATURAL DISASTERS – ARTIFICIAL RAINFALL,
ASTRONOMICAL HAZARDS, EARTHQUAKE, BUSH
FIRE & EPIDEMIC CONTROL

*Event*: Technology for humanity and global security summit

*Venue*: Natural disaster security hall, Technology park : Sanada

*Time Schedule*: 2p.m. - 6p.m., 15.8.2020

*Agents*: Representatives of various global organizations Technology management experts from science and technology forums, Earth science experts, environmental engineers, scientists.

Topic of discussion and key focus areas: Natural disaster, artificial rainfall, cloud physics, astronomical hazards, epidemic and pandemic control, bushfire.

Keynote speakers : Prof. Nick Jones, Prof. Gramy Woods, Prof. Bob Roy, Mr. Hansie Rabada, Dr. Kalin Croft, Dr. Hariharan, Mr. Lin, Dr. Han

1. SCOPE

Scope Analytics

Agents: Earth scientists, engineers;

Moves : Requirements management, threat analytics;

Disasters: call threat analytics → assess threats (drought, flood, heavy rainfall, snowfall, storm, sand storm, cyclone, earthquake, volcano, bushfire, woodfire, epidemic, pandemic, astronomical hazards, environmental pollution (air, water, soil, sound, sunlight), (attacks of wild beasts and pastes);

Prof. Nick Jones and Mr. Lin are exploring the scope of emerging technologies against natural disaster. *Natural disaster* is a critical causal factor of poverty of human society globally. Can emerging technologies be used to fight against natural calamities effectively? How can we assess and mitigate risks of various types of natural disaster such as flood, drought, storm, earthquake, volcano, woodfire, snowfall, epidemic and pandemic outbreak, astronomical hazards, environmental pollution and attack of malicious pastes and wild animals effectively?

Can You recall the tune of 'The rain must fall' by Yanni? This session is analyzing the technological innovation associated with the problem of water security through artificial rainfall and rational, fair and correct resource allocation and sharing among multiple entities. Such type of technological innovation is essential to fight against natural calamities such as drought and flood. It is an emerging technology for humanity. We have analyzed the technological innovation through seven elements of the deep analytics i.e. scope, system, structure, security, strategy, staff-resources and skill-style support of the deep analytics. We have shown various strategic moves of artificial rainfall such as weather modification and rain enhancement through cloud seeding, glaciogenic seeding, hygroscopic seeding and laser induced rainfall. At present, the technology is at emergence phase of
technological life-cycle. We have shown SWOT analysis on various methods of artificial rainfall. This work also outlines a water sharing mechanism (WSM) based on collaborative intelligence. However, the efficiency of the resource sharing mechanism is associated with several critical success factors such as good governance, corporate social responsibilities, law and order, rational positive thinking and political goodwill. An intelligent broadcast protocol is expected to enhance public awareness of rational usage of water by the common people and restrict wastage of water in swimming pools, water amusement parks, luxurious use of air conditioners and air coolers by the rich and super rich classes of our society; tap at roadside and construction works and leakage from pipelines, saving water bodies and conservation of water. Academic institutes and government are expected to play responsible and rational role in regional urban and rural development planning globally. Finally, this work outlines the innovation, diffusion and adoption on emerging laser induced artificial rainfall technology, very large scale integrated smart water grid and water sharing mechanisms based on collaborative planning, forecasting and replenishment.

The expert panel are considering the problem of water security. The people in today's world are facing with significant challenges in this utility sector such as shortage of water, high cost of generation, storage and distribution, wastage or loss and pollution. We must set an efficient national and global utility policy to promote the development of a sustainable system which should be viable environmentally, socially and economically. The sustainability in such resource management not only requires a balanced approach between natural and artificial rainfall but also encourages rational and efficient use of water by minimizing wastage and pollution. There are many strategic moves of efficient water resource management. This work is focused on two specific strategic moves to fight against flood and drought: artificial rainfall and multi-agent collaborative resource sharing. Can we dream of a collaborative enterprise model in this context?

This session is also exploring the problem of private search and presents three private search algorithms for three test cases – (a) real-time moving target search to detect astronomical hazards; (b) Real time private moving target search for robot navigation and (c) Private Light Beam Search. The basic objective of a search is to identify an object and the position of the target. The target's position may be uncertain or there may be complete or incomplete information about its location in terms of a probability distribution. The target may be stationary or in motion. The target distribution is associated with discrete or continuous search space. The problem of optimal search is to maximize the probability of detecting a target subject to the constraints of resources, effort and time. Adaptive security and dynamic data management (DDM) is an essential part of space technology that can monitor the space in real-time to detect any anomalies, vulnerabilities or malicious traffic congestion. If a threat is detected, the technology should be able to mitigate the risks through a set of preventive, detective, retrospective and predictive capabilities and measures. This session also presents Real-time Probabilistic Search Mechanism (RPSM). The probabilistic search approach addresses the incomplete information on the target location by location probability. The problem is probabilistic from the perspectives of the location, size, distance and timing of the
moving target(s) and distribution of the search efforts. The effectiveness of probabilistic search procedure can be verified on the basis of various properties of adaptive secure multiparty computation such as correctness, privacy, transparency, reliability and consistency. The search space can be divided into a set of private blocks; adequate number of sensors should be assigned to each private block; each block is monitored independently. This work highlights the complexity analysis of RPSM from the perspectives of computational cost and security intelligence. It also exercises case based reasoning on a test case of astronomical hazards and explores the scope of RPSM to assess and mitigate those threats. The universe is basically a computer, its history is being computed continuously. The astronomical hazards may be really dangerous threats against the sustainability of today's human civilization and the existence of a safe earth. This type of probabilistic search problem is really hard to solve, it is not a trivial problem. It is also challenging to deploy automated real-time search in reality and seeks extensive support, coordination, planning and corporate social responsibilities from various space research organizations and earth science institutes globally. Today, we have been dreaming of the moon and Mars voyage in space research. It may be a hard target. But, the sustainability of our earth from the dangerous astronomical hazards should be a top priority in space research globally: isn't it rational?

The basic objective of a search is to identify an object and the position of the target. The target's position may be uncertain or there may be complete or incomplete information about its location in terms of a probability distribution. The target may be stationary or in motion. The target distribution is associated with discrete or continuous search space. Search is conducted with various types of sensors such as CCTVs, cameras, telescopes, satellites and eyes of human agents. A detection function gives the probability of detection for a search as a function of effort (e.g. swept area, time). The detection function evaluates the effectiveness of search efforts in terms of probability of detecting the target object. The problem of optimal search is to maximize the probability of detecting a target subject to the constraints of resources, effort and time. The search space can be divided into a set of private blocks; adequate number of resources (sensors) can be assigned to each private block; each block is monitored independently.

In a search problem, a searching agent tries to find a hidden object by screening a certain defined area. The search space may be either discrete or continuous. In a continuous space, the target may move in various ways such as random, Markovian or Brownian moves. If the location of the target is known, then it may be complete-information tractable search problem and it may detect the target with a minimal number of search moves. The exact location of the target is generally unknown to the searching agent in incomplete information search and the problem is addressed using the concepts of fuzzy logic or probability theory. The probabilistic search approach addresses the incomplete information on the target location by location probability. The problem is probabilistic from the perspectives of the location of the target and distribution of the search efforts. The effectiveness of probabilistic search procedure can be verified on the basis of various properties of secure multiparty computation: correctness (i.e. correct identification of the targets), privacy, transparency, reliability and consistency.
The problem of optimal search for a moving target in both discrete and continuous space has been investigated extensively in various research articles on operations research and artificial intelligence. This work is an attempt to extend the study on the basis of related literature review and case based reasoning. This work is organized as follows. Section 1 is focused on scope; it defines the problem of probabilistic search of moving targets in discrete and continuous space.

This session also explores the scope of other natural disasters including epidemic and pandemic outbreak and bushfire and suggests a set of intelligent strategic moves as countermeasures. It is rational to adopt an efficient system; an optimal mix of e-governance (e.g. online grievance management system), broadcast communication protocol and artificial immune mechanism to fight against natural disaster, epidemic and pandemic outbreak. There is threat of bio-terrorism on the soft targets (e.g. life-science supply chain and healthcare service chain). Is the conflict between security intelligence and business intelligence inevitable? It is an interesting observation that technologies for humanity can be effectively applied to fight against disasters. Pandemic is more dangerous than epidemic. When an epidemic spreads globally, it is called pandemic. In case of epidemic, a disease spreads at very fast rate within a particular period among one or more communities. For example, WHO has recently declared the outbreak of novel Corona virus as Pandemic, but it is controllable. There are other various types of threats of epidemic globally due to environmental pollution such as air, water, soil, light and sound pollution.

a) Epidemic due to *air pollution* like dust at construction sites and industrial plants; smoke from vehicles; paste control problem (e.g. mosquitoes, flies), malnutrition in slum areas, improper cleaning of garbages and stool of street animals?

b) Epidemic due to *water pollution* in supply of dirty drinking tap water caused by leakage in pipelines, contamination and jerms in water storage system, malfunctioning of tube wells, water filtering problem, mixing of water from drainage system and tap water pipeline, unprotected selling of unhealthy food (e.g. oily spicy biriyani) and beverages at retail outlets and by hawkers being contaminated by flies; risks of diahorrea, stomach upset and loose motion.

c) Epidemic due to *soil pollution* and earthquake caused by random digging of soil for construction projects, erosion of soil at riverbeds; jamming in drains due to plastics, improper cleaning of drainage and sewage system;

d) Epidemic due to *light pollution* in slum areas, unplanned urban development planning, blockage of sufficient sunlight into residential areas (e.g. houses, flats, multi-storied buildings)

e) Epidemic due to *sound pollution* caused by playing loud and wild music, fireworks, activities at construction sites and industrial belts.

The key focus areas of this session are artificial rainfall, cloud seeding, collaborative intelligence, resource sharing mechanism, water sharing, compensation, collaborative planning, forecasting & replenishment, political will, power play, conspiracy for war, corporate social responsibilities, artificial intelligence, probabilistic light beam search, predictive threat analytics, astronomical hazards, reactive and proactive security, private search, adaptive security, dynamic data management, natural disaster, epidemic control, pandemic
outbreak, intelligent broadcast, online grievance management system, artificial immune mechanism, self-Nonself classification, danger signal, clonal selection, hotspot, cluster, social distancing, security intelligence, business intelligence, bio-terrorism, life science supply chain and healthcare service chain.

## 2. SYSTEM

### System Analytics

**Agents**: Earth scientists, environmental engineers;

**Moves**: requirements engineering, system design, prototype testing, erection, installation, testing, commissioning;

**Emerging technologies**: innovate a set of emerging technologies based on threats.

* **Drought and heatwave**: Earth science – artificial rainfall, cloud seeding using laser technology and cloud physics; civil – water storage system, irrigation system; mechanical – air conditioning system, air coolers, water pipeline; electrical – solar water pumps; logistics engineering – rail, ship, truck;

* **Flood / heavy rainfall**: Earth science – cloud diversion; civil – dams, water conservation system, storage, drainage and irrigation system; mechanical – boats, helicopters, drones, planes; electrical – motors and pumps;

* **Snowfall**: mechanical – excavators, ice breakers; electrical – room heaters;

* **Storm / cyclone / sandstorm**: Earth science – predictive analytics; civil – robust housing structure; mechanical – excavator, electrical – fault analytics, power cut, standalone roof top solar panel;

* **Earthquake**: Earth science – predictive analytics, geographical information system; civil – earthquake proof light weight, flexible housing structure; mechanical and metallurgical – steel, aluminium, glass; electrical – protection from open circuit, short circuit, transients and power cut;

* **Volcano**: Earth science – predictive analytics; logistics system – evacuation system, rail cars, bus, trucks, planes, helicopters; civil – safety chambers;

* **Bushfire**: mechanical – water piepine, electrical – solar water pumps, lightning arrester; chemical – smoke, fire extinguishing system;

* **Epidemic / pandemic**: Pharmaceutical engineering – vaccines, precision medicines, healthcare engineering, biotechnology engineering;

* **Astronomical hazards**: Earth science and digital technology – predictive analytics, real-time moving target search, satellite communication; civil – safety shelter, cave, robust housing structure; mechanical – strong, reliable materials; electrical – fault analytics; chemical – fire extinguishing system;

* **Environmental pollution**: environmental engineering (air, water, soil, sound, sunlight); civil – soil erosion control, restriction on soil digging, drainage system, nano housing architecture; mechanical – smoke emission control from vehicles, waste treatment plants;

* **Attacks of wild beasts and pastes**: conservation of forests; civil – wall; mechanical, rubber bullet, electrical – fence; chemical – gas bombs; digital / electronics – music system;
Prof. Bob Roy and Dr. Hariharan are discussing on the system associated with emerging technologies against various types of natural disaster, Natural rainfall requires conservation of forests and green plantation in smart cities and villages and along riverside. It is essential to save the rivers. Massive cut of trees and green plants in urban zone occurs due to construction of residential flats and other civil infrastructure (e.g. flyovers, bridges and transportation infrastructure). It may lead to droughts. The basic objectives of rainmaking or artificial precipitation or artificial rainfall is to artificially induce or increase precipitation using airplanes or rockets to sow to the clouds with catalysts. This method makes rain or increase precipitation and drought like situation.

<figure><img src="image_16.png" /><figcaption>Figure 2.1: Cloud Seeding</figcaption></figure>

Cloud seeding is a weather modification method by dispersing substances into the air; results condensation of cloud and alter the microphysical processes within the cloud [6-10]. The most common chemicals used for cloud seeding are salt powder (e.g. silver iodide, potassium iodide), dry ice and liquid propane. Cloud seeding chemicals may be dispersed by aircraft or drones or by dispersion devices located on the ground (e.g. firing from anti-aircraft guns or rockets) (Figure 2.1).

Glaciogenic cloud seeding use glaciogenic materials such as Silver Iodide which increase the ice crystal concentration in clouds by freezing cloud droplets. Static cloud seeding is applicable to cold cloud. Dynamic seeding results increased rainfall as compared to the static approach. The seeding of super cooled cloud with large amount of ice nuclei cause glaciation of the cloud. The super cooled liquid water is converted into ice flakes releasing latent heat, increasing buoyancy, growing larger and increased precipitation. Hygroscopic cloud seeding is a form of warm cloud seeding which enhances rainfall through coalescence process using fine spray of hygroscopic salt.

The other emerging techniques for artificial rainfall are laser induced cloud generation and ion generation method. Lasers may help causing rain; rainclouds form when airborne pockets of tiny particles condense water vapor around them (Figure 2.2). It is possible to control over moisture using laser. Weather control may get their next rainmaking tool in the form of an infrared laser. Precipitation is formed after lightning and heavy rain follows due to dissociation, ionization and natural seeding process in the atmosphere. Plasma laser pulse can be used for artificial rain making; for example $2.2 \times 10^{19} \text{ gm}$ of water drops are formed in the
atmosphere by laser pulse of energy 500 mJ. Plasma laser pulse creates high
temperature (up to 3000°C) which breaks bonds N₂ and O₂ into excited and unstable
N* and O* and form NO and O₃. These endothermic reactions absorb heat from the
clouds and condensation creates water drops. Simultaneously N₂ and O₂ will be
ionized and become big clustered ions through several

<figure><img src="image_17.png" /><figcaption>Figure 2.2 : Laser induced artificial rain</figcaption></figure>

reaction. The big ions act as seed and results precipitation and rain.

Self-guided filaments generated by ultra short laser pulses is an emerging
technology to assist water condensation, in an under saturated free atmosphere
based on photo-oxidative chemistry and electrostatic effects. The phenomenon is
used for remote characterization of humid atmosphere, triggering of water
precipitation and cloud formation. Laser based condensation is a remote sensing
nucleation processes in clouds influencing or triggering water precipitation. Self-
guided laser filaments result from a nonlinear propagation regime of ultra-short
laser pulses. Beyond a critical power (Pcr = 3 GW in air at a wavelength of 800 nm),
the beam self-focuses due to the optical Kerr effect until its intensity is sufficient to
allow multi-photon ionization of air molecules generating a cold plasma. At this
point, the released free electrons (typically 10¹⁵–10¹⁶ cm) and the negative higher-
order Kerr terms tend to defocus the beam and dynamically balance Kerr self-
focusing. one or several self-guided filaments with a diameter of 100 µm are
generated over distances much longer than the Rayleigh length, up to hundreds of
meters. Filaments can be initiated at predefined remote distances and propagate
through adverse conditions including fog and clouds, turbulence or reduced
pressures.

Test case : Private real-time moving target search for astronomical hazards

Real-Time Probabilistic Search Mechanism (RPSM)

Input: search space, goal state, target distribution, detection function;
Output: Identify objects (e.g. moving targets);

Critical parameters : Target goal state, search space, detection function

Moves:

* Adaptive security for dynamic data protection through preventive, detective, retrospective and predictive capabilities;

* Real-time search;

* Automated data stream mining by intelligent threat analytics,

* Adaptive secure multi-party computation

* Access control: audit authentication, authorization and correct identification, privacy;

* Secure computation: verify correctness, fairness, rationality, trust, commitment, transparency, accountability;

* System verification: verify safety, reliability, consistency, liveness, deadlock-freeness, reachability, resiliency;

Procedure (Probabilistic Search):

Divide the search space into a set of private blocks;

Assign resources to each private block;

Project light beam on private search space → move forward and backward;

Search discrete or continuous search space → sense data stream → filter data stream;

Detect target → verify correctness → give alert.

Security measures: (a) Proactive (b) Reactive.

The aforesaid mechanism (RPSM) is defined by a set of elements: system, searching agents, a finite set of inputs, a finite set of outcomes as defined by output function, a set of objective functions and constraints, an optimal set of moves, revelation principle, security measures and search procedure. It evaluates a system which is defined by a set of states (e.g. initial, goal, local and global) and state transition relations. The mechanism seeks the support of an intelligent reasoning system i.e. threat analytics.

Private Light Beam Search Algorithm : Let us analyze the aforesaid private search algorithm which is basically an interactive search. The cost of computation of probabilistic search depends on light beam projection on private search space. Let us show an illustration of private search. The basic steps of an interactive search algorithm which operates between a decision making agent (DMA) and the mediator agent (MA) are as follows: (a) MA computes an initial feasible solution. (b) MA interacts with the DMA and (c) MA obtains a (or a set of) new solution. If the new solution or one of the previous solutions is acceptable to the DMA, stop. Otherwise, go to step 2. The design of interactive search methods depends on various issues: (a) The form through which DMA gives information, (b) The approach by which a multi-objective problem is transformed into a single objective problem, (c) The type of data used for interaction with DMA, (d) Number of non-dominated points to be presented to the DMA (a single point or a sample of points) and (e) How the DMA evaluates a set of alternatives?
Agents : A decision-making agent (DMA) and the mediator agent (MA).

Input : The mediator holds the deterministic problem; The DMA holds its aspiration point, reservation point, indifferent threshold, strong and weak preference threshold and veto threshold.

Output: DMA knows a set of solutions; MA can not know the output.

1. MA requests the DMA to specify its preferential parameters (P<sub>A</sub>,P<sub>R</sub>,I<sub>th</sub>, P<sub>th</sub>, S<sub>th</sub>, W<sub>th</sub>, V<sub>th</sub>).

2. The DMA generates (n-1) random set of preferential parameters and appends its desired set of preferential parameters at a random position. The DMA sends to MA the list H = (H<sub>1</sub>,...,H<sub>n</sub>) where for a secret index 1≤j≤n, H<sub>j</sub> = ( P<sub>A</sub>, P<sub>R</sub>, I<sub>th</sub>, P<sub>th</sub>, S<sub>th</sub>, W<sub>th</sub>, V<sub>th</sub>).

3. Repeat until the DMA is satisfied with a solution or concludes that no compromise point exists for the present constraints

a. MA computes a middle point (MP) alongwith characteristic neighbors for each set of preferential parameters.

b. The DMA gets back the results of k middle points alongwith characteristic neighbors using k-out-of-n oblivious transfer protocol where k<n.; DMA scans the inner area of the current neighborhood and stores its preferred solutions in a private list L<sub>1</sub>; it stores the invalid middle points in a private list L<sub>2</sub>.

c. Case

1. *The DMA wants to define a new aspiration and/or reservation point and/or updates preferential thresholds:*

* The DMA adds a set of new aspiration and/or reservation points and/or new preferential thresholds to the list H and sends H to MA.

* MA projects the aspiration points onto the non-dominated set and generates middle points with characteristic neighborhood.

* The DMA gets back the result of desired middle point alongwith characteristics neighbors using 1-out-of-n oblivious transfer protocol.

2. *The DMA wants a point from the current neighborhood to be the new middle point or wants to return to one of the stored points of L<sub>1</sub>:*

* The DMA adds the desired middle point to the list L<sub>2</sub> and sends L<sub>2</sub> to MA;

* MA generates neighborhood of the middle points.

* The DMA gets back the result of desired middle point alongwith characteristics neighbors using 1-out-of-n oblivious transfer protocol.

Let us consider a specific interactive search procedure called *Light Beam Search* (LBS) method. The idea of light beam search is analogous to projecting a focused beam of light from the aspiration point onto the search space [Figure 2.3]. The lighted part of the frontier changes if the *aspiration point* or the point of interest in the non-dominated set is changed. This interactive search occurs between a DMA and the MA. The mediator asks the DMA to specify its preference in the form of aspiration and reservation point and various types of preferential thresholds. At each iteration of LBS procedure, MA generates a sample of non-dominated points.