bluuebunny/crossref_metadata_2025 huggingface dataset:
Unique Approach of Movie Rating Prediction Using Support Vector Machine Algorithm and K-Nearest Neighbors Algorithm
Authors: Ganesh R  , Kalaiarasi S
Published: November 2022

The aim is to predict movie rating using Support Vector Machine Algorithm and K-Nearest Neighbors Algorithm. An aggregate of 392 examples were gathered from film datasets and these datasets were taken fromkaggle dataset. Two experimental calculations were performed, one with K-Nearest Neighbors algorithm and another with Novel Support Vector Machine algorithm. Sample size of N=5 is taken for both algorithms. The computation processes were executed and verified for exactness. SPSS was used for predicting significance value of the dataset considering G-Power value as 80%. Novel Support Vector Machine calculation was applied and it had accomplished mean accuracy of 91.8% when compared with mean accuracy of 53% of K-Nearest Neighbors algorithm. The outcomes were obtained with a significance value of 0.03 (p<0.05). A unique approach model is affirmed to have higher exactness than K-Nearest Neighbors calculation.
Diabetes Prediction Using Blood Sample Data with Novel Voting Classifier over Random Forest
Authors: Suresh Reddya M  , Ramakrishnan V
Published: November 2022

This study focuses on how to predict diabetes using blood sample data and machine learning algorithms like the Voting Classifier over the Random Forest technique. The proposed prediction models were trained and evaluated on a dataset that included seven variables: glucose level, diastolic blood pressure, blood thickness, insulin levels, BMI, age, and skin. The new Voting classifier (VC) and Random Forest (RF) algorithms are used on a diabetes dataset of 1495 records with 10 features, sample size=5, and two groups with a g-power value of 80%. With a threshold of 0.05, a confidence interval of 95 percent, and a standard deviation of one standard deviation, the patients’ information was acquired from a variety of websites. The framework was built using blood sample data and the VC over Random Forest machine learning algorithm, resulting in a successful research of diabetes prediction using blood sample data and the Voting Classifier (95%) over Random Forest machine learning technique (85 percent). With a 95 percent confidence interval, the two-tailed t-test revealed a statistical significance value of 0.001 (p0.05). This research shows that the VC algorithm’s results are more accurate than the RF approach, which was written in Python.
Efficient Cloud Environment for Secure User Authentication to Prevent Cyber Attacks Using DES Over AES
Authors: Jaya Prakash Reddy C  , Padmapriya G
Published: November 2022

The proposed work is aimed at providing an efficient cloud environment for secured cloud-based users to improve user authentication and storage security to prevent cyber-attacks based on the Data Encryption Standard algorithm over the Advanced Encryption Standard algorithm. A total of 160 data samples were collected for both the Data Encryption Standard algorithm and the Advanced Encryption Standard algorithm, with a G power of 80%, a threshold of 0.05%, a confidence interval of 95%, mean and standard deviation. size of the training and test datasets is 70% and 30% respectively. The Data Encryption Standard Accuracy is 94% and the Decryption Time value is (66 ns), while the Advanced Encryption Standard Accuracy is 80% and the Decryption Time value is (72 ns), with a significance value of 0.003 (p 0.05). Encryption Standard achieved significantly better authentication than the Advanced Encryption Standard for the identification of lack of security due to cyber-attacks.
Analyzing Ola Data for Precise Price Prediction Using XGBoost Technique Comparing with LASSO Regression
Authors: Venkat Sai Tarun G. , Sriramya P.
Published: November 2022

XGBoost algorithm and Lasso regression and compare r-square, Mean Square Error (MSE), Root MSE, and RMSLE values. The algorithm should be efficient enough to produce the exact fare amount of the trip before the trip starts. The sample size for implementing this work was N=10 for each of the groups considered. It was iterated 20 times for efficient and accurate prediction of cab price prediction with G power in 80% and threshold 0.05%, CI 95% mean and standard deviation. The sample size calculation was done with clincle. The pretest analysis was kept at 80%. The sample size calculation was done using clincalc. The statistical analysis shows that the significance value for calculating r-squared and MSE was 0.63 and 0.581(p>0.05), respectively. The XGBoost algorithm gives a slightly better accuracy rate with a mean r-squared percentage of 72.62%, and the Lasso regression algorithm has a mean r-square of 70.47%. Through this, the prediction is made for the online booking of cabs or taxis, and the Xgboost algorithm gives a slightly better r-squared value and MSE values than the Lasso regression algorithm.
Proposed Novel Methodology for Automatic Drainage Block Identification in Smart Cities Using Internet of Things
Authors: Sheeba J  , Mesia Dhas John T , Shiny Angel T.S.
Published: November 2022

Internet of Things (IoT) is the dynamic multidimensional web of technological heterogeneous artifacts. It is changing smart style of human life into ultra tech vibrant multi style of life. The usage of technology in the layman’s life and profession is the ultimate objective of modern scientific inventions. The drainage block is one of the tedious problems faced by all the developing countries. The drainage block yields many sanitation problems, highly infected diseases and flood monsoon seasons. The maintenance of drainage systems is highly expensive; the humans are involving the cleaning and maintenance activities. This is killing thousands of humans every year. This research article is proposing a new technique and giving the solution for finding the blockage of drainage using air, temperature and pressure constants. The sensors are used to identify the values for each parameter. The Automatic Drainage Block Identification Algorithm (ADBIA) is analyzing the values and predicting whether the drainage is blocked or not.
Smart Surveillance System Using Face and Optical Character Recognition for Secure Environment
Authors: HarikaPalivela Lakshmi , Ashok Kumar P.M. , Rama Krishna V.V.
Published: November 2022

One of the important applications in the smart surveillance system is to identify unknown suspicious persons. Instead of manually and tediously monitoring the cameras continuously, this system can be used to identify and recognize suspected individuals and consequently send a warning message on the occurrence of such recognition. In this work, we propose a novel methodology, in which the system is connected to a database containing images of the Aadhar cards of the malfunctioning individuals. Since SQL database cannot directly store images, the Aadhar card pictures will have to be stored in a NoSQL database. The proposed system was built utilizing the Python language and the PyCharm IDE. The input video stream is processed frame by frame using the OpenCV library. The system will process each frame to check for the presence of a face. If a face is found, it matches the detected face with each of the faces present in the Aadhar card photos stored in the connected database. This process is carried out using a face recognition algorithm. If a match is found, the system uses OCR to isolate and recognize the text present in the image in order to get the name and Aadhar card number of the offender, and subsequently an alert will be displayed to the corresponding moderators with the name and Aadhar card number of that individual, in order to facilitate the necessary action.
Underwater Fish Species Classification Using Alexnet
Authors: Bhanumathi M  , Rithika Ravi , Roshni R  , Selvaraj Sona
Published: November 2022

There has been a constant need for the classification of fish species for a better understanding of the underwater ecological balance. Identifying the characteristics of different fish species plays a significant role in knowing the insights of marine ecology and is a great deal to many fisheries and industries. Manually classifying fish species is time-consuming and requires high sampling efforts. The behaviour of fishes can be well understood using an automated system that accurately classifies various fish species effectively. The classification of underwater images has difficulties like background noise interruption, image disruption, lower quality of images, occlusion. The proposed model lights up on the assortment of fish species using Alexnet. The knowledge of the previously trained model is given to the alexnet for improving the system. The performance of our improved model is demonstrated with real-world data from a research organization called Kaggle. CNN has used several layers trained for precise identification of the distinct features of a species and classify them accordingly. This paper ensures increased accuracy than the existing systems.
Support Vector Machine-Based Scheme Identification System for the Marginalized People
Authors: Varghese K  , JanarthananPillai M.
Published: November 2022

Scheme identification system is required to improve the socio-economic status of marginalized people. The absence of technological innovations creates more complications during online scheme identifications for the development of marginalized people. In order to increases the service availability to marginalized people, a scheme identification system is proposed. In this, the previous beneficiary data collected from Kerala state SC, women development and Handicapped Persons cooperation are used. The Support Vector Machine (SVM) with linear and non-linear kernels identifies the best suitable schemes based on the input features. Finally, SVM with Gaussian Radial Basis Function (RBF) kernel achieved 91.23 %, 95.65% and 94.23% accuracy during scheme identification for SC, Women development and Handicapped Persons cooperation data.
Improved Accuracy for Fake News in Social Media Using Logistic Regression Comparing Naive Bayes Classifier
Authors: Jeevan K. , Kanimozhi K.V.
Published: November 2022

The fundamental purpose of the work is to detect the Fake News in Social Media with the use of Machine Learning Algorithms.TRUE and FAKE dataset is used to detect false news. This dataset carries the record of data i.e. TRUE or FAKE news. Fake News detection is accomplished via Logistic Regression and Naive Bayes classifier. Naive-Bayes algorithm is a simple approach mainly used for classification. Sample size has been determined to be 20 for both the groups using G Power 80%. Logistic Regression algorithm provides mean accuracy of 97.5% when compared to Naive Bayes algorithm with mean accuracy of 89.43%. Statistical significance value is obtained as 0.002 (p<0.05). Logistic Regression has extensively higher accuracy than Naive Bayes algorithm.
Enhancing Real-Time Driver Drowsiness Detection System with Facial Recognition Using Support Vector Machine
Authors: MadigubbaRughveda  , Senthil Kumar R  , KirubakaranS Stewart
Published: November 2022

The aim of the research is to detect driving fatigue and determine whether the driver is sleeping or awake. There are several approaches for detecting tiredness based on extensive learning, but in this instance we will use machine learning algorithms. The alarm sounds when the driver becomes sleepy to prevent accidents. Factors are compared using various groups, and 20 samples are used to obtain an 80 percent Gpower accuracy rate. The EAR method (84.60 percent) and the SVM algorithm (90.00 percent) both have high retrieval accuracy. The data is gathered from resources for networking capabilities that were previously context-sensitive, such as applying the brakes via an alarm system, and the driver is alerted using the OpenCV package and Python environment by employing two machine learning techniques.
Higher Prediction Accuracy on Parkinson Disease Patients Using Random Forest Algorithm over DTA
Authors: Manoj Suvvala , Rajendran T
Published: November 2022

Improving the prediction accuracy for Parkinson’s patients is achieved by applying Innovative Parkinson’s disease prediction utilizing classifiers that use machine learning methods and evaluating their performance. In this proposed work, the Innovative Parkinson’s disease prediction has been carried out using the Random Forest algorithm and the Decision Tree algorithm. It was tested over a dataset consisting of 757 records. Both algorithms were subjected to a programming experiment in which N=10 iterations were used to discover the symptoms of Innovative Parkinson’s disease prediction and their accurate analysis. The G-power test is around 80% accurate. From the implemented experiment by performing the independent sample t-test, the Random Forest algorithm’s Parkinson’s disease prediction accuracy is significantly (0.028) better than the Decision Tree algorithm. The accuracy of Innovative Parkinson’s disease prediction was compared between two algorithms, and the Random Forest algorithm appears to be higher at 93% than the Decision Tree algorithm’s accuracy of 91%. This research will use the most up-to-date Machine Learning Classifiers to create an innovative Parkinson’s disease prediction technique for the early detection of Parkinson’s disease and other related issues.
Classification Model for Tumor Detection in Breast Cancer Patients Using K-Neighborhood and Decision Tree
Authors: Sai Monika R. , Sriramya P.
Published: November 2022

Breast cancer is a malignancy affecting many women worldwide and is associated with a high fatality rate. The paper’s main objective is to detect the breast cancer tumor using a K-neighborhood and compare it with Decision Tree classification to evaluate accuracy using the Machine Learning technique. The k-neighborhood algorithm is applied to 10 images from a dataset of more than 300. For the same, the accuracy values are evaluated. It consists of breast cancer images in the research study of K-neighborhood machines and a decision tree with 20 sample sizes. Based on the statistical analysis, the significance value for calculating accuracy was p<0.05. Breast cancer detection is performed using a K-neighborhood, which means 87.5% and 80.5% in the Decision Tree classification. The performance of the K-neighborhood is considerably improved than the Decision Tree classification in terms of accuracy.
Challenges of a Transdisciplinary Team in the Design of a Lithium-Ion Battery Pack for Small Urban Electric Vehicles: Lessons Learned
Authors: Werlich Samuel Henrique , Gotz Joelton Deonei , Corrêa Fernanda Cristina , Borsato Milton
Published: November 2022

Electric vehicles (EVs) are becoming a viable alternative to eliminate emissions, reduce dependence on fossil fuels, and gradually replace vehicles with an internal combustion engine (ICE). The traction battery of these vehicles has the primary function of supplying the energy necessary for the electric motor to work. Its design is complex and represents one of the most significant difficulties in reducing the cost of vehicular electrification. This article describes the challenges faced by a transdisciplinary team in designing a lithium-ion battery pack with a battery management system (BMS) applied to a small urban vehicle developed in the context of the “Program Route 2030”. Through the case study approach, we will present the interaction between 29 researchers from three Brazilian Science and Technology Institutions (ICT) (UTFPR-PG; UTFPR-CT; SENAI-PR) and two subsidiaries of multinationals (Renault; Clarios) in the automotive sector also based in Brazil. Preliminary results show the importance of transdisciplinary work in leveling the team’s knowledge about the product, determining target specifications through applying the House of Quality (HoQ) tool, specifying cell chemistry, proposing the modules’ physical arrangement, and developing the BMS.
Design for Producibility: A Case Study on Theory, Practice and Gaps
Authors: Areth Koroth Rohith , Elgh Fredrik , Lennartsson Martin , Raudberget Dag
Published: November 2022

Changing customer requirements, regulations, technology and regulations, shift to automated assembly and product variety are common challenges faced by many manufacturing industries and alignment between product and production system is critical for business success. Design engineers should be aware of production constraints and capabilities to ensure efficient manufacture and assembly of products that are developed. This requires different and detailed support to guide the work, evaluate different design solutions, enable continuous and concurrent work with design for producibility and production preparation. A study was conducted in three companies to understand alignment and integration of product development and production preparation processes. Also, utilization of production requirements, design for manufacture and assembly (DFMA) and failure modes and effect analysis (FMEA) to support design for producibility (DFP) was studied. Currently, production preparation is done through discussions between design and production engineers. Production preparation and work with DFMA and FMEA is skill and experience dependent. Definition, structuring and sharing of production requirements on different system levels, from production and product perspectives are identified as critical to supporting design for producibility and production preparation. The work with FMEA and DFMA can be developed and improved with systematic and structured way of working with production requirements.
A Food Supply Chain Model Directing Food Loss and Waste to Kodomo-Shokudos
Authors: Narazaki Kayoko , Kimura Tomiya , Sato Mizuho , Sugihara Midori , Toma Tetsuya
Published: November 2022

Food loss and waste has become a major social problem all over the world. To reduce “Food loss and waste,” laws have been put in place to turn it into animal feed and fertilizers, relax delivery deadlines, and reduce serving sizes of meals. In addition, there are other activities promoted by the government, such as the use of food banks. However, even if all these activities are utilized, food loss and waste can never be reduced to zero. Another way to reduce food loss, other than these laws and activities, is to provide food materials to restaurants that provide inexpensive meals in the city, called “Kodomo-Shokudo.” By using food that would otherwise be wasted at the “Kodomo-Shokudo,” we can contribute to reducing food loss and lowering the operating costs of the these restaurants. Our aim is to clarify the difference between the food bank and “Kodomo-Shokudo” and create a route model that supplies food losses to the “Kodomo-Shokudo.” Kodomo-Shokudo is a community place where local residents or NPOs take the initiative to provide meals to children for free or at a low cost. Consequently, Monte Carlo simulations show that the resulting transportation costs could be significantly reduced.
Modelling for the UK’s Utility-Scale Solar Regulation Change: Lessons for Transdisciplinary Engineering in Policy Practice
Authors: Liote Laurent
Published: November 2022

Meeting the UK’s net-zero greenhouse gases target by 2050 requires transdisciplinary engineering, it requires efficient exchange and collaboration between engineering and social science, between engineers and policy makers within the national government. Based on ethnographic fieldwork conducted within the UK’s department for Business, Energy and Industrial Strategy (BEIS), this paper explores how technical and policy expertise were mobilized and combined in a recent change in utility-scale solar policy. Taking a model developed by BEIS’ engineering advice team in collaboration with the established renewable policy team, this paper looks at what it means to give and receive engineering advice in the context of utility-scale solar regulation. Looking at the model design process from both the engineer’s and policy advisor’s perspectives highlights how concepts of expertise, disciplinarity compatibility and opposition impact policy and outcomes. The modelling process was successful in helping the negotiation and reconciliation of technical and social concerns to enable a change in utility-scale solar regulation satisfactory to industry and constituents. By drawing on this case, this paper ends on a wider discussion about how the generation of mutual trust and development of interactional knowledge between engineers and policy advisers enables TE in policy practice.
Engineering Systems Design Capabilities for a Resilient Green Transformation
Authors: Oehmen Josef , Willumsen Pelle , Mattulat Filipovic Andy
Published: November 2022

Early calls for environmental protection and sustainability centered on empathic arguments for curtailing consumerism and enacting personal and societal changes in our way of life. However we have now evolved to embrace a ‘technology fix’ approach, where we aim to reconcile our need to maintain our standard of living with our sustainability goals by developing and deploying ‘green technologies’ in our critical infrastructure at an unprecedented speed. While cost and sustainability are already in focus, the current discussion misses the third critical performance objective, resilience. In this paper, we explore how we can conceptualize resilience for critical socio-technical systems, and what the unresolved design challenges are to successfully develop and deploy them.
Effects of Data Exchange Methods on Perceived Risk and Trust in Digital Engineering
Authors: Avsar Alkim Z. , Chiesi Stephanie S. , Grogan Paul T.
Published: November 2022

Data exchange is a critical part of collaborative design and digital engineering. Design teams need effective communication to exchange vital information and achieve desired outcomes. Literature shows high data quality is matched with higher levels of trust and lower levels of perceived risk which influence design decisions. This paper conducts a survey of industry professionals as a preliminary investigation of how data exchange methods implemented in digital engineering affect perceived trust and risk levels of engineers. The survey adopts questions from the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT2) questionnaires to measure risk and trust perception in digital engineering environments for three data exchange methods and interfaces: direct data integration, air-gapped import and export, and semantic data exchange. Survey results show engineers have different trust and perceived risk levels towards different data exchange methods. Engineers behavioral intentions towards using a data exchange method depend on perceiving the method as less risky and trustworthy. Understanding how data exchange methods and interfaces impact engineers’ trust and risk perception can inform effective implementation of digital engineering and lead to more successful collaborative engineering design projects.
Generic User Interface for Inclusive Interactive Simulation
Authors: Winder Ira , Hiekata Kazuo
Published: November 2022

Inclusive interactive simulations are boundary objects that make it possible for engineering know-how to be placed directly into the hands of non-technical stakeholders. However, interactive simulations are also complex technologies that are difficult to implement on a bespoke basis. In order to make deployment of interactive simulations more feasible for engineers, especially those without prior background in user interface design, we created a generic, open-source simulation user interface (OpenSUI) with an associated communication schema. OpenSUI is agnostic to domains, and can communicate with any computational simulation that follows its schema. To demonstrate, we convert an existing computational simulation for real estate, FuzzyIO, into an inclusive interactive simulation using OpenSUI. We then review preliminary, qualitative feedback from non-technical users and discuss our intent for further validation through experimentation at scale.
A Survey of Artificial-Intelligence Enabled Digital Transformation in Elderly Healthcare Field
Authors: Lee Ching-Hung , Wang Chang , Li Fan , Deng Qingqing , Chang Danni
Published: November 2022

With the rapid growth of the elderly population, traditional elderly healthcare services can hardly meet the dynamic and uncertain demand and higher customer expectations. With the digital transformation with digital technological enablers of the elderly-related industry rising globally, Artificial Intelligence (AI) has begun to play a disruptive and critical role in the elderly healthcare industry. But there is still a lack of a holistic review about AI-enabled digital transformation (DT) in the elderly healthcare field. Therefore, 59 works of literature published from 2000 through 2021 were extracted and analysed from Web of Science. This study focuses on the application scenarios, the AI approach used, and the benefits of AI-enabled DT. And this literature review lays the foundation for future research in the elderly healthcare field.
Preliminary Evolutionary Network Model for Efficient Collaboration in Systems-of-Systems
Authors: Boyd Myron , Mazzuchi Thomas , Sarkani Shahram
Published: November 2022

Collaborative systems-of-systems (CSoSs) are defined by the Systems Engineering Body of Knowledge as groups of constituent systems that voluntarily work with each other toward a common goal. As the complexity, sociotechnical interactions, and cooperation of real systems increases, so too does our need to understand how to design and manage collaboration across disciplines. An agent-based model is developed that combines network evolution mechanisms with evolutionary game theory to simulate CSoSs. Collaboration efficiency (CE) is introduced as a metric by which collaboration may be measured and performance compared. Cost and strategy parameters of constituent systems are tested via CSoS model simulation to develop insights into best collaboration practices for CSoSs. Results suggest a reactive collaboration strategy or a reinforcement algorithm-based strategy produce the highest CE under certain conditions. Applicable to systems in sociotechnical enterprises, logistics, energy, infrastructure, and more, this research can improve the design and operation of any CSoS.
Conceptual Design of Space Missions Integrated with Real-Time, In Situ Sensors
Authors: Chell Brian , Levine Matthew J. , Capra Leigha , Sellers Jerry J. , Grogan Paul T.
Published: November 2022

Technological advances have enabled new types of distributed space missions (DSMs) that can improve the data resolution along many dimensions over monolithic, “flagship” spacecraft. Future DSMs will fuse data from a wide variety of sensors including other spacecraft and various ground- and air-based in situ platforms. The New Observing Strategies Testbed (NOS-T) is a new digital engineering environment based on systems engineering principles for simulating DSMs using a loosely coupled, event-driven architecture that manages communication between logically and geographically distributed user-developed applications. This paper demonstrates how NOS-T can evaluate new operational modes for satellite constellations using real-time stream gauge data from the U.S. Geological Survey (USGS) National Water Information System (NWIS) to decrease the latency of targeted spacecraft observations of flooded areas. The test case uses real-time data from NWIS stream gauges in the U.S., artificially triggers a flooding event, subsequently tasks satellite observations, and downlinks data to a ground station. It demonstrates how NOS-T enables the transfer of information between in situ and space-based sensors in a digital engineering environment to aid conceptual design of future DSMs across organizational boundaries.
Machine Learning and Digital Twin-Based Path Planning for AGVs at Automated Container Terminals
Authors: Gao Yinping , Chen Chun-Hsien , Chang Daofang , Chen Songlin , Song Xue Ting
Published: November 2022

The complex and dynamic environment in an automated container terminal (ACT) increases the difficulty of path planning, especially for automated guided vehicles (AGVs). Digital twin is an essential means of characterizing complex production systems as the physical objects can be synchronized in the virtual space. Machine learning is also a popular way to solve path planning problems. This study combines digital twin and machine learning to tackle AGV path planning problems in the time-changing operation environment. A digital twin-based AGV scheduling approach is developed to obtain the real-time data from the physical ACT. Based on the information of the dynamic factors obtained, a mathematical model is formulated to minimize the transportation time of AGVs. Subsequently, the path planning schemes solved by machine learning are used asinput to the virtual ACT for validation and optimization. The optimized solution is further compared to a common path plan algorithm without applying digital twin. The experimental results show the superiority of the proposed method, which can provide better decision support for ACT operation.
Cost-Optimal Pathfinding Model for Multi-Echelon Logistics Network Design and Optimization: A Fourth-Party Logistics (4PL) Perspective
Authors: Lim Kendrik Yan Hong , Dang Le Van , Chen Chun-Hsien , Chew Kim Hoe
Published: November 2022

The adoption and integration of e-commerce strategies into existing business models have allowed many companies to broaden their customer base and boost profits. However, the lack of a cost-efficient logistics planning model often results in the unsatisfactory performance of complex multi-echelon supply chain networks. As transportation planning and scheduling are typically managed via independent entities within the supply network, one key challenge is achieving maximum asset utilization rates considering production flow, logistics cost, and delivery time constraints. This study leverages digital twin capabilities to propose a 4PL-oriented heuristics search model for omnichannel logistics planning and scheduling. The approach aims to enhance transportation flow and resource utilization while shortening waiting times within multi-echelon networks. An industrial case study is featured to validate its cost-effectiveness.
Digital Visualisation Tools to Bridge Communication Across Manufacturing – A Transdisciplinary Journey
Authors: Clausen Pernille , Mathiasen John Bang
Published: November 2022

The future of manufacturing is happening. Today’s cyber technologies allow real-time visual control of various production processes in manufacturing through the intelligent utilisation of data. Having access to knowledge is power. Providing shop floor practitioners with the opportunity to gain and share knowledge across boundaries in manufacturing in the right way at the right time is a prerequisite to doing a proper job controlling a production line. Carrying out shop floor operations without visual control leads to actions performed in blindness based on gut-feeling decision-making and is often prone to errors. This paper investigates a practical problem of securing knowledge integration across production units on the shop floor and managerial stakeholders via a digital visualisation tool (a visualisation board (VB)). The study constitutes an instrumental case study and follows a transdisciplinary engineering process, outlining a collaborative approach with practitioners across various disciplines in a large manufacturing company in the Renewable Energy sector. The findings illustrate an unsuccessful approach to designing and deploying a digital VB. The paper’s contribution is a set of lessons learned from an unsuccessful attempt that highlights the importance of socio-technical solutions to digital transformations as opposed to purely technical solutions.
A Transdisciplinary Approach to a Manufacturing Problem with a Machine Learning Solution
Authors: Wilson Peter , Kinnell Peter , Goh Y. Mey , Pretty Chris , Walpole Andrew
Published: November 2022

The application of machine learning to high cost, low volume (HCLV) manufacture is challenging due to prohibitive costs and low data volumes. An example HCLV application is linear friction welding (LFW) of Blisks (Bladed Disks). LFW is a solid-state joining process, typically used in high integrity aerospace applications. The successful application of machine learning (ML) has the potential to predict quality metrics and enable timely interventions to machine maintenance for avoidance of machine damage or deterioration. This paper proposes a methodology that combines expert knowledge with machine learning to minimise the quantity of weld data required to generate a robust and accurate ML model. Expert knowledge incorporation requires methods of elicitation, capture, standardisation and quantification of information (it can be qualitative, experiential and subjective) and conversion to a quantitative, data driven and digital format for input into a ML algorithm. This paper will describe the methodology developed to enable a combined data science and engineering approach to address complex manufacturing problems. If successful, this methodology will be used as a standard framework for application to HCLV manufacture.
A Model-Based Hybrid System for Human Resource Allocation in Multi-Project Management
Authors: Khatun Mst Taskia , Hiekata Kazuo
Published: November 2022

Multi-project management (MPM) effectively handles project-based organizations that use multi-skilled shared resources to execute projects, which is crucial for engineering teamwork and transdisciplinary research. Hence, allocating appropriate resources to projects is a significant challenge. Furthermore, projects are often executed in dynamic contexts with various sources of uncertainty, necessitating resource reallocation and rescheduling, which might influence other projects due to interdependencies. While mathematical approaches can help with low complexity problems or in a relatively static environment, they have limitations in characterizing interrelationships in multi-project settings and adapting to dynamic change. Considering these problems, we propose a model-based hybrid simulation system comprising system dynamics (SD) and agent-based simulation (ABS). SD can refine the complexity of uncertainty, while ABS provides decision-making support for dynamic changes. ABS models both projects and resources as agents, whereas SD reproduces the cause-and-effect relationship between project activities. Projects require resources to accomplish their scheduled work, while resources provide their skills and staffing. The outcome helps get insights into the impact of dynamic changes on allocation to the project execution process. The system is used as a decision-support tool for evaluating and obtaining feasible resource allocation solutions considering dependencies, uncertain occurrences, and resource constraints. Another intention is to motivate a transdisciplinary-enabling framework to support the systematic integration of knowledge with stakeholders.
A Transdisciplinary Spatial Approach to Creating a Vibrant Entrepreneurial Ecosystem for Regional Development
Authors: Aldana Carlos Mario , Rodríguez Miguel Angel , Ozuna Jr Teofilo
Published: November 2022

Defining vibrancy as the levels of entrepreneurial activity promoted by the coordination of the network and the cooperation that represents an entrepreneurial ecosystem, practitioners and academicians have identified core attributes to create value based on the complex connection of factors to make it vibrant. Nevertheless, some ecosystems do not have efficient activity levels or are not well interconnected, preventing all stakeholders’ total involvement or spillover (value creation and outcomes such as socio-economic development, well-being, and happiness). The contribution of this work is to use a transdisciplinary spatial method (used in measuring the spatial concentration of airline traveling) to measure the vibrancy of an entrepreneurial ecosystem. The framework procures the measurement of vibrancy and its sustainability for value creation in turbulent environments. A vibrant entrepreneurial ecosystem imbues regional development. A case will present a numerical comparison between the level of vibrancy between ecosystems and illustrations of its activity levels for the creation of networks and regional development.
A Transdisciplinary Framework for Engineering Education, Developing Tactical Engineering Decision Making Skills
Authors: Trigos Federico , Tamayo-Enriquez Francisco
Published: November 2022

The use of digital simulators is a well-known practice in managerial courses as tools for strategic decision-making. On the other side, engineering students are used to practicing theoretical knowledge in laboratories or real factories due to the tactical nature of the decisions involved. During the COVID pandemic, universities were forced to limit or cancel access to physical facilities. Engineering professors were challenged to keep educational schedules using digital tools. The contribution of this work is a transdisciplinary framework on how to design engineering practices through digital simulation models to keep or improve prepandemic learning levels. The societal challenge involves a change in paradigm for professors, students, and practitioners. The proposed framework was used to design, implement, and feedback a senior student Six Sigma project course, using a tailormade web-based simulator. Two iterations of the framework are currently deployed: one-way information flow, and two-way interaction. The information obtained so far was the base for the third iteration of the framework which involves three-dimensional virtual reality interaction. Case-based learning and management simulators have been successful at bridging theory and practice for management students. The work in this paper builds on these management practices to achieve equivalent learning levels for engineering students.
A Holistic Education for the 21st Century Engineer Based on Wisdom and Multiplexity
Authors: Qadir Junaid
Published: November 2022

In a volatile, uncertain, complex, and ambiguous (VUCA) 21st-century world, there is a great need for drawing upon capacity for holistic thinking and wisdom. We need to leverage multiple transdisciplinary thinking that allows a multiplexity of ways of thinking and knowing to deal with multi-layered complex reality. Our society is rife with elusive “wicked problems” that defy narrow technological solutions emerging from disciplinary siloed thinking. A narrow education in such times is not only sub-optimal but also dangerous. With the advancement in artificial intelligence, we now have too advanced a technology to be able to survive without wisdom. We require a broad range of experiences and models to study the same problem and the wisdom to choose and combine different models in appropriate ways. We need not only mathematics, engineering, and sciences for engineers but also an exposure to the important ideas in philosophy, psychology, history, economics, sustainability, and significant insights about subjects of enduring interest to human beings.
Learner-Centered Design of Online Courses: A Transdisciplinary Systems Engineering Case Design
Authors: McCormack Cassandra M. , Caldwell Barrett S.
Published: November 2022

Societal shifts associated with the COVID-19 pandemic have exposed challenges associated with online engineering education. These challenges encompass both those inherent to the digital learning environment and those associated with the scalable presentation of content to learners with a range of different backgrounds, learning goals, and user attributes. Universal design principles can be applied to benefit all learners in some cases (e.g., modularized content, captioning of audiovisual material). However, some interface configurations for content presentation and contextualization may benefit one type of learner at the expense of others (e.g., the expertise-reversal effect). Such examples of conflicting user needs indicate a demand for adaptable interfaces that inform the information architecture and user experience interface design. A case design approach applied to a transdisciplinary systems engineering course identifies three primary interface components to target for adaptation: (1) the initial topical “entry point” into the course content, (2) the preferred presentation medium (e.g., text notes, presentation slides, or video), and (3) the navigation mechanisms supporting exploration of the learning environment and highlighting interconnections amongst the material. These adaptations address diversity in backgrounds, learning priorities, presentation preferences, and levels of expertise to appropriately scaffold the learning process for the diversity of learners experiencing transdisciplinary courses.
European Union Conceptualisation of Industry 5.0: Opportunities and Challenges for Transdisciplinary Engineering
Authors: Lattanzio Susan , Goh Yee Mey , Houghton Robert , Garcia Lazaro Aida , Newnes Linda
Published: November 2022

‘Industry 5.0’ has been used as a term to describe alternative visions of the future of industry. Recently, a European Union Research and Innovation Policy Brief used the label Industry 5.0 to define a vision which is not driven by a new technology, but by a changed perception of value founded on human-centricity, sustainability and resilience. Transdisciplinary Engineering (TE) seeks to integrate knowledge and understanding to reduce the negative effect of engineering innovation on the environment and society, thus there likely exists a natural synergy between Industry 5.0 and TE. Consequently, in this paper we seek to understand what the opportunities and challenges of the emergence of Industry 5.0 might be for the field of TE now and in the future. A workshop involving multidisciplinary experts was convened to brainstorm and then explore perceptions. Opportunities include new research areas and potential access to funding. Challenges center on the extent that TE could, or should, align itself with Industry 5.0, and the lack of consensus around definitions of TE. Conclusions find that to attract funding, the community should clearly articulate how TE differs from complimentary and overlapping fields such as interdisciplinarity and systems engineering.
Motivations to Participate in Transdisciplinary COVID-19 Projects
Authors: Sajdakova Jana , Newnes Linda , Carey Emily , Dhokia Vimal
Published: November 2022

Transdisciplinary projects claim to be driven by societal needs and hence it would be expected that motivations are driven by the societal and project beneficiaries. Projects conducted in respect of COVID-19 instinctively meet transdisciplinary status with societal benefit being paramount. This paper presents an analysis of six transdisciplinary COVID-19 projects, assessing the motivations of twenty-nine participants involved. Primary data was collated through semi-structured individual interviews and thematic analysis was used to evaluate the reasons for individual participation. The findings show that of the motivations for participation, ethical motivation was 16%, personal fulfilment was 21% and being able to help was 19%. The extrinsic motivations such as expected rewards and benefits was still present but remained very low at 6%. The qualitative responses from the interviews give an indication that although a societal challenge, the motivations remained more of a personal nature aligning with the societal need.
Standards and Principles to Enable Interoperability and Integration of 5P Medicine Ecosystems
Authors: Blobel Bernd , Ruotsalainen Pekka , Giacomini Mauro
Published: November 2022

Health and social care ecosystems are currently a matter of foundational organizational, methodological and technological paradigm changes towards personalized, preventive, predictive, participative precision (5P) medicine. For designing and implementing such advanced ecosystems, an understanding and correct representation of structure, function and relations of their components is inevitable. To guarantee consistent and conformant processes and outcomes, the specifications and principles must be internationally standardized. Summarizing the first author’s Keynotes over the last 15 years of pHealth conferences, the paper discusses concepts, standards and principles of 5P medicine ecosystems including their design and implementation. Furthermore, a guidance to find and to deploy corresponding international standards in practical projects is provided.
Obstacles of eHealth Capacity Building and Innovation Promotion Initiative in African Countries
Authors: Asah Flora Nah , Kaasbøll Jens Johan , Anthun Kirsti Sarheim
Published: November 2022

eHealth applications and tools have the potential to improve coordination, knowledge, and information sharing between health professionals as well as continuity of care. One of the main obstacles hindering its full integration and use, particularly in the healthcare sector in developing and low and middle-income countries is the lack of qualified staff and healthcare personnel. To explore obstacles that hinder capacity and innovation promotion initiatives, a survey was conducted among BETTEReHEALTH partners. A questionnaire was used to collect quantitative data from 37 organizations. Although there are different buckets of capacity-building and innovation promotion activities going on, the findings showed very few targeting policymakers and eHealth specialists. The findings found that obstacles to capacity building and innovation promotion include lack of finance, poor infrastructure, poor leadership, and governance, and these obstacles are context or region specific. Findings from our study concur with those from previous research on the need to identify practical solutions and simple interventions to address eHealth obstacles to capacity building in developing countries. As measures to mitigate these obstacles, our study proposed the need for adequate policies, strong political commitment, the development of academic modules to be integrated into existing educational programs, and the creation of more in-country and on-site capacity-building activities. While this study contributes to the discourse on eHealth capacity-building and innovation promotion initiatives among healthcare and public health professionals, the study has a limitation as data was collected only from BETTEReHEALTH partners.
Response Center Services: An Exploratory Study
Authors: Sun Luyi , Yang Bian
Published: November 2022

The population aging has facilitated a growing number of welfare technologies and smart home solutions. These technologies enable clinical staff and health care professionals to provide health services in an intelligent way with the trend of patient-centric digital health platforms. As one of the health services, response center service is facing new challenges when connected with welfare technologies, such as false alarms, security threats, privacy leakage, etc. This paper introduces the mechanism of the response center and the role it plays in healthcare. We conduct an exploratory study to find out the benefits and challenges of the response center service from the results of a structured interview. Based on the findings, we identify the required services to improve the intelligent response center mechanism