bluuebunny/crossref_metadata_2025 huggingface dataset:
An improved coupled PDE system applied to the inverse image denoising problem
Authors: Hakoume Abdelmajid El , Afraites Lekbir , Laghrib Amine
Published: May 2022

The problem of interest in this paper is the mathematical and numerical analysis of a new non-variational model based on a high order non-linear PDE system resulting from image denoising. This model is motivated by involving the decomposition approach of $ H^{-1} $ norm suggested by Guo et al. [1,2] which is more appropriate to represent the small details in the textured image. Our model is based on a diffusion tensor that improves the behavior of the Perona-Malik diffusion directions in homogeneous regions and the Weickert model near tiny edges with a high diffusion order. A rigorous analysis of the existence and uniqueness of the weak solution of the proposed reaction-diffusion model is cheked in a suitable functional framework, using the Schauder fixed point theorem. Finally, we carry out a numerical result to show the effectiveness of our model by comparing the results obtained with some competitive models.
Comparison of exact and approximate approaches to UAVs mission contingency planning in dynamic environments
Authors: Radzki Grzegorz , Bocewicz Grzegorz , Wikarek Jaroslaw , Nielsen Peter , Banaszak Zbigniew
Published: May 2022

This paper presents a novel approach to the joint proactive and reactive planning of deliveries by an unmanned aerial vehicle (UAV) fleet. We develop a receding horizon-based approach to contingency planning for the UAV fleet's mission. We considered the delivery of goods to spatially dispersed customers, over an assumed time horizon. In order to take into account forecasted weather changes that affect the energy consumption of UAVs and limit their range, we propose a set of reaction rules that can be encountered during delivery in a highly dynamic and unpredictable environment. These rules are used in the course of designing the contingency plans related to the need to implement an emergency return of the UAV to the base or handling of ad hoc ordered deliveries. Due to the nonlinearity of the environment's characteristics, both constraint programming and genetic algorithm paradigms have been implemented. Because of the NP-difficult nature of the considered planning problem, conditions have been developed that allow for the acceleration of calculations. The multiple computer experiments carried out allow for comparison representatives of the approximate and exact methods so as to judge which approach is faster for which size of the selected instance of the UAV mission planning problem.
Evaluation of Light-Logging Geolocators to Study Mottled Duck Nesting Ecology
Authors: Ringelman Kevin M. , Brasher Michael G. , Marty Joseph R. , Butler David A. , McDowell Stephen , Temple Richard
Published: May 2022

Geolocators are small devices that record and store time-stamped light levels that researchers typically use to approximate the latitude and longitude of small birds across the annual cycle. However, when geolocators are affixed to leg bands of larger-bodied birds, nest incubation by females interrupts the daily pattern of light and darkness. Thus, geolocators can provide information on nesting propensity, nest success, and renesting intensity; these demographic parameters are both difficult to measure unobtrusively and are critically important in determining population dynamics of birds, especially ducks. Here, we deployed 240 geolocators on mottled ducks Anas fulvigula in Louisiana and Texas in 2018–2019 to evaluate their utility in providing nesting data. During July 2018–January 2022, we recovered 16 geolocators from hunter-harvested birds, and learned of 6 other unreported recoveries, yielding a realized recovery rate of 7.1% (9.1% unrealized). Three of the recovered units provided breeding-season data. Two of these clearly indicated a single nest initiation in the early spring of 2019, and one of the units also logged an attempt in spring of 2020. Ducks incubated all three nests for approximately a month, suggesting that they all successfully hatched. The final geolocator logged five putative nest attempts over the course of 2 y. In 2019, both attempts were unsuccessful (incubated ≤ 10 d). In 2020, we documented three attempts spanning 20 February–10 June, all of which appeared to have failed. For all failed attempts, the hen left the nest at dusk or overnight and did not return, which is suggestive of mammalian predation. Geolocators successfully provided information on breeding-season activities of mottled ducks, and we documented renesting rates following nest depredation. However, we achieved a smaller sample size than anticipated (three usable returns), resulting in an effective cost of $11,800 per usable return. Where possible in other species, capturing birds immediately prior to the breeding season, and improvements to geolocator attachment have the potential to improve recovery rates and increase cost effectiveness of the technique.
À la table des stéréotypes
Authors: Fidolini Vulca , Fournier Tristan
Published: May 2022

Cet article interroge les liens entre genre et alimentation à l’heure où les technologies et services alimentaires se démocratisent et où la norme égalitariste progresse. Il s’appuie sur un dialogue fictif entre un homme et une femme au restaurant, dialogue volontairement empreint de stéréotypes de genre de manière à mieux les saisir pour ensuite les discuter et les mettre en perspective avec la littérature scientifique. Quatre thèmes sont investigués : 1) le corps et l’alimentation au prisme des consommations genrées, 2) la place des hommes en cuisine, aux échelles domestique et professionnelle, 3) la question de la complémentarité des rôles sociaux et 4) les influences du phénomène d’urbanisation sur le genre et l’alimentation. Nous constatons une relative permanence des inégalités de genre au sein de la sphère alimentaire tout en étant attentifs aux formes d’ajustement à l’œuvre, que le champ de recherche sur les masculinités contribue à rendre visibles. Au final, plutôt que d’envisager le phénomène d’urbanisation comme un moteur de réduction de ces inégalités, nous attirons surtout l’attention sur la nécessité d’appréhender les modes d’alimentation urbaine de manière intersectionnelle.
Drug Discovery With XAI Using Deep Learning
Authors: Iswarya B.  , Manimekalai K.
Published: May 2022

Deep learning has potential in the process of discovering drug, with enhanced method for analyzing image, structure of molecule and function prediction, along with preset synthesis based on the novel enzymatic structure along tailored features and its applications. Even with expanding quantity based on effective potential approaches, the statistical systems and Machine Learning algorithms that underpin them are sometimes difficult to grasp by the human mind. To meet the required recent paradigm for the automated structure of molecules, for the purpose of 'Explainable Artificial Intelligence' with deep learning approaches. In current era, there is a need for XAI with methods of deep learning to discourse the demand for a developed machine language of the molecular science. This review outlines the important concepts in XAI, possible approaches, and obstacles. It promotes to further development of XAI techniques.
Basic Issues and Challenges on Explainable Artificial Intelligence (XAI) in Healthcare Systems
Authors: Dauda Oladipo Idowu , Awotunde Joseph Bamidele , Muyideen AbdulRaheem  , Salihu Shakirat Aderonke
Published: May 2022

Artificial intelligence (AI) studies are progressing at a breakneck pace, with prospective programs in healthcare industries being established. In healthcare, there has been an extensive demonstration of the promise of AI through numerous applications like medical support systems and smart healthcare. Explainable artificial intelligence (XAI) development has been extremely beneficial in this direction. XAI models allow smart healthcare equipped with AI models so that the results generated by AI algorithms can be understood and trusted. Therefore, the goal of this chapter is to discuss the utility of XAI in systems used in healthcare. The issues, as well as difficulties related to the usage of XAI models in the healthcare system, were also discussed. The findings demonstrate some examples of XAI's effective medical practice implementation. The real-world application of XAI models in healthcare will significantly improve users' trust in AI algorithms in healthcare systems.
Overview of Web Dawdler Outline and FKNN Utilizing Cluster-Based Secret Net
Authors: Mahor Vinod , Bijrothiya Sadhna , Bhujade Rakesh Kumar , Mandloi Jasvant , Mandloi Harshita , Asthana Stuti
Published: May 2022

Everyone uses the world wide web for their everyday tasks. The WWW is divided into several side by sides, deep-net, and black net. It is dangerous to use the world wide web to obtain the deep or secret-net. In this chapter, the dawdler structure will be used for a faster and safer search. Various types of data will be gathered during the search process and kept in the data. The data categorization procedure will be used to determine the webpage's side by side. The fuzzy k-nearest neighbor approach is used in the arrangement procedure. The fuzzy k-nearest neighbor approach categories the results of the dawdling structure stored in the data. Data will be generated in the form of webpage addresses, page information, and other data by the dawdling structure. the fuzzy k-nearest neighbor arrangement result will result in web side by side data. In this chapter, to focus on many dataset tests, there is as much as 30% of the online surface net, 25% deep-net, 24.5% charter, and 30% secret web.
Implementation of Machine Learning Techniques for Analyzing Crime and Dark Web Data
Authors: Sarangi Sanjaya Kumar , Vadlamudi Muniraju Naidu , Balram G  , Sarma C. Sasidhar , Saidulu D. , Sakthidasan Sankaran K.
Published: May 2022

The dark web is a virtually untraceable hidden layer of the internet that is frequently used to store and access secret data. However, a number of situations have been documented in which this platform has been used to covertly undertake illicit and unlawful operations. Traditional crime-solving procedures are inadequate to meet the demands of the current crime environment. Machine learning can be used to detect criminal patterns. Past crime records, social media sentiment analysis, meteorological data, and other sources of data can be used to feed this machine learning technique. Using machine learning, there are five phases to predicting crime. These are data gathering, data classification, pattern recognition, event prediction, and visualization. Using crime prediction technologies, law enforcement agencies can make better use of their limited resources. In this chapter, the authors show the importance of learning the principles of various policies on the dark web and cyber crimes, guiding new researchers through cutting-edge methodologies.
Image Processing for Criminal Pattern Detection Using Machine Learning in the Dark Web
Authors: Dhariwal Sumit , Sharma Avani , Shaoo Biswa Mohan
Published: May 2022

Data mining has been used to present difficulties in this chapter in order to reduce crime. Crime is a major issue for which we must pay a high price in a variety of ways. Here, the authors look at how machine-learning techniques may be used in an image processing method to aid in the detection of and speed up the investigative process by identifying criminal tendencies. The authors look at the supervised approaches, as well as some enhancements, to help in the identification of criminal trends. They tested their approaches with real-world crime data from a police department, and they were satisfied with the results. They also employed clustering approaches to improve the prediction accuracy of criminal record categorization. They also utilized a clustering approach to assist classify crime records and improve prediction accuracy. They created a weighting system for the characteristics below. The machine-learning framework works with geospatial crime plotting and helps detectives and other law enforcement professionals increase their efficiency. It may also be used to combat terrorism and ensure national security.
Internet of Things Security Challenges and Concerns for Cyber Vulnerability
Authors: Khan Shadab Pasha , Rizvi M. A. , Khan Rehan
Published: May 2022

The future is all about machines or devices. Smart devices are strong enough to create a network, establish connections among nodes, and exchange data as and when required. Internet and IoT are two sides of the same coin responsible for ubiquitous connectivity (24x7x365). The modern digital society depends upon IoT in the future to come, which would inherently invite various threats and new challenges due to its diversity. The entire internet is divided into multiple levels (i.e., surface web, deep web, and dark web), which is further explored in terms of providing anonymity. Non-electronic devices can also be connected which then forms the internet of everything (IoE). Security is often ignored by many manufacturers due to the rise in demand which leads to privacy issues. Over the period of time due to IIoT, Industry 4.0, new threats and high-profile IoT-driven cyber-attacks have been identified. For IoT security, we must switch to ML and DL methodologies to identify and resolve new threats and vulnerabilities.
Forecasting the Traits of Cyber Criminals Based on Case Studies
Authors: Bansod Nandini , Kamble Dinesh Baban , Mishra Rina , Kuliha Megha
Published: May 2022

The COVID-19 virus has affected every country on the globe; India is amongst the most with over 3.39 billion people who have been infected, and computer use has expanded since. As cybercrime (breaching, spoofing, DDOS assault, and phishing) is one of the most serious problems facing society today, it's crucial to understand what causes such attacks. Although many methods have been proposed to detect cybercrime, criminological theory of crime is one of them. But the most successful method for detecting these malicious activities is machine learning. This is because most of the cyberattacks have some common characteristics which can be identified by machine learning methods. In this context, an approach has been made in the chapter to review machine learning methods to understand the traits of cyber-criminals and crime committed on the dark web along with suitable methods to tackle them.
Dark Web for the Spread of Illegal Activities Using Tor
Authors: Mahor Vinod , Bijrothiya Sadhna , Bhujade Rakesh Kumar , Mandloi Jasvant , Mandloi Harshita , Asthana Stuti
Published: May 2022

The dark web has been in existence since about the emergence of the internet. There is still a wealth of material indexed on the web that is freely available to anyone with internet connection, regardless of region. There is even more information and data that is concealed and needs specific rights to access. Tor is well-known and extensively used anonymity software that is built on the Tor network and provides secrecy over the vulnerable web. The personal data defense is generally beneficial. This chapter provides a brief summary of established methods to gain access to this section of the web as well as examples of its talents being abused.
A Primer for Developing Computer-Mediated Solutions for the Modern Workforce
Authors: Bednar Edward
Published: May 2022

With perceptual capabilities, computers can intelligently function as a part of our everyday lives, helping us make sense of what is happening as we experience and navigate through many different types of situations. In this way, computing systems can be situated when they combine machine perception with an individual's background knowledge to observe, explore, and interpret human and environmental activity in a way that supports decision-making. Many of the current, prominent situated computing solutions are consumer-focused in nature. But these systems will, in time, change the way we work as well as how we learn. Many disciplines are adapting and changing in the process, and many opportunities remain, including the use of situated computing systems for workforce education. This chapter offers many opportunities on how to innovate and improve workforce education by leveraging the power and affordances of personal mobile devices and intelligent personal assistant technologies to turn everyday situations into everyday learning opportunities.
Experiential Learning for Telehealth in Sports Science and Allied Health
Authors: Mentor Dominic , Leach Lloyd
Published: May 2022

This chapter offers concepts to leverage experiential learning in electronic (e-) and mobile (m-) learning environments with examples from talent development and allied telehealth disciplines. Learning goals need to cater to increasingly complex and ever-changing contexts. Experiential learning can deliver such outcomes in a digital world. E-learning and m-learning cater to the needs of learners who seek dynamic, interactive, multimodal, situated, and personalized learning. How do we incorporate experiential features in e-/m-learning talent development, sport, allied health training, and other workplace contexts? The authors weave the learnings from pilot and action research projects, as well as from real world examples to apply a model of experiential learning for e-/m-learning environments. The model will help learners critically evaluate learning applications in digital environments with tools to design their own experiential learning for e-/m-blended, a/synchronous learning environments.
Mobile Assessment Applications and Training for Professional Examinations
Authors: Sahin Fusun , Mentor Dominic
Published: May 2022

Mobile-assessment applications provide various conveniences to working professionals who prepare for a certification exam. The purposes of this chapter are highlighting the usefulness of m-assessment apps to learners and informing the field about the needs in this area. The chapter focuses on the m-assessment apps in terms of preparing for project management certification examinations. The authors developed nine criteria based on learning theories and literature on m-assessment. These criteria organized important features of m-apps under three categories: 1) general user experience, 2) learning experience, and 3) practice test-experience. The authors also evaluate the features of m-assessment apps found in the app spaces using the criteria. Results communicate how the examined apps perform against the criteria and emphasize different ways that these features can help learners. The chapter concludes with future directions based on the updated requirements of the project management certification exam and the need for more apps that implement theoretically supported features.
African Multi-Wives Culture
Authors: Okeke Ifeanyi J.
Published: May 2022

Human life is the most prized and valued of all creation. No wonder all human activities, government policies, and the entire engagements of humanity are anchored on the betterment and sanctity of human life. Religions all over the world recognize the sacredness of human life as the highest form of creation. Sadly, the sacredness of human life has been facing a downward movement never experienced in the history of mankind in the main as a result of incidences of couples killing each other on a mere or flimsy reason of suspected marital infidelity. Nigeria media quite recently have been agog with daily reportage of such dastardly acts without a corresponding reportage of possible solution located in the multi-wives marriage or culture of the African traditional society. This work focuses on the gains of multi-wives marriage in Africa, which has been in existence since the origin of humanity in Africa. This work is a wake-up call on the media in Nigeria to rise up to the occasion and do the needful and challenge the Western culture of individualistic existentialism.
Rethinking the Idea of Sustainable Development in Africa
Authors: Ugwu Vitalis Chukwuemeka
Published: May 2022

Africa has a long history of exogenous development initiatives usually directed at addressing various developmental challenges in the continent. For instance, at the dawn of the 21st century, there was the Millennium Development Goals (MDGs) meant to address the indignity of poverty especially in Africa. After that, came the New Partnership for Africa's Development (NEPAD). Like the MDGs before it, the objective of NEPAD was to eradicate poverty and place Africa on a sustainable process of development. The latest in the list of these development initiatives was the 2030 Sustainable Development Goals (SDGs) meant again to address the issues of poverty, hunger, health, gender equality, among others. However, in spite of this long history of exogenous development initiatives, Africa remains largely underdeveloped – a pointer to something intrinsically and specially wrong with Africa. This chapter therefore calls for a more endogenous approach to tackling African development challenges as the continent has peculiar problems requiring endogenous solutions.
Expounding the Edge/Fog Computing Infrastructures for Data Science
Authors: Raj Pethuru , J. Pushpa
Published: May 2022

Data is the new fuel for any system to deliver smart and sophisticated services. Data is being touted as the strategic asset for any organization to plan ahead and provide next-generation capabilities with all the clarity and confidence. Whether data is internally sourced or aggregated from different and distributed source, it is essential for all kinds of data to be continuously and consciously collected, transmitted, cleansed, and hosted on storage systems. There are several types of analytical methods and machines to do deeper and decisive analytics on those curated and consolidated data to extract actionable insights in real-time. Precise and concise analytics guarantee perfect decision-making and action. We need competent and highly integrated analytics platform for speeding up, simplifying and streamlining data analytics, which is becoming a hard nut to crack due to the multi-structured and massive quantities of data. On the infrastructure front, we need highly optimized compute, storage and network infrastructure for achieving data analytics with ease. Another noteworthy point is that there are batch, real-time, and interactive processing of data. Most of the personal and professional applications need real-time insights in order to produce real-time applications. That is, real-time capture, processing, and decision-making are being insisted and hence the edge or fog computing concept has become very popular. This chapter is exclusively designed in order to tell all on how to accomplish real-time analytics on fog devices data.
Lightweight Virtualization for Edge Computing
Authors: Rossi Fabio Diniz , Neves de Castro Bruno Morais , Batista dos Santos Matheus Breno
Published: May 2022

In infrastructure as a service (IaaS), the edge computing paradigm proposes the network node distribution of storage and computing resources to ensure swift access throughout a fog environment. The edge platform landscape fragmentation requires flexible and scalable approaches. Based on the above, the most recent works highlight lightweight virtualization, the process of making any hardware shares its resources with other applications without impacting on performance issues. In this sense, this chapter conveys current concepts, techniques and open challenges of lightweight virtualization for edge computing.
Edge-of-Things Computing-Based Smart Healthcare System
Authors: Yacchirema Diana , Palau Carlos , Esteve Manuel
Published: May 2022

Any delay introduced in healthcare applications could critically affect the health of patients. Edge computing paradigm has been introduced recently as an alternative to develop such applications, where a rapid response is necessary to ensure the immediate assistance of patients when they need help. In this chapter, the authors propose an edge-of-things computing-based architecture, which illustrates the benefits of the realization of IoT under edge computing approach. The proposed architecture offers significant advantages: 1) it reduces the latency time in the data transmission for processing and analysis; 2) it improves the response time of the delivery of notifications or emergency alerts; 3) it provides real-time processing and big data analysis in the proximity of data sources; 4) it enables interoperability between heterogeneous devices; and 5) it provides security and QoS in the data transmission. The usefulness and relevance of the proposed architecture is evaluated through the implementation of a smart healthcare system applied to a medical case study.
Probabilistic-QoS-Aware Multi-Workflow Scheduling Upon the Edge Computing Resources
Authors: Tang Tao , Ma Yuyin , Feng Wenjiang
Published: May 2022

Edge computing is an evolving decentralized computing infrastructure by which end applications are situated near the computing facilities. While the edge servers leverage the close proximity to the end-users for provisioning services at reduced latency and lower energy costs, their capabilities are constrained by limitations in computational and radio resources, which calls for smart, quality-of-service (QoS) guaranteed, and efficient task scheduling methods and algorithms. For addressing the edge-environment-oriented multi-workflow scheduling problem, the authors consider a probabilistic-QoS-aware approach to multi-workflow scheduling upon edge servers and resources. It leverages a probability-mass function-based QoS aggregation model and a discrete firefly algorithm for generating the multi-workflow scheduling plans. This research conducted an experimental case study based on varying types of workflow process models and a real-world dataset for edge server positions. It can be observed the method clearly outperforms its peers in terms of workflow completion time, cost, and deadline violation rate.
Constructive Solutions for Security and Privacy Issues at the Edge
Authors: S. Indra Priyadharshini , Ulaganathan Pradheeba , M. Vigilson Prem , B. R. Yuvaraj
Published: May 2022

The evolution in computing strategies has shown wonders in reducing the reachability issue among different end devices. After centralized approaches, decentralized approaches started to take action, but with the latency in data pre-processing, computing very simple requests was the same as for the larger computations. Now it's time to have a simple decentralized environment called edge that is created very near to the end device. This makes edge location friendly and time friendly to different kinds of devices like smart, sensor, grid, etc. In this chapter, some of the serious and non-discussed security issues and privacy issues available on edge are explained neatly, and for a few of the problems, some solutions are also recommended. At last, a separate case study of edge computing challenges in healthcare is also explored, and solutions to those issues concerning that domain are shown.
The Role of Edge/Fog Computing Security in IoT and Industry 4.0 Infrastructures
Authors: Mutluturk Meltem , Kor Burcu , Metin Bilgin
Published: May 2022

The development of information and communication technologies (ICT) has led to many innovative technologies. The integration of technologies such as the internet of things (IoT), cloud computing, and machine learning concepts have given rise to Industry 4.0. Fog and edge computing have stepped in to fill the areas where cloud computing is inadequate to ensure these systems work quickly and efficiently. The number of connected devices has brought about cybersecurity issues. This study reviewed the current literature regarding edge/fog-based cybersecurity in IoT to display the current state.
Next Generation Multi-Access Edge-Computing Fiber-Wireless-Enhanced HetNets for Low-Latency Immersive Applications
Authors: Ebrahimzadeh Amin , Maier Martin
Published: May 2022

Next generation optical access networks have to cope with the contradiction between the intense computation and ultra-low latency requirements of the immersive applications and limited resources of smart mobile devices. In this chapter, after presenting a brief overview of the related work on multi-access edge computing (MEC), the authors explore the potential of full and partial decentralization of computation by leveraging mobile end-user equipment in an MEC-enabled FiWi-enhanced LTE-A HetNet, by designing a two-tier hierarchical MEC-enabled FiWi-enhanced HetNet-based architecture for computation offloading, which leverages both local (i.e., on-device) and nonlocal (i.e., MEC/cloud-assisted) computing resources to achieve low response time and energy consumption for mobile users. They also propose a simple yet efficient task offloading mechanism to achieve an improved quality of experience (QoE) for mobile users.
Edge Computing-Induced Caching Strategy for National Traditional Sports Video Resources by Considering Unusual Items
Authors: Pan Wenwen , Liu Bei , Song Zhiliang
Published: May 2022

In order to promote the development of national traditional sports to carry forward the spirit and culture of a country or nation, this paper designs a system for national traditional sports video distribution with the help of software-defined network and mobile edge computing technologies. Thus, the popular national traditional sports resources can be cached in mobile edge computing servers, which can reduce the delay time from cloud center directly. In order to improve the hit rate of the cached videos, the ant colony-stimulated annealing is used as the caching strategy. The experimental results show that the ant colony-stimulated annealing caching strategy can increase the hit rate of the contents in mobile edge computing servers as well as decrease the delay time of the request videos. The ant colony-stimulated annealing caching strategy performs better than previous caching strategies for updating contents in mobile edge computing servers.
A Comprehensive Survey on Edge Computing for the IoT
Authors: Pravin A.  , Jacob Prem , Nagarajan G.
Published: May 2022

The IoT concept is used in various applications and it uses different devices for collecting data and processing the data. Various sets of devices such as sensors generate a large amount of data and the data will be forwarded to the appropriate devices for processing. The devices used will range from small devices to larger devices. The edge computing becomes the major role in overcoming the difficulties in cloud computing, the nearby devices are used as servers for providing better services. Most of the issues such as power consumption, data security, and response time will be addressed. The IoT plays a major role in many real-world applications. In this chapter, the basics and the use of the Edge computing concept in different applications are discussed. Edge computing can be used to increase the overall performance of the IoT. The performance of various applications in terms of edge computing and other methodologies are analyzed.
Edge Computing-Based Internet of Things Framework for Indoor Occupancy Estimation
Authors: Rastogi Krati , Lohani Divya
Published: May 2022

Indoor occupancy estimation has become an important area of research in the recent past. Information about the number of people entering or leaving a building is useful in estimation of hourly sales, dynamic seat allocation, building climate control, etc. This work proposes a decentralized edge computing-based IoT framework in which the majority of the data analytics is performed on the edge, thus saving a lot of time and network bandwidth. For occupancy estimation, relative humidity and carbon dioxide concentration are used as inputs, and estimation models are developed using multiple linear regression, quantile regression, support vector regression, kernel ridge regression, and artificial neural networks. These estimations are compared using execution speed, power consumption, accuracy, root mean square error, and mean absolute percentage error.
Challenges for Convergence of Cloud and IoT in Applications and Edge Computing
Authors: S. Rashmi , S. Roopashree , V. Sathiyamoorthi
Published: May 2022

Cloud computing and internet of things (IoT) are two disparate technologies that can be united for a common purpose as in an operating profit. The technologies are integral parts of modern sophisticated human life. In the future, it is destined to proliferate boundlessly covering utmost spheres. This chapter describes the challenges faced in adopting the two technologies. Edge computing includes both computing and processing the information are carried at the edge of the IoT devices where vast information gathered instead of relying on the central location. Benefits include avoiding latency issues, improving the performance of the application, and cost effectiveness as it reduces the data volume to be processed in cloud/centralized location. In the advent of IoT devices, edge computing is a vital step in building any of its application which sends and receives enormous information to and from the cloud over the course of operations. Applications such as virtual reality and smart systems are benefited by edge computing as they expect higher rate of response and processing speed. A case study on video surveillance is done in this chapter.
A Constrained Static Scheduling Strategy in Edge Computing for Industrial Cloud Systems
Authors: Ma Yuliang , Han Yinghua , Wang Jinkuan , Zhao Qiang
Published: May 2022

With the development of industrial internet, attention has been paid for edge computing due to the low latency. However, some problems remain about the task scheduling and resource management. In this paper, an edge computing supported industrial cloud system is investigated. According to the system, a constrained static scheduling strategy is proposed to over the deficiency of dynamic scheduling. The strategy is divided into the following steps. Firstly, the queue theory is introduced to calculate the expectations of task completion time. Thereupon, the task scheduling and resource management problems are formulated and turned into an integer non-linear programming (INLP) problem. Then, tasks that can be scheduled statically are selected based on the expectation of task completion and constrains of various aspects of task. Finally, a multi-elites-based co-evolutionary genetic algorithm (MEB-CGA) is proposed to solve the INLP problem. Simulation result shows that the MEB-CGA significantly outperforms the scheduling quality of greedy algorithm.
Let's Talk About Artificial Intelligence
Authors: Watanabe Alice
Published: May 2022

Internationally, the interest in applying AI technologies in higher education is growing rapidly. Currently, the focus is on extensive research and implementation efforts through which AI applications are to be transferred into higher education contexts. Neglected aspects are the questions of how university agents can grasp the complexity of AI and how the exchange about AI can be strengthened. The chapter starts with this consideration and presents the scholarship of teaching and learning (SoTL) concept as a possible basis to strengthen the AI discourse between teachers and researchers. To this end, it first discusses different aspects of AI in higher education and then shows how AI projects can be structured. This is followed by an introduction to the SoTL concept. Subsequently, the concept will be examined as an example of the extent to which SoTL can be used to process AI projects.
Postsecondary Administrative Leadership and Educational AI
Authors: Selznick Benjamin S. , Titareva Tatjana N.
Published: May 2022

The purpose of this chapter is to introduce an ethical, shared approach to postsecondary administrative leadership in the current and future-facing landscape of AIEd. The chapter opens by providing coverage of both AIEd and ethical, shared, and administrative leadership in higher education settings. Next, it demonstrates applications of AIEd that confront administrative leaders and warrant sustained attention. The chapter then offers the parameters for an ethical discourse, one that is mission-responsive, transparent/accountable, and equity-minded. The chapter closes by providing implications for practices reflecting knowledge sharing, policy, and a student-centered ethic of care.
How Is Socially Responsible Academic Performance Prediction Possible?
Authors: Keller Birte , Lünich Marco , Marcinkowski Frank
Published: May 2022

The availability of big data at universities enables the use of artificial intelligence (AI) systems in almost all areas of the institution: from administration to research, to learning and teaching, the use of AI systems is seen as having great potential. One promising area is academic performance prediction (APP), which is expected to provide individual feedback for students, improve their academic performance and ultimately increase graduation rates. However, using an APP system also entails certain risks of discrimination against individual groups of students. Thus, the fairness perceptions of affected students come into focus. To take a closer look at these perceptions, this chapter develops a framework of the “perceived fairness” of an ideal-typical APP system, which asks critical questions about input, throughput and output, and based on the four-dimensional concept of organizational justice, sheds light on potential (un-)fairness perceptions from the students' point of view.
The Challenges Brought About by the IoT Revolution
Authors: Odhiambo Marcel Ohanga , Mwashita Weston
Published: May 2022

The internet of things (IoT) revolution is affecting a wide range of academic and industrial disciplines in positive ways. Consumer applications like smart home devices and wearables are giving way to mission-critical applications like public safety, emergency response, industrial automation, self-driving cars, and the internet of medical things. This chapter provides an overview of the internet of things as well as its history. Even though the IoT market is booming, several obstacles are keeping the technology from reaching its full potential. Many of the issues that exist are highlighted and clearly explained in this chapter, with the goal of making it easier for a wide range of scholars/researchers to provide feasible solutions to the challenges. Businesses who embrace IoT ideas and learn to harness the data generated by the internet of things will survive and thrive in the future.
An Effective Secured Privacy-Protecting Data Aggregation Method in IoT
Authors: Pramanik Sabyasachi
Published: May 2022

Because privacy concerns in IoT devices are the most sensitive of all the difficulties, such an extreme growth in IoT usage has an impact on the privacy and life spans of IoT devices, because until now, all devices communicated one to one, resulting in high traffic that may shorten the life of unit nodes. In addition, delivering data repeatedly increases the likelihood of an attacker attacking the system. Such traffic may exacerbate security concerns. The employment of an aggregator in the system as an intermediary between end nodes and the sink may overcome these problems. In any system with numerous sensors or nodes and a common controller or sink, we can use an intermediate device to combine all of the individual sensor data and deliver it to the sink in a single packet. Aggregator is the name given to such a device or component. Data aggregation is carried out to decrease traffic or communication overhead. In general, this strategy helps to extend the life of a node while also reducing network transmission.
Synthesis and Characterization Nanosilica from Geothermal Waste by Wet Grinding Method as an Enhanced Oil Recovery (EOR)
Authors: Andriyan Mohammad Wahyu , Umiati Ngurah Ayu Ketut , Gunawan Vincensius , Subagio Agus
Published: May 2022

In this study, nanosilica for Enhanced Oil Recovey (EOR) has been successfully synthesized using geothermal waste. The method used is wet grinding method with deposition time variations to determine its effect on time deposition on particle size. Characterization using X-Ray Fluoroscene (XRF), X-Ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) were also carried out to determine the characteristics of the material. Wettability of nanosilica then was confirmed by contact angle test. Based on the results, it was shown that the purified silica has silica content up to 98.85% with an amorphous phase. The results of Particle Size Analyzer (PSA) show that deposition time determines nanoparticles size. The longer time deposition, the smaller particles can be produced with the best result at 4 days deposition because it can produced the smallest particle at 71.5 nm. The morphology of nanosilica is granular that has been confirmed by SEM. Contact angle test reveals that nanosilica geothermal waste has ability to change wettability. Nanosilica can decrease contact angel of crude oil from 23,3o to 13,9o.
Oxygen Electrochemical Production from Air by High Performance Anion Exchange Membrane Electrode Assemble Devices
Authors: Tian Bo Yuan , Deng Zhan Feng , Xu Gui Zhi , Song Xiao Yun , Zhang Gao Qun , Deng Jia Yao
Published: May 2022

CoNi and FeCoNi hydroxide with narrow voltage distance between oxygen reduction reaction and oxygen evolution reaction was synthetized by electro-deposition in low solvent concentration. 5cm2 Membrane electrode assemble (MEA) electrolyzers composed with anion exchange membrane, homogenerated catalyst on both cathode and anode gas diffusion layer (GDL) was fabricated for oxygen electrochemical production from air. The current and yield of binary CoNi device reached up to 466.7mA and 4.4mmol/h (94.7% conversion rate) at 1.2V. The ternary FeCoNi device showed only 0.5% degradation from 394.0mA during 12h. The applicability of oxygen production from air by high performance electrochemical devices was demonstrated.
Plastic Waste Recycle into Pellet: Economic Analysis and Processing Temperature Effects
Authors: Badu Risti Ristianingsih , Purnomo Chandra Wahyu , Kamulyan Budi
Published: May 2022

Waste from plastic products can be considered complex materials, making recycling a challenge. PET and LDPE are common type of plastic that is easily found in garbage pile. This research was conducted to study the potential of plastic waste conversion into solid fuel to reduce the accumulation of this waste in the environment. The conversion method is to use an extruder. An economic analysis was performed to measure the feasibility of this recycling method. PET and LDPE plastic waste were recycled into pellets using simple heated-extruder equipment after being shredded into small flakes. The extruder temperature was varied at 120OC, 130OC, 140OC, 150OC, 160OC, and 180OC. The optimum temperature for LDPE pellets is 120OC, and PET pellets is 130OC. The highest density LDPE pellets is 966.7 kg/m3, and PET pellets is 1320 kg/m3. The highest compressive strength LDPE pellets is 1041.1 kg/m2, and PET pellets is 615.2 kg/m2. The calorific value of the recycled LDPE and PET pellets is 43.52 mJ/kg and 27.26 mJ/kg, respectively. The moisture value in each plastic pellet did not change significantly by temperature difference. Meanwhile, the economic feasibility analysis shows an NPV of Rp4,394,049,079; IRR of 25.18%; B/CR of 1.465; and PP of 5.95 years.
Numerical Comparison of the Elastic Response of Stochastic Tow-Based Composites with Different Chip Consolidation Methods
Authors: Ryatt Jeremy , Ramulu Mamidala
Published: May 2022

Stochastic tow based discontinuous composites (STBDC) are fabricated from the compression molding of chips made from chopped and slit pre-impregnated uni-directional carbon fiber reinforced polymer (CFRP) tape. The discontinuous mesostructure gives the material system increased moldability versus continuous fiber composites allowing complex three-dimensional parts to be manufactured. However, the discontinuous mesostructure creates challenges for engineers designing parts as the effective properties are variable. Furthermore, the properties have been shown to be a function of the consolidation method of the chips. This study uses finite element analysis simulations of mesostructural representative volume elements to compare the elastic response and characteristics. The results are compared to the available literature regarding the elastic response STBDCs.
Effect of Hydrogen Peroxide Concentration to Fluorescence Properties of Carbon Dot from HDPE
Authors: Lestari Ratih , Kartini Indriana , Wahyuningsih Tutik Dwi
Published: May 2022

This research paper describes the effect of hydrogen peroxide concentrations (0; 1; 3; 5 and 7 wt%) on fluorescence properties of carbon dots (CDs) from high-density polyethylene (HDPE) plastic. Synthesis of CDs has been carried out using modified pyrolysis and hydrothermal methods. The CDs obtained were characterized by FTIR, XRD, UV-Visible and fluorescence spectrophotometer. Based on UV-Visible spectra, the maximum wavelength of carbon dot ranges from 287 to 291 nm, indicating there is an π -π* electron transition belonging to the core site (C=C), then the absorption widens to 400 nm which indicated n-π* electron transition relating to the CDs surface functional group (carbonyl, hydroxyl, and carboxyl). The use of hydrogen peroxide (5 wt%) can produce CDs with the best fluorescence properties based on fluorescence spectra. CDs has a structure like graphite which is rich in functional groups on its surface. The photocatalytic activity of carbon dot has been tested and it showed that CDs can degrade methylene blue (MB) dye under visible light (80.3%).
Antithrombotic Therapy after Transcatheter Aortic Valve Replacement
Authors: Mousa Tariq A. M. , Mahfouz Ahmed , Mohammed Nazar
Published: May 2022

Transcatheter aortic valve replacement (TAVR) is a treatment option for patients with asymptomatic severe aortic stenosis who are candidates for a bioprosthesis across the entire spectrum of risk. TAVR carries a risk for thrombotic and bleeding events, focusing on the importance of defining the optimal antithrombotic regimen. Patients undergoing TAVR are mostly elderly and have many comorbidities such as atrial fibrillation (AF) requiring oral anticoagulants (OACs) or coronary artery disease requiring antiplatelet agents. After TAVR among patients without baseline indications for OAC, recent data suggest dual-antiplatelet therapy is associated with an increased risk for bleeding events, particularly early postprocedure compared with single-antiplatelet therapy with aspirin. The risk of leaflet thrombosis in patients undergoing TAVR raised concern about the use of OAC in patients without an initial indication for anticoagulation therapy. Although it showed effectiveness in modulating thrombus formation at the valve level, the bleeding hazard has shown to be unacceptably high, and the net benefit of combining antiplatelet and OAC therapy is unproven. For patients with indications for the use of long-term OAC, such as those with AF, adding antiplatelet therapy increases bleeding events. A favorable effect of new OAC agents over Vitamin K antagonists is debatable. Overall, single-antiplatelet therapy and OAC appear to be reasonable strategies in patients without and with indications for concurrent anticoagulation, respectively. This article aims to review the available published studies and recommendations in the literature regarding the use of antithrombotic therapy post-TAVR.