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This article examines the datafication of ageing by drawing on a practice approach toward care. We describe the datafication of ageing as a matter of care, achieved through the local tinkering of actors – technology designers, care staff, older adults, and highlighting the practices necessary to develop, maintain and implement data infrastructures. This paper draws on research conducted in a qualitative interview study in a LTC facility that uses AI-supported sensors to detect, predict and alarm care staff about falls of older residents. 18 interviews with developers, staff, residents and interest groups were conducted, as well as 24 h of participant observation in the care facility. The results reveal how AI-development for older target groups is characterized by absent data on these populations. Designers turn to practices that decontextualize data from the realities of older adults, relying on domain experts or synthetic data. This decontextualization of data requires recontextualization, with staff and older residents ensuring that the system functions smoothly, adapting their behavior, protecting the system from making false decisions and making existing care arrangements 'fit' the databases used to monitor activities in these arrangements. The ambivalent position of older adults in this data assemblage is further highlighted, as their caring practices are made invisible by different actors through ageist stereotypes, positioning them as being too frail to understand and engage with the system. While their bodily behavior is core for the databases, their perspective on and engagements with the operating system are marginalized, rendering some aspects of ageing hyper-visible, and others invisible. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is a rapidly developing field with growing importance in engineering, mainly as it serves to better understand and manipulate big data. However, the literature is sparse on how to educate engineers on the use of AI applications. For many years, a role-playing simulation known as the 'Beer Distribution Game' has been a staple of industrial engineering education and an established vehicle for teaching the fundamentals of supply chain management (SCM) technology. The purpose of the study is to suggest an alternative and effective way to introduce AI and web-based games to students. This paper introduces and applies two types of web-based AI-enabled tools in a second-year industrial engineering classroom including OpexAnalytics (a web-based AI-enabled Beer Distribution Game learning module) and CompareAssess (a web-based AI-enabled tool to promote 'learning by evaluation'). Findings provide evidence of this 5-week web-based AI-enabled learning module's effectiveness in improving student perceptions and learning outcomes related to the intersection between supply chain management and artificial intelligence. The intention for this module was not to sufficiently prepare students for data science and artificial intelligence work assignments; however, this module offers a starting point for further skill development in higher-level coursework. [ABSTRACT FROM AUTHOR]

Climate change, the growth in world population, high levels of food waste and food loss, and the risk of new disease or pandemic outbreaks are examples of the many challenges that threaten future food sustainability and the security of the planet and urgently need to be addressed. The fourth industrial revolution, or Industry 4.0, has been gaining momentum since 2015, being a significant driver for sustainable development and a successful catalyst to tackle critical global challenges. This review paper summarizes the most relevant food Industry 4.0 technologies including, among others, digital technologies (e.g., artificial intelligence, big data analytics, Internet of Things, and blockchain) and other technological advances (e.g., smart sensors, robotics, digital twins, and cyber-physical systems). Moreover, insights into the new food trends (such as 3D printed foods) that have emerged as a result of the Industry 4.0 technological revolution will also be discussed in Part II of this work. The Industry 4.0 technologies have significantly modified the food industry and led to substantial consequences for the environment, economics, and human health. Despite the importance of each of the technologies mentioned above, ground-breaking sustainable solutions could only emerge by combining many technologies simultaneously. The Food Industry 4.0 era has been characterized by new challenges, opportunities, and trends that have reshaped current strategies and prospects for food production and consumption patterns, paving the way for the move toward Industry 5.0. [ABSTRACT FROM AUTHOR]

This collection on 'Artificial intelligence, big data, algorithms and Industry 4.0 in firms and clusters' is introduced exploring the themes discussed by the nine papers and grouped into three categories to uncover new dynamics and identify future research opportunities for clusters and organizations in these transformative times. The first group explores theoretical aspects of AI and its evolution in social sciences, focusing on industry 4.0, smart cities, big data, and other related topics. The second group examines the role of industrial robots in employment, productivity, and knowledge absorption in industrial districts. The third group discusses innovation in the context of local production systems, AI ecosystems, and the growth and potential of the Metaverse. [ABSTRACT FROM AUTHOR]

With the emergence of the Industrial Revolution 4.0, modern technologies such as cloud computing, artificial intelligence, and big data are profoundly transforming the education ecosystem. The development of education is not only faced with huge challenges but also contains rare opportunities. New concepts such as deep learning, adaptive learning, and blended learning do not break through the inherent barriers of learning theory, and they also expedite the restoration of the educational technology ecosystem. Based on the ASSURE model, this paper systematically discusses the new development concept of educational technology. Heinich, Russell, and Molenda proposed the ASSURE model in 1989. The “A” stands for Analyze Learner, the first “S” is assumed as Objectives and State Standards; the second “S” for Selecting Media, Strategies, Materials, and Technology; the “U” for Utilizing Materials, Media, and Technology; the “R” stands for Required Learner Participation; and the “E” is assigned for Revise and Evaluate. It is widely used in classroom instruction, online learning, and organizational training. Educational technology must embrace new technology, renew the concept of educational development, shape the new pattern of educational ecology, and better serve the needs of cultivating innovative talents. [ABSTRACT FROM AUTHOR]

In order to explore the intelligent path of agricultural sustainable development, this paper combines big data technology to process agricultural sustainable development data with the support of machine learning technology. Moreover, in order to solve the evaluation problem of regional agricultural sustainable development as well as understand the evolution path and evolution law of regional agricultural sustainable growth, this paper starts from the framework model and combines big data in addition to artificial intelligence expertise to construct a sustainable agricultural analysis model. In addition, this paper combines the needs of sustainable agricultural development to set the system function and analyse the realisation process of the system function. At last, this paper proposes a digital simulation experiment to authenticate the accomplishment of the model built in this paper. Through experimental research, it can be known that the intelligent agricultural sustainable development system constructed in this paper has certain effects, and this system can be used in subsequent research to analyse agricultural sustainable development. Based on algorithm verification as well as evaluation of the model on agricultural sustainability analysis, the statistical results are varying from 80 to 90. [ABSTRACT FROM AUTHOR]

Legal Decision Support Systems in Cuba as yet show few results, but now a resurgence of this field is possible. This new opportunity is due to the worldwide boom in AI & Law research. In addition to the current efforts towards the digital transformation of society. This paper aims to review some antecedents, and discuss some proposals for the evolution of this domain in the country. We first identify the strengths and constraints of some previous contributions and outline the current state of this topic in Cuba. Then, we propose some ideas for future projects, considering the main international approaches and state of the art in Artificial Intelligence, Soft Computing, Big data, and Open Data. We argue the importance of creating different forms of legal knowledge representation, the development of specific algorithms based on the most appropriate techniques for each task, updating of jurists' and computer specialists' curricula, and the legislative process improvement. Besides, we advocate the feasibility of creating recommendations in natural language, and we exemplify their types and uses. Finally, the paper calls attention to the protection of personal data and the importance of jurists to participate in the development of systems and to understand how they operate. [ABSTRACT FROM AUTHOR]

Green technology or renewable energy system (RES) is an advancement of the electricity generation which creates pollution free environment. Since last 15–20 years, renewable power plant works in the traditional way over the world and needs technology enhancement. This paper presents a complete review of application of recent advanced technology in the field of RES. The paper analyses and discusses the potential application of artificial intelligence, the internet of things, cloud computing, big data, blockchain and game theory in the area of RESs such as solar energy, wind energy, biomass energy, etc. At the end of this paper, the ordinal assessment of application of recent technology for different objectives of RES is presented. [ABSTRACT FROM AUTHOR]

This paper discusses various forms and sources of algorithmic discrimination. In particular, we explore the connection between – at first glance – 'voluntary' sharing or selling of one's data on the one hand and potential risks of automated decision-making based on big data and artificial intelligence on the other. We argue that the implementation of algorithm-driven profiling or decision-making mechanisms will, in many cases, disproportionately disadvantage certain vulnerable groups that are already disadvantaged by many existing datafication practices. We call into question the voluntariness of these mechanisms, especially for certain vulnerable groups, and claim that members of such groups are oftentimes more likely to give away their data. If these existing datafication practices exacerbate prior disadvantages, they 'compound historical injustices' (Hellman, 2018) and thereby constitute forms of morally wrong discrimination. To make matters worse, they are even more prone to further algorithmic discriminations based on the additional data collected from them. [ABSTRACT FROM AUTHOR]