Search

In this practice-based pedagogical paper, we, the university faculty of Education, Food and Nutrition, Sociology, and History in the U.S., describe how we started a Reflective Practice Teaching Circle at our institution for interdisciplinary dialogue about the effective use of social media (SM) for teaching and learning. Our discussions led to the design of the Social Media Entry Model that educators can use for decision-making. We begin this paper with a brief synthesis of scholarly literature describing students' SM use and how higher education faculty use SM to support 21st-century skills development. Next, we describe the institutional, individual, and pedagogical barriers that prevent faculty from embracing SM as a teaching and learning tool. Based on our shared vision and praxis, we present the Social Media Entry Model and describe how educators can use it when deciding how to integrate SM into the formal or informal curriculum. Through our narratives, we illustrate how we use a variety of SM platforms and different entry points in the model to enhance students' 21st-century skills. We also discuss the legal and ethical issues that educators must consider to ensure that university students use SM in a socially responsible manner.

This paper presents a midterm review of a 4-year factor analysis project aimed at validating an outcome-based assessment of study-abroad programs attended by Japanese students. This paper outlines how the results from the initial two years captured changes in perceptions and reasons for studying abroad. It found that students have become increasingly focused on how the experience will impact their future careers. This is a shift from those who studied abroad before the COVID-19 pandemic. Those students motivations for studying abroad were primarily internal and experiential, such as wanting to improve their language skills and experience life in another country, or external and passive reasons arising from the circumstances or opinions of family or friends. The research also indicates how awareness of this shift could assist administrators in designing and conducting successful international experiences.

This design case details the design process of a multiple-choice assessment of socio-scientific systems thinking. This assessment is situated within a larger project that aims to understand the ways students use multiple scientific models to understand complex socio-scientific issues. In addition to the research component, this project entails developing curriculum and assessment resources that support science teaching and learning. We begin this paper by framing the needs that motivated the design of this assessment and introducing the design team. We then present a narrative outlining the design process, focusing on key challenges that arose and the ways these challenges influenced our final design. We conclude this paper with a discussion of the compromises that had to be made in the process of designing this instrument.

ICT advancements have enabled more online activities, resulting in several cyberethical behaviours. The literature documents the prevalence of plagiarism and online fraud, among others. While behaviour has been explained by several theories, as scholarship and research advance, frameworks are modified to include more constructs. This paper proposes a developed framework for studying cyberethical behaviour in North Central Nigeria, with a focus on exploring the factors that influence individuals' attitudes, awareness, and perception of cyberethics. The framework is based on a modified reasoned action approach model and incorporates key constructs such as attitude, subjective norm, perceived behavioral control, and awareness of cyber ethics. An adapted questionnaire was used as an instrument for collecting quantitative data. This study used multistage sampling. A sample size of 989 north-central Nigerian female university postgraduate students was selected from a population of 9,000. Perception and attitude toward cyberethical behaviour were positively correlated. Perception, awareness, and attitude toward cyberethical behaviour correlated. Perception and awareness significantly affected cyberethical behaviour. The study aims to provide insights into the factors that shape individuals' decision-making processes regarding cyberethics and to identify potential areas for intervention and education. The paper also discusses the importance of promoting cyberethics in Nigeria, given the increasing use of technology and the growing threat of cybercrime. The proposed framework offers a valuable tool for researchers and policymakers seeking to understand and address cyberethical behaviour in North Central Nigeria.

The financial motivation to earn advertising revenue has been widely conjectured to be pivotal for the production of online misinformation 1-4 . Research aimed at mitigating misinformation has so far focused on interventions at the user level 5-8 , with little emphasis on how the supply of misinformation can itself be countered. Here we show how online misinformation is largely financed by advertising, examine how financing misinformation affects the companies involved, and outline interventions for reducing the financing of misinformation. First, we find that advertising on websites that publish misinformation is pervasive for companies across several industries and is amplified by digital advertising platforms that algorithmically distribute advertising across the web. Using an information-provision experiment 9 , we find that companies that advertise on websites that publish misinformation can face substantial backlash from their consumers. To examine why misinformation continues to be monetized despite the potential backlash for the advertisers involved, we survey decision-makers at companies. We find that most decision-makers are unaware that their companies' advertising appears on misinformation websites but have a strong preference to avoid doing so. Moreover, those who are unaware and uncertain about their company's role in financing misinformation increase their demand for a platform-based solution to reduce monetizing misinformation when informed about how platforms amplify advertising placement on misinformation websites. We identify low-cost, scalable information-based interventions to reduce the financial incentive to misinform and counter the supply of misinformation online., (© 2024. The Author(s).)

Decision prediction based on neurophysiological signals is of great application value in many real‐life situations, especially in human–AI collaboration or counteraction. Single‐trial analysis of electroencephalogram (EEG) signals is a very valuable step in the development of an online decision‐prediction system. However, previous EEG‐based decision‐prediction methods focused mainly on averaged EEG signals of all decision‐making trials to predict an individual's general decision tendency (e.g., risk seeking or aversion) over a period rather than on a specific decision response in a single trial. In the present study, we used a rock–paper–scissors game, which is a common multichoice decision‐making task, to explore how to predict participants' single‐trial choice with EEG signals. Forty participants, comprising 20 females and 20 males, played the game with a computer player for 330 trials. Considering that the decision‐making process of this game involves multiple brain regions and neural networks, we proposed a new algorithm named common spatial pattern‐attractor metagene (CSP‐AM) to extract CSP features from different frequency bands of EEG signals that occurred during decision making. The results showed that a multilayer perceptron classifier achieved an accuracy significantly exceeding the chance level among 88.57% (31 of 35) of participants, verifying the classification ability of CSP features in multichoice decision‐making prediction. We believe that the CSP‐AM algorithm could be used in the development of proactive AI systems. [ABSTRACT FROM AUTHOR]

An editorial is presented on the release of a white paper by the International Federation of Dental Hygienists (IFDH), summarizing findings from 5 global surveys on oral health conducted between 2019 and 2023. Topics discussed include toothpaste and electric toothbrush recommendations, oral-systemic links, sustainable dentistry, and oral hygiene instruction practices, revealing gaps between evidence-based knowledge and clinical decision making among dental hygienists.

Contemporary wildlife disease management is complex because managers need to respond to a wide range of stakeholders, multiple uncertainties, and difficult trade-offs that characterize the interconnected challenges of today. Despite general acknowledgment of these complexities, managing wildlife disease tends to be framed as a scientific problem, in which the major challenge is lack of knowledge. The complex and multifactorial process of decision-making is collapsed into a scientific endeavor to reduce uncertainty. As a result, contemporary decision-making may be oversimplified, rely on simple heuristics, and fail to account for the broader legal, social, and economic context in which the decisions are made. Concurrently, scientific research on wildlife disease may be distant from this decision context, resulting in information that may not be directly relevant to the pertinent management questions. We propose reframing wildlife disease management challenges as decision problems and addressing them with decision analytical tools to divide the complex problems into more cognitively manageable elements. In particular, structured decision-making has the potential to improve the quality, rigor, and transparency of decisions about wildlife disease in a variety of systems. Examples of management of severe acute respiratory syndrome coronavirus 2, white-nose syndrome, avian influenza, and chytridiomycosis illustrate the most common impediments to decision-making, including competing objectives, risks, prediction uncertainty, and limited resources., (© ([0‐9]+) Society for Conservation Biology.)

Aim/Purpose: The purpose of this paper is to address the challenges posed by disinformation in an educational context. The paper aims to review existing information assessment techniques, highlight their limitations, and propose a conceptual design for a multimodal, explainable information assessment system for higher education. The ultimate goal is to provide a roadmap for researchers that meets current requirements of information assessment in education. Background: The background of this paper is rooted in the growing concern over disinformation, especially in higher education, where it can impact critical thinking and decision-making. The issue is exacerbated by the rise of AI-based analytics on social media and their use in educational settings. Existing information assessment techniques have limitations, requiring a more comprehensive AI-based approach that considers a wide range of data types and multiple dimensions of disinformation. Methodology: Our approach involves an extensive literature review of current methods for information assessment, along with their limitations. We then establish theoretical foundations and design concepts for EMIAS based on AI techniques and knowledge graph theory. Contribution: We introduce a comprehensive theoretical framework for an AI-based multimodal information assessment system specifically designed for the education sector. It not only provides a novel approach to assessing information credibility but also proposes the use of explainable AI and a three-pronged approach to information evaluation, addressing a critical gap in the current literature. This research also serves as a guide for educational institutions considering the deployment of advanced AI-based systems for information evaluation. Findings: We uncover a critical need for robust information assessment systems in higher education to tackle disinformation. We propose an AI-based EMIAS system designed to evaluate the trustworthiness and quality of content while providing explanatory justifications. We underscore the challenges of integrating this system into educational infrastructures and emphasize its potential benefits, such as improved teaching quality and fostering critical thinking. Recommendations for Practitioners: Implement the proposed EMIAS system to enhance the credibility of information in educational settings and foster critical thinking among students and teachers. Recommendation for Researchers: Explore domain-specific adaptations of EMIAS, research on user feedback mechanisms, and investigate seamless integration techniques within existing academic infrastructure. Impact on Society: This paper's findings could strengthen academic integrity and foster a more informed society by improving the quality of information in education. Future Research: Further research should investigate the practical implementation, effectiveness, and adaptation of EMIAS across various educational contexts.

Post-secondary institutions are required to complete a variety of quality assurance activities including external program review, a process of inviting external reviewers from outside the institution to provide constructive feedback on all aspects of an academic program. While external program reviews have been completed for decades, there has been little work to ensure that meaningful change occurs as a result. Assessment of outcomes at the department level are limited and research specifically considering external academic program review as a primary quality assurance activity is scant. Each of these limitations is even more significant when considered at the institutional level. From our experience, there is minimal consideration in research or practice to ensure that external review recommendations have an impact on educational planning at the institutional level. To help address this concern, in this paper we describe an approach involving secondary research and thematic document analysis that can be leveraged to enhance institutional decision-making processes by uncovering institutional themes evident across external reviews. We suggest that our approach may be helpful for other higher education institutions striving to make sense of program review recommendations from an institutional perspective.

Chinese students studying abroad have been increasing rapidly in the past decades and become a significant financial contribution to receiving countries. Accordingly, understanding their enrollment choice is essential to facilitate college marketing and admission strategies. Though the decision process is believed to be different from domestic students, empirical analysis of Chinese students' enrollment choices is still lacking. This paper fills the void by examining the influential factors of Chinese students' enrollment choice with novel student-level data. We find that in addition to factors domestic students typically consider, such as financial aid and academic quality, Chinese students particularly emphasize college ranking, reputation, and location in their decision process. Furthermore, unlike domestic students who usually prefer colleges with proximity to home, Chinese students' location preference is linked to job prosperity. We also find that the impact of the factors varies for students from different regions of China, which can be attributable to uneven economic development within the country.

Economic man ("homo economicus") in the view of classical economics is an individual who is independent, autonomous, and rational, who pursue maximum satisfaction with minimum costs. However, as a human being, we often make irrational or unreasonable decisions because of cognitive biases. The irrationality of the individual due to instincts or feelings that are used as a consideration in making decisions without using rationality in thinking. This topic can be studied in the discussion of Behavioral "Economics." The dominance of neoclassical economics in educational curricula is a prominent concern. However, promoting behavioral economics may encourage students to make better economic decisions. This study aims to develop and assess the feasibility and effectiveness of learning materials in behavioral economics for higher education in order to help students make complex decisions. This paper followed the development procedures outlined by Dick and Carey for instructional design and was tested in the classroom using an experimental approach. Four experts validated the content, and 30 undergraduate students at Universitas Negeri Surabaya participated in the experimental study. The findings reveal that the learning material development was reviewed and validated by experts and underwent some revisions and enhancement. Based on the analysis, the development feasibility level scored 81, and the N-Gain was 56.22%, indicating that the teaching material was quite feasible and effective. Therefore, the behavioral economics teaching material can be used for higher education.

The emergence of the COVID-19 pandemic led to a significant transition towards online education in pre-K-12 schools, prompting school administrators to confront the disparities revealed by the remote learning model. This paper includes the findings of a multi-phase research project exploring the intersection of educational leadership, technology, and systemic inequities aimed to guide administrator preparation programs to meet relevant, post-pandemic leadership standards. Phase One of the research project, conducted early in the pandemic, highlights the findings from a survey administered to technology directors in Minnesota. The survey aimed to understand how school districts were addressing the technology disparities encountered by students and families during hybrid and distance learning models. Phase Two of the research project, conducted as the pandemic waned, focused on school leaders' evaluation of which practices developed and implemented during remote learning should be sustained. Technology directors participated in a focus group and asserted that the pandemic was an opportunity for educational leaders to reimagine schools for the success of all students. Findings call for revised administrator preparation standards, the implementation of technology plans in every state, and continued focus on identifying and addressing educational inequities.

Determining one's confidence in a decision is a vital part of decision-making. Traditionally, psychological experiments have assessed a person's confidence by eliciting confidence judgments. The notion that such judgments can be elicited without impacting the accuracy of the decision has recently been challenged by several studies which have shown reactivity effects-either an increase or decrease in decision accuracy when confidence judgments are elicited. Evidence for the direction of reactivity effects has, however, been decidedly mixed. Here, we report three studies designed to specifically make reactivity effects more prominent by eliciting confidence judgment contemporaneously with perceptual decisions. We show that confidence judgments elicited contemporaneously produce an impairment in decision accuracy, this suggests that confidence judgments may rely on a partially distinct set of cues/evidence than the primary perceptual decision and, additionally, challenges the continued use of confidence ratings as an unobtrusive measure of metacognition., (© 2024. The Author(s).)

This paper explores how the culture within a Danish university study programme affects students' aspirations of becoming a high school chemistry teacher. We draw on ethnographic fieldwork and qualitative interviews to describe and analyse the culture of the programme. In the analysis we foreground the case of two students to understand their identity negotiations and changes in their teacher aspirations over time. By combining a 'domains-of-power framework' with the concept of 'science identity', we show how everyday practices formed a culture of power that positioned research in the centre and teaching as a less attractive career path. By unfolding the subtle mechanisms that together form a culture of power, which works both to include and exclude, we show the ways in which the two student's aspirations for becoming high school chemistry teachers somewhat diminished over time. The study concludes that negotiating a science identity at a research-intensive university programme, makes it almost impossible to develop and keep an aspiration for becoming a high school teacher. To address the current and future shortage of science teachers across Europe, our findings underline the need for a more inclusive culture at the university to support science students' diverse aspirations.

The low prevalence effect (LPE) is a cognitive limitation commonly found in visual search tasks, in which observers miss rare targets. Drivers looking for road hazards are also subject to the LPE. However, not all road hazards are equal; a paper bag floating down the road is much less dangerous than a rampaging moose. Here, we asked whether perceived hazardousness modulated the LPE. To examine this, we took a dataset in which 48 raters assessed the perceived dangerousness of hazards in recorded road videos (Song et al. in Behav Res Methods, 2023. https://doi.org/10.3758/s13428-023-02299-8) and correlated the ratings with data from a hazard detection task using the same stimuli with varying hazard prevalence rates (Kosovicheva et al. in Psychon Bull Rev 30(1):212-223, 2023. https://doi.org/10.3758/s13423-022-02159-0). We found that while hazard detectability increased monotonically with hazardousness ratings, the LPE was comparable across perceived hazardousness levels. Our findings are consistent with the decision criterion account of the LPE, in which target rarity induces a conservative shift in criterion. Importantly, feedback was necessary for a large and consistent LPE; when participants were not given feedback about their accuracy, the most dangerous hazards showed a non-significant LPE. However, eliminating feedback was not enough to induce the opposite of the LPE--prevalence induced concept change (Levari et al. in Science 360(6396):1465-1467, 2018. https://doi.org/10.1126/science.aap8731), in which participants adopt a more liberal criterion when instances of a category become rare. Our results suggest that the road hazard LPE may be somewhat affected by the inherent variability of driving situations, but is still observed for highly dangerous hazards.

As the demand for business intelligence (BI) professionals continues to grow, educators need to calibrate their instruction to accommodate the demand of practitioners for specific technical skills while also providing college students with a broader foundation that includes a general understanding of BI concepts and problem-solving skills that are applicable across disciplines. This paper describes a pedagogical method called report-oriented learning which seeks to combine the established methods of problem-based learning and case-based learning. Report-oriented learning requires students to reflect on the knowledge gained during the conceptual parts of the course and use critical thinking and storytelling skills as they prepare and present several comprehensive reports in class. We applied the report-oriented method in a business intelligence course that consists of four instructional approaches: (1) section preview, (2) lectures and quizzes on basic concepts, (3) application of concepts and development of practical skills with hands-on projects, and (4) comprehensive reflection and inquiry in the form of reports. We surveyed students with anonymous questionnaires in the report-oriented BI courses from 2021-2023. The results indicate that the method was effective and perceived by students as having improved their critical thinking and practical skills related to the application of BI techniques and the professional presentation of their findings.

The laboratory is a complex environment where the three levels of the chemistry triplet coincide. As the laboratory environment places a large demand on the working memory of students, cognitive load theory can address overload which causes barriers to learning. Breaking down barriers requires iterative phases of analysis/exploration, design/construction and evaluation/reflection over multiple cycles which are the hallmarks of design-based research. In a complex setting, managing change and redressing teaching approaches can be difficult to navigate. Design-based research incorporates iterative phases in which theory informs decision making. This paper uses the context of a laboratory exercise of emission spectra to illustrate how the cognitive load theory can be used in tandem with design-based research to support student learning in the exercise. Using this approach, it was possible to show how barriers to student understanding, including task demands and conceptual demands were supported through proposed approaches focusing on extraneous, intrinsic and ultimately germane cognitive load.

Although child-centred care is increasingly referred to within the nursing literature, a clear definition of child-centred care and clarity around the concept is yet to be achieved. The objectives of this review were to examine the following: (1) What constitutes the concept of child-centred care in healthcare? (2) How has the concept of child-centred care developed? (3) What is the applicability of child-centred care and what are its limitations? (4) How does the concept of child-centred care benefit and inform children's healthcare? In total, 2984 papers were imported for screening, and, following the removal of duplicates and screening, 21 papers were included in the scoping review. The findings suggest that child-centred care is an emerging, ambiguous poorly defined concept; no clear consensus exists about what constitutes child-centred care. Although it seems antithetical to argue against child-centred care, little robust evidence was identified that demonstrates the impact and benefit of child-centred care. If child-centred care is to be a sustainable, convincing model to guide practice and compete with other models of care, it needs to establish robust evidence of its effectiveness, the impact on children and their families, as well as the wider impacts on the healthcare system. [ABSTRACT FROM AUTHOR]

Foresight methods have been used by governments to reduce the margin of error in decision-making, but there is no golden rule for foresight activity; rather, several methods are combined to support decision-making. This article proposes an index number to support Technology Foresight in the field of Pharmacology/Pharmacy. The index number was formed by the relationship between bibliometric and human resources variables. First, Principal Components Analysis was used to reduce the initial bibliometric variables proposed by literature. Finally, Data Envelopment Analysis was used to calculate the number of Decision-Making Units (DMU), which are the most prolific institutions in the study country. The study examined 12 DMUs with 2,744 human resources (100% with academic degree) and 1,515 with research category (55.2%) from these, 217 granted patents (17.1% cited patents) and 1,017 papers (92.3% cited papers) were obtained. A simple but robust index was obtained to support decision-making in Technology Foresight. The results obtained from DMUs affect the Technology Foresight Index due to some institutions with low levels of scientific and technological activity and others with many highly qualified personnel. Technology foresight should be performed periodically by governments to reduce uncertainty in the innovation process and to develop highly competitive technologies. In this sense, this index is reliable for decision-making in the field of pharmacology/pharmaceuticals. It proposes a novel index relating bibliometric variables (output indicator) and human resources variables (input indicator) to foresee the scientific and technological development in the field of Pharmacology/Pharmacy at the national level. In addition, this study includes variables representing scientific (paper) and technological (patent) activity, as well as the impact of both at the international level. [ABSTRACT FROM AUTHOR]

The article announces the winner of the 2023 Clemen-Kleinmuntz Decision Analysis Best Paper Award, which comes with a $2,000 prize. The winning paper, titled "Information Density in Decision Analysis," introduces the concept of "information density" as a new approach to sensitivity analysis in decision analysis. The paper provides a technical grounding of information density and offers examples for real-world implementation. Another paper, titled "An Empirical Comparison of Rank-Based Surrogate Weights in Additive Multiattribute Decision Analysis," was identified as a finalist for the award and provides valuable insights into converting ordinal rankings into attribute weights. The article encourages authors to continue submitting high-quality manuscripts for future consideration. [Extracted from the article]

After emergencies occur, decision-makers can reference historical cases with similar causes to take similar emergency response measures. However, information about emergencies is usually recorded and stored in textual form, and it is difficult for decision-makers to obtain effective information from large amounts of textual data and make decisions that balance various factors. To address these issues, this paper presents an emergency assisted decision-making model based on a knowledge graph and many-objective optimization. First, we preprocess the information by extracting entities to construct a knowledge graph of emergencies to make the event information more structured and easier to use. A knowledge graph is also used to narrow the range of matching historical events. Second, we construct a many-objective model with four objectives: similarity, diversity, processing time, and resource cost. Finally, combining the model characteristics, we design genetic operations for duplicate location matching and substitution removal strategies to obtain the nonrepetitive VaEA algorithm. This algorithm is used to optimize the model and generate a list of reference cases in the reduced matching range to provide rescue strategy suggestions for the current situation. The experimental results show that the algorithm outperforms the comparison algorithms under all four evaluation metrics. This indicates that the method in this paper can match the higher-quality historical emergency solutions applicable to the current decision-making situation in the case base and provide support for decision-makers to respond reasonably in emergencies. [ABSTRACT FROM AUTHOR]

Application of Artificial Intelligence (AI) in tunnel construction has the potential to transform the industry by improving efficiency, safety, and cost-effectiveness. This paper presents a comprehensive literature review and analysis of hotspots and frontier topics in artificial intelligence-related research in tunnel construction. A total of 554 articles published between 2011 and 2023 were collected from the Web of Science (WOS) core collection database and analyzed using CiteSpace software. The analysis identified three main study areas: Tunnel Boring Machine (TBM) performance, construction optimization, and rock and soil mechanics. The review highlights the advancements made in each area, focusing on design and operation, performance prediction models, and fault detection in TBM performance; computer vision and image processing, neural network algorithms, and optimization and decision-making in construction optimization; and geo-properties and behaviours, tunnel stability and excavation, and risk assessment and safety management in rock and soil mechanics. The paper concludes by discussing future research directions, emphasizing the integration of AI with other advanced technologies, realtime decision-making systems, and the management of environmental impacts in tunnel construction. This comprehensive review provides valuable insights into the current state of AI research in tunnel engineering and serves as a reference for future studies in this rapidly evolving field. [ABSTRACT FROM AUTHOR]

The sugarcane crop has great socioeconomic relevance because of its use in the production of sugar, bioelectricity, and ethanol. Mainly cultivated in tropical and subtropical countries, such as Brazil, India, and China, this crop presented a global harvested area of 17.4 million hectares (Mha) in 2021. Thus, decision making in this activity needs reliable information. Obtaining accurate sugarcane yield estimates is challenging, and in this sense, it is important to reduce uncertainties. Currently, it can be estimated by empirical or mechanistic approaches. However, the model's peculiarities vary according to the availability of data and the spatial scale. Here, we present a systematic review to discuss state-of-the-art sugarcane yield estimation approaches using remote sensing and crop simulation models. We consulted 1398 papers, and we focused on 72 of them, published between January 2017 and June 2023 in the main scientific databases (e.g., AGORA-FAO, Google Scholar, Nature, MDPI, among others), using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. We observed how the models vary in space and time, presenting the potential, challenges, limitations, and outlooks for enhancing decision making in the sugarcane crop supply chain. We concluded that remote sensing data assimilation both in mechanistic and empirical models is promising and will be enhanced in the coming years, due to the increasing availability of free Earth observation data. [ABSTRACT FROM AUTHOR]

Evaluating and selecting players to suit football clubs and decision-makers (coaches, managers, technical, and medical staff) is a difficult process from a managerial-financial and sporting perspective. Football is a highly competitive sport where sponsors and fans are attracted by success. The most successful players, based on their characteristics (criteria and sub-criteria), can influence the outcome of a football game at any given time. Consequently, the D-day of selection should employ a more appropriate approach to human resource management. To effectively address this issue, a detailed study and analysis of the available literature are needed to assist practitioners and professionals in making decisions about football player selection and hiring. Peer-reviewed journals were selected for collecting published papers between 2018 and 2023. A total of 66 relevant articles (journal articles, conference articles, book sections, and review articles) were selected for evaluation and analysis. The purpose of the study is to present a systematic literature review (SLR) on how to solve this problem and organize the published research papers that answer our four research questions. [ABSTRACT FROM AUTHOR]

In today's world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One way to reduce this waste is by detecting, learning, and predicting when people are present in buildings. To do this, buildings need to become "smart" and "cognitive" and use modern technologies to sense when and how people are occupying the buildings. By leveraging this information, buildings can make smart decisions based on recently developed methods. In this paper, we provide a comprehensive overview of recent advancements in Internet of Things (IoT) technologies that have been designed and used for the monitoring of indoor environmental conditions within buildings. Using these technologies is crucial to gathering data about the indoor environment and determining the number and presence of occupants. Furthermore, this paper critically examines both the strengths and limitations of each technology in predicting occupant behavior. In addition, it explores different methods for processing these data and making future occupancy predictions. Moreover, we highlight some challenges, such as determining the optimal number and location of sensors and radars, and provide a detailed explanation and insights into these challenges. Furthermore, the paper explores possible future directions, including the security of occupants' data and the promotion of energy-efficient practices such as localizing occupants and monitoring their activities within a building. With respect to other survey works on similar topics, our work aims to both cover recent sensory approaches and review methods used in the literature for estimating occupancy. [ABSTRACT FROM AUTHOR]

This paper undertakes a revisit of the sunk cost fallacy, which refers to the tendency of people to persist investing resources into something, even if it is destined to have no good outcome. We emphasize that the utilities associated with different alternatives are not static for decision makers, which is exactly opposite to the traditional perspective. This paper argues that the utility of an option may change due to the choice of another option, suggesting that decisions considered irrational by the traditional analytical method, i.e., sunk cost fallacy, may be rational. We propose a novel analytical method for decision making with sunk cost when considering the utility change and validate the effectiveness of this method through mathematical modeling and computational experiments. This paper mathematically describes such decision-making problems, analyzing the impact of changes in the utilities across different alternatives on decision making with a real-world example. Furthermore, we develop a two-stage stochastic optimization model for such decision-making problems and employ the sample average approximation (SAA) method to solve them. The results from computational experiments indicate that some decisions traditionally considered irrational are, in fact, rational when the utility of an option changes as a result of choosing another option. This paper, therefore, highlights the significance of incorporating utility changes into the decision-making process and stands as a valuable addition to the literature, offering a refreshed and effective decision-making method for improved decision making. [ABSTRACT FROM AUTHOR]

The ability of fast restoration reflects power system resilience and safety. This paper proposes a partitioning decision-making method for parallel restoration based on the label propagation algorithm and the cooperative game theory. The topological and physical characteristics of blackout systems are considered as the edge weight and the node importance simultaneously for formulating the partitioning model. By the label propagation algorithm, various labels representing different subsystems mark all buses. To accelerate the speed of convergence and prevent the label oscillation, this paper proposes a game strategy of cooperation between buses and subsystems by evaluating the Shapley value of buses. The partitioning constraints are integrated into each label propagation process to assess the feasibility of the partitioning strategy. Finally, case studies on the IEEE 39- and 118-bus test systems and an actual urban power system in South China verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article is an editorial that focuses on the topic of decision-making and planning for multi-agent systems. Multi-agent systems are used in various real-world applications, but there are challenges in coordinating the behaviors of multiple agents. The article discusses the difficulties in translating single-agent techniques to the multi-agent domain, including complex interaction dynamics, constrained communication, and heterogeneity. The objective of this research topic is to report on recent advances in multi-agent planning and decision-making and to address the existing challenges in this area. The article includes summaries of five papers that cover different aspects of multi-agent decision-making, such as emergent communication, motion planning, reinforcement learning, and cooperative navigation. These papers provide insights into the complexity of multi-agent systems and suggest potential areas for further research. [Extracted from the article]

The topic of electric vehicles is currently generating significant interest worldwide. They are beneficial for the environment as they save up fuel, reduce pollution and carbon footprint too, but their market consumption is at the nascent level. The demand for these electric vehicles in developing countries is currently very slow and not that steady. The importance of Electric Vehicles (EVs) in the upcoming future is high and so it is crucial to figure out why these EVs are not being sold as much as we expect them in the market today. This paper tries to find out the multiple reasons why these vehicles are not in high demand, and why their counterparts, gasoline-fuelled vehicles are more in demand in the market. This paper discusses, after reviewing multiple other papers, why the consumption of EVs is low. Why their market demand is not where it should be and also multiple other facets. Finally, the paper finds that reasons like higher costs and low government incentives among multiple others are some factors leading to a low market demand for EVs. Also, the paper discusses the future scope of these EVs and how we can increase their consumption in the market. [ABSTRACT FROM AUTHOR]

The ability to make informed decisions in complex scenarios is crucial for intelligent automotive systems. Traditional expert rules and other methods often fall short in complex contexts. Recently, reinforcement learning has garnered significant attention due to its superior decision-making capabilities. However, there exists the phenomenon of inaccurate target network estimation, which limits its decision-making ability in complex scenarios. This paper mainly focuses on the study of the underestimation phenomenon, and proposes an end-to-end autonomous driving decision-making method based on an improved TD3 algorithm. This method employs a forward camera to capture data. By introducing a new critic network to form a triple-critic structure and combining it with the target maximization operation, the underestimation problem in the TD3 algorithm is solved. Subsequently, the multi-timestep averaging method is used to address the policy instability caused by the new single critic. In addition, this paper uses Carla platform to construct multi-vehicle unprotected left turn and congested lane-center driving scenarios and verifies the algorithm. The results demonstrate that our method surpasses baseline DDPG and TD3 algorithms in aspects such as convergence speed, estimation accuracy, and policy stability. [ABSTRACT FROM AUTHOR]

Background: Deaths in paediatric intensive care units (PICUs) are not uncommon. End-of-life care in PICUs is generally considered more challenging than other settings since it is framed within a context where care is focused on curative or life-sustaining treatments for children who are seriously ill. This review aimed to identify and synthesise literature related to the essential elements in the provision of end-of-life care in the PICU from the perspectives of both healthcare professionals (HCPs) and families. Methods: A systematic integrative review was conducted by searching EMBASE, CINAHL, MEDLINE, Nursing and Allied Health Database, PsycINFO, Scopus, Web of Science, and Google Scholar databases. Grey literature was searched via Electronic Theses Online Service (EthOS), OpenGrey, Grey literature report. Additionally, hand searches were performed by checking the reference lists of all included papers. Inclusion and exclusion criteria were used to screen retrieved papers by two reviewers independently. The findings were analysed using a constant comparative method. Results: Twenty-one studies met the inclusion criteria. Three elements in end-of-life care provision for children in the PICUs were identified: 1) Assessment of entering the end-of-life stage; 2) Discussion with parents and decision making; 3) End of life care processes, including care provided during the dying phase, care provided at the time of death, and care provided after death. Conclusion: The focus of end-of-life care in PICUs varies depending on HCPs' and families' preferences, at different stages such as during the dying phase, at the time of death, and after the child died. Tailoring end-of-life care to families' beliefs and rituals was acknowledged as important by PICU HCPs. This review also emphasises the importance of HCPs collaborating to provide the optimum end-of-life care in the PICU and involving a palliative care team in end-of-life care. [ABSTRACT FROM AUTHOR]

Arctic melting is an effect of climate change; the use of fossil fuels in marine shipping emits large amounts of air emissions that impact climate change, and Arctic aquatic and human life. Swedish pulp and paper mills generate large amounts of waste and side streams that could be utilized. The production of forest-based biofuel may be a promising solution to achieve sustainable Arctic marine shipping. This review highlights the socio-economic impacts associated with the production of forest-based biofuel in Sweden, the related opportunities, challenges, knowledge gaps, and further need of research. From the economic perspective, it was found that the production and use of forest-based biofuel have short and long-term economic sustainability benefits: (a) short-term benefits, the use of the waste and side streams of the pulp and paper industry is a low-cost available feedstock, unlike first-generation biofuel from crops like corn forest-based biofuels neither require additional land use, water resources nor compete with food. (b) Long-term benefits: (i) the Swedish shipping sector depends on imported fossil fuels, these new biofuels can replace partly those imported fossil fuels that will reduce shipping costs, and generate economic benefits for local consumers. (ii) Usage of forest-based biofuels as blends with conventional fuels in existing engines will reduce greenhouse gas emissions from the Arctic shipping to the set limits in the region. (iii) One of the important socioeconomic impacts of forest-based biofuel production and use is the new job creation and employment opportunities that will impact the local communities and livelihoods of indigenous people in the area. From a societal perspective, stakeholder involvement is essential to address the sustainability challenges of biofuel production: EU policymakers need to encourage the production and use of biofuels by developing policies that promote biofuel use. Further studies are needed to develop more efficient and low-cost biofuel production routes, more investments in related research and development are required as well. Local indigenous communities must be involved in the decision-making process through surveys, local dialogues, and research studies. The production of forest-based biofuels has great potential and many social-economic impacts alongside the environmental benefits. [ABSTRACT FROM AUTHOR]

The primary objective of this research paper is to conduct a comprehensive investigation into the process of developing briefs for Public–Private Partnership (PPP) projects. The study aims to outline the fundamental stages, standard processes, and critical decision points involved in this process. This is achieved through a comparative analysis of briefing frameworks used in three countries that are among the top in the PPP Market Maturity chart: The United Kingdom, Australia, and Canada. The paper discusses the stages and decision points within these countries' briefing processes, highlighting their challenges, similarities, and differences. It emphasizes the fundamental role of effective communication and coordination in PPP projects, which involve multiple stakeholders. By examining these interconnected activities and analyzing the phases, stages, and key processes constituting the briefing process, the study provides valuable insights into the PPP briefing processes in these nations. The paper concludes by proposing a conceptual process framework for brief development in PPP projects, which will be further tailored for adaptation in the PPP market in the United Arab Emirates. [ABSTRACT FROM AUTHOR]

Remote condition monitoring (RCM) aims to ensure the availability of railway assets. Previous work has indicated the importance of a user-centred RCM design approach based on cognitive principles, but there has been no known demonstration of the application of these principles. The following paper takes this theory-based approach and applies it to the design of an RCM system for the rail pantograph/Overhead Line (OHL) system. The paper first presents a high-level conceptual architecture, based on four stages of cognitive decision-making (notification, acceptance, analysis and clearance), linked to the wider monitoring architecture. Second, the paper uses cognitive principles to propose demonstration Human–Machine Interface designs for the OHL system. These HMIs were presented in an evaluation with subject matter experts. The outcomes of the process generated user-centred design recommendations for RCM. Furthermore, the evaluation suggested the importance of multiple paths through the HMI dependent on the type and urgency of fault. Finally, the outcomes of the evaluation also highlighted the importance of considering context when deploying user-centred RCM. [ABSTRACT FROM AUTHOR]

Featured Application: Future 5G/6G and DTT networks implementation in the UHF spectrum. This paper analyzes practical solutions for implementing 5G/6G and Digital Terrestrial Television (DTT) network coexistence in the UHF band. Two potential scenarios are explored using the case study of a nationwide DTT network: spectrum splitting and spectrum interleaving. Both scenarios are thoroughly examined based on current UHF spectrum usage and the DTT network structure based on a case study of Poland. The analysis includes potential future benefits and limitations of each scenario. The results presented in this paper can guide administrations in implementing decisions made at the International Telecommunication Union—Radiocommunication Sector, World Radiocommunication Conference 2023 (ITU-R WRC23), improving spectrum efficiency and 5G/6G networks development. [ABSTRACT FROM AUTHOR]

Background: After curative surgery for early-stage breast cancer, patients face a decision on whether to undergo surgery alone or to receive one or more adjuvant treatments, which may lower the risk of recurrence. Variations in survival outcomes are often marginal but there are differences in the side effects and other features of the options that patients may value differently. Hence, the patient's values and preferences are critical in determining what option to choose. It is well-researched that the use of shared decision making and patient decision aids can support this choice in a discussion between patient and clinician. However, it is still to be investigated what impact the timing and format of the patient decision aid have on shared decision making outcomes. In this trial, we aim to investigate the impact of a digital pre-consult compared to a paper-based in-consult patient decision aid on patients' involvement in shared decision making, decisional conflict and preparedness to make a decision. Methods: The study is a randomised controlled trial with 204 patients at two Danish oncology outpatient clinics. Eligible patients are newly diagnosed with early-stage breast cancer and offered adjuvant treatments after curative surgery to lower the risk of recurrence. Participants will be randomised to receive either an in-consult paper-based patient decision aid or a pre-consult digital patient decision aid. Data collection includes patient and clinician-reported outcomes as well as observer-reported shared decision making based on audio recordings of the consultation. The primary outcome is the extent to which patients are engaged in a shared decision making process reported by the patient. Secondary aims include the length of consultation, preparation for decision making, preferred role in shared decision making and decisional conflict. Discussion: This study is the first known randomised, controlled trial comparing a digital, pre-consult patient decision aid to an identical paper-based, in-consult patient decision aid. It will contribute evidence on the impact of patient decision aids in terms of investigating if pre-consult digital patient decisions aids compared to in-consult paper-based decision aids support the cancer patients in being better prepared for decision making. Trial registration: ClinicalTrials.gov (NCT05573022). [ABSTRACT FROM AUTHOR]

Acknowledging the importance of climate challenges to our environment, landscape and cities, this review focuses on the exploration of visual methods (e.g., design, drawing, sketches) in relation to a deeper understanding of climate emergency and sustainability on a spatial scale. It provides an overview of existing research and highlights the role design and drawing can play in landscape-led projects, as well as the impact these might have on behavioural change and decision-making. Looking at how design and drawing are perceived in landscape architecture and what their contribution is to the narrative of a project as well as the decisions made, this paper establishes a connection between pictorial forms and landscape. At the same time, this paper explores whether visualisations are used in relation to the climate and environmental challenges we face, sharing some light on the role they can play with regards to climate awareness and sustainability and how important they might be for our communities, cities and regions. This review highlights the need for further research around this topic and explains that there is a minor part of the literature looking at ways in which landscape design can be integrated into the wider climate emergency agenda and how this might influence behavioural change and the decision-making of various stakeholders. [ABSTRACT FROM AUTHOR]

When facing problems in the aerial pursuit game, most of the current unmanned aerial vehicles (UAVs) have good maneuverability performance, but it is difficult to utilize the overload maneuverability of UAVs properly; further, UAVs tend to be more costly, and it is often difficult to effectively prevent the enemy from reaching the tailgating position behind the UAV in the aerial pursuit game. Therefore, there is a pressing need for a maneuvering algorithm that can effectively allow a UAV to quickly protect itself in a disadvantageous position, stably and effectively select a maneuver with the maneuvering algorithm, and stably and effectively establish an advantage by moving to an advantageous position. Therefore, this paper establishes a cloud model-based UAV-maneuvering aerial pursuit decision-making model based on pursuit-and-evasion game positions. Based on the evaluation of the latter, when the UAV is at a disadvantage, we use the constructed defensive maneuver expert pool to abandon the disadvantageous position. When the UAV is at an advantage, we use cloud model-based pursuit-and-evasion game maneuvering decision making to establish an advantageous position. According to the results of the simulation examples, the maneuvering decision-making method designed in this paper confirms that the UAV can quickly abandon its position and establish an advantage in case of parity or disadvantage and that it can also stably establish a tail-chasing position in case of advantage. [ABSTRACT FROM AUTHOR]

In a democratic regime, voting is crucial to making collective decisions. Unfortunately, although this activity has great significance and value, little effort has been made to improve the way we vote. Paper ballots are still the most used method, although this method is relatively simple, brings many inconveniences, and represents a contradiction to the modern world and its advances. This paper mostly focuses on a review study of blockchain-based voting systems. It aims at identifying the strategies and the guidelines as well as provides a comprehensive end-to-end electronic voting system based on blockchain, with the help of cryptographic techniques such as zero-knowledge proofs to improve privacy. The novelty of this paper is that we tackle the limitations of electronic voting systems found in the literature, including cost, identity management, and scalability problems. Our purpose is to provide key elements for organizations on how to design their proper electronic voting system based on blockchain technology. [ABSTRACT FROM AUTHOR]

The rapid growth of on-demand ride service platforms has made it increasingly important for these platforms to efficiently match services by understanding driver characteristics and consumer preferences. This paper aims to investigate the pricing strategy by considering the impact of consumer preference heterogeneity and the different service types offered by drivers. The findings of this study reveal the need for the platform to strike a balance between service cost and the benefits of high-quality drivers, which can be referred to as the "cost-performance ratio". If the "cost-performance ratio" that attracts high-quality drivers is high, the platform will attract high-quality drivers or drivers of all types to participate while offering differentiated services. Otherwise, the platform will only provide services through low-quality drivers. Furthermore, the platform will also consider when to offer differentiated services based on network externalities and service quality. When the network externalities of the two types of services are similar, the platform will differentiate them based on service quality differences. Overall, considering consumer preference heterogeneity, drivers of service types, and network externalities, this paper provides guidance for platforms to make optimal decisions that enhance their service offerings and improve overall customer satisfaction. [ABSTRACT FROM AUTHOR]

Rural communities are geographically isolated from large urban areas, affecting access to definitive care, specialists and other health services that only service urban areas. Rural decision-makers are often faced with numerous challenges regarding the availability, capacity, sustainability and performance of health systems in rural and remote areas. We evaluated the current body of literature on educational initiatives being used in under-resourced areas to increase the knowledge or skills of healthcare workers. This rapid review followed the methods laid out by the Cochrane Rapid Reviews Methods Group and included published articles from any of three databases that described and evaluated an educational intervention, in which healthcare workers were the learners and which took place in an under-resourced area. Papers were excluded if they were deemed to be too resource intensive, were an opinion or concept paper or took place in an urban area. Results were synthesised descriptively. Ten studies were identified that contained information on educational initiatives in a variety of countries. The healthcare workers targeted in the studies varied from physicians, nurses and midwives to community health workers and students. The quality of studies also varied and included randomised control trials, systematic reviews and both prospective and retrospective studies. Initiatives involving simulation or point-of-care ultrasound were most common and showed the most benefit to a learner's knowledge and skill development. A limited body of literature exists on educational initiatives for healthcare workers in under-resourced areas. While simulation and hands-on learning showed positive results, the opportunity remains for a low-cost, high-yield educational initiative tailored to the unique needs of healthcare workers in under-resourced areas. Résumé Les communautés rurales sont géographiquement isolées des grandes zones urbaines, ce qui affecte l'accès à des soins définitifs, à des spécialistes et à d'autres services de santé qui ne desservent que les zones urbaines. Les décideurs ruraux sont souvent confrontés à de nombreux défis concernant la disponibilité, la capacité, la durabilité et la performance des systèmes de santé dans les zones rurales et éloignées. Nous avons évalué l'ensemble de la littérature actuelle sur les initiatives éducatives utilisées dans les zones sous-dotées pour améliorer les connaissances ou les compétences des travailleuses et travailleurs de la santé. Cette examen rapide a suivi les méthodes définies par le Cochrane Rapid Reviews Methods Group et a inclus des articles publiés dans l'une des trois bases de données qui décrivaient et évaluaient une intervention éducative dans laquelle les travailleuses et travailleurs de la santé étaient les apprenants et qui SE déroulait dans une zone manquant de ressources. Des articles jugés trop gourmands en ressources, des opinions, des documents conceptuels ou en lien avec des zones urbaines, ont été exclus. Les résultats ont été synthétisés de manière descriptive. 10 études ont été identifiées, contenant des informations sur des initiatives éducatives dans divers pays. Les travailleurs de la santé ciblés dans les études variaient des médecins, des infirmières et des sages-femmes aux travailleurs de la santé communautaire et aux étudiants. La qualité des études était également variable et comprenait des essais contrôlés randomisés, des revues systématiques et des études prospectives et rétrospectives. Les initiatives impliquant la simulation ou le POCUS étaient les plus courantes et présentaient le plus d'avantages pour le développement des connaissances et des compétences de l'apprenant. Il existe un nombre limité de documents sur les initiatives éducatives destinées aux travailleurs de la santé dans les zones sous-dotées. Bien que la simulation et l'apprentissage pratique aient donné des résultats positifs, il est toujours possible de mettre en place une initiative éducative peu coûteuse et à haut rendement, adaptée aux besoins spécifiques des travailleuses et travailleurs de la santé dans les zones sous-dotées. Mots-clés: Formation, éducation, rural, éloigné, manque de ressources, première ligne, prestataire de soins de santé [ABSTRACT FROM AUTHOR]

With the successful application of artificial intelligence technology in various fields, deep reinforcement learning (DRL) algorithms have applied in active corrective control in the power system to improve accuracy and efficiency. However, the "black-box" nature of deep reinforcement learning models reduces their reliability in practical applications, making it difficult for operators to comprehend the decision-making mechanism. process of these models, thus undermining their credibility. In this paper, a DRL model is constructed based on the Markov decision process (MDP) to effectively address active corrective control issues in a 36-bus system. Furthermore, a feature importance explainability method is proposed, validating that the proposed feature importance-based explainability method enhances the transparency and reliability of the DRL model for active corrective control. [ABSTRACT FROM AUTHOR]

To steadily promote the reform of sales electricity prices, China has innovatively proposed to carry out grid enterprises' agency power purchase business (GEAPPB) as a transitional scheme for industrial and commercial users to enter the power market in the future. However, because the GEAPPB covers a wide range of users and involves complex business processes, the risks throughout the process of the GEAPPB will affect the organization and operation of the business. This paper proposes a GEAPPB risk assessment technique. First, a risk evaluation index system containing 10 indicators is designed based on the characteristics and development process of GEAPPB. Second, a risk assessment model based on the fuzzy full consistency method (FUCOM-F) and gray relational analysis (GRA)-improved MARCOS is constructed. Finally, seven grid enterprises in China are taken as examples to verify the validity and scientificity of the model. The results show that improving the accuracy of power forecasting, optimizing the agency power purchase strategy, and strengthening demand response management are the key schemes to reduce the risk of GEAPPB. Compared with the other three evaluation models, the proposed model has good applicability and effectiveness for the risk evaluation of GEAPPB. [ABSTRACT FROM AUTHOR]

This paper presents an intuitive explanation about why and how Rawlsian Theory of Justice (Rawls in A theory of justice, Harvard University Press, Harvard, 1971) provides the foundations to a solution for algorithmic bias. The contribution of the paper is to discuss and show why Rawlsian ideas in their original form (e.g. the veil of ignorance, original position, and allowing inequalities that serve the worst-off) are relevant to operationalize fairness for algorithmic decision making. The paper also explains how this leads to a specific MinMaxfairness solution, which addresses the basic challenges of algorithmic justice. We combine substantive elements of Rawlsian perspective with an intuitive explanation in order to provide accessible and practical insights. The goal is to propose and motivate why and how the MinMaxfairness solution derived from Rawlsian principles overcomes some of the current challenges for algorithmic bias and highlight the benefits provided when compared to other approaches. The paper presents and discusses the solution by building a bridge between the qualitative theoretical aspects and the quantitative technical approach. [ABSTRACT FROM AUTHOR]