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Four decades of research into the influences of time pressure on risky decisions have produced widely contrasting findings: 38.5% of the effects indicate that time pressure increases risk preferences, whereas 61.5% show the opposite. A theoretical framework with four conceptual categories of moderators is proposed to explain these heterogeneous findings: nature of the time constraint, negative outcome salience, negative outcome severity, and vulnerability to the outcomes. This framework is tested through a meta-analysis of 213 effect sizes reported in 83 papers, representing 65,574 unique respondents. The four categories of moderators effectively resolve notable conflicts. For example, regarding the nature of the time constraint, an absolute versus relative constraint increases risk preferences, but an ambiguous versus objective constraint decreases risk preferences. In terms of negative outcome salience, risk preferences decrease if the risk is learned about from a description (vs. experience) or the outcome is framed as a loss (vs. gain). Negative outcome severity also exerts an effect, as discrete choices lower risk preferences compared with attitudinal risk. In addition to managerial and public policy implications based on simulations, a comprehensive research agenda that builds on the robust insights of this meta-analysis is offered. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this paper, using the intuitionistic fuzzy (IF)–set based Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method, is to create multicriteria decision-making (MCDM) mechanism that evaluates the papers according to the offset print quality, separately for each of the CMYK colors. Thanks to the mechanism, the most suitable and the most unsuitable paper for printing is determined. Design/methodology/approach: With the IF PROMETHEE method, respectively, deviations are calculated by linear criteria type; the overall IF preference relation matrix is obtained. Finally, the positive and negative outranking flows are calculated; by obtaining net outranking flows, all papers are ranked from best to worst for printability. Findings: Based on the results of the MCDM algorithm; the best offset printing quality is Matte Coated 115 gr. for cyan, magenta and black and Glossy Coated 115 gr. for yellow. The worst offset printing quality for all papers is 70 gr. III. low grade paper. According to the findings, when all colors are examined, the best paper for offset printing quality is coated papers; the worst paper is III. low grade papers. Originality/value: The creation of mechanism that evaluates all criteria together leads to the most accurate result. Cases of hesitation are also addressed using an IF-based algorithm; all criteria were assigned individual importance levels, and a single result was obtained by activating all of the criteria simultaneously. Therefore reasons, this is paper that will bring innovation and shed light on studies in this field. [ABSTRACT FROM AUTHOR]

As more people express their thoughts on products on various online shopping platforms, the feelings expressed in these opinions are becoming a significant source of information for marketers and buyers. These opinions have a big impact on consumers' decision to buy the best quality product. When there are too many features or a small number of records to analyze, the decision-making process gets difficult. A recent stream of study has used the conventional quantitative star score ratings and textual content reviews in this context. In this research, a decision-making framework is proposed that relies on feature-based opinions to analyze the textual content of reviews and classify buyer's opinions, thereby assisting consumers in making long-term purchases. The framework is proposed in this paper for product purchase decision making based on feature-based opinions and deep learning. Framework consists of four components: i) Pre-processing, ii) Feature extraction, iii) Feature-based opinion classification, and iv) Decision-making. Web scraping is used to obtain the dataset of Smartphone reviews, which is subsequently clean and pre-processed using tokenization and POS tagging. From the tagged dataset, noun labeled words are retrieved, and then the probable product's features are extracted. These feature-based sentences or reviews are processed using a word embedding to generate review vectors that identify contextual information. These word vectors are used to construct hidden vectors at the word and sentence levels using a hierarchical attention method. With respect to each feature, reviews are divided into five classes: extremely positive, positive, extremely negative, negative, and neutral. The proposed method may readily detect a customer's opinion on the quality of a product based on a certain attribute, which is beneficial in making a purchase choice. [ABSTRACT FROM AUTHOR]

Web development frameworks help in streamlining and speedy-tracking the system of constructing web packages. There is an array of frameworks which makes it hard for developers to decide the nice option to use in their tasks. This survey paper is extensive and a comparative internet development framework, thinking about the primary additives, strengths and weaknesses of every utility. This paper involves stringent assessment technique for performance measurement metrics inclusive of reaction time, scalability, and memory. Optimization of techniques and the issues on protection for each framework also are taken into consideration. To wrap up, this paper offers steering to be able to help the readers to choose the most appropriate framework depending on their task needs. The data offered on this survey are supposed to guide developers as well as decision makers towards making quality decisions that culminate in appropriate utility on websites. [ABSTRACT FROM AUTHOR]

Reviews have long been recognized as among the most important forms of scientific communication. The rapid growth of the primary literature has further increased the need for reviews to distill and interpret the literature. This review on Reviews and Reviewing: Approaches to Research Synthesis encompasses the evolution of the review literature, taxonomy of review literature, uses and users of reviews, the process of preparing reviews, assessment of review quality and impact, the impact of information technology on the preparation of reviews, and research opportunities for information science related to reviews and reviewing. In addition to providing a synthesis of prior research, this review seeks to identify gaps in the published research and to suggest possible future research directions. [ABSTRACT FROM AUTHOR]

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]

We propose an outdoor activity for game theory courses in which students are invited to fly self-made paper airplanes to earn points in three tests. The main goal of this outdoor classroom experiment is to help students learn by experiencing concepts of uncertainty in the domain of profit, specifically risk aversion. After the experiment, students are encouraged to reflect on decision making under uncertainty. We present a theoretical model to explain the decisions made by the participants, their optimal behavior and any deviations from it. Our activity aims to introduce the study of decision making under uncertainty through an experience. [ABSTRACT FROM AUTHOR]

An editorial is presented discussing the crucial role of practitioner papers in health marketing, emphasizing the need for collaboration between academics and practitioners. Topics include the unique value of practitioner papers, barriers and enablers to publishing, and guidance for both practitioners and editors to facilitate successful publication.

Many researchers in the construction field have explored the utilization of augmented reality (AR) and its impact on the industry. Previous studies have shown potential uses for AR in the construction industry. However, a comprehensive critical review exploring the ways in which AR supports synchronized communication is still missing. This paper aims to fill this gap by examining trends identified in the literature and by analyzing both beneficial and challenging attributes. This work was performed by collecting numerous journal and conference papers, using keywords including "augmented reality", "construction", and "synchronous communication". The papers were then categorized based on the reported attributes that were indicated to be challenges or benefits. Throughout the analysis, several benefits were consistently reported, including training, visualization, instantly sharing information, decision making, and intuitive interaction. Similarly, several challenges were consistently reported, such as difficulty in manipulation, unfriendly interface, device discomfort, and sun brightness. Regarding other attributes, such as field of view, cost, safety hazards, and hands-free mode, researchers provided divergent reports regarding whether they were beneficial or detrimental to AR communication. These findings provide valuable guidance for future researchers and practitioners, enabling them to leverage AR for synchronized communication in ways that consistently offer value. [ABSTRACT FROM AUTHOR]

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]

Career Guidance is a very critical process that helps individual in making informed decisions about their educational and professional paths. Traditional career guidance systems were dependent on career exploration resources, standardized assessments and human counselors. However, the expanding multiplicity of the workforce and the dynamic nature of the job market, underscored the need for more personalized and advanced career guidance system. The advent of Intelligent Career Guidance System (ICGS), uses Artificial Intelligence (AI) and Machine Learning (ML) which solves this problem. The advance algorithms and data analytics used by the ICGS, helps in the identification of correlations and patterns among the huge datasets of individual profiles and career information. The Intelligent Career Guidance Systems (ICGS) equipped with the power of Artificial Intelligence (AI) and Machine Learning (ML) provides more personalized and customized recommendations lined up with an individual’s unique profile of skills, interests and aptitudes. This thorough review delves into the current condition of ICGS, explaining their various functionalities, components and potential advantages. It also crucially examines the limitations and challenges of ICGS, laying the foundation for future research endeavors. [ABSTRACT FROM AUTHOR]

Academic journals rely crucially upon the willingness of academics to review papers and for them to provide high-quality review reports. The purpose of this Editorial is to assist reviewers for Tourism and Hospitality in providing review reports that will be helpful to the journal editors in making decisions about whether to accept, reject or require revisions to papers that are submitted to the journal. It provides reviewers with a 'peep behind the curtain' of what can sometimes seem a rather opaque paper review process, with the aim of clarifying the purpose and format of the review process, and thereby to gain a greater appreciation of what it required of them. Several tips for writing high-quality review reports are also presented. [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]

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]

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 this paper, Peter Ellis discusses the topic of mental capacity. What is legal capacity, and why is it so important? [ABSTRACT FROM AUTHOR]

When an order arrives at a warehouse it is usually assigned to a batch and a decision is made on how long to wait before assigning the batch to a picker and starting the picking tour. If the idle time of the pickers is minimised, the batch is immediately assigned, and the picking starts. Alternatively, if a time window is introduced, other orders may arrive, and more efficient batches may be formed. The method to decide how long to wait (the time-window strategy) is therefore important but, surprisingly, almost completely overlooked in the literature. In this paper, we demonstrate that this lack of attention is unwarranted, and that the time-window method significantly influences the overall warehouse performance. In the context of the online order batching problem (OOBP), we first demonstrate that the effects of different time-window strategies are independent of the methods used to solve the other subproblems of the OOBP (batching and routing). Second, we propose two new time-window strategies, compare them to existing methods, and prove that our methods outperform those in the literature under various scenarios. Finally, we show how time-window methods influence different objective functions of the OOBP when varying numbers of orders and pickers. [ABSTRACT FROM AUTHOR]

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This paper documents how extensive economic education can reduce the risk of getting into financial trouble by comparing people who enter business and economics programs with people who enter other higher education programs. To identify the causal effect, I exploit GPA admission thresholds that quasi-randomize applicants near the thresholds into different higher education programs. I find that admission to an economics program reduces the probability of loan default and delinquency by one half. This large reduction is associated with changes in financial behavior, but it is not associated with differences in the level or stability of people's income. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) are important liver enzymes in clinical settings. Their levels are known to be elevated in individuals with underlying liver diseases and those consuming hepatotoxic drugs. Serum ALT and AST levels are crucial for diagnosing and assessing liver diseases. Serum ALT is considered the most reliable and specific candidate as a disease biomarker for liver diseases. ALT and AST levels are routinely analyzed in high-risk individuals for the bioanalysis of both liver function and complications associated with drug-induced liver injury. Typically, ALT and AST require blood sampling, serum separation, and testing. Traditional methods require expensive or sophisticated equipment and trained specialists, which is often time-consuming. Therefore, developing countries have limited or no access to these methods. To address the above issues, we hypothesize that low-cost biosensing methods (paper-based assays) can be applied to the analysis of ALT and AST levels in biological fluids. The paper-based biodetection technique can semi-quantitatively measure ALT and AST from capillary finger sticks, and it will pave the way for the development of an inexpensive and rapid alternative method for the early detection and diagnosis of liver diseases. This method is expected to significantly reduce the economic burden and aid routine clinical analysis in both developed and underdeveloped countries. The development of low-cost testing platforms and their diagnostic utility will be extremely beneficial in helping millions of patients with liver disorders. [ABSTRACT FROM AUTHOR]

We aimed to investigate differences in patients' anxiety and satisfaction between patients undergoing paper-based patient decision aid (PDA) for shared decision-making (SDM) and those receiving computer-based PDA. We retrospectively collected questionnaires before and after SDM. Basic demographic data as well as anxiety, satisfaction, knowledge acquisition, and participation in SDM were recorded. We divided our population into subgroups according to use of paper-based or computer-based PDA. In addition, Pearson correlation analysis was applied to assess the relationships among variables. In total, 304 patients who visited our Division of Nephrology were included in the final analysis. Overall, over half of the patients felt anxiety (n = 217, 71.4%). Near half of the patients felt a reduction in anxiety after SDM (n = 143, 47.0%) and 281 patients (92.4%) were satisfied with the whole process of SDM. When we divided all the patients based on use of paper-based or computer-based PDA, the reduction of anxiety level was greater in the patients who underwent paper-based PDA when compared with that of those who underwent computer-based PDA. However, there was no significant difference in satisfaction between the two groups. Paper-based PDA was as effective as computer-based PDA. Further studies comparing different types of PDA are warranted to fill the knowledge gaps in the literature. [ABSTRACT FROM AUTHOR]

The rapid spread of the COVID-19 virus and the massive influx of patients exceeded the intensive care units capacity limit. Hospitals have engaged community partners to request resources or transfer patients to facilities with available resources. This setting fosters the need for rationing decisions to allocate scarce resources consistently, rather than basing decisions on clinicians' intuition in the heat of the moment. In this paper, we identify the inter-hospital COVID-19 patients' transfer criteria, and we apply a multi-criteria decision-making methodology to classify and rank these criteria in order to determine the transfer priority scores between hospitals. We then develop an optimisation model for patient systematic referral from saturated to recipient hospitals with contingency capacity expansion and a ventilator donation scheme. To deal with the uncertainty related to the number of ill patients requesting beds in hospitals, a two-stage stochastic model is presented and solved using the sample average approximation. A case with real data related to the COVID-19 French context is presented, which allows us to assess the resilience of the hospital network under the proposed solutions. The results show how local and regional coalitions can avert the saturation of the hospital system and reduce the number of rejected patients. [ABSTRACT FROM AUTHOR]

Background: Advance care planning discussions are crucial in the management and support of individuals with life-limiting or life-threatening conditions. Few studies have examined best practices for advance care planning with adolescents and young adults. Aim: To identify core components of current guidelines, frameworks and tools for advance care planning discussions with adolescents and young adults with life-limiting or life-threatening conditions and their families. Design: A scoping review of the literature was conducted followed by a thematic analysis of the included papers. The scoping review was reported according to the Joanna Briggs Institute approach to the conduct of scoping reviews. Data sources: Five databases [Cochrane Central Register of Controlled Trials (CENTRAL), Cochrane Database of Systematic Reviews, PsycInfo, PubMed and Scopus] were searched for English-language papers published between inception until January 2023. Results: The search yielded 2976 papers, of which 9 met the inclusion criteria. Five main themes were identified: (i) utilisation of standardised documents and protocols; (ii) shared decision-making between the adolescents and young adults, their families and the healthcare team; (iii) the importance of open and honest communication with adolescents and young adults during advance care planning discussions; (iv) individualisation and flexibility in the advance care planning process and (v) timing of advance care planning initiation. Conclusions: Results highlight the importance of engaging adolescents and young adults in advance care planning and considering their unique needs when initiating and framing these discussions. Our findings can be used by healthcare professionals to inform advance care planning in this group. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to explore ChatGPT's (generative pre-trained transformers) potential as a tool for retailers to improve customer experience and boost sales. While it provides benefits like personalized recommendations and 24/7 assistance, there are limitations, like difficulty in understanding unconventional language. The paper stresses careful integration to overcome these limitations and create a better customer experience. Additionally, it discusses the potential for further development and integration of ChatGPT in retail, such as generating product descriptions and virtual try-on experiences. Finally, the paper encourages retailers to embrace ChatGPT to meet their customer needs. Design/methodology/approach: Case-based methodology involves using specific cases or examples to explore a broader issue or phenomenon. Researchers have analysed real-world cases to identify patterns, themes and insights that can be applied to other contexts or situations. This was useful for understanding complex and multifaceted issues as it allowed us to delve deeper into specific examples and explore the nuances of the situation. Findings: While ChatGPT is a powerful tool for retailers, limitations such as difficulty in understanding non-standard accents and unconventional language can arise, causing customer frustration. Retail managers must integrate ChatGPT in a way that enhances customer experience. In the future, ChatGPT has the potential to generate product descriptions, provide virtual try-on experiences and integrate with augmented or virtual reality technology to offer more immersive experiences. Careful consideration and integration can help retailers overcome these limitations and offer personalized recommendations, round-the-clock assistance and an engaging customer experience that improves sales. Originality/value: The case topic is very much in a novel stage of research and writing. [ABSTRACT FROM AUTHOR]

The decision-making of generator start-up sequence plays a pivotal role in the power system restoration process following the blackout. In this paper, an optimized deep Q-learning network (DQN) algorithm is proposed to address this challenge. The generator start-up process is modeled as a Markov Decision Process (MDP) based on its characteristics. The DQN is tasked with deciding both the generator start-up sequence and the corresponding restoration path. To address the limitations of DQN, such as low exploration efficiency and slow convergence, the study incorporates the Artificial Potential Field (APF) algorithm to refine the reward function of it. This integration results in the development of the F-DQN (APF-DQN) algorithm, which enhances training efficiency. The effectiveness of this proposed method is demonstrated through the IEEE 39-bus test system. The results reveal that the DQN algorithm is capable of efficiently solving the model of the generator start-up sequence after the blackout. Moreover, the F-DQN algorithm exhibits superior learning efficiency, faster convergence, and higher-quality optimal solutions compared to the DQN. This paper also discusses the applicability of this method under partial blackouts. When compared to other decision-making algorithms, the proposed method offers a restoration scheme that is both time-efficient and results in increased electricity generation. [ABSTRACT FROM AUTHOR]

Extensive research has been carried out on reinforcement learning methods. The core idea of reinforcement learning is to learn methods by means of trial and error, and it has been successfully applied to robotics, autonomous driving, gaming, healthcare, resource management, and other fields. However, when building reinforcement learning solutions at the edge, not only are there the challenges of data-hungry and insufficient computational resources but also there is the difficulty of a single reinforcement learning method to meet the requirements of the model in terms of efficiency, generalization, robustness, and so on. These solutions rely on expert knowledge for the design of edge-side integrated reinforcement learning methods, and they lack high-level system architecture design to support their wider generalization and application. Therefore, in this paper, instead of surveying reinforcement learning systems, we survey the most commonly used options for each part of the architecture from the point of view of integrated application. We present the characteristics of traditional reinforcement learning in several aspects and design a corresponding integration framework based on them. In this process, we show a complete primer on the design of reinforcement learning architectures while also demonstrating the flexibility of the various parts of the architecture to be adapted to the characteristics of different edge tasks. Overall, reinforcement learning has become an important tool in intelligent decision making, but it still faces many challenges in the practical application in edge computing. The aim of this paper is to provide researchers and practitioners with a new, integrated perspective to better understand and apply reinforcement learning in edge decision-making tasks. [ABSTRACT FROM AUTHOR]

The excessive use of energy from fossil fuels, which corresponds to population, industrialisation, and unsustainable economic growth, is the cause of carbon dioxide production and climate change. The Water–Energy–Food (WEF) nexus is an applicable conceptual framework that helps manage the balance between human development and natural resource constraints, and it becomes a valuable tool to address the challenges of resource depletion and clean energy. This article aims to analyse the relationship between the WEF nexus and clean energy through a statistical analysis and a systematic review of knowledge on energy sustainability. The methodology involves the selection of bibliographic information databases such as Scopus and Web of Science (WoS), a statistical analysis, specifically the Scientometrics applied to the information obtained, and the identification of 179 scientific publications related to the study variables through a screening process called Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The interaction of green energy and sustainability with the WEF nexus has been approached mainly by developed countries such as China (10%), the United States (9.49%), India (7.26%), and Brazil (5.02%). The review of 55 scientific papers identified strategies that balance economic growth and environmental impact, applying clean energy systems (e.g., solar and wind), and the importance of adaptation to the subsystems of the WEF nexus. This study also highlights adaptability to climate change in rural and urban communities. Therefore, it is consistent with the four pillars of sustainable development (Economic: 7 papers, Environmental: 22, Social: 18, and Cultural: 8). This study highlights the following: (a) the importance of the use of renewable energy either in mixed systems and consumption, energy storage, and seeking energy efficiency in systems adapted to diverse environments and (b) the relevance of community participation in the decision-making process for the use of clean energy, such as a strategy for climate change adaptation. [ABSTRACT FROM AUTHOR]

This paper implements a two-staged ensemble learning model for traffic forecasting, focusing on the interpretability of predictions. The stacking model leverages the advantages of its diverse component learning models. Experiments on high-dimensional and sparse data validate the stacking model's superior predictive accuracy compared to baseline models, including LightGBM and XGBoost. In addition to validating the stacking model's outstanding predictive performance, this paper emphasizes the interpretability of its predictions by proposing an innovative explanation model based on feature contributions. This explanation model addresses the high dimension and sparsity in data prevalent in transportation engineering with its integration of resampling and consensus clustering, offering a scalable, stable, and computationally efficient solution ideal for real-time and large-scale applications. The paper presents theoretical justification, experimental results, and empirical validation of the interpretation model. Extensive experiments demonstrate the model's enhanced stability compared to traditional shapley additive explanations (SHAP) implementations such as kernel SHAP. Investigating trade-offs between stability and computational efficiency of resampling provides insights for optimal configuration choices. This paper contributes to traffic flow prediction with broad applicability in real-time and large-scale traffic management scenarios, underscoring the vital role of ensemble learning and interpretable machine learning in contemporary data-driven decision making processes. [ABSTRACT FROM AUTHOR]

With the development of automated vehicles, researches related to automated vehicle platoon (AVP) have received more and more attention. AVP is considered one of the effective means to alleviate traffic congestion and reduce vehicle energy consumption. This paper studies a three‐layer method of avoiding obstacle vehicles in traffic by switching the formation for the automated heavy vehicle platoon. In the decision‐making layer, a decision‐making system based on the finite‐state machine is established for formation switching. In the second layer, the lane‐changing trajectory is optimized based on the quantic polynomial curve fitting for vehicles that need to change lanes. In terms of vehicle control layer, each vehicle has a longitudinal controller based on sliding mode control and a lateral controller based on model predictive control to track the planned trajectory to complete the target formation. Finally, the proposed method is simulated in MATLAB/TruckSim. The simulation results show that the proposed method could effectively avoid the obstacle vehicles by switching the formation and has a small average value of errors in speed tracking and trajectory tracking. [ABSTRACT FROM AUTHOR]

Floating cities have emerged as an efficient long-term solution over unsustainable practiced solutions to combat the rising seas problem; nevertheless, the world lacks an international, official, and comprehensive framework regarding floating cities. Although previous research approached modular floating city design; however, resulted in configurations with various critical design restrictions mainly regarding interlocking capabilities and space utilization. The purpose of this paper is to offer a new systematic strategy for configuring modular and expandable floating cities without such restrictions. This paper explores Euclidean tilings as a strategy to offer numerous configurations based on regular, semi-regular, and demi-regular tilings. Selecting the ideal configuration is complicated; therefore, both quantitative and qualitative data methods were implemented to attain the objectives. Via an extensive literature review, this research derives key factors for configuring floating cities, then sets a brainstorming session with experts for group decision making before providing findings upon calculations via analytic hierarchy process, one of the most used quantitative data methods of multiple-criteria decision analysis. Through comprehensive literature review: seakeeping, modularity, zoning and circulation, and feasibility have been identified as the most significant criteria in floating city research. It explores the qualities and limitations of triangular, squared, hexagonal, octagonal, and dodecagonal platforms. Regarding criteria, seakeeping was the most significant criterion for platform selection by 53.6%. Regarding platforms, the hexagonal platform scored the highest with 25.31%. Relying on this method and the design considerations presented, numerous dynamic configurations can be offered and assessed through specific contexts without any of the past restrictions. [ABSTRACT FROM AUTHOR]

Background: The concept of a financial scarcity mindset has raised much attention as an explanation for poor decision-making and dysfunctional behavior. It has been suggested that financial scarcity could also impair dietary behavior, through a decline in self-control. Underlying cognitive mechanisms of tunneling (directing attention to financial issues and neglecting other demands), cognitive load (a tax on mental bandwidth interfering with executive functioning) and time orientation (a shift towards a present time horizon, versus a future time horizon) may explain the association between financial scarcity and self-control related dietary behavior. The current scoping review gathers recent evidence on how these mechanisms affect dietary behavior of people experiencing financial scarcity. It builds on a theoretical framework based on insights from behavioral economics and health psychology. Methods: A literature search was executed in six online databases, which resulted in 9.975 papers. Search terms were tunneling, cognitive load and time orientation, financial scarcity, and dietary behavior. Screening was performed with ASReview, an AI-ranking tool. In total, 14 papers were included in the scoping review. We used PRISMA-ScR guidelines for reporting. Results: Limited evidence indicates that a scarcity mindset could increase tunneling, through attentional narrowing on costs of food, which then directly impacts dietary behavior. A scarcity mindset involves experiencing financial stress, which can be understood as cognitive load. Cognitive load decreases attentional capacity, which could impair self-control in dietary choices. Financial scarcity is related to a present time orientation, which affects dietary choices by shifting priorities and decreasing motivation for healthy dietary behavior. Conclusions: A scarcity mindset affects dietary behavior in different ways. Tunneling and a shift in time orientation are indicative of an attentional redirection, which can be seen as more adaptive to the situation. These may be processes indirectly affecting self-control capacity. Cognitive load could decrease self-control capacity needed for healthy dietary behavior because it consumes mental bandwidth. How a changing time orientation when experiencing financial scarcity relates to motivation for self-control in dietary behavior is a promising theme for further inquiry. [ABSTRACT FROM AUTHOR]

Background: The concept of a financial scarcity mindset has raised much attention as an explanation for poor decision-making and dysfunctional behavior. It has been suggested that financial scarcity could also impair dietary behavior, through a decline in self-control. Underlying cognitive mechanisms of tunneling (directing attention to financial issues and neglecting other demands), cognitive load (a tax on mental bandwidth interfering with executive functioning) and time orientation (a shift towards a present time horizon, versus a future time horizon) may explain the association between financial scarcity and self-control related dietary behavior. The current scoping review gathers recent evidence on how these mechanisms affect dietary behavior of people experiencing financial scarcity. It builds on a theoretical framework based on insights from behavioral economics and health psychology. Methods: A literature search was executed in six online databases, which resulted in 9.975 papers. Search terms were tunneling, cognitive load and time orientation, financial scarcity, and dietary behavior. Screening was performed with ASReview, an AI-ranking tool. In total, 14 papers were included in the scoping review. We used PRISMA-ScR guidelines for reporting. Results: Limited evidence indicates that a scarcity mindset could increase tunneling, through attentional narrowing on costs of food, which then directly impacts dietary behavior. A scarcity mindset involves experiencing financial stress, which can be understood as cognitive load. Cognitive load decreases attentional capacity, which could impair self-control in dietary choices. Financial scarcity is related to a present time orientation, which affects dietary choices by shifting priorities and decreasing motivation for healthy dietary behavior. Conclusions: A scarcity mindset affects dietary behavior in different ways. Tunneling and a shift in time orientation are indicative of an attentional redirection, which can be seen as more adaptive to the situation. These may be processes indirectly affecting self-control capacity. Cognitive load could decrease self-control capacity needed for healthy dietary behavior because it consumes mental bandwidth. How a changing time orientation when experiencing financial scarcity relates to motivation for self-control in dietary behavior is a promising theme for further inquiry. [ABSTRACT FROM AUTHOR]

Natural language inference (NLI) is a fundamental task of natural language processing (NLP). Most recent NLI research has focused on explaining the model's decisions in generating causal explanations (i.e., why did a premise/hypothesis pair as input lead to their inference relation as output?). As layer-based language models can learn language structure information, this paper conducts a sample-by-sample analysis of the linguistic feature relation between premise and hypothesis that is expected to guide NLI modeling and interpretation better. Our empirical study verifies that the linguistic feature relation of premise/hypothesis pairs can be seen in NLI inference models, which can be used to interpret inference samples. Meanwhile, experimental results show that these linguistic features relation interpretation can help the NLI model achieve comparable inference accuracy compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Sequential decision-making under multiple objective functions includes the problem of exhaustively searching for a Pareto-optimal policy and the problem of selecting a policy from the resulting set of Pareto-optimal policies based on the decision maker's preferences. This paper focuses on the latter problem. In order to select a policy that reflects the decision maker's preferences, it is necessary to order these policies, which is problematic because the decision-maker's preferences are generally tacit knowledge. Furthermore, it is difficult to order them quantitatively. For this reason, conventional methods have mainly been used to elicit preferences through dialogue with decision-makers and through one-to-one comparisons. In contrast, this paper proposes a method based on inverse reinforcement learning to estimate the weight of each objective from the decision-making sequence. The estimated weights can be used to quantitatively evaluate the Pareto-optimal policies from the viewpoints of the decision-makers preferences. We applied the proposed method to the multi-objective reinforcement learning benchmark problem and verified its effectiveness as an elicitation method of weights for each objective function. [ABSTRACT FROM AUTHOR]

This paper delves into the role of an Action Learning Coach in real-world scenarios. The coach addresses challenges such as team members leaving for phone calls, disruptions caused by important participants and the team returning from a break visibly shaken. As the coach, I employed an approach involving awareness, team queries, and collaborative decision-making. The paper emphasizes the vital function of language in coaching, advocating for a non-judgmental, future-positive approach to instill a learning mindset. The paper underscores the transformative potential of action learning, a process that encourages constant questioning and questioning everything. Action learning coaching leverages coaching at the group level, fostering self-awareness, goal setting and feedback. The article concludes by highlighting the impact of coaching on participants' goal orientation and self-efficacy, stressing the importance of a learning orientation for building powerful, growth-oriented teams. [ABSTRACT FROM AUTHOR]

In order to improve the performance of UAV’s autonomous maneuvering decision-making, this paper proposes a decision-making method based on situational continuity. The algorithm in this paper designs a situation evaluation function with strong guidance, then trains the Long Short-Term Memory (LSTM) under the framework of Deep Q Network (DQN) for air combat maneuvering decision-making. Considering the continuity between adjacent situations, the method takes multiple consecutive situations as one input of the neural network. To reflect the difference between adjacent situations, the method takes the difference of situation evaluation value as the reward of reinforcement learning. In different scenarios, the algorithm proposed in this paper is compared with the algorithm based on the Fully Neural Network (FNN) and the algorithm based on statistical principles respectively. The results show that, compared with the FNN algorithm, the algorithm proposed in this paper is more accurate and forwardlooking. Compared with the algorithm based on the statistical principles, the decision-making of the algorithm proposed in this paper is more efficient and its real-time performance is better. [ABSTRACT FROM AUTHOR]

Background: Low- and middle-income countries (LMICs), including South Africa, are currently experiencing multiple epidemics: HIV and the rising burden of non-communicable diseases (NCDs), leading to different patterns of multimorbidity (the occurrence of two or more chronic conditions) than experienced in high income settings. These adversely affect health outcomes, increase patients' perceived burden of treatment, and impact the workload of self-management. This paper outlines the methods used in a qualitative study exploring burden of treatment among people living with HIV/NCD multimorbidity in South Africa. Methods: We undertook a comparative qualitative study to examine the interaction between individuals' treatment burden (self-management workload) and their capacity to take on this workload, using the dual lenses of Burden of Treatment Theory (BoTT) and Cumulative Complexity Model (CuCoM) to aid conceptualisation of the data. We interviewed 30 people with multimorbidity and 16 carers in rural Eastern Cape and urban Cape Town between February-April 2021. Data was analysed through framework analysis. Findings: This paper discusses the methodological procedures considered when conducting qualitative research among people with multimorbidity in low-income settings in South Africa. We highlight the decisions made when developing the research design, recruiting participants, and selecting field-sites. We also explore data analysis processes and reflect on the positionality of the research project and researchers. Conclusion: This paper illustrates the decision-making processes conducting this qualitative research and may be helpful in informing future research aiming to qualitatively investigate treatment burden among patients in LMICs. [ABSTRACT FROM AUTHOR]

We address a key theoretical issue in the literature of dominant coalitions by explaining why, contrary to conventional wisdom, we observe the emergence of different coalitions under identical equity holdings of shareholders. Shifting the focus from exploring drivers of dominant coalition configurations in isolation (e.g., equity holdings), we expand on a relational perspective to explore how relational ties (soft power) among family shareholders interplay with equity holdings (hard power) in generating individual effective power to influence decision-making in the family firm. Drawing from and expanding on a cooperative game theory approach, this paper breaks new theoretical ground by providing insights into why and how the formation of relational ties (and lack thereof) among family shareholders affects the distribution of effective power in decision-making processes, over and beyond the share in equity holdings. Our work trains a microfoundation lens on the study of dominant coalitions by disentangling and assessing individual effective power in coalitions of shareholders in family firms. We further propose a new perspective and a formal modeling approach to explain and measure why and how relational ties among members shape their effective power to influence decision-making, thereby shedding light on why and how members participate in different coalitional configurations. Finally, we contribute to the literature on ownership structure by promoting a comprehensive perspective that incorporates both equity holdings and relational ties among family shareholders. [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]