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Extracorporeal life support systems (ECLS) are life-sustaining measures for severe cardiovascular diseases, serving as bridging treatment either until cardiovascular function is restored or alternative treatment, such as heart transplantation or the implantation of permanent ventricular assist devices is performed. Given the insufficient evidence and frequent urgency of implantation without initial patient consent, the ethical challenges and psychological burden for patients, relatives and the interprofessional intensive care team are significant. As with any treatment, an appropriate therapeutic goal for ECLS treatment based on the indications and patient informed consent is mandatory. In order to integrate the necessary ethical considerations into everyday clinical practice, a structured algorithm for handling ECLS is proposed here, which takes ethical aspects into due account. [ABSTRACT FROM AUTHOR]

Real-world information is often characterized by uncertainty and partial reliability, which led Zadeh to introduce the concept of Z-numbers as a more appropriate formal structure for describing such information. However, the computation of Z-numbers requires solving highly complex optimization problems, limiting their practical application. Although linguistic Z-numbers have been explored for their computational straightforwardness, they lack theoretical support from Z-number theory and exhibit certain limitations. To address these issues and provide theoretical support from Z-numbers, we propose a Z-number linguistic term set to facilitate more efficient processing of Z-number-based information. Specifically, we redefine linguistic Z-numbers as Z-number linguistic terms. By analyzing the hidden probability density functions of these terms, we identify patterns for ranking them. These patterns are used to define the Z-number linguistic term set, which includes all Z-number linguistic terms sorted in order. We also discuss the basic operators between these terms. Furthermore, we develop a multi-criteria group decision-making (MCGDM) model based on the Z-number linguistic term set. Applying our method to predict the acceptance of academic papers, we demonstrate its effectiveness and superiority. We compare the performance of our MCGDM method with five existing Z-number-based MCGDM methods and eight traditional machine learning clustering algorithms. Our results show that the proposed method outperforms others in terms of accuracy and time consumption, highlighting the potential of Z-number linguistic terms for enhancing Z-number computation and extending the application of Z-number-based information to real-world problems. [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]

Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy is one of the preferred techniques for the analysis of forensic samples. However, the potential of ATR-FTIR spectroscopy for the classification of papers has not been established and remains unclear. In this work, a total of 150 ATR-FTIR spectra of white copy paper of three different manufacturers were analyzed using two advanced statistical methods, i.e., principal component analysis (PCA) and classification and regression tree (CART). Based on simple visual examination alone, the IR spectra did not allow any definite discrimination. However, the IR spectral differences between the papers were revealed by PCA and CART methods. A region at 1500–1200 cm–1 was determined to be the most discriminative. In conclusion, coupling of IR spectrum and CART provides a novel and non-destructive alternative for solving classification tasks in forensic context. [ABSTRACT FROM AUTHOR]

In this work, a fine correlation among destructive and diagnostic parameters of transformer's paper insulation is presented. The degree of polymerization (DP) directly assesses the condition of the paper insulation strength and is measured through destructive procedure. Generally, 2-FAL, CO2 and CO are the aging products of paper decomposition and referred as diagnostic parameters. An Adaptive Neuro Fuzzy Inference System (ANFIS) is proposed and developed to estimate the value of DP as the function of amount of diagnostic parameters. The proposed system has an advantage of diagnosing the health of solid insulation without performing destructive tests. The diagnostic parameters are taken as inputs to the system to determine the value of DP. The system uses 630 data points for training the ANFIS model and follows a ten-fold cross-validation approach. The average validation error has been determined to be. 0.0029. Further, the model's performance has been assessed using experimental data. The optimal ANFIS model has been achieved by suitably selecting the number and type of membership function. The estimated value of DP has been found to conform to the experimental measurements for every case under test. The performance of this model has also been compared with a fuzzy inference system (FIS) model in the reported literature. The comparison shows that the ANFIS model determines the DP values with a greater degree of accuracy than the FIS model. [ABSTRACT FROM AUTHOR]

This column is intended to address the kinds of knotty problems and dilemmas with which many scholars grapple in studying health professions education. In this article, the authors address the question of who should be listed as an author on a given publication and provide advice as to how to navigate potential tensions in the authorship decision-making process. [ABSTRACT FROM AUTHOR]

This paper aims to provide a review of the basis for application of AI in radiology, to discuss the immediate ethical and professional impact in radiology, and to consider possible future evolution. Even if AI does add significant value to image interpretation, there are implications outside the traditional radiology activities of lesion detection and characterisation. In radiomics, AI can foster the analysis of the features and help in the correlation with other omics data. Imaging biobanks would become a necessary infrastructure to organise and share the image data from which AI models can be trained. AI can be used as an optimising tool to assist the technologist and radiologist in choosing a personalised patient's protocol, tracking the patient's dose parameters, providing an estimate of the radiation risks. AI can also aid the reporting workflow and help the linking between words, images, and quantitative data. Finally, AI coupled with CDS can improve the decision process and thereby optimise clinical and radiological workflow. [ABSTRACT FROM AUTHOR]

With the ever-increasing number of submissions in top-tier conferences and journals, finding good reviewers and meta-reviewers is becoming increasingly difficult. Writing a meta-review is not straightforward as it involves a series of sub-tasks, including making a decision on the paper based on the reviewer's recommendation and their confidence in the recommendation, mitigating disagreements among the reviewers, and other such similar tasks. In this work, we develop a novel approach to automatically generate meta-reviews that are decision-aware and which also take into account a set of relevant sub-tasks in the peer-review process. More specifically, we first predict the recommendation scores and confidence scores for the reviews, using which we then predict the decision on a particular manuscript. Finally, we utilize the decision signals for generating the meta-reviews using a transformer-based seq2seq architecture. Our proposed pipelined approach for automatic decision-aware meta-review generation achieves significant performance improvement over the standard summarization baselines as well as relevant prior works on this problem. We make our codes available at https://github.com/saprativa/seq-to-seq-decision-aware-mrg. [ABSTRACT FROM AUTHOR]

This paper describes an interactive decision support system called Opti-Link which has been developed for a company operating in the area of waste and raw material management. Built around a specific transportation problem, the system is used to maximize the revenue generated by selling waste paper to paper mills. Furthermore, the dual variables of the linear program allow the planner to identify upper bounds for setting bid prices to buy waste paper from waste collection companies. First operational results indicate a significant increase in profit while at the same time the duration of the planning process could be cut by more than half. [ABSTRACT FROM AUTHOR]

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]

Graph We are sorry to report that Peter C. Fishburn died on June 10, 2021, in Racine, Wisconsin. In that paper, Fishburn gives a new proof of the theorem and explains issues in the literature with the correctness of the theorem. In paper (Fishburn & Gehrlein, [13]), Fishburn and Gehrlein discuss the propensities of different voting procedures to elect the simple majority candidate when one exists in a multicandidate election. For example, papers (Fishburn, [5]) and (Fishburn, [7]) deal with subjective expected utility theory. [Extracted from the article]

The prediction of current scientific impact of papers and authors has been extensively studied to help researchers find valuable papers and recent research directions, also help policymakers make recruitment decisions or funding allocation. However, how to accurately evaluate the future impact of them, especially for new papers and young researchers, is the focus of scientific impact prediction research, and is less explored. Existing graph-based methods heavily depend on the global structure information of heterogeneous academic network and ignore the local structure information and text information, which may provide important clues to identify influential papers and authors with novel perspective. In this paper, we propose a hybrid model called ESMR to predict the future influence of papers and authors by mainly exploiting these information mentioned above. Specifically, we first put forward a novel network embedding-based model, which can capture not only the local structure information, but also the text information of papers into a unified embedding representation. Then, the future impact of papers and authors is mutually ranked by integrating the learned embedding representations into a multivariate random-walk model. Empirical results on two real datasets demonstrate that the proposed method significantly outperforms the existing state-of-the-art ranking methods. [ABSTRACT FROM AUTHOR]

The special issue on Advanced Statistical Modeling and Causal Inference with Complex data for Better Decision Making has been inspired by the developments of innovative models and methods to answer challenging substantive questions and support policy choices and decisions. It includes a selection of twelve papers with cutting-edge methodological developments motivated by unique applications with challenging data structures from various fields of science. [ABSTRACT FROM AUTHOR]

In recent years, more and more attention is paid to the OWA operator in the academy. Growth curve analysis, which is often used in ecosystem studies, also indicates that this growth trend will continue. However, prior literature has not made a big picture to help researchers make clear of the development of this field by identifying the evolution path. The classic main path analysis is an excellent method combining quantitative analysis and qualitative analysis. We conducted the classic main path analysis and its variants on a citation network with 1474 papers to probe the development trajectories and research topics of OWA. We obtained several findings by constructing local and global main path, and multiple main paths. The path results indicate that weight generation and operator generalization run through the overall OWA domain, show that the multiple criteria decision making process assumed in the related research begins to be dynamic and multi-period, and reveal that some theories such as social network theory are introduced into the OWA operator and the applications are also greatly expanded. [ABSTRACT FROM AUTHOR]

Comparing with cities, rural communities especially those declining rural communities have become vulnerable to natural disasters owing to their backward socioeconomic conditions. Taking Xun County of China's Henan Province as the study area, the paper aims to evaluate rural community resilience to flood by unveiling the connection between individuals' cognition, follow-up actions and the community resilience. Research results show that: (1) The logic chain exists as individual's cognition to disaster leads to their constructive actions to cope with disaster, which contribute to community resilience. (2) At the cognition dimension, individual's knowledge reserve of disaster prevention and their recognition to local authority are playing an important role in their decision making and follow-up behaviors when disaster occurs. (3) At the action dimension, individual's familiarity with the disaster preparedness, efficient information transmission when disaster occurs and villagers' following order and their unity of action all contribute to community resilience to disaster. The paper proposes ways to improve rural community resilience to disasters based on the research findings. [ABSTRACT FROM AUTHOR]

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The authors revisit their 1991 Bigglestone Award winning Fire Technology paper “Fire Risk Analysis: General Conceptual Framework for Describing Models” and comment on what has changed, what has evolved, and what has remained the same in our understanding of good practice in the conduct of fire risk analysis. Also, the authors refer to the present issues and the future prospects in this regard. [ABSTRACT FROM AUTHOR]

In Europe, lung cancer ranks third among the most common cancers, remaining the biggest killer. Since the publication of the first European Society of Radiology and European Respiratory Society joint white paper on lung cancer screening (LCS) in 2015, many new findings have been published and discussions have increased considerably. Thus, this updated expert opinion represents a narrative, non-systematic review of the evidence from LCS trials and description of the current practice of LCS as well as aspects that have not received adequate attention until now. Reaching out to the potential participants (persons at high risk), optimal communication and shared decision-making will be key starting points. Furthermore, standards for infrastructure, pathways and quality assurance are pivotal, including promoting tobacco cessation, benefits and harms, overdiagnosis, quality, minimum radiation exposure, definition of management of positive screen results and incidental findings linked to respective actions as well as cost-effectiveness. This requires a multidisciplinary team with experts from pulmonology and radiology as well as thoracic oncologists, thoracic surgeons, pathologists, family doctors, patient representatives and others. The ESR and ERS agree that Europe's health systems need to adapt to allow citizens to benefit from organised pathways, rather than unsupervised initiatives, to allow early diagnosis of lung cancer and reduce the mortality rate. Now is the time to set up and conduct demonstration programmes focusing, among other points, on methodology, standardisation, tobacco cessation, education on healthy lifestyle, cost-effectiveness and a central registry.Key Points• Pulmonologists and radiologists both have key roles in the set up of multidisciplinary LCS teams with experts from many other fields.• Pulmonologists identify people eligible for LCS, reach out to family doctors, share the decision-making process and promote tobacco cessation.• Radiologists ensure appropriate image quality, minimum dose and a standardised reading/reporting algorithm, together with a clear definition of a "positive screen".• Strict algorithms define the exact management of screen-detected nodules and incidental findings.• For LCS to be (cost-)effective, it has to target a population defined by risk prediction models. [ABSTRACT FROM AUTHOR]

The paper by Ma et al. ([20]) investigates the supply chain coordination issues arising out of a green supply chain consisting of a manufacturer and a retailer under a cost-sharing contract with uncertain information. 20 Ma N, Gao R, Wang X, Li P. Green supply chain analysis under cost sharing contract with uncertain information based on confidence level. 30 Yang S, Ding P, Wang G, Wu X. Green investment in a supply chain based on price and quality competition. 33 Zhang P, Liu Y, Yang G, Zhang G. A distributionally robust optimization model for designing humanitarian relief network with resource reallocation. [Extracted from the article]

The purpose of the paper is to provide a conceptualization and an aligned game-theoretic operationalization of the dynamic coopetitive managerial decision-making process. The proposed two-step sequential game uses existing game theoretical constructs but combines them in a unique way. One of its important positive features is its capacity to make the performance implications of all potential coopetitive decisions in a relationship episode explicit, together with the interplay among them. The lack of such a feature has been recognized as a severe limitation of extant literature, hampering further theoretical and methodological development. The proposed solution gives insights into how moment-by-moment managerial decisions unfold over time and possibly result in disequilibrium states, generating tension. Managerial attributes play a key role in effectively managing such moments, it is suggested. Based on the proposed operationalization, behavioral experiments can be designed through which future empirical research can develop appropriate data sets and test the widely hypothesized roles of these attributes, which can result in more reliable and generalizable research results. Besides the theoretical and empirical value of the proposed conceptualization and operationalization, the paper is of value to decision makers, as it makes the internal complexity of the coopetitive strategizing and its inherent interdependencies explicit, providing practical insights into this complex phenomenon. [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]

The task of multi-attribute decision making (MADM) involves selecting the best alternative/course of action from a set of feasible options in the presence of conflicting objectives (criteria). Till date, several MADM techniques having their own merits and demerits have emerged out to aid the decision making task. Among them, multi-objective optimization on the basis of ratio analysis (MOORA) method is one of the simplest techniques with uncomplicated computational steps. In this method, the appraisal score of an alternative is calculated for its subsequent ranking as the difference between the sum of performance scores for the beneficial criteria and sum of performance scores for the non-beneficial criteria. In this paper, a comprehensive literature review on the applications of MOORA method is conducted based on more than 200 scholarly articles published during 2008–2021. Those articles are broadly classified into ten application areas, i.e. (a) process optimization, (b) design, engineering and manufacturing, (c) supply chain management and logistics, d) business and financial management, (e) energy management, (f) human resources management, (g) education, (h) civil engineering, water resource management and waste management, (i) health, safety, hospitality and risk management, and (j) miscellaneous. The reviewed papers are also critically analyzed with respect to combination or comparison with other methods, year-wise publication, journal-wise publication, authors' nationality, institution-wise publication, relation with other keywords and collaborative works. This review paper would act as a data repository to meet the requirements of the decision making practitioners in solving realistic MADM problems using MOORA while utilizing its simple mathematical steps and robust ranking performance. A framework for future research directions is also proposed to promote MOORA research. [ABSTRACT FROM AUTHOR]

Decision making is the process of making choices by identifying a decision, gathering information, and assessing alternative resolutions. Therefore, in this paper, they proposed an attention-based hierarchical denoised deep clustering (AHDDC) algorithm to solve the problem, which enables GCN to learn multiple layers of hidden information and uses the attention mechanism to strengthen the information. These accepted articles make contributions and novel aspects of different research in social media data scenarios, traffic data scenarios, data security scenarios, medical data scenarios, knowledge graph learning, and the other graph based deep learning models. [Extracted from the article]

In order to solve the problem that the Proximal Policy Optimization (PPO) algorithm is difficult to converge in the air-sea battle scenarios with high dynamics, strong interference, and complex state space, the Ray-LAPPO algorithm based on Long Short-Term Memory (LSTM) and Attention mechanism is proposed in this paper under the distributed training framework Ray. Firstly, the idea of Centralized Training Distributed Execution (CTDE) is adopted to extend the PPO algorithm to the field of multi-agent and the policy entropy is added to the loss function to encourage the exploration of agents; Secondly, the LSTM network is added to the actor and critic networks to explore the timing relationship between non-independent and identically distributed samples and improve the learning performance of the UAV; In addition, the Attention mechanism is introduced to obtain the states at different time steps and establish a weighted differentiation model of the final value function; Finally, the simulation experiments on the self-developed heterogeneous UAV collaborative decision-making environment show that Ray-LAPPO can get the most advanced performance in different scenarios, and also possesses potential value for large-scale real-world applications. [ABSTRACT FROM AUTHOR]

The paper investigates the intricate relationship between oil and stock markets in the context of different oil price shocks and a country's energy profile. Focusing on five major oil-producing and consuming countries, the analysis reveals a significant, time-varying dimension in the oil-stock relationship. It highlights the importance of distinguishing between various types of oil shocks and a nation's reliance on oil revenues. The paper finds that countries heavily reliant on oil revenues exhibit robust responses to oil supply and demand shocks. In contrast, oil producers show more moderate reactions due to lower oil revenue dependence. This evidences the importance of recognizing the multifaceted impacts of oil shocks on different economies for making well-informed decisions in asset allocation, portfolio management, and policymaking. [ABSTRACT FROM AUTHOR]

Introduction: Behaviourally informed soft policies, such as nudges, have become popular in areas like health, environment, and energy use as cost-effective instruments to change behaviour and decision-making. However, the effectiveness of soft policies in the transport sector is modest at best. One reason for this relative ineffectiveness might be their one-size-fits-all nature, and personalizing soft interventions has been suggested to increase their effectiveness. The Trans-theoretical Model (TTM) suggests that people progress through five stages of behavioural change, from pre-contemplating a behaviour to maintaining the behaviour, and behavioural interventions could be designed for specific stages. However, it is not always feasible to conduct surveys to place people at different stages of the TTM. Methods: This paper explores whether it is possible to use multimodality data taken from a travel diary to place people at different stages of the TTM. The analysis uses an existing dataset from 826 respondents that includes self-reported TTM stages regarding cycling and data on multimodality. In the analysis, the multimodality data are used to allocate respondents to categories and assign them to TTM stages. The performances of the stage assignment approaches are evaluated using the self-reported TTM data and confusion matrices. Findings: The accuracy of the allocation of participants to TTM stages using multimodality data is approximately 75%. The accuracy is higher for early stages (pre-contemplation) and later stages (maintenance) of the TTM. A data-driven approach to dealing with multimodality data performs slightly better than an approach that relies on pre-defined categorization. Conclusion: The paper suggests that it will be possible in the future to personalise behavioural interventions according to the stages of the TTM even in the absence of self-reported survey data that classifies people to TTM stages if objective multimodality data are available. [ABSTRACT FROM AUTHOR]

Background: Addressing Patient Reported Outcomes (PROs) is essential for patient-centred care, shared decision making and improved health outcomes. Value-based health care systems in New South Wales (NSW) have a growing focus on collecting and using PROs that matter most to patients to improve their healthcare outcomes. Developing oral health patient reported outcomes measures (OH-PROM) is a first step towards value-based oral health care. This paper describes the development process of an adult and child OH-PROM tool that can be piloted for NSW public dental patients. Methods: An expert panel was assembled to undertake a systematic process of developing OH-PROMs for NSW Health. Key methodological considerations included: (1) forming an expert panel to specify the target population and context of implementation, (2) rapid literature review and environmental scan to identify existing validated OH-PROM tools for adults and children. (3) consensus gathering with the expert panel (4) consumer feedback, and (5) finalisation of the tool for electronic oral health record (eOHR) integration to establish a set of questions, that were relevant, context-appropriate, and important to oral healthcare outcomes for patients using public dental services. Results: The panel considered a total of 59 questions from two child (15), and four adult (44) Oral Health Related Quality of Life (OHRQoL) questionnaires used to collect OH-PROMs. These questions were mapped to the four key dimensions of OHRQoL for OH-PROMs: Oral Function, Orofacial Pain, Orofacial Appearance, and Psychosocial Impact. The consensus resulted in seven questions that aligned with these four dimensions to form two new NSW OH-PROM tools: one for adults and one for children. The tools were tested with consumers for understandability and usefulness before being incorporated into the electronic oral health record system, in readiness for future pilot testing. Conclusion: The process for developing new OH-PROMs for NSW public dental services took a pragmatic approach that combined literature appraisal, expert consensus, and consumer consultation. Future work will assess the implementation of the OH-PROM tool and test its validity for broader use as an outcome measure for value-based oral healthcare. [ABSTRACT FROM AUTHOR]

The advancement of foundation models, such as large language models (LLMs), vision-language models (VLMs), diffusion models, and robotics foundation models (RFMs), has become a new paradigm in robotics by offering innovative approaches to the long-standing challenge of building robot autonomy. These models enable the development of robotic agents that can independently understand and reason about semantic contexts, plan actions, physically interact with surroundings, and adapt to new environments and untrained tasks. This paper presents a comprehensive and systematic survey of recent advancements in applying foundation models to robot perception, planning, and control. It introduces the key concepts and terminology associated with foundation models, providing a clear understanding for researchers in robotics and control engineering. The relevant studies are categorized based on how foundation models are utilized in various elements of robotic autonomy, focusing on 1) perception and situational awareness: object detection and classification, semantic understanding, mapping, and navigation; 2) decision making and task planning: mission understanding, task decomposition and coordination, planning with symbolic and learning-based approaches, plan validation and correction, and LLM-robot interaction; 3) motion planning and control: motion planning, control command and reward generation, and trajectory generation and optimization with diffusion models. Furthermore, the survey covers essential environmental setups, including real-world and simulation datasets and platforms used in training and validating these models. It concludes with a discussion on current challenges such as robustness, explainability, data scarcity, and real-time performance, and highlights promising future directions, including retrieval augmented generation, on-device foundation models, and explainability. This survey aims to systematically summarize the latest research trends in applying foundation models to robotics, bridging the gap between the state-of-the-art in artificial intelligence and robotics. By sharing knowledge and resources, this survey is expected to foster the introduction of a new research paradigm for building generalized and autonomous robots. [ABSTRACT FROM AUTHOR]

This paper investigates how the introduction of AI to decision making increases moral distance and recommends the ethics of care to augment the ethical examination of AI decision making. With AI decision making, face-to-face interactions are minimized, and decisions are part of a more opaque process that humans do not always understand. Within decision-making research, the concept of moral distance is used to explain why individuals behave unethically towards those who are not seen. Moral distance abstracts those who are impacted by the decision and leads to less ethical decisions. The goal of this paper is to identify and analyze the moral distance created by AI through both proximity distance (in space, time, and culture) and bureaucratic distance (derived from hierarchy, complex processes, and principlism). We then propose the ethics of care as a moral framework to analyze the moral implications of AI. The ethics of care brings to the forefront circumstances and context, interdependence, and vulnerability in analyzing algorithmic decision making. [ABSTRACT FROM AUTHOR]

Over the last few decades, researches related to the impact of production and industrial manufacturing on environmental sustainability, coupled with multi-dimensional multi-criteria decision analysis and performance evaluation has consistently gained importance. However, no significant work in which an effort to optimize the total production process with the consideration of the positive and negative impacts of each input and output on Environmental sustainability has been noticed. The main aim of our paper is to address this gap. In this paper, an effort has been made to develop a realistic optimization model wherein a manufacturing firm has been considered to function with several decision-making units taking into account the effects of factors like profit, employment generation, social development, environmental pollution and sustainability. The various inputs employed and outputs produced by the firm are responsible for having negative impacts on the environment, besides creative value generation. With due consideration to both these values instead of mere profit, the model determines and explores the firm's decision considering different objectives. The optimization problem has been solved by a GA developed by us and demonstrated with numerical examples. The solutions of the numerical examples point towards some interesting revelations and indicate some prospective future research directions which contributes to this field of research. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of improving information infrastructure and decision support systems in dynamic external and internal organizational environments. It provides an overview of the new event-driven process methodology notation for information infrastructure and the assessing information processing effectiveness. The main types of information processes, their differences and research methods are presented. The procedural models given in this paper are intended for research and comparison of information processes according to specified performance criteria. Different information processes types and their formalization techniques are given. [ABSTRACT FROM AUTHOR]

Background: Virtual fracture clinics (VFCs) have become widely adopted, aiming to improve efficiency, standardise patient care and reduce clinic appointments for injuries that can be managed conservatively. A variety of means exist to manage VFC referrals and assessment, including paper-based and digital methods. This study assesses VFC referral quality and outcomes before and after implementation of a digital VFC referral and management system. Methods: A retrospective analysis was conducted of all VFC referrals and assessments from July 2017–March 2020 in a large UK district general hospital. All referrals and assessments were analysed for quality and completeness of referral information, grade of assessor, outcome of assessment, referral-to-assessment time, and assessment-to-surgery time (for those requiring operative management). Results: 3038 paper and 9,228 digital referrals were analysed by 2 separate reviewers. Quality and completeness of referral information showed significant improvement in 11 predetermined key data points with the digital referral system (p < 0.001). Date and mechanism of injury were the most commonly missing data criteria (67.5% and 68.2%, respectively) with paper referrals. Significant improvements were noted in the proportion of Consultant delivered VFC assessments (84.2% vs 71.0%; p < 0.001), VFC discharge rate (20.8% vs 13.1%; p < 0.001) and patients recalled for urgent review (6.2% vs 0.8%; p < 0.001) with digital referrals. Mean referral-to-assessment (31.2 vs 49.9 h; p < 0.001) and assessment-to-surgery (9.2 vs 13.0 days; p = 0.01) times also reduced significantly with referral digitisation. Conclusion: Improvements in virtual referral quality and completeness directly lead to facilitation of more thorough, detailed and appropriate virtual assessments; improving timely decision-making, reducing unnecessary appointments, and permitting better prioritisation of workload and earlier surgery for patients requiring operative treatment. Purpose-built digital solutions are an excellent means of achieving these aims. [ABSTRACT FROM AUTHOR]

Predicting and modeling human behavior and finding trends within human decision-making processes is a major problem of social science. Rock Paper Scissors (RPS) is the fundamental strategic question in many game theory problems and real-world competitions. Finding the right approach to beat a particular human opponent is challenging. Here we use an AI (artificial intelligence) algorithm based on Markov Models of one fixed memory length (abbreviated as "single AI") to compete against humans in an iterated RPS game. We model and predict human competition behavior by combining many Markov Models with different fixed memory lengths (abbreviated as "multi-AI"), and develop an architecture of multi-AI with changeable parameters to adapt to different competition strategies. We introduce a parameter called "focus length" (a positive number such as 5 or 10) to control the speed and sensitivity for our multi-AI to adapt to the opponent's strategy change. The focus length is the number of previous rounds that the multi-AI should look at when determining which Single-AI has the best performance and should choose to play for the next game. We experimented with 52 different people, each playing 300 rounds continuously against one specific multi-AI model, and demonstrated that our strategy could win against more than 95% of human opponents. [ABSTRACT FROM AUTHOR]

Healthcare Industry 4.0 refers to intelligent operation processes in the medical industry. With the development of information technology, large-scale group decision making (GDM), which allows a larger number of decision makers (DMs) from different places or sectors to participate in decision making, has been rapidly developed and applied in Healthcare Industry 4.0 to help to make decisions efficiently and smartly. To make full use of GDM methods to promote the developments of the medical industry, it is necessary to review the existing relevant achievements. Therefore, this paper conducts an overview to generate a comprehensive understanding of GDM in Healthcare Industry 4.0 and to identify future development directions. Bibliometric analyses are conducted in order to learn the development trends from published papers. The implementations of GDM methods in Healthcare Industry 4.0 are reviewed in accordance with the paradigm of the general GDM process, which includes information representation, dimension reduction, consensus reaching, and result elicitation. We also provide current research challenges and future directions regarding medical GDM. It is hoped that our study will be helpful for researchers in the field of GDM in Healthcare Industry 4.0. [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]

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]

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]

This paper draws from data collected from 500+ surveys, distributed twice from the same respondents (2020 and 2021), and forty-five face-to-face interviews (2022). The location studied is a metropolitan county in Colorado (USA). The research examined the discourses and practices having to do with organic and natural food consumption—note, too, the data were collected at the height of the COVID-19 pandemic. The findings upend conventional understandings of, and frameworks used to explain, consumer behavior. What are often presented as motivations in prior studies are shown, instead, to be justifications; rationalizations after-the-fact. The paper troubles decision-making frameworks that cast motivations, attitudes, and intentions as "antecedents" to consumer behavior. Rather, the findings point to the significance of social networks, and in particular network diversity, for understanding and explaining the sayings (discourses) and doings (practices) of "individual" consumers. Discourses linked to health are also shown to be salient variables, though when situated within social networks those discourses are shown to have politics. Particular attention is devoted to explaining dietary shifts among those who reported the largest increases in the consumption of organic and natural foods between 2020 and 2021/22. The paper concludes discussing what the data mean from the standpoint of envisioning just and inclusive food system futures and agrifood policy that delivers on those ends. [ABSTRACT FROM AUTHOR]

The probabilistic linguistic term sets (PLTSs) are very powerful in solving the multi-criteria decision-making (MCDM) problems. The previous comparison methods associated with PLTSs are finite and unreasonable. Hence, developing a more effective way to compare PLTSs and proposing a reasonable decision-making method to cope with MCDM problems are important work of this paper. Firstly, a new possibility degree is proposed and a visual comparison method is given to present the process of comparing PLTSs. Subsequently, a similarity measure for PLTSs is also proposed to make up for the lack of similarity measure. Combining the new comparison method with the similarity measure, the TOPSIS is extended to solve real-life problems under probabilistic linguistic environment. Finally, a numerical example considers the selection of public opinion monitoring systems and the comparative analyses are shown to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Recommender systems have been successfully applied to alleviate overloaded information and assist decision making in various domains and applications. Recently, several new research directions emerged and novel techniques were proposed to advance the development of recommender systems. In this special issue, we invited authors to submit the revised and extended version of their accepted papers in the track on recommender systems associated with ACM Symposium on Applied Computing in 2020 and 2021. Each submission was reviewed by at least two experts and revised according to the reviewers' comments to ensure the quality of the paper. We hope this special issue can motivate researchers in the area of recommender systems to take the next step beyond traditional algorithm development and seek more opportunities in their research work. [ABSTRACT FROM AUTHOR]

Every year many earthquakes occurs around the world and some of them are highly destructive. Therefore, the study of the vulnerable areas to the earthquakes seems crucial. Current paper presents a novel method for the assessment of areas, which are prone to the earthquakes, using relative tectonic activity index (IAT). IAT is calculated via six morphometric indices, including Asymmetry factor, Mountain Front Sinuosity, Valley Floor width, Hypsometric Integral, Average of Channel Mean Slope and Drainage Basin Shape. Every index has some limits to show the magnitude of tectonic activity and sometimes they are not clear enough and need a reliable and accurate scoring method to reduce the uncertainty of the calculation results. Morphometric Indices, as the key factors in determining the tectonics situation of an area, were analyzed via fuzzy inference system to improve the accuracy of decision making in the IAT calculation, as the suggested method in present paper for the earthquake vulnerability assessment. Torbat-e-Jam basin was selected as a case of paper to operate the proposed method. Results showed that about 0.87 percent of paper area has the high IAT and is high risk of earthquake, consequently. About 82.47 and 16.66 percent of paper are determined as the moderate and low IAT, respectively. The map of the earthquake intensity of the paper area was plotted, afterwards, to check whether the results of presented method are reliable, and it approved that. The above method, thus, is applicable in those areas, which do not have enough seismologic data. [ABSTRACT FROM AUTHOR]

In Victoria, Australia, senior secondary mathematics students are expected to use technology and thus need to make decisions about using pen-and-paper (P&P) or technology when solving mathematics problems. The predominant technology is a Computer Algebra System (CAS). This study investigated the beliefs about CAS held by twelve Year 11 students as they learnt to use CAS and whether these beliefs were stable over time. These students held a range of beliefs related to the usefulness of CAS, speed of CAS compared to P&P, whether CAS is proper mathematics, choice of CAS or P&P, ease of use, the correctness of answers and solving problems in Mathematical Methods (i.e. the mathematics subject studied). Beliefs are often described as being stable (e.g. McLeod, 1992), but some researchers stress stability needs to be determined empirically rather than being seen as a characteristic of beliefs (e.g. Liljedahl et al., 2012). For this sample of students, stability (rather than instability) is a feature of students' beliefs about CAS. [ABSTRACT FROM AUTHOR]

The paper presents a specific method of environmental impact assessment applied in Strategic Environmental Assessment (SEA) for the National Emission Reduction Plan (NERP) in the Republic of Serbia, based on air quality. The specificity of the approach is in the application of a semiquantitative method of multicriteria evaluation based on air dispersion modeling and the integration of SEA goals, indicators and criteria for assessing the impact of the NERP on the quality of air and other environmental elements in this method. When predicting changes in air quality for the planning horizon to 2028, the physical, geographical and climatic characteristics of the area were taken into account, as well as technical measures to reduce SO2 emissions, since this was the dominant pollutant from the Serbian coal-fired power plants studied by the NERP. Air pollution modeling was carried out using the AERMOD software package based on the data collected, and the quantitative results obtained were used in a multicriteria evaluation as part of the SEA. The results of the research indicated the importance of applying this approach in order to significantly increase objectivity in the SEA process, since it is an important element of decision making at the strategic level. In addition, a comparative presentation of the modeling results before and after application of the NERP was an important part of the SEA process, and it provided a clear insight into expected changes in the air quality. This is a key argument for making appropriate policy decisions on spatial, energy, environmental and socio-economic development in the Republic of Serbia, which, like other developing countries, is sluggishly following global trends in energy transition. [ABSTRACT FROM AUTHOR]

The paper investigates and refines the proportionalist solution to the causal exclusion problem developed by Menzies and List. First and foremost, it explores the implications of their inter-level compatibility result. It is highlighted that in theory the inter-level causal compatibility of realizer and realized properties allows for scenarios where the higher-level property is multiply realized. By developing concrete illustrations, the paper proves this to be an empirically plausible option. Further non-trivial implications of the framework are unpacked to show that the sensitivity of causal relations to background conditions is as important in deciding on the existence and the direction of exclusion as sensitivity to the realization of the cause. This insight also opens the way to further refinements: a richer reconceptualization of upwards exclusion and a plausible answer to a critic of the Menzies and List project. [ABSTRACT FROM AUTHOR]

Digital health technologies (DHTs) have the potential to modernize drug development and clinical trial operations by remotely, passively, and continuously collecting ecologically valid evidence that is meaningful to patients' lived experiences. Such evidence holds potential for all drug development stakeholders, including regulatory agencies, as it will help create a stronger evidentiary link between approval of new therapeutics and the ultimate aim of improving patient lives. However, only a very small number of novel digital measures have matured from exploratory usage into regulatory qualification or efficacy endpoints. This shows that despite the clear potential, actually gaining regulatory agreement that a new measure is both fit-for-purpose and delivers value remains a serious challenge. One of the key stumbling blocks for developers has been the requirement to demonstrate that a digital measure is meaningful to patients. This viewpoint aims to examine the co-evolution of regulatory guidance in the United States (U.S.) and best practice for integration of DHTs into the development of clinical outcome assessments. Contextualizing guidance on meaningfulness within the larger shift towards a patient-centric drug development approach, this paper reviews the U.S. Food and Drug Administration (FDA) guidance and existing literature surrounding the development of meaningful digital measures and patient engagement, including the recent examples of rejections by the FDA that further emphasize patient-centricity in digital measures. Finally, this paper highlights remaining hurdles and provides insights into the established frameworks for development and adoption of digital measures in clinical research. [ABSTRACT FROM AUTHOR]

Deploying systems-theoretical conceptuality, this paper improves understanding of the organisational consequences of the intensified societal engagement of a research university. Aligning its work with Luhmannian organisational analysis, it addresses the dynamic interplay between two modes of administrative decision-making communication, namely, the traditional professional administration and the New-Public-Management-oriented (NPM) managerial techniques. Our research observes how the politico-economic conditions of the society translate into the university's decisions concerning an initiative to engage in start-up entrepreneurship. The article contributes to higher education literature by showing that the university's professional administration is a discrete organisational function internally differentiated into specialised administrative branches, each of which operates according to a sense-making regime associated with its primary societal system reference, such as education, science and the economy. The article also demonstrates the structurally conditioned differences in branch-specific temporalisations of the entrepreneurial initiative during decision-making. Inspired by the Luhmannian view on temporality, we demonstrate how administrative decisions synchronise the varied structural time horizons within the university's professional administration. Focus on temporality in decision-making thus allows us to see how the NPM-inspired managerial techniques are operationalised in administrative communication at universities. Consequently, the paper argues that university administration is a complex dynamic entity, which varyingly aligns itself to national policy scripts, and only selectively enacts features of a global trend known as NPM. [ABSTRACT FROM AUTHOR]

The price prediction is valuable in energy management system (EMS) because it allows making informed decisions and solving the problem of the uncertainty related to the future ignorance based only on the past knowledge. To this goal, we present in this paper a two-steps EMS in order to control the different operations of a micro-grid (MG). In the first step, we exploit the advantages of the Bidirectional Long-Short Term Memory (BiLSTM) deep learning model to predict the next daily electricity price based on time series. In the second step, we use a type-2 fuzzy logic controller to decide which energy source will exploit the excess energy produced or meet the MG need. Real data is used in this paper to test the effectiveness of the proposed EMS whose superiority is proved through the test period. The BiLSTM forecasting model better performs compared to other related algorithms designed to the electricity price prediction. In addition, the proposed decision-making process can reduce the total MG cost and protect the batteries against the deep discharge and maximum charge in order to prolong their lifespan. We expect that this work can contribute to meet the real-world needs in the management of the electrical system. [ABSTRACT FROM AUTHOR]

How a name influences its holder has been studied for a long time. This paper labels the fundamental literature in this discipline into three categories: how a name influences its owner's personality, decision-making, and life outcomes. We also provide suggestions for future studies concerning this topic. [ABSTRACT FROM AUTHOR]

Catholic schooling in the United States is suffering from a persistent enrollment crisis that has triggered the need for system-wide organizational reforms. However, most of the changes that the sector has experienced has taken place in individual schools making decisions about how to operationally sustain their individual school community. In this article, we present findings from a qualitative analysis of 26 superintendents of (arch)diocesan Catholic school systems in order to better understand why there has been an absence of system-level change in the Catholic sector in the U.S. at a time when systems thinking has started to spread throughout other sectors domestically and internationally. We show through the findings presented in this paper that many Catholic school systems in the United States do not sustain system-level change because they rely on a decentralized "system of schools" organizational form that superintendents believe limits the possibility for sector-wide organizational reform. We highlight in this paper the ways superintendents are forced to navigate these organizational and political limitations and suggest what the implications of this limited possibility for system-level change are for the Catholic sector and other similarly organized sectors. [ABSTRACT FROM AUTHOR]