Search

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]

Manifold visualisation techniques are commonly used to visualise high-dimensional datasets in physical sciences. In this paper, we apply a recently introduced manifold visualisation method, slisemap, on datasets from physics and chemistry. slisemap combines manifold visualisation with explainable artificial intelligence. Explainable artificial intelligence investigates the decision processes of black box machine learning models and complex simulators. With slisemap, we find an embedding such that data items with similar local explanations are grouped together. Hence, slisemap gives us an overview of the different behaviours of a black box model, where the patterns in the embedding reflect a target property. In this paper, we show how slisemap can be used and evaluated on physical data and that it is helpful in finding meaningful information on classification and regression models trained on these datasets. [ABSTRACT FROM AUTHOR]

The challenge of detecting research topics in a specific research field has attracted attention from researchers in the bibliometrics community. In this study, to solve two problems of clustering papers, i.e., the influence of different distributions of citation links and involved textual features on similarity computation, the authors propose a hybrid self-optimized clustering model to detect research topics by extending the hybrid clustering model to identify “core documents”. First, the Amsler network, consisting of bibliographic coupling and co-citation links, is created to calculate the citation-based similarity based on the cosine angle of papers. Second, the cosine similarity is also used to compute the text-based similarity, which consists of the textual statistical and topological features. Then, the cosine angle of the linear combination of citation- and text-based similarity is considered as the hybrid similarity. Finally, the Louvain method is applied to cluster papers, and the terms based on term frequency are used to label clusters. To test the performance of the proposed model, a dataset related to the data envelopment analysis field is used for comparison and analysis of clustering results. Based on the benchmark built, different clustering methods with different citation links or textual features are compared according to evaluation measures. The results show that the proposed model can obtain reasonable and effective clustering results, and the research topics of data envelopment analysis field are also analyzed based on the proposed model. As different features are considered in the proposed model compared with previous hybrid clustering models, the proposed clustering model can provide inspiration for further studies on topic identification by other researchers. [ABSTRACT FROM AUTHOR]

Although PPP(Public-private partnership) mode has been applied for a long time in infrastructural project, the success rate is not very high. The sustainability of PPP projects is still influenced by many factors. In order to examine the evolutionary stable strategies (ESSs) of social capital, government, and paying consumers, a tripartite evolutionary game model is established in this work. In order to further promote consumer participation, it is necessary to make the assumption that customer oversight and review can have an impact on service prices. The results show: i)The strategy choice of consumer depends on the comparison between supervision cost of consumer and price coefficient for consumer to social capital. ii)Consumer supervision can promote the provision of high-quality services by social capital. iii)The difference between high-quality cost and low-quality cost, subsidy coefficient, price coefficient and supervision cost of consumer are critical factors influencing both evolutionary results and trajectories. This paper also puts forward policy implications for the three stakeholders to promote social capital's high-quality strategy so as to maintain the sustainability of PPP projects. [ABSTRACT FROM AUTHOR]

Maintenance and rehabilitation (M&R) is necessary to keep pavement networks in good condition. Due to the capital intensity, M&R funding is always insufficient. The annual budget, determining the available funding, is a critical criterion when planning M&R treatments. However, its values are often given, and the determination of the values is seldom discussed. To fill the gap, this paper focuses on both the determination of annual budgets and the budget allocations, and therefore enhances the network-level decision-making on M&R by developing a Multi-Objective Optimization (MOO) method. This method does not only optimize and trade off the annual budgets and their consequences, but also allocates the funding across the entire network through generating the optimized M&R decisions. According to a case study with 50 segments, the developed method successfully and effectively identified non-linear discrete relationship between the minimized annual budgets and the maximized M&R benefits subject to all the constraints, and generated the optimized annual budget allocation for each M&R decision. The achievements of this paper can be used to enhance the efficiency of M&R decisions and contribute to informed pavement management. [ABSTRACT FROM AUTHOR]

The measurement of productivity change in decision-making units (DMUs) is crucial for assessing their performance and supporting efficient decision-making processes. In this paper, we propose a new approach for measuring productivity change using the Malmquist productivity index (MPI) within the context of two-stage network data envelopment analysis (TSNDEA) under data uncertainty. The two-stage network structure represents a realistic model for DMUs in various fields, such as insurance companies, bank branches, and mutual funds. However, traditional DEA models do not adequately address the issue of data uncertainty, which can significantly impact the accuracy of productivity measurements. To address this limitation, we integrate the MPI methodology with an uncertain programming framework to tackle data uncertainty in the productivity change measurement process. Our proposed approach enables the evaluation of productivity change by capturing both technical efficiency and technological progress over time. By incorporating fuzzy mathematical programming into the DEA framework, we model the inherent uncertainty in input and output data more effectively, enhancing the robustness and reliability of productivity measurements. The utilization of the proposed approach provides decision-makers with a comprehensive analysis of productivity change in DMUs, allowing for better identification of efficiency improvements or potential areas for enhancement. The findings from our study can enhance the decision-making process and facilitate more informed resource allocation strategies in real-world applications., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Shojaie et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Prior literature finds senior executives can influence auditor decision making. However, few studies have discussed the impact of employee's personal characteristics. Our research aims to fill the above research gaps by examining the impact of employee level education on audit costs. Taking A-share listed companies in Shanghai and Shenzhen from 2006 to 2021 as the research object, this paper examines the impact of employee education on audit fees. It is found that highly educated employees can effectively reduce the audit fees borne by the company, but the implementation of the Labor Protection Law weakens this inhibitory effect. In the case of low marketization level and weak Confucian culture intensity, employee education level has a more significant inhibitory effect on audit fees of listed companies. This study provides a basis for empirical research on the impact of employee attributes on auditor decision making, provides a new research perspective on the impact of labor protection law at the corporate micro level, and enriches the theoretical research on corporate governance rooted in traditional Chinese culture. We contribute to the practice that implications for evaluating the effectiveness of adopting labor protection., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Shen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This paper introduces N-bipolar hypersoft (N-BHS) sets, a versatile extension of bipolar hypersoft (BHS) sets designed to effectively manage evaluations encompassing both binary and non-binary data, thereby exhibiting heightened versatility. The major contributions of this framework are twofold: Firstly, the N-BHS set introduces a parameterized representation of the universe, providing a nuanced and finite granularity in perceiving attributes, thereby distinguishing itself from conventional binary BHS sets and continuous fuzzy BHS sets. Secondly, this model signifies a new area of research aimed at overcoming limitations inherent in the N-bipolar soft set when handling multi-argument approximate functions. Through the strategic partitioning of attributes into distinct subattribute values using disjoint sets, the N-BHS set emerges as a powerful tool for effectively addressing uncertainty-related problems. In pursuit of these objectives, the paper outlines various algebraic definitions, including incomplete N-BHS sets, efficient N-BHS sets, normalized N-BHS sets, equivalence under normalization, N-BHS complements, and BHS sets derived from a threshold, exemplified through illustrative examples. Additionally, the article explores set-theoretic operations within the N-BHS sets framework, such as relative null/whole N-BHS sets, N-BHS subsets, and two distinct approaches to N-BHS extended/restricted union and intersection. Finally, it proposes and compares decision-making methodologies regarding N-BHS sets, including a comprehensive comparison with relevant existing models. [ABSTRACT FROM AUTHOR]

This paper investigates N-bipolar hypersoft topology (N-BHST), a novel extension of both the well-established N-hypersoft topology (N-HST) and hypersoft topology (HST). Deviating significantly from its precursor, the N-bipolar hypersoft (N-BHS) set, N-BHST introduces a multi-opinion approach to decision-making, augmenting robustness and adaptability. This innovative framework addresses identified limitations in N-bipolar soft topology (N-BST), especially in managing multi-argument approximate functions. The study analyzes various operators (closure, interior, exterior, and boundary) within the N-BHST framework, elucidating their interrelationships. Additionally, an examination is carried out on the enhancement of multi-criteria group decision-making (MCGDM) using N-BHST, setting it apart from existing models. A numerical example is presented to illustrate its application in real-world decision scenarios., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Musa, Asaad. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Customer churn prediction is vital for organizations to mitigate costs and foster growth. Ensemble learning models are commonly used for churn prediction. Diversity and prediction performance are two essential principles for constructing ensemble classifiers. Therefore, developing accurate ensemble learning models consisting of diverse base classifiers is a considerable challenge in this area. In this study, we propose two multi-objective evolutionary ensemble learning models based on clustering (MOEECs), which are include a novel diversity measure. Also, to overcome the data imbalance problem, another objective function is presented in the second model to evaluate ensemble performance. The proposed models in this paper are evaluated with a dataset collected from a mobile operator database. Our first model, MOEEC-1, achieves an accuracy of 97.30% and an AUC of 93.76%, outperforming classical classifiers and other ensemble models. Similarly, MOEEC-2 attains an accuracy of 96.35% and an AUC of 94.89%, showcasing its effectiveness in churn prediction. Furthermore, comparison with previous churn models reveals that MOEEC-1 and MOEEC-2 exhibit superior performance in accuracy, precision, and F-score. Overall, our proposed MOEECs demonstrate significant advancements in churn prediction accuracy and outperform existing models in terms of key performance metrics. These findings underscore the efficacy of our approach in addressing the challenges of customer churn prediction and its potential for practical application in organizational decision-making. [ABSTRACT FROM AUTHOR]

Prospect Theory, proposed and developed by Kahneman and Tversky, demonstrated that people do not make rational decisions based on expected utility, but are instead biased by specific cognitive tendencies leading to neglect, under- or over- consider information, depending on the context of presentation. In this vein, the present paper focuses on whether and how individual decision-making attitudes are prone to change in the presence of globally challenging events. We ran three partial replications of the Kahneman and Tversky (1979) paper, focusing on a set of eight prospects, after a terror attack (Paris, November 2015, 134 subjects) and during the Covid-19 pandemic, both during the first lockdown in Italy (Spring 2020, 176 subjects) and after the first reopening (140 subjects). The results confirm patterns of choice characterizing uncertain times, as shown by previous literature. In particular, we note significant increase of risk aversion, both in the gain and in the loss domains, that consistently emerged in the three replications. Given the nature of our sample, and the heterogeneity between the three periods investigated, we suggest that the phenomenon we present can be explained stress-related effects on decision making rather than by other economic effects, such as the income effect. [ABSTRACT FROM AUTHOR]

Existing Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods are mostly suitable for simple systems with fewer factors, and lack effective integration of expert knowledge and experience from large-scale group populations, resulting in a potential compromise of the quality of the initial direct relation (IDR) matrix. To make DEMATEL better suited for the identification of critical factors in complex systems, this paper proposes a hierarchical DEMATEL method for large-scale group decision-making. Considering the limitations of expert knowledge and experience, a method based on expert consistency network for constructing the expert weight matrix is designed. The expert consistency network is constructed for different elements, and the weights of experts in different elements are determined using the clustering coefficient. Following the principles of the classic DEMATEL method, the steps for identifying key elements in complex systems using the large-scale group-hierarchical DEMATEL method are summarized. To objectively test the effectiveness and superiority of the decision algorithm, the robustness of the algorithm is analyzed in an interference environment. Finally, the superiority of the proposed method and algorithm is verified through a case study, which demonstrating that the proposed decision-making method is suitable for group decision-making in complex systems, with high algorithm stability and low algorithm deviation., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The paper presents the results of research on the influence of risk factors on the implementation of railway investments in Poland (build stage) and deals with a detailed diagnosis of relation between factors. The application of DEcision MAking Trial and Evaluation Laboratory (DEMATEL) method for the analyses allowed to develop a cause-and-effect model of key factors and analyse the importance of the factors. Eleven factors identified in eariel studies as the most important risk factors were examined. It was found that the factors: errors in the preparation of tender documents (10.38%), errors in project documentation (10.02%), improperly estimated time of completion of the investment by the Employer (9.82%), internal regulations of PKP Polskie Koleje Państwowe S.A. (Polish State Railways) not coordinated with the provisions of contracts (9.51%) have the highest degree of importance. Factors: too many external institutions involved in the investment process and internal regulations of PKP Polskie Koleje Państwowe S.A. (Polish State Railways) not coordinated with the provisions of contracts, have the greatest net impact on the other factors. The relations between the factors and factors importance are valuable knowledge for engineers, enabling the project to be implemented according to the planned schedule and investment cost. [ABSTRACT FROM AUTHOR]

This paper presents an optimized preparation process for external ointment using the Definitive Screening Design (DSD) method. The ointment is a Traditional Chinese Medicine (TCM) formula developed by Professor WYH, a renowned TCM practitioner in Jiangsu Province, China, known for its proven clinical efficacy. In this study, a stepwise regression model was employed to analyze the relationship between key process factors (such as mixing speed and time) and rheological parameters. Machine learning techniques, including Monte Carlo simulation, decision tree analysis, and Gaussian process, were used for parameter optimization. Through rigorous experimentation and verification, we have successfully identified the optimal preparation process for WYH ointment. The optimized parameters included drug ratio of 24.5%, mixing time of 8 min, mixing speed of 1175 rpm, petroleum dosage of 79 g, liquid paraffin dosage of 6.7 g. The final ointment formulation was prepared using method B. This research not only contributes to the optimization of the WYH ointment preparation process but also provides valuable insights and practical guidance for designing the preparation processes of other TCM ointments. This advanced DSD method enhances the screening approach for identifying the best preparation process, thereby improving the scientific rigor and quality of TCM ointment preparation processes. [ABSTRACT FROM AUTHOR]

Introduction: Women at increased risk of developing pre-eclampsia are advised to take a daily low-dose of aspirin from 12 weeks of pregnancy to reduce their risks. Despite the well-established prophylactic effect of aspirin, adherence to this therapy is low. This systematic review aimed to summarise evidence on the barriers and facilitators of adherence to low-dose aspirin to inform intervention development to support decision making and persistence with aspirin use for pre-eclampsia prevention. Materials and methods: A systematic review and meta-synthesis of qualitative research was co-produced by representatives from charities, and public, clinical and academic members. Eight electronic databases (MEDLINE, PsycINFO, CINAHL, Web of Science, Scopus, EMBASE, Prospero, OpenGrey), archives of charities and professional organisations were searched (between October and November 2023 and re-run in August 2023) using predefined search terms. Studies containing qualitative components related to barriers and facilitators of adherence to low-dose aspirin during pregnancy were included. Quality assessment was performed using the Critical Appraisal Skills Programme checklist for qualitative research. A combination of the COM-B framework with phases of adherence process as defined by international taxonomy was used as the coding framework. Co-production activities were facilitated by use of 'Zoom' and 'Linoit'. Results: From a total of 3377 papers identified through our searches, five published studies and one dissertation met our inclusion criteria. Studies were published from 2019 to 2022 covering research conducted in the USA, Canada, UK, Netherlands and Australia. Barriers and facilitators to adherence were mapped to six categories of the COM-B for three phases of adherence: initiation, implementation, and discontinuation. The discontinuation phase of adherence was only mentioned by one author. Four key themes were identified relating to pregnancy: 'Insufficient knowledge', 'Necessity concerns balance', 'Access to medicine', 'Social influences', and 'Lack of Habit'. Conclusions: The COM-B framework allowed for detailed mapping of key factors shaping different phases of adherence in behavioural change terms and now provides a solid foundation for the development of a behavioural intervention. Although potential intervention elements could be suggested based on the results of this synthesis, additional co-production work is needed to define elements and plan for the delivery of the future intervention. Trial registration: PROSPERO CRD42022359718. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022359718. [ABSTRACT FROM AUTHOR]

The multiple global environments have triggered changes in the international environment, leading to a sharp decline of foreign direct investment (FDI) compared to pre-pandemic level. To evaluate the investment risk of FDI and make optimal investment decision becomes the most important issue for investors. This paper focuses on the evaluation of investment risk for FDI. First, an index system for risk evaluation of FDI is constructed. Then, we introduce the probabilistic linguistic entropy and cross entropy measures, based on which, a programming model is developed to identify the objective attribute weights. A composite weight derivation method, which takes both the objective attribute weights and the subjective attribute weights into account, is further introduced. In view of attributes' uncertainty and fuzziness and the conflicting characteristics of some attributes, the VIKOR (the Serbian name: VlseKriterijumska Optimizacija I Kompromisno Resenje, means multi-criteria optimization and compromise solution) method is used to evaluate the risk of FDI under the probabilistic linguistic environment. Furthermore, a case study is presented to illustrate the proposed method. The comparative analysis and some further discussions verify the validity of the proposed method for the FDI risk evaluation. [ABSTRACT FROM AUTHOR]

Charities differ, among other things, alongside the likelihood that their interventions succeed and produce the desired outcomes and alongside the extent that such likelihood can even be articulated numerically. In this paper, we investigate what best explains charitable giving behaviour regarding charities that have interventions that will succeed with a quantifiable and high probability (sure-thing charities) and charities that have interventions that only have a small and hard to quantify probability of bringing about the desired end (probabilistic charities). We study individual differences in risk/ambiguity attitudes, empathy, numeracy, optimism, and donor type (warm glow vs. pure altruistic donor type) as potential predictors of this choice. We conduct a money incentivised, pre-registered experiment on Prolific on a representative UK sample (n = 1,506) to investigate participant choices (i) between these two types of charities and (ii) about one randomly selected charity. Overall, we find little to no evidence that individual differences predict choices regarding decisions about sure-thing and probabilistic charities, with the exception that a purely altruistic donor type predicts donations to probabilistic charities when participants were presented with a randomly selected charity in (ii). Conducting exploratory equivalence tests, we find that the data provide robust evidence in favour of the absence of an effect (or a negligibly small effect) where we fail to reject the null. This is corroborated by exploratory Bayesian analyses. We take this paper to be contributing to the literature on charitable giving via this comprehensive null-result in pursuit of contributing to a cumulative science. [ABSTRACT FROM AUTHOR]

Globally, millions of women and their children suffer due to preventable morbidity and mortality, associated with both underuse and overuse of maternal and newborn care. An effective system of risk selection that differentiates between what care should be provided and who should provide it is a global necessity to ensure women and children receive appropriate care, at the right place and the right time. Poor conceptualization of risk selection impedes evaluation and comparison of models of risk selection across various settings, which is necessary to improve maternal and newborn care. We conducted a scoping review to enhance the understanding of risk selection in maternal and newborn care. We included 210 papers, published over the past four decades, originating from 24 countries. Using inductive thematic analysis, we identified three main dimensions of risk selection: (1) risk selection as an organisational measure to optimally align women's and children's needs and resources, (2) risk selection as a practice to detect and assess risk and to make decisions about the delivery of care, and (3) risk selection as a tool to ensure safe care. We found that these three dimensions have three themes in common: risk selection (1) is viewed as both requiring and providing regulation, (2) has a provider centred focus and (3) aims to avoid underuse of care. Due to the methodological challenges of contextual diversity, the concept of risk selection needs clear indicators that capture the complexity of care to make cross-setting evaluation and comparison of risk selection possible. Moreover, a comprehensive understanding of risk selection needs to consider access disparity, women's needs, and unnecessary medicalization. [ABSTRACT FROM AUTHOR]

Young drivers (18–24 years) are over-represented in sleep-related crashes (comprising one in five fatal crashes in developed countries) primarily due to decreased sleep opportunity, lower tolerance for sleep loss, and ongoing maturation of brain areas associated with driving-related decision making. Impaired driving performance is the proximal reason for most car crashes. There is still a limited body of evidence examining the effects of sleep loss on young drivers’ performance, with discrepancies in the methodologies used, and in the definition of outcomes. This study aimed to identify the direction and magnitude of the effects of sleep loss on young drivers’ performance, and to appraise the quality of current evidence via a systematic review. Based on the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA) approach, 16 eligible studies were selected for review, and their findings summarised. Next, critical elements of these studies were identified, and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) guidelines augmented to rate those elements. Using those criteria, the quality of individual papers was calculated and the overall body of evidence for each driving outcome were assigned a quality ranking (from ‘very low’ to ‘high-quality’). Two metrics, the standard deviation of lateral position and number of line crossings, were commonly reported outcomes (although in an overall ‘low-quality’ body of evidence), with significant impairments after sleep loss identified in 50% of studies. While speed-related outcomes and crash events (also with very low- quality evidence) both increased under chronic sleep loss, discrepant findings were reported under conditions of acute total sleep deprivation. It is crucial to obtain more reliable data about the effects of sleep loss on young drivers’ performance by using higher quality experimental designs, adopting common protocols, and the use of consistent metrics and reporting of findings based on GRADE criteria and the PRISMA statement. Key words: Young drivers, sleep loss, driving performance, PRISMA, the GRADE, systematic review. [ABSTRACT FROM AUTHOR]

The rise of retail groups has strengthened their voice in the supply chain, drawing more attention from supply chain members to the issue of profit fairness. To explore the influence of fairness concerns on operational decisions in closed-loop supply chains after the formation of retail groups. In this paper, we first construct a secondary dual-channel closed-loop supply chain led by a retail group and followed by a manufacturer. Next, the corresponding game models are constructed under three scenarios, namely, fairness neutrality (FN), fairness concerns of the retail groups (FR), and fairness concerns of the manufacturer (FM), respectively. Finally, the game models are solved and analyzed. It turns out that it is easier for the manufacturer to satisfy its demands for fairness by adjusting the wholesale price. Furthermore, we find that fairness concerns do not enhance the recycling rate of used products and the greenness of remanufacturing. For retail groups, fairness concerns can hurt their profits, but appropriate fairness concerns can contribute to profitable growth in their retail business. Interestingly, the manufacturer's fairness concerns do not affect the total profitability of the supply chain system, but the retail group's fairness concerns do. This paper identifies dual changes in the scope of operations and power structure of retail groups in closed-loop supply chains, as well as analyzes the fairness concerns raised by these changes, which will lead to new recommendations for operational decision-making in firms., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Tan, Guo. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Decision Avoidance (DA) strategies allow people to forego or abandon effortful deliberation by postponing, bypassing, or delegating a decision. DA is thought to reduce regret, primarily by allowing decision makers to evade personal responsibility for potential negative outcomes. We tested this relation between DA and post-decision regret in a multilevel meta-analysis of 59 effect estimates coming from 13 papers. Five DA strategies were considered: status quo preservation, action omission, inaction inertia, choice delegation and choice deferral. Across all effects and DA strategies, there was a non-significant trend toward DA reducing regret (Hedges' g = -0.23, p = 0.063). When assessing individual strategies, we found that only status quo preservation reduced regret reliably (Hedges' g = -0.45, p = 0.006). The relationship between DA and regret was unclear for the other DA strategies. We tested a number of moderators for the effect. Only 'previous experience' (i.e., the outcome of a previous decision) influenced the relation between DA and regret reliably. That is, if participants choose the DA option when the same choice previously led to a negative outcome, regret is actually enhanced. Overall, there is clear evidence that status quo preservation can reduce regret, but it is currently unclear whether the same holds for other DA strategies., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Han et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

When the in/consistency in Pairwise Comparisons (PCs) is taken into consideration as the subarea of the Multi Attribute Decision Making (MADM) scientific field, it has many repercussions in various types of research areas including different modelling scenarios e.g. reduction of inconsistency during PCs, deriving appropriate consistency thresholds for inconsistent Pairwise Comparison Matrices (PCMs), completing of incomplete PCMs, aggregating of individual PCMs in relation to Group Decision Making (GDM) aspects, and PCMs in/consistency relation to credibility of Priority Vectors (PV) derived from PCMs with the application of various Priorities Deriving Methods (PDMs). The examination objective in the latter area of research is the uncertainty related to the inexactitude of prioritization based on derived PVs. However, only few research studies examine this problem from the perspective of PCM applicability for credible designation of decision maker's (DM) priorities in the way that leads to minimization of the prioritization uncertainty related to possible, and sometimes very probable, ranking fluctuations. This problem constitutes the primary area of interest for this research paper as no research study was thus far identified that examines this problem from the perspective of consistent PCMs. Hence, a research gap was identified. Thus, the objective of this research paper is to fill in this scientific gap. The research findings have serious repercussions in relation to prioritization quality with the application of PCs methodology, mostly in relation to the interpretation and reliability evaluation of prioritization results. Firstly, the research study outcome changes the perspective of the rank reversal phenomenon, which shed new light on many research studies that have been presented in the subject's literature for many decades. Secondly, the research study results throw new light on the discussion concerning the fuzziness of AHP's results. Last but not least, the effect of the research opens the unique opportunity to evaluate the prioritization outcome obtained within the process of consistent PCs from the well-known perspective of statistical hypothesis testing i.e. the probability designation of the chance that accepted ranking results which were considered as correct due to low probability of change may be incorrect, hence they should be rejected, and the probability designation of the chance that rejected ranking results which were considered as incorrect due to high probability of change may be correct and should be accepted. The paramount finding of the research is the fact that consistent PCMs provide PVs, which elements cannot be considered as established, but only approximated within certain confidence intervals estimated with a certain level of probability. As problems related to heuristics can be analyzed only via a computer simulation process, because they cannot be mathematically determined, the problem examined in this research paper is examined via Monte Carlo simulations, appropriately coded and executed with the application of Wolfram's Mathematica Software. It is believed that this research findings should be very important and useful for all decision makers and researchers during their problems' examinations that relate to prioritization processes with the application of PCs methodology., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Pawel Tadeusz Kazibudzki. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: The CONSORT extension for patient reported outcomes (PROs) aims to improve reporting, but guidance on the optimal integration with clinical data is lacking. This study examines in detail the reporting of PROs and clinical data from randomized controlled trials (RCTs) in gastro-intestinal cancer to inform design and reporting of combined PRO and clinical data from trials to improve the ‘take home’ message for clinicians to use in practice. Materials and Methods: The case study was undertaken in gastro-intestinal cancer trials. Well-conducted RCTs reporting PROs with validated instruments were identified and categorized into those combining PRO and clinical data in a single paper, or those separating data into linked primary and supplemental papers. Qualitative methods were developed to examine reporting of the critical interpretation of the trial results (trial exegesis) in the papers in relation of the PRO and clinical outcomes and applied to each publication category. Results were used to inform recommendations for practice. Results: From 1917 screened abstracts, 49 high quality RCTs were identified reported in 36 combined and 15 linked primary and supplemental papers. In-depth analysis of manuscript text identified three categories for understanding trial exegesis: where authors reported a “detailed”, “general”, or absent PRO rationale and integrated interpretation of clinical and PRO results. A total of 11 (30%) and 6 (16%) combined papers reported “detailed” PRO rationale and integrated interpretation of results although only 2 (14%) and 1 (7%) primary papers achieved the same standard respectively. Supplemental papers provide better information with 11 (73%) and 3 (20%) achieving “detailed” rationale and integrated interpretation of results. Supplemental papers, however, were published a median of 20 months after the primary RCT data in lower impact factor journals (median 16.8 versus 5.2). Conclusion: It is recommended that single papers, with detailed PRO rationale and integrated PRO and clinical data are published to optimize trial exegesis. Further work to examine whether this improves the use of PRO data to inform practice is needed. [ABSTRACT FROM AUTHOR]

Background: Until recently, the Chagas disease vector, Triatoma infestans, was widespread in Arequipa, Perú, but as a result of a decades-long campaign in which over 70,000 houses were treated with insecticides, infestation prevalence is now greatly reduced. To monitor for T. infestans resurgence, the city is currently in a surveillance phase in which a sample of houses is selected for inspection each year. Despite extensive data from the control campaign that could be used to inform surveillance, the selection of houses to inspect is often carried out haphazardly or by convenience. Therefore, we asked, how can we enhance efforts toward preventing T. infestans resurgence by creating the opportunity for vector surveillance to be informed by data? Methodology/principal findings: To this end, we developed a mobile app that provides vector infestation risk maps generated with data from the control campaign run in a predictive model. The app is intended to enhance vector surveillance activities by giving inspectors the opportunity to incorporate the infestation risk information into their surveillance activities, but it does not dictate which houses to surveil. Therefore, a critical question becomes, will inspectors use the risk information? To answer this question, we ran a pilot study in which we compared surveillance using the app to the current practice (paper maps). We hypothesized that inspectors would use the risk information provided by the app, as measured by the frequency of higher risk houses visited, and qualitative analyses of inspector movement patterns in the field. We also compared the efficiency of both mediums to identify factors that might discourage risk information use. Over the course of ten days (five with each medium), 1,081 houses were visited using the paper maps, of which 366 (34%) were inspected, while 1,038 houses were visited using the app, with 401 (39%) inspected. Five out of eight inspectors (62.5%) visited more higher risk houses when using the app (Fisher’s exact test, p < 0.001). Among all inspectors, there was an upward shift in proportional visits to higher risk houses when using the app (Mantel-Haenszel test, common odds ratio (OR) = 2.42, 95% CI 2.00–2.92), and in a second analysis using generalized linear mixed models, app use increased the odds of visiting a higher risk house 2.73-fold (95% CI 2.24–3.32), suggesting that the risk information provided by the app was used by most inspectors. Qualitative analyses of inspector movement revealed indications of risk information use in seven out of eight (87.5%) inspectors. There was no difference between the app and paper maps in the number of houses visited (paired t-test, p = 0.67) or inspected (p = 0.17), suggesting that app use did not reduce surveillance efficiency. Conclusions/significance: Without staying vigilant to remaining and re-emerging vector foci following a vector control campaign, disease transmission eventually returns and progress achieved is reversed. Our results suggest that, when provided the opportunity, most inspectors will use risk information to direct their surveillance activities, at least over the short term. The study is an initial, but key, step toward evidence-based vector surveillance. [ABSTRACT FROM AUTHOR]

In order to further improve the computing power of the information aggregation operator in the q-rung orthopair probabilistic hesitant fuzzy environment, this paper proposes a multi-attribute decision-making method based on the q-rung orthopair probabilistic hesitant fuzzy Schweizer-Sklar power weighted Hamy mean operator. Firstly, the algorithm of q-rung orthopair probabilistic hesitant fuzzy set is improved based on the Schweizer-Sklar T-norm. In order to better reflect the degree of hesitation of decision-making experts, a new q-rung orthopair probabilistic hesitant fuzzy distance measure is proposed, which provides a basis for subsequent power weighted calculations. Furthermore, considering the correlation between attributes and the influence of data extremes, some information aggregation operators and their power weighted forms are proposed. Finally, a multi-attribute decision-making model based on the q-rung orthopair probabilistic hesitant fuzzy Schweizer-Sklar power weighted Hamy mean operator is established, and the reliability and validity of the research content in this paper are verified through decision-making examples and comparative analysis. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic required significant public health interventions from local governments. Although nonpharmaceutical interventions often were implemented as decision rules, few studies evaluated the robustness of those reopening plans under a wide range of uncertainties. This paper uses the Robust Decision Making approach to stress-test 78 alternative reopening strategies, using California as an example. This study uniquely considers a wide range of uncertainties and demonstrates that seemingly sensible reopening plans can lead to both unnecessary COVID-19 deaths and days of interventions. We find that plans using fixed COVID-19 case thresholds might be less effective than strategies with time-varying reopening thresholds. While we use California as an example, our results are particularly relevant for jurisdictions where vaccination roll-out has been slower. The approach used in this paper could also prove useful for other public health policy problems in which policymakers need to make robust decisions in the face of deep uncertainty. [ABSTRACT FROM AUTHOR]

Family and cultural contexts can constrain the effectiveness of evidence-based interventions designed to improve the health and wellbeing of women and their children. Unequal power relationships within the household may underlie the failure of many programs targeting women to achieve their intended impact. To reduce these unequal power dynamics within the households, many programs or interventions aim to both assess and improve the gender dynamics between husbands and wives within the household. Decision-making is one important facet of these dynamics and has been linked to health outcomes for women and children. However, household decision-making is rarely observed and often difficult to capture. This study aimed to use qualitative research to further understand one aspect of decision-making, namely on how to spend money. In two regions of Tanzania, we used surveys and interviews to explore different perspectives on spending and allocation of resources among 58 couples in rural farming households. While many men and women initially reported that they made decisions jointly, most women stated they would often concede if there was a disagreement or argument around spending. These results highlight the different perceptions of joint decision-making between men and women. We compared these results to survey responses on decision-making and found differences within and between couples across interview and survey responses. Based on the differences in qualitative and survey responses within couples and how they reported dealing with disagreement, our study found households were on a spectrum from no cooperation in decision-making to full cooperation. Our results highlight challenges for assessing decision-making on spending and ultimately improving these decision-making dynamics within the household. These challenges are especially important for maternal and child behavioral change and provide insights on why many interventions aimed at improving women's decision- making power on money may not reach their full potential. [ABSTRACT FROM AUTHOR]

Discrete choice experiments (DCEs) are a way to assess priority-setting in health care provision. This approach allows for the evaluation of individuals’ preferences as a means of adding criteria to traditional quality-adjusted life year analysis. The aim of this systematic literature review was to identify attributes for designing a DCE in order to then develop and validate a framework that supports decision-making on health technologies. Our systematic literature review replicated the methods and search terms used by de Bekker-Grob et al. 2012 and Clark et al. 2014. The Medline database was searched for articles dated between 2008 and 2015. The search was limited to studies in English that reflected general preferences and were choice-based, published as full-text articles and related to health technologies. This study included 72 papers, 52% of which focused on DCEs on drug treatments. The average number of attributes used in all included DCE studies was 5.74 (SD 1.98). The most frequently used attributes in these DCEs were improvements in health (78%), side effects (57%) and cost of treatment (53%). Other, less frequently used attributes included waiting time for treatment or duration of treatment (25%), severity of disease (7%) and value for money (4%). The attributes identified might inform future DCE surveys designed to study societal preferences regarding health technologies in order to better inform decisions in health technology assessment. [ABSTRACT FROM AUTHOR]

Reviewer assignment is critical to peer review systems, such as peer-reviewed research conferences or peer-reviewed funding applications, and its effectiveness is a deep concern of all academics. However, there are some problems in existing peer review systems during reviewer assignment. For example, some of the reviewers are much more stringent than others, leading to an unfair final decision, i.e., some submissions (i.e., papers or applications) with better quality are rejected. In this paper, we propose a context-aware reviewer assignment for trust enhanced peer review. More specifically, in our approach, we first consider the research area specific expertise of reviewers, and the institution relevance and co-authorship between reviewers and authors, so that reviewers with the right expertise are assigned to the corresponding submissions without potential conflict of interest. In addition, we propose a novel cross-assignment paradigm, and reviewers are cross-assigned in order to avoid assigning a group of stringent reviewers or a group of lenient reviewers to the same submission. More importantly, on top of them, we propose an academic CONtext-aware expertise relevanCe oriEnted Reviewer cross-assignmenT approach (CONCERT), which aims to effectively estimate the “true” ratings of submissions based on the ratings from all reviewers, even though no prior knowledge exists about the distribution of stringent reviewers and lenient reviewers. The experiments illustrate that compared with existing approaches, our proposed CONCERT approach can less likely assign more than one stringent reviewers or lenient reviewers to a submission simultaneously and significantly reduce the influence of ratings from stringent reviewers and lenient reviewers, leading to trust enhanced peer review and selection, no matter what kind of distributions of stringent reviewers and lenient reviewers are. [ABSTRACT FROM AUTHOR]

Background: Shared decision making (SDM) is a core element in the meeting between patient and healthcare professionals, but has proved difficult to implement and sustain in routine clinical practice. One of five Danish regions set out to succeed and to develop a model that ensures lasting SDM based on learnings from large-scale real-world implementation initiatives that go beyond the 'barriers' and 'facilitators' research approach. This paper describes this process and development of a generic implementation model, SDM:HOSP., Methods: This project was carried out in the Region of Southern Denmark with five major hospital units. Based on existing theory of SDM, SDM implementation, implementation science and improvement methodology, a process of four phases were described; development of conceptual elements, field-testing, evaluation, and development of the final implementation model. The conceptual elements developed aimed to prepare leaders, train SDM teachers, teach clinicians to perform SDM, support development of patient decision aids, and support systematic planning, execution and follow-up. Field testing was done including continuous participant evaluations and an overall evaluation after one year., Results: Data from field testing and learnings from the implementation process, illustrated the need for a dynamic and easy adjustable model. The final SDM:HOSP model included four themes; i)Training of Leaders, ii) Training of Teachers and Clinicians, iii) Decision Helper, and iv) 'Process', each with details in three levels, 1) shared elements, 2) recommendations, and 3) local adaption., Conclusions: A feasible and acceptable model for implementation of SDM across hospitals and departments that accounts for different organizations and cultures was developed. The overall design can easily be adapted to other organizations and can be adjusted to fit the specific organization and culture. The results from the ongoing and overall evaluation suggest promising avenues for future work in further testing and research of the usability of the model., Competing Interests: The authors have declared that no competing interests exist, (Copyright: © 2023 Steffensen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

As an important supplement to my country's financial institutions, micro-loan companies serve "agriculture, rural areas and farmers", small and micro enterprises, and individuals, to a certain extent, alleviating the financing difficulties of such groups and regulating private finance. However, micro-loan companies only lend but do not deposit. In the process of lending, due to inadequate risk management, the risk problem has become increasingly prominent. With the continuous growth of the loan amount of rural credit and the continuous increase of microfinance groups lending customers, it faces certain problems in its risk management, which increases the risks of the company in all aspects. The performance evaluation of sustainable microfinance groups lending is a classical MAGDM issues. In such paper, the Hamming distances of single-valued neutrosophic sets (SVNSs) and maximizing deviation method (MDM) is used to obtain the attribute weights and the single-valued neutrosophic numbers MABAC(SVNN-MABAC) method is structured for MAGDM under SVNSs. Finally, an example about performance evaluation of sustainable microfinance groups lending and some comparative decision analysis are given to proof the SVNN-MABAC., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Hui Ran. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

As an important basis of navigation safety decisions, ship domains have always been a pilot concern. In the past, model parameters were usually obtained from statistics of massive historical cumulative data, but the results were mostly historical analysis and static data, which obviously could not meet the needs of pilots who wish to master the ship domain in real time. To obtain and update the ship domain parameter online in time and meet the real-time needs of maritime applications, this paper obtains CRI as the weight coefficient-based PSO-LSSVM method and proposes to use short-term AIS data accumulation through the risk-weighted least squares method online rolling identification method, which can filter nonhazardous targets and improve the identification accuracy and real-time performance of nonlinear models in the ship domain. The experimental examples show that the method can generate the ship domain dynamically in real time. At the same time, the method can be used to study the dynamic evolution characteristics of the ship domain over the course of navigation, which provides a reference for navigation safety decisions and the analysis of ship navigation behavior. [ABSTRACT FROM AUTHOR]

Bibliographic analysis has been a very powerful tool in evaluating the effective contributions of a researcher and determining his/her future research potential. The lack of an absolute quantification of the author’s scientific contributions by the existing measurement system hampers the decision-making process. In this paper, a new metric system, Absolute index (Ab-index), has been proposed that allows a more objective comparison of the contributions of a researcher. The Ab-index takes into account the impact of research findings while keeping in mind the physical and intellectual contributions of the author(s) in accomplishing the task. The Ab-index and h-index were calculated for 10 highly cited geneticists and molecular biologist and 10 young researchers of biological sciences and compared for their relationship to the researchers input as a primary author. This is the first report of a measuring method clarifying the contributions of the first author, corresponding author, and other co-authors and the sharing of credit in a logical ratio. A java application has been developed for the easy calculation of the Ab-index. It can be used as a yardstick for comparing the credibility of different scientists competing for the same resources while the Productivity index (Pr-index), which is the rate of change in the Ab-index per year, can be used for comparing scientists of different age groups. The Ab-index has clear advantage over other popular metric systems in comparing scientific credibility of young scientists. The sum of the Ab-indices earned by individual researchers of an institute per year can be referred to as Pr-index of the institute. [ABSTRACT FROM AUTHOR]

Background: Routine Data Quality Assessments (RDQAs) were developed to measure and improve facility-level electronic medical record (EMR) data quality. We assessed if RDQAs were associated with improvements in data quality in KenyaEMR, an HIV care and treatment EMR used at 341 facilities in Kenya. Methods: RDQAs assess data quality by comparing information recorded in paper records to KenyaEMR. RDQAs are conducted during a one-day site visit, where approximately 100 records are randomly selected and 24 data elements are reviewed to assess data completeness and concordance. Results are immediately provided to facility staff and action plans are developed for data quality improvement. For facilities that had received more than one RDQA (baseline and follow-up), we used generalized estimating equation models to determine if data completeness or concordance improved from the baseline to the follow-up RDQAs. Results: 27 facilities received two RDQAs and were included in the analysis, with 2369 and 2355 records reviewed from baseline and follow-up RDQAs, respectively. The frequency of missing data in KenyaEMR declined from the baseline (31% missing) to the follow-up (13% missing) RDQAs. After adjusting for facility characteristics, records from follow-up RDQAs had 0.43-times the risk (95% CI: 0.32–0.58) of having at least one missing value among nine required data elements compared to records from baseline RDQAs. Using a scale with one point awarded for each of 20 data elements with concordant values in paper records and KenyaEMR, we found that data concordance improved from baseline (11.9/20) to follow-up (13.6/20) RDQAs, with the mean concordance score increasing by 1.79 (95% CI: 0.25–3.33). Conclusions: This manuscript demonstrates that RDQAs can be implemented on a large scale and used to identify EMR data quality problems. RDQAs were associated with meaningful improvements in data quality and could be adapted for implementation in other settings. [ABSTRACT FROM AUTHOR]

Background: The practice of female genital mutilation is associated with harmful social norms promoting violence against girls and women. Various studies have been conducted to examine the prevalence of female genital mutilation and its associated factors. However, there has been limited studies conducted to assess the association between female genital mutilation and markers of women's autonomy, such as their ability to negotiate for safer sex. In this study, we examined the association between female genital mutilation and women's ability to negotiate for safer sex in sub-Saharan Africa (SSA). Methods: We pooled data from the most recent Demographic and Health Surveys (DHS) conducted from 2010 to 2020. Data from a sample of 50,337 currently married and cohabiting women from eleven sub-Saharan African countries were included in the study. A multilevel binary logistic regression analysis was used to examine the association between female genital mutilation and women's ability to refuse sex and ask their partners to use condom. Adjusted odds ratios (aORs) with a 95% confidence interval (CI) were used to present the findings of the logistic regression analysis. Statistical significance was set at p<0.05. Results: Female genital mutilation was performed on 56.1% of women included in our study. The highest and lowest prevalence of female genital mutilation were found among women from Guinea (96.3%) and Togo (6.9%), respectively. We found that women who had undergone female genital mutilation were less likely to refuse sex from their partners (aOR = 0.91, 95% CI = 0.86, 0.96) and ask their partners to use condoms (aOR = 0.82, 95% CI = 0.78, 0.86) compared to those who had not undergone female genital mutilation. Conclusion: Female genital mutilation hinders women's ability to negotiate for safer sex. It is necessary to implement health education and promotion interventions (e.g., decision making skills) that assist women who have experienced female genital mutilation to negotiate for safer sex. These interventions are crucial to enhance sexual health outcomes for these women. Further, strict enforcement of policies and laws aimed at eradicating the practice of female genital mutilation are encouraged to help contribute to the improvement of women's reproductive health. [ABSTRACT FROM AUTHOR]

When deciding whether to reciprocate trust, people are typically strongly influenced by how much trust their interaction partner has originally shown them. If a partner has placed a lot of trust in you, there is a strong motivation to reciprocate, and indeed this factor often outweighs pro-self considerations to maximize one's own financial payout. However, one important unanswered question in this regard is what people decide to do when this prior information is ambiguous; that is, when they do not know for sure exactly how trusting their partner has been. How then do people decide to reciprocate? This study utilizes a novel version of the Trust Game to directly address this question. Here, we develop, and validate, a computational model-based approach to quantify and categorize how participants assessed the trustworthiness of an unfamiliar partner when making reciprocity decisions. We find that participants spontaneously use their prior experience about the trustingness of game partners in general to inform their reciprocity decisions, even when they had the opportunity to strategically assume that their new, unfamiliar, partners were untrusting, and hence could have justified lower reciprocation rates. [ABSTRACT FROM AUTHOR]

To tackle the global water imbalance problem, a multitude of inter-basin water transfer projects have been built worldwide in recent decades. Nevertheless, given the complexity and safety challenges associated with project operation, effective emergency decision-making is crucial for addressing unforeseen incidents. Hence, this research has developed a two-stage emergency decision-making framework to tackle the uncertainty in the development trends of emergencies in inter-basin water transfer projects. (1) The first stage mainly utilizes case-based reasoning techniques to extract historical case information and disposal plans for inter-basin water transfer projects. Subsequently, a holistic similarity model is built by employing structural similarity and local attribute similarity algorithms to identify highly similar historical cases. (2) The second stage involves the optimization and adjustment of decision-making plans based on the dynamic evolution characteristics of emergencies. It utilizes the theory of decision-makers regret psychology and combines it with practical case studies to verify the scientific rationality of the method. This enables it to achieve effective multidimensional expression and rapid matching of scenarios, satisfying the decision-making requirements of "scenario response". Finally, this study compares the results obtained from this method with those computed using the traditional TOPSIS method and fuzzy comprehensive evaluation method, further validating its feasibility and effectiveness. In practice, this method can provide effective support for decision-makers work. [ABSTRACT FROM AUTHOR]

Design concept evaluation is a huge challenge in the R&D stage of new product development. The information in the assessments often depends on the decision-makers' individual preferences. However, sometimes the decision-makers cannot give precise and complete information because it is very difficult for them to be familiar with all the criteria. In this situation, an incomplete information decision-making matrix is established. In this paper, decision-making methods based on incomplete information are compared in the literature review. Incomplete information determination method using trust mechanism is proved as a proper way to solve this problem, and the missing information are computed based on the alternatives However, in design concept evaluation, experts commonly provide their preferences using linguistic words according to the different attributes. Hence, we propose a three-step Multiple Attributes Group Decision-making (MAGDM) method where the missing value are determined by attributes. In step one, a data repairing method is proposed based on trust theory. After that, in step two, a comprehensive weight determination method combining AHP and entropy is proposed to obtain the weight of index attributes. Finally, the Rough-TOPSIS method is applied in the design scheme ranking step. In the case study, the proposed method is implemented in a tourism product design process to show its effectiveness., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In multiple-criteria decision making/aiding/analysis (MCDM/MCDA) weights of criteria constitute a crucial input for finding an optimal solution (alternative). A large number of methods were proposed for criteria weights derivation including direct ranking, point allocation, pairwise comparisons, entropy method, standard deviation method, and so on. However, the problem of correct criteria weights setting persists, especially when the number of criteria is relatively high. The aim of this paper is to approach the problem of determining criteria weights from a different perspective: we examine what weights' values have to be for a given alternative to be ranked the best. We consider a space of all feasible weights from which a large number of weights in the form of n-tuples is drawn randomly via Monte Carlo method. Then, we use predefined dominance relations for comparison and ranking of alternatives, which are based on the set of generated cases. Further on, we provide the estimates for a sample size so the results could be considered robust enough. At last, but not least, we introduce the concept of central weights and the measure of its robustness (stability) as well as the concept of alternatives' multi-dominance, and show their application to a real-world problem of the selection of the best wind turbine., Competing Interests: The authors have declared that no competing interests exist.

Sustainability, as a trend of social development and the embodiment of corporate social responsibility, has begun to receive more attention. To achieve this goal, sustainable supplier selection (SSS) and order allocation (OA) are seen as the crucial activities in corporate management. In the process of SSS, the psychological behavior of decision-makers (DMs) could play a critical role in the evaluation results. Therefore, introducing it into the decision-making process may lead to decision in line with the actual situation. In the uncertain multi-criteria group decision-making (MCGDM) problem described by probability linguistic term sets (PLTS), the DMs can evaluate the criteria of each supplier based on his own preference and hesitation, which is useful to avoid the loss of information. For this reason, this study develops a novel multi-criteria group decision-making combined with fuzzy multi-objective optimization (MCGDM-FMOO) model for SSS/OA problems by considering the triple bottom line (TBL) in which includes economic, environmental and social factors. The proposed method includes four stages. (1) the best-worst method (BWM) and entropy weight method are utilized to assign the weights of criteria to obtain the comprehensive weight. According to the output weights, the an acronym for interactive and multi-criteria decision-making in Portugese (TODIM) approach is applied to rank the suppliers under PLTS environment; (2) a FMOO model that can effectively deal with uncertainties and dynamic nature of parameter is formulated for allocating optimal order quantities; (3) two novel approaches are utilized to solve the FMOO model in order to obtain the richer Pareto frontier; and (4) the final OA solution is achieved by technique for order preference by similarity to ideal solution (TOPSIS) method. Finally, the validity and practicability of proposed MCGDM-FMOO model are verified by an example and comparative analysis with other classical MCGDM methods., Competing Interests: The authors declare that there is no conflict of interests regarding the publication of this paper.

Background: Decision impact studies have become increasingly prevalent in genomic medicine, particularly in cancer research. Such studies are designed to provide evidence of clinical utility for genomic tests by evaluating their impact on clinical decision-making. This paper offers insights into understanding of the origins and intentions of these studies through an analysis of the actors and institutions responsible for the production of this new type of evidence. Methods: We conducted bibliometric and funding analyses of decision impact studies in genomic medicine research. We searched databases from inception to June 2022. The datasets used were primarily from Web of Science. Biblioshiny, additional R-based applications, and Microsoft Excel were used for publication, co-authorship and co-word analyses. Results: 163 publications were included for the bibliometric analysis; a subset of 125 studies were included for the funding analysis. Included publications started in 2010 and increased steadily over time. Decision impact studies were primarily produced for proprietary genomic assays for use in cancer care. The author and affiliate analyses reveal that these studies were produced by 'invisible colleges' of researchers and industry actors with collaborations focused on producing evidence for proprietary assays. Most authors had an industry affiliation, and the majority of studies were funded by industry. While studies were conducted in 22 countries, the majority had at least one author from the USA. Discussion: This study is a critical step in understanding the role of industry in the production of new types of research. Based on the data collected, we conclude that decision impact studies are industry-conceived and -produced evidence. The findings of this study demonstrate the depth of industry involvement and highlight a need for further research into the use of these studies in decision-making for coverage and reimbursement. [ABSTRACT FROM AUTHOR]

Correlation is considered the most important factor in analyzing the data in statistics. It is used to measure the movement of two different variables linearly. The concept of correlation is well-known and used in different fields to measure the association between two variables. The hesitant 2-tuple fuzzy linguistic set (H2FLS) comes out to be valuable in addressing people's reluctant subjective data. The purpose of this paper is to analyze new correlation measures between H2FLSs and apply them in the decision-making process. First and foremost, the ideas of mean and variance of hesitant 2-tuple fuzzy linguistic elements (H2FLEs) are introduced. Then, a new correlation coefficient between H2FLSs is established. In addition, considering that different H2FLEs may have different criteria weights, the weighted correlation coefficient and ordered weighted correlation coefficient are further investigated. A practical example concerning the detailed procedure of solving problems is exemplified to feature the reasonableness and attainability of the proposed technique in situations where the criteria weights are either known or unknown. When the weight vector is unknown, the best-worst method (BWM) is used to acquire the criteria weights in the context of a hesitant 2-tuple fuzzy linguistic environment. Furthermore, a comparative study is undertaken with current techniques to provide a vision into the design decision-making process. Finally, it is verified that the proposed correlation coefficient between H2FLSs is more satisfactory than the extant ones, and the correlation coefficient with the weights of criteria being either known or unknown is applicable., Competing Interests: The authors have declared that no competing interests exist.

Global warming has seriously affected the local climate characteristics of cities, resulting in the frequent occurrence of urban waterlogging with severe economic losses and casualties. Aiming to improve the effectiveness of disaster emergency management, we propose a novel emergency decision model embedding similarity algorithms of heterogeneous multi-attribute based on case-based reasoning. First, this paper establishes a multi-dimensional attribute system of urban waterlogging catastrophes cases based on the Wuli-Shili-Renli theory. Due to the heterogeneity of attributes of waterlogging cases, different algorithms to measure the attribute similarity are designed for crisp symbols, crisp numbers, interval numbers, fuzzy linguistic variables, and hesitant fuzzy linguistic term sets. Then, this paper combines the best-worst method with the maximal deviation method for a more reasonable weight allocation of attributes. Finally, the hybrid similarity between the historical and the target cases is obtained by aggregating attribute similarities via the weighted method. According to the given threshold value, a similar historical case set is built whose emergency measures are used to provide the reference for the target case. Additionally, a case of urban waterlogging emergency is conducted to demonstrate the applicability and effectiveness of the proposed model, which exploits historical experiences and retrieves the optimal scheme for the current disaster emergency with heterogeneous multi attributes. Consequently, the proposed model solves the problem of diverse data types to satisfy the needs of case presentation and retrieval. Compared with the existing model, it can better realize the multi-dimensional expression and fast matching of the cases., Competing Interests: The authors have declared that no competing interests exist.

Frailty is a common condition in older age and is a public health concern which requires integrated care and involves different stakeholders. This meta-synthesis focuses on experiences, understanding, and attitudes towards screening, care, intervention and prevention for frailty across frail and healthy older persons, caregivers, health and social care practitioners. Studies published since 2001 were identified through search of electronic databases; 81 eligible papers were identified and read in full, and 45 papers were finally included and synthesized. The synthesis was conducted with a meta-ethnographic approach. We identified four key themes: Uncertainty about malleability of frailty; Strategies to prevent or to respond to frailty; Capacity to care and person and family-centred service provision; Power and choice. A bottom-up approach which emphasises and works in synchrony with frail older people's and their families' values, goals, resources and optimisation strategies is necessary. A greater employment of psychological skills, enhancing communication abilities and tools to overcome disempowering attitudes should inform care organisation, resulting in more efficient and satisfactory use of services. Public health communication about prevention and management of frailty should be founded on a paradigm of resilience, balanced acceptance, and coping. Addressing stakeholders’ views about the preventability of frailty was seen as a salient need. [ABSTRACT FROM AUTHOR]

This paper explores the perceptions and attitudes of married couples which prevent them from using modern contraceptive for purpose of family planning, based on semi-structured interviews with 16 married couples from rural Pakistan. This study, with married couples, not using any modern contraceptives, discussed issues of spousal communication and religious norms using qualitative methods. Despite near universal knowledge of modern contraceptives among married Pakistani women, the use continues to be low, with high unmet need. Understanding the couple context about reproductive decision making, pregnancy and family planning intentions is imperative to helping individuals fulfil their reproductive desires. Married couples may have varying intentions and desires about family size; a lack of alignment between partners may lead to unintended pregnancies and affect uptake and use of contraception. This study specifically explored the factors which prevent married couples from using LARCs for family planning, despite their availability, at affordable prices in the study area of rural Islamabad, Pakistan. Findings show differences between concordant and discordant couples regarding desired family size, contraceptive communication and influence of religious beliefs. Understanding the role that male partners play in family planning and use of contraceptives is important in preventing unintended pregnancies and improving service delivery programmes. This study also helped identify the challenges married couples, particularly men have in understanding family planning and contraceptive use. The results also show that while men's participation in family planning decision making is limited, there is also a lack of programs and interventions for Pakistani men. The study findings can support development of appropriate strategies and implementation plans. [ABSTRACT FROM AUTHOR]

Leprosy, also known as Hansen's, is one of the listed neglected tropical diseases as a major health problem global. Treatment is one of the main alternatives, however, the scarcity of medication and its poor distribution are important factors that have driven the spread of the disease, leading to irreversible and multi-resistant complications. This paper uses a distribution methodology to optimize medication administration, taking into account the most relevant attributes for the epidemiological profile of patients and the deficit in treatment via Polychemotherapy. Multi-criteria Decision Methods were applied based on AHP-Electre model in a database with information from patients in the state of Para between 2015 and 2020. The results pointed out that 84% of individuals did not receive any treatment and, among these, the method obtained a gain in the distribution of 68% in patients with positive diagnosis for leprosy. [ABSTRACT FROM AUTHOR]

In estimation theory, the use of auxiliary information significantly improves precision while estimating population parameters. In this paper, an efficient class of ratio cum median estimators of the population median is suggested using simple random sampling without replacement. The expressions for bias and mean square error of the proposed class are derived theoretically. The condition for the asymptotic optimum estimator is obtained with its bias and mean square error expressions. Under certain realistic conditions, the asymptotic optimum estimator is more proficient, based on analytical and numerical comparisons with some existing estimators that are members of the suggested class of estimators. The superiority of the proposed ratio cum median estimators is shown through real data applications. Such a new proposed estimator will be useful in the future for data analysis and making decisions. [ABSTRACT FROM AUTHOR]

In many tasks, human behavior is far noisier than is optimal. Yet when asked to behave randomly, people are typically too predictable. We argue that these apparently contrasting observations have the same origin: the operation of a general-purpose local sampling algorithm for probabilistic inference. This account makes distinctive predictions regarding random sequence generation, not predicted by previous accounts—which suggests that randomness is produced by inhibition of habitual behavior, striving for unpredictability. We verify these predictions in two experiments: people show the same deviations from randomness when randomly generating from non-uniform or recently-learned distributions. In addition, our data show a novel signature behavior, that people's sequences have too few changes of trajectory, which argues against the specific local sampling algorithms that have been proposed in past work with other tasks. Using computational modeling, we show that local sampling where direction is maintained across trials best explains our data, which suggests it may be used in other tasks too. While local sampling has previously explained why people are unpredictable in standard cognitive tasks, here it also explains why human random sequences are not unpredictable enough. Author summary: When explicitly asked to be random, people are not random enough. Previous accounts of these random generation tasks have argued that people are effortfully trying not to be predictable. In many other tasks, however, people also show random behavior, even when it is unnecessary or outright disadvantageous. Here, we try to bridge this apparent gap. We hypothesize that the randomness people produce when trying to be random and the randomness that they display when trying to make the best choice has the same common mechanism: drawing mental samples to make judgments and decisions. In two experiments, we compare previous random generation accounts, which are task-specific in nature, to the more general account of mental sampling that has been used to explain how people behave in many other domains. We find that the flexibility of human random generation in our data is better explained by the mental sampling account. We also find a novel empirical signature of momentum in random generation, which points to a new kind of mental sampling algorithm. If mental sampling governs behavior in random generation tasks and elsewhere, then this task has great promise in helping to understand wider human behavior. [ABSTRACT FROM AUTHOR]

Rapid urbanization and population growth have increased the need for optimizing the location of health services in highly urbanized countries like Kingdom of Saudi Arabia (KSA). This study employs a multiple-criteria decision making (MCDM) approach, e.g., fuzzy overlay technique by combining the P-Median location-allocation model, for optimizing health services. First, a geodatabase, containing public hospitals, road networks and population districts, was prepared. Next, we investigated the location and services of five public hospitals in Jeddah city of KSA, by using a MCDM model that included a fuzzy overlay technique with a location-allocation model. The results showed that the allocated five hospitals served 94 out of 110 districts in the study area. Our results suggested additional hospitals must be added to ensure that the entire city is covered with timely hospital services. To improve the existing situation, we prioritized demand locations using the maximize coverage (MC) location problem model. We then used the P-Median function to find the optimal locations of hospitals, and then combined these two methods to create the MC-P-Median optimizer. This optimizer eliminated any unallocated or redundant information. Health planners can use this model to determine the best locations for public hospitals in Jeddah city and similar settings. [ABSTRACT FROM AUTHOR]