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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]

Ferry transport has witnessed numerous fatal accidents due to unsafe navigation; thus, it is of paramount importance to mitigate risks and enhance safety measures in ferry navigation. This paper aims to evaluate the navigational risk of ferry transport by a continuous risk management matrix (CRMM) based on the fuzzy Best-Worst Method (BMW). Its originalities include developing CRMM to figure out the risk level of risk factors (RFs) for ferry transport and adopting fuzzy BWM to estimate the probability and severity weights vector of RFs. Empirical results show that twenty RFs for ferry navigation are divided into four zones corresponding to their risk values, including extreme-risk, high-risk, medium-risk, and low-risk areas. Particularly, results identify three extreme-risk RFs: inadequate evacuation and emergency response features, marine traffic congestion, and insufficient training on navigational regulations. The proposed research model can provide a methodological reference to the pertinent studies regarding risk management and multiple-criteria decision analysis (MCDA). [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]

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]

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 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.)

In order to reduce the encoding complexity and stream size, improve the encoding performance and further improve the compression performance, the depth prediction partition encoding is studied in this paper. In terms of pattern selection strategy, optimization analysis is carried out based on fast strategic decision-making methods to ensure the comprehensiveness of data processing. In the design of adaptive strategies, different adaptive quantization parameter adjustment strategies are adopted for the equatorial and polar regions by considering the different levels of user attention in 360 degree virtual reality videos. The purpose is to achieve the optimal balance between distortion and stream size, thereby managing the output stream size while maintaining video quality. The results showed that this strategy achieved a maximum reduction of 2.92% in bit rate and an average reduction of 1.76%. The average coding time could be saved by 39.28%, and the average reconstruction quality was 0.043, with almost no quality loss detected by the audience. At the same time, the model demonstrated excellent performance in sequences of 4K, 6K, and 8K. The proposed deep partitioning adaptive strategy has significant improvements in video encoding quality and efficiency, which can improve encoding efficiency while ensuring video quality. [ABSTRACT FROM AUTHOR]

Big data pertains to extensive and intricate compilations of information that necessitate the implementation of proficient and cost-effective evaluation and analysis tools to derive insights and support decision making. The Fermatean fuzzy set theory possesses remarkable capability in capturing imprecision due to its capacity to accommodate complex and ambiguous problem descriptions. This paper presents the study of the concepts of dynamic ordered weighted aggregation operators in the context of Fermatean fuzzy environment. In numerous practical decision making scenarios, the term "dynamic" frequently denotes the capability of obtaining decision-relevant data at various time intervals. In this study, we introduce two novel aggregation operators: Fermatean fuzzy dynamic ordered weighted averaging and geometric operators. We investigate the attributes of these operators in detail, offering a comprehensive description of their salient features. We present a step-by-step mathematical algorithm for decision making scenarios in the context of proposed methodologies. In addition, we highlight the significance of these approaches by presenting the solution to the decision making problem and determining the most effective big data analytics platform for YouTube data analysis. Finally, we perform a thorough comparative analysis to assess the effectiveness of the suggested approaches in comparison to a variety of existing techniques. [ABSTRACT FROM AUTHOR]

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]

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]

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]

This study examines the potential negative effects of city-county mergers on firm-level total factor productivity (TFP). A theoretical framework is established to explain how city-county mergers could lead to higher institutional and non-institutional costs, thus influencing government decision-making and resource allocation. As a result, firms operating in the economy will absorb these costs and adjust their behavior accordingly. By employing Chinese firm-level data in a difference-in-differences approach, this study finds a statistically significant negative effect of transitioning county-level cities into municipal districts (TCD) on firm-level productivity. Additionally, this study identifies the financial situation of reformed governments and the internal environment of firms as two mechanisms through which the TCD reform influences firm-level productivity. [ABSTRACT FROM AUTHOR]

Purpose: A cross-sectional study was conducted to investigate the feasibility of implementing routine collection of the Euro-Qol 5 dimensions (EQ-5D) questionnaire, to inform drug and health technology reimbursement decision making. Methods: Women with breast cancer were recruited during scheduled clinic visits to an academic cancer centre. EQ-5D-5L was self-administered using electronic tablets. Diagnostic and treatment data were abstracted from patient charts. Feasibility was assessed primarily by the proportion of patients who fully completed EQ-5D-5L and by their willingness to complete the instrument at each clinic visit. Results: 588 women were approached for study participation, 341 were enrolled. Fully completed EQ-5D-5L questionnaires were obtained in 323 participants (95% of participants, 95% CI 92–97%). Median time for EQ-5D-5L completion was 1.5 minutes (range:0.35 to 14.7). Mean age of participants was 58 years old. Most women who completed EQ-5D were White, born outside Canada and presented a high education level; one-quarter had metastatic disease. Most participants reported "No problems" in all EQ-5D-5L dimensions. Mean EQ-5D-5L index and mean EQ-5D-5L VAS values for all participants were respectively 0.83 (SD 0.13) and 75.7 (SD 17.45), with patients with metastatic disease scoring the lowest values. Seventy-eight percent of participants were willing to complete EQ-5D-5L at each clinic visit; lower Charlson comorbidity index and higher education level were predictors of willingness to continue to answer EQ-5D-5L. Conclusions: Tablet-based collection of EQ-5D-5L in the context of routine clinical practice proved to be feasible. However, many patients declined study participation or reported being in full health, raising concerns about whether this method of collecting EQ-5D adequately represents the health status of all breast cancer patients. [ABSTRACT FROM AUTHOR]

Background: Pulmonary Rehabilitation (PR) services typically offer programmes to support individuals living with COPD make rehabilitation choices that best meet their needs, however, uptake remains low. Shared Decision-Making (SDM; e.g., Patient Decision Aids (PtDA)) interventions increase informed and values-based decision-making between individuals and healthcare professionals (HCPs). We aimed to develop an intervention to facilitate PR SDM which was acceptable to individuals living with COPD and PR HCPs. Methods: An iterative development process involving qualitative methods was adopted. Broad overarching frameworks included: complex intervention development framework, the multiple stakeholder decision making support model, and the Ottawa Decision Support Framework. Development included: assembling a steering group, outlining the scope for the PtDA, collating data to inform the PtDA design, prototype development, alpha testing with individuals with COPD (n = 4) and PR HCPs (n = 8), PtDA finalisation, and design and development of supporting components. This took nine months. Results: The PtDA was revised six times before providing an acceptable, comprehensible, and usable format for all stakeholders. Supporting components (decision coaching training and a consultation prompt) were necessary to upskill PR HCPs in SDM and implement the intervention into the PR pathway. Conclusions: We have developed a three-component SDM intervention (a PtDA, decision coaching training for PR healthcare professionals, and a consultation prompt) to support individuals living with COPD make informed and values-based decision about PR together with their PR healthcare professional. Clear implementation strategies are outlined which should support its integration into the PR pathway. [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]

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]

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]

Decision-making in the real world involves multiple abilities. The main goal of the current study was to examine the abilities underlying the Preschool Gambling task (PGT), a preschool variant of the Iowa Gambling task (IGT), in the context of an integrative decision-making framework. Preschoolers (n = 144) were given the PGT along with four novel decision-making tasks assessing either decision-making under ambiguity or decision-making under risk. Results indicated that the ability to learn from feedback, to maintain a stable preference, and to integrate losses and gains contributed to the variance in decision-making on the PGT. Furthermore, children's awareness level on the PGT contributed additional variance, suggesting both implicit and explicit processes are involved. The results partially support the integrative decision-making framework and suggest that multiple abilities contribute to individual differences in decision-making on the PGT. [ABSTRACT FROM AUTHOR]

Implicit statistical learning, whereby predictable relationships between stimuli are detected without conscious awareness, is important for language acquisition. However, while this process is putatively implicit, it is often assessed using measures that require explicit reflection and conscious decision making. Here, we conducted three experiments combining an artificial grammar learning paradigm with a serial reaction time (SRT-AGL) task, to measure statistical learning of adjacent and nonadjacent dependencies implicitly, without conscious decision making. Participants viewed an array of six visual stimuli and were presented with a sequence of three auditory (nonsense words, Expt. 1; names of familiar objects, Expt. 2) or visual (abstract shapes, Expt. 3) cues and were asked to click on the corresponding visual stimulus as quickly as possible. In each experiment, the final stimulus in the sequence was predictable based on items earlier in the sequence. Faster responses to this predictable final stimulus compared to unpredictable stimuli would provide evidence of implicit statistical learning, without requiring explicit decision making or conscious reflection. Despite previous positive results (Christiansen et al. 2009 and Misyak et al. 2010) we saw little evidence of implicit statistical learning in any of the experiments, suggesting that in this case, these SRT-AGL tasks were not an effective measure implicit statistical learning. [ABSTRACT FROM AUTHOR]

Attending to the behaviors of eyewitnesses at police lineups could help to determine whether an eyewitness identification is accurate or mistaken. Eyewitness identification decision processes were explored using augmented reality holograms. Children (n = 143; Mage = 10.79, SD = 1.12 years) and adults (n = 152; Mage = 22.12, SD = 7.47 years) viewed staged crime videos and made identification decisions from sequential lineups. The lineups were presented in augmented reality. Children were less accurate than adults on the lineup task. For adults, fast response times and high post-identification confidence ratings were both reflective of identification accuracy. Fast response times were also reflective of accuracy for children; however, children's confidence ratings did not reflect the likely accuracy of their identifications. A new additional measure, the witness' proximity to the augmented reality lineup, revealed that children who made mistaken identifications moved closer to the lineup than children who correctly identified the person from the crime video. Adults who moved any distance towards the lineup were less accurate than adults who did not move at all, but beyond that, adults' proximity to the lineup was not reflective of accuracy. The findings give further evidence that behavioral indicators of deliberation and information-seeking by eyewitnesses are signals of low lineup identification reliability. The findings also suggest that when assessing the reliability of children's lineup identifications, behavioral measures are more useful than metacognitive reports. [ABSTRACT FROM AUTHOR]

Anthropometric prevalence indicators such as stunting, wasting, and underweight are widely-used population-level tools used to track trends in childhood nutrition. Threats to the validity of these data can lead to erroneous decision making and improper allocation of finite resources intended to support some of the world's most vulnerable populations. It has been demonstrated previously that aggregated prevalence rates for these indicators can be highly sensitive to biases in the presence of non-directional measurement errors, but the quantitative relationship between the contributing factors and the scale of this bias has not been fully described. In this work, a Monte Carlo simulation exercise was performed to generate high-statistics z-score distributions with a wide range of mean and standard deviation parameters relevant to the populations in low- and middle-income countries (LMIC). With the important assumption that the distribution's standard deviation should be close to 1.0 in the absence of non-directional measurement errors, the shift in prevalence rate due to this common challenge is calculated and explored. Assuming access to a given z-score distribution's mean and standard deviation values, this relationship can be used to evaluate the potential scale of prevalence bias for both historical and modern anthropometric indicator results. As a demonstration of the efficacy of this exercise, the bias scale for a set of 21 child anthropometry datasets collected in LMIC contexts is presented. [ABSTRACT FROM AUTHOR]

Background: If communicated adequately, numerical decision-relevant information can support informed and shared decision making. Visual formats are recommended, but which format supports patients depending on their health literacy (HL) levels for specific decisions is unclear. Study aim: The aim of this study is to investigate: 1) the effect of survival rates and side-effects presentation formats on comprehension and 'feeling informed'; 2) differential effects among women with higher/lower HL, with adjuvant systemic breast cancer therapy as case example. Methods: Two online experiments among women from the Dutch population without a history of breast cancer were conducted. Experiment 1 had a 3 (survival rate format: text block–bar graph–icon array) x 2 (HL: low–high) between-subjects design. Experiment 2 had a 5 (side-effects format: no probability information–probability information in numbers with or without a visualisation–probability information in numbers with or without a visualisation accompanied by a description of the side-effects) x 2 (HL: low–high) design. Primary outcomes were comprehension and feeling informed (Experiment 2 only). Formats were previously designed in co-creation with patients. Results: In Experiment 1, presentation format did not affect gist or verbatim comprehension. Higher HL was associated with higher gist comprehension. Experiment 2 showed an interaction between presentation format and HL on 'feeling informed'. When provided with visualised probability information without a description of the side-effects, women with lower HL felt better informed than women with higher HL. Conclusion: Visual formats did not enhance comprehension of survival rate information beyond a well-designed text block format. However, none of the formats could overcome HL differences. When designing decision-relevant information, visualisations might not necessarily provide an advantage over structured numerical information for both patients with lower and higher HL. However, a deeper understanding of presenting side-effect information is warranted. [ABSTRACT FROM AUTHOR]

In this research, we developed an epidemic model with a combination of Atangana-Baleanu Caputo derivative and classical operators for the hybrid operator's memory effects, allowing us to observe the dynamics and treatment effects at different time phases of syphilis infection caused by sex. The developed model properties, which take into account linear growth and Lipschitz requirements relating the rate of effects within its many sub-compartments according to the equilibrium points, include positivity, unique solution, exitance, and boundedness in the feasible domain. After conducting sensitivity analysis with various parameters influencing the model for the piecewise fractional operator, the reproductive number R0 for the biological viability of the model is determined. Generalized Ulam-Hyers stability results are employed to preserve global stability. The investigated model thus has a unique solution in the specified subinterval in light of the Banach conclusion, and contraction as a consequence holds for the Atangana-Baleanu Caputo derivative with classical operators. The piecewise model that has been suggested has a maximum of one solution. For numerical solutions, piecewise fractional hybrid operators at various fractional order values are solved using the Newton polynomial interpolation method. A comparison is also made between Caputo operator and the piecewise derivative proposed operator. This work improves our knowledge of the dynamics of syphilis and offers a solid framework for assessing the effectiveness of interventions for planning and making decisions to manage the illness. [ABSTRACT FROM AUTHOR]

We make a novel investigation of welfare costs associated with various suboptimal decisions made by retirees, both analytically and numerically. We utilize a unique framework that incorporates recursive utility with housing, and also encompasses expected utility and recursive utility without housing as special cases. Our findings indicate that under-investment in stocks incurs lower welfare costs compared to an equivalent over-investment. Suboptimal allocations in bond holdings result in higher costs than similar misallocations in stocks. Choosing not to participate in the stock market is less detrimental than avoiding the bond market. Should retirees opt to simplify their decision-making by investing solely in one type of asset, it is less costly for them to invest exclusively in bonds. Overconsumption of housing is less costly than an equivalent underconsumption. Suboptimal consumption imposes the highest welfare cost. Decisions regarding consumption, housing, and savings are found to be more crucial than the choice of how to distribute liquid savings between stocks and bonds. Additionally, recursive utility model better captures retirees' preference for bonds over stocks than expected utility model. Our results, which are consistent across various parameter settings, provide valuable insights for academics and policymakers aiming to enhance retiree welfare. [ABSTRACT FROM AUTHOR]

The Ultimatum Game is an effective tool for understanding how social decision-making is influenced by emotions in both research and clinical settings. Previous findings have shown that the Ultimatum Game can evoke negative emotions, especially anger and aggression. In a sample of non-clinical adults (N = 143) we evaluated the sensitivity of an anger-infused version of the Ultimatum Game to individual differences in anger and irritability. Findings showed significant relationships between anger and aggressive behaviors in the Ultimatum game, but no association between irritability and aggressive behavior were observed. This indicates that the anger-infused Ultimatum Game is a promising method for studying individual differences in trait anger and anger expression. However, the relationship between decision-making in the anger-infused Ultimatum Game and irritability is less straight forward and needs further investigation. Therefore, when studying the behavioral responses of irritability, it would be beneficial to capture other behaviors beyond aggressive responses. [ABSTRACT FROM AUTHOR]

In order to assess the quality of senior tourism services in vacation destinations, we examine complex interval valued intuitionistic Fuzzy Dombi Hamy Mean (CIVIFDHM) operators. These operators successfully manage imprecision and uncertainty in the preferences of senior tourists. However, the Hamy mean (HM) operator can identify the connections between various input data sets and produce excellent outcomes when combining and evaluating information. We illustrate their usefulness and efficacy through a case study, providing a strong instrument for improving service quality for senior citizens and promoting an inclusive and fulfilling travel experience. In this work, we develop the HM operator and Dombi operations with Complex interval valued intuitionistic fuzzy numbers (CIVIFNs). We recommend the CIVIFDHM operator, complex interval valued intuitionistic fuzzy weighted Dombi Hamy mean (CIVIFWDHM), complex interval valued intuitionistic fuzzy dual Dombi Hamy mean (CIVIFDDHM), and complex interval valued intuitionistic fuzzy weighted dual Dombi Hamy mean (CIVIFWDD) operators. Next, multiple attribute decision making (MADM) models are constructed with the help of CIVIFWDHM and CIVIFWDDHM operators. We provided an evaluation of an older tourism operator in a tourist area as an example to show the suggested models. [ABSTRACT FROM AUTHOR]

Background: Shared decision-making (SDM) has yet to be successfully adopted into routine use in psychiatric settings amongst people living with severe mental illnesses. Suboptimal rates of SDM are particularly prominent amongst patients with psychotic illnesses during antipsychotic treatment choices. Many interventions have been assessed for their efficacy in improving SDM within this context, although results have been variable and inconsistent. Aims: To generate an in-depth understanding of how, why, for whom, and to what extent interventions facilitating the application of SDM during antipsychotic treatment choices work and the impact of contextual factors on intervention effectiveness. Methods: This review will use realist review methodology to provide a causal understanding of how and why interventions work when implementing SDM during antipsychotic treatment choices. The cohort of interest will be those experiencing psychosis where ongoing treatment with an antipsychotic is clinically indicated. The review will take place over five stages; (1) Locating existing theories, (2) Searching for evidence, (3) Selecting articles, (4) Extracting and organising data and (5) Synthesizing evidence and drawing conclusions. An understanding of how and why interventions work will be achieved by developing realist programme theories on intervention effectiveness through iterative literature reviews and engaging with various stakeholder groups, including patient, clinician and carer representatives. Discussion: This is the first realist review aiming to identify generative mechanisms explaining how and why successful interventions aimed at improving SDM within the parameters outlined work and in which contexts desired outcomes are most likely to be achieved. Review findings will include suggestions for clinicians, policy and decision-makers about the most promising interventions to pursue and their ideal attributes. [ABSTRACT FROM AUTHOR]

Nowadays, federated learning is one of the most prominent choices for making decisions. A significant benefit of federated learning is that, unlike deep learning, it is not necessary to share data samples with the model owner. The weight of the global model in traditional federated learning is created by averaging the weights of all clients or sites. In the proposed work, a novel method has been discussed to generate an optimized base model without hampering its performance, which is based on a genetic algorithm. Chromosome representation, crossover, and mutation—all the intermediate operations of the genetic algorithm have been illustrated with useful examples. After applying the genetic algorithm, there is a significant improvement in inference time and a huge reduction in storage space. Therefore, the model can be easily deployed on resource-constrained devices. For the experimental work, sports data has been used in balanced and unbalanced scenarios with various numbers of clients in a federated learning environment. In addition, we have used four famous deep learning architectures, such as AlexNet, VGG19, ResNet50, and EfficientNetB3, as the base model. We have achieved 92.34% accuracy with 9 clients in the balanced data set by using EfficientNetB3 as the base model using a GA-based approach. Moreover, after applying the genetic algorithm to optimize EfficientNetB3, there is an improvement in inference time and storage space by 20% and 2.35%, respectively. [ABSTRACT FROM AUTHOR]

Intolerance of Uncertainty (IU) is thought to lead to maladaptive behaviours and dysfunctional decision making, both in the clinical and healthy population. The seminal study reported by Luhmann and collaborators in 2011 showed that IU was negatively associated with choosing a delayed, but more certain and valuable, reward over choosing an immediate, but less certain and valuable, reward. These findings have been widely disseminated across the field of personality and individual differences because of their relevance to understand the role of IU in maladaptive behaviours in anxiety-related disorders. We conducted a study to replicate and extend Luhmann et al.'s results with a sample of 313 participants, which exceeded the size necessary (N = 266) to largely improve the statistical power of the original study by using the small telescopes approach. The results of our well powered study strongly suggest that the relationship between IU and the tendency to prefer an immediate, but less certain and less valuable reward is virtually negligible. Consequently, although this relationship cannot be definitely discarded, we conclude that it cannot be detected with Luhmann et al.'s (2011) decision-making task. [ABSTRACT FROM AUTHOR]

This article delves into the current popular phenomenon of live streaming e-commerce, with a specific focus on issues related to product quality and after-sales service. It constructs an evolutionary game model that encompasses three key stakeholders: e-commerce platforms, consumers, and streamers. The study conducts a thorough analysis of the interactions and strategic choices among these entities, investigating the stability of equilibrium strategy combinations within the game system and the influence of various factors on decision-making behaviors. Furthermore, the validity of the analytical conclusion is corroborated through the application of simulation analysis methods. The study finds that for the consumer, strategies such as reducing losses encountered due to quality issues under strict demands, enhancing compensation in these scenarios, and increasing benefits for maintaining stringent requirements during live streaming sessions can motivate them to adopt more stringent strategies. For the streamer, essential factors in promoting the selection of high-quality products include increasing the benefits associated with such choices and reducing the probability of quality issues, or alternatively, decreasing the gains from lower-quality selections and increasing the likelihood of encountering quality problems with these products. For the e-commerce platform, strategically adjusting the profit-sharing ratio to maintain collaborative momentum and influence the enthusiasm of both consumers and streamers is a critical strategy to avert market scenarios akin to prisoner's dilemmas and tragic outcomes. Overall, this research offers profound insights into the complex strategic evolution within the live commerce market, providing valuable guidance for interaction strategies among e-commerce platforms, consumers, and streamers. Its implications for practical decision-making optimization and strategic formulation are of significant importance. [ABSTRACT FROM AUTHOR]

Background: The birth plan is a document expressing a pregnant woman's childbirth preferences, enabling communication of expectations and facilitating discussions among women, their partners, and healthcare providers for key birthing decisions. There has been limited research on the role of birth plans in shared decision-making (SDM). Our study aims to explore how the use of birth plans can contribute to SDM from women's, partners, and healthcare providers' perspectives. Methods: We conducted in-depth interviews with women, their partners, and their healthcare providers. We used a thematic analysis to identify themes and subthemes. Furthermore, we created a grounded theory about the role of birth plans as a tool in SDM. Results: Three main themes were created: "Creating a birth plan", "Getting all on board" and "Birth plans in the daily practice of decision-making". Most women, partners, and healthcare providers agreed that birth plans can facilitate communication and SDM. Women and their partners viewed the birth plan as a tool to prepare for birth. Most healthcare providers mentioned the birth plan as a tool to get to know the women, their partners, and their preferences. Barriers are the attitude of healthcare providers toward birth plans, such as their evident resistance to the birth plan itself or to certain preferences. Another barrier is the assumption women and their partners may have that these plans can accurately predict the childbirth experience, enhancing the chance of a disappointing, negative experience. Some healthcare providers view birth plans as barriers to SDM. Conclusion: The use of a birth plan seems to promote women's, partners', and healthcare providers' involvement in the birth process, and seems suitable to facilitate SDM. Further research is required to explore strategies for overcoming barriers, including healthcare providers' attitudes toward birth plans and the expectations of women and their partners regarding their role. [ABSTRACT FROM AUTHOR]

The use of multi-criteria decision analysis (MCDA) for disease prioritization at the sub-national level in sub-Sahara Africa (SSA) is rare. In this research, we contextualized MCDA for parallel prioritization of endemic zoonoses and animal diseases in The Adamawa and North regions of Cameroon. MCDA was associated to categorical principal component analysis (CATPCA), and two-step cluster analysis. Six and seven domains made of 17 and 19 criteria (out of 70) respectively were selected by CATPCA for the prioritization of zoonoses and animal diseases, respectively. The most influencing domains were "public health" for zoonoses and "control and prevention" for animal diseases. Twenty-seven zoonoses and 40 animal diseases were ranked and grouped in three clusters. Sensitivity analysis resulted in high correlation between complete models and reduced models showing the robustness of the simplification processes. The tool used in this study can be applied to prioritize endemic zoonoses and transboundary animal diseases in SSA at the sub-national level and upscaled at the national and regional levels. The relevance of MCDA is high because of its contextualization process and participatory nature enabling better operationalization of disease prioritization outcomes in the context of African countries or other low and middle-income countries. [ABSTRACT FROM AUTHOR]

Drug-induced QT prolongation (diLQTS), and subsequent risk of torsade de pointes, is a major concern with use of many medications, including for non-cardiac conditions. The possibility that genetic risk, in the form of polygenic risk scores (PGS), could be integrated into prediction of risk of diLQTS has great potential, although it is unknown how genetic risk is related to clinical risk factors as might be applied in clinical decision-making. In this study, we examined the PGS for QT interval in 2500 subjects exposed to a known QT-prolonging drug on prolongation of the QT interval over 500ms on subsequent ECG using electronic health record data. We found that the normalized QT PGS was higher in cases than controls (0.212±0.954 vs. -0.0270±1.003, P = 0.0002), with an unadjusted odds ratio of 1.34 (95%CI 1.17–1.53, P<0.001) for association with diLQTS. When included with age and clinical predictors of QT prolongation, we found that the PGS for QT interval provided independent risk prediction for diLQTS, in which the interaction for high-risk diagnosis or with certain high-risk medications (amiodarone, sotalol, and dofetilide) was not significant, indicating that genetic risk did not modify the effect of other risk factors on risk of diLQTS. We found that a high-risk cutoff (QT PGS ≥ 2 standard deviations above mean), but not a low-risk cutoff, was associated with risk of diLQTS after adjustment for clinical factors, and provided one method of integration based on the decision-tree framework. In conclusion, we found that PGS for QT interval is an independent predictor of diLQTS, but that in contrast to existing theories about repolarization reserve as a mechanism of increasing risk, the effect is independent of other clinical risk factors. More work is needed for external validation in clinical decision-making, as well as defining the mechanism through which genes that increase QT interval are associated with risk of diLQTS. [ABSTRACT FROM AUTHOR]

What is the role of working memory over the course of non-native speech category learning? Prior work has predominantly focused on how working memory might influence learning assessed at a single timepoint. Here, we substantially extend this prior work by examining the role of working memory on speech learning performance over time (i.e., over several months) and leverage a multifaceted approach that provides key insights into how working memory influences learning accuracy, maintenance of knowledge over time, generalization ability, and decision processes. We found that the role of working memory in non-native speech learning depends on the timepoint of learning and whether individuals learned the categories at all. Among learners, across all stages of learning, working memory was associated with higher accuracy as well as faster and slightly more cautious decision making. Further, while learners and non-learners did not have substantially different working memory performance, learners had faster evidence accumulation and more cautious decision thresholds throughout all sessions. Working memory may enhance learning by facilitating rapid category acquisition in initial stages and enabling faster and slightly more careful decision-making strategies that may reduce the overall effort needed to learn. Our results have important implications for developing interventions to improve learning in naturalistic language contexts. [ABSTRACT FROM AUTHOR]

Cognitive rehabilitation, STEM (science, technology, engineering, and math) skill acquisition, and coaching games such as chess often require tutoring decision-making strategies. The advancement of AI-driven tutoring systems for facilitating human learning requires an understanding of the impact of evaluative feedback on human decision-making and skill development. To this end, we conduct human experiments using Amazon Mechanical Turk to study the influence of evaluative feedback on human decision-making in sequential tasks. In these experiments, participants solve the Tower of Hanoi puzzle and receive AI-generated feedback while solving it. We examine how this feedback affects their learning and skill transfer to related tasks. Additionally, treating humans as noisy optimal agents, we employ maximum entropy inverse reinforcement learning to analyze the effect of feedback on the implicit human reward structure that guides their decision making. Lastly, we explore various computational models to understand how people incorporate evaluative feedback into their decision-making processes. Our findings underscore that humans perceive evaluative feedback as indicative of their long-term strategic success, thus aiding in skill acquisition and transfer in sequential decision-making tasks. Moreover, we demonstrate that evaluative feedback fosters a more structured and organized learning experience compared to learning without feedback. Furthermore, our results indicate that providing intermediate goals alone does not significantly enhance human learning outcomes. [ABSTRACT FROM AUTHOR]

To construct and internally and externally validate a nomogram model for predicting the severity of acute pancreatitis (AP) based on the CT severity index (CTSI).A retrospective analysis of clinical data from 200 AP patients diagnosed at the Hefei Third Clinical College of Anhui Medical University from June 2019 to June 2022 was conducted. Patients were classified into non-severe acute pancreatitis (NSAP, n = 135) and severe acute pancreatitis (SAP, n = 65) based on final clinical diagnosis. Differences in CTSI, general clinical features, and laboratory indicators between the two groups were compared. The LASSO regression model was used to select variables that might affect the severity of AP, and these variables were analyzed using multivariate logistic regression. A nomogram model was constructed using R software, and its AUC value was calculated. The accuracy and practicality of the model were evaluated using calibration curves, Hosmer-Lemeshow test, and decision curve analysis (DCA), with internal validation performed using the bootstrap method. Finally, 60 AP patients treated in the same hospital from July 2022 to December 2023 were selected for external validation.LASSO regression identified CTSI, BUN, D-D, NLR, and Ascites as five predictive factors. Unconditional binary logistic regression analysis showed that CTSI (OR = 2.141, 95%CI:1.369–3.504), BUN (OR = 1.378, 95%CI:1.026–1.959), NLR (OR = 1.370, 95%CI:1.016–1.906), D-D (OR = 1.500, 95%CI:1.112–2.110), and Ascites (OR = 5.517, 95%CI:1.217–2.993) were independent factors influencing SAP. The established prediction model had a C-index of 0.962, indicating high accuracy. Calibration curves demonstrated good consistency between predicted survival rates and actual survival rates. The C-indexes for internal and external validation were 0.935 and 0.901, respectively, with calibration curves close to the ideal line.The model based on CTSI and clinical indicators can effectively predict the severity of AP, providing a scientific basis for clinical decision-making by physicians. [ABSTRACT FROM AUTHOR]

Acute behavioural disturbance (ABD), sometimes called 'excited delirium', is a medical emergency. In the UK, some patients presenting with ABD are managed by advanced paramedics (APs), however little is known about how APs make restraint decisions. The aim of this research is to explore the decisions made by APs when managing restraint in the context of ABD, in the UK pre-hospital ambulance setting. Seven semi-structured interviews were undertaken with APs. All participants were experienced APs with post-registration, post-graduate advanced practice education and qualifications. The resulting data were analysed using reflexive thematic analysis, informed by critical realism. We identified four interconnected themes from the interview data. Firstly, managing complexity and ambiguity in relation to identifying ABD patients and determining appropriate treatment plans. Secondly, feeling vulnerable to professional consequences from patients deteriorating whilst in the care of APs. Thirdly, negotiating with other professionals who have different roles and priorities. Finally, establishing primacy of care in relation to incidents which involve police officers and other professionals. A key influence was the need to characterise incidents as medical, as an enabler to establishing clinical leadership and decision-making control. APs focused on de-escalation techniques and sought to reduce physical restraint, intervening with pharmacological interventions if necessary to achieve this. The social relationships and interactions with patients and other professionals at the scene were key to success. Decisions are a source of anxiety, with fears of professional detriment accompanying poor patient outcomes. Our results indicate that APs would benefit from education and development specifically in relation to making ABD decisions, acknowledging the context of inter-professional relationships and the potential for competing and conflicting priorities. A focus on joint, high-fidelity training with the police may be a helpful intervention. [ABSTRACT FROM AUTHOR]

Rice, being a staple food in many countries, necessitates the identification of reliable suppliers to ensure a steady supply. Consequently, it is vital to establish trustworthy vendors for various types of this essential grain who can meet stringent product quality standards. This study aims to identify, analyze, rank, and select primary rice suppliers. The study emphasizes the importance of selecting and managing suitable providers to meet customer demands, proposes a ranking model for rice suppliers, and introduces developed fuzzy MCDM techniques. It proposes an integrated model for selecting rice suppliers, considering factors related to the processes before, during, and after selecting providers within a defined framework. The outcomes shows that rice supplier selection strategy can efficiently identify reliable rice suppliers, improve buyer value, reduce procurement risk, enhance efficiency, and establish strong supply chain relationships in complex decision-making processes. To assess suppliers, the study introduces two advanced integrated approaches and compares them. The fuzzy entropy weight method (EWM) was used to determine the criteria weights. The ranking of rice suppliers was achieved using a fuzzy multi-objective optimization based on ratio analysis (MOORA), fuzzy complex proportional assessment (COPRAS), and combinations of these two methods in different approaches. The methodology supports decision-makers in a rapidly evolving global environment by assisting importers, traders, suppliers, procurement, and logistics management, particularly for non-rice-cultivating countries in rice importation and supplier selection. The numerical analysis is grounded in a real-world case study of selecting rice suppliers in Jordan. The findings reveal that the various strategies yield both similar and different results. Furthermore, the integrated method is considered the most accurate for evaluating rice imports and suppliers, aligning closely with the reality of the current situation. [ABSTRACT FROM AUTHOR]

Background: Ductal carcinoma in situ with microinvasion (DCIS-MI) is a special type of breast cancer. It is an invasive lesion less than 1.0 mm in size related to simple ductal carcinoma in situ (DCIS). Lymph node metastasis (LNM) in DCIS-MI often indicates a poor prognosis. Therefore, the management of lymph nodes plays a vital role in the treatment strategy of DCIS-MI. Since DCIS-MI is often diagnosed by postoperative paraffin section and immunohistochemical detection, to obtain the best clinical benefits for such patients, we aim to establish and verify a nomogram to predict the possibility of lymph node metastasis in DCIS-MI patients and help preoperative or intraoperative clinical decision-making. Methods: A retrospective analysis of patients with DCIS-MI in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2019 was performed. The study cohort was randomly divided into a training cohort and a validation cohort at a ratio of 7:3. The risk factors were determined by univariate and multivariate logistic regression analyses in the training cohort, and a nomogram was constructed. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram in the training set and validation set. An independent data cohort was obtained from the Shanghai Jiao Tong University Breast Cancer Database (SJTU-BCDB) for external validation. Results: This study included 3951 female patients from SEER with DCIS-MI, including 244 patients with regional lymph node metastasis, accounting for 6.18% of the total. An independent test set of 323 patients from SJTU-BCDB was used for external validation. According to the multifactorial logistic regression analysis results, age at diagnosis, ethnicity, grade, and surgical modality were included in the prediction model. The areas under the ROC curves (AUCs) were 0.739 (95% CI: 0.702~0.775), 0.732 (95% CI: 0.675~0.788), and 0.707 (95%CI: 0.607–0.807) in the training, validation and external test groups, suggesting that the column line graphs had excellent differentiation. The calibration curves slope was close to 1, and the model's predicted values were in good agreement with the actual values. The DCA curves showed good clinical utility. Conclusion: In this study, we constructed accurate and practical columnar maps with some clinical benefit to predict the likelihood of lymph node metastasis in patients with postoperatively diagnosed DCIS-MI and provide a reference value for specifying treatment strategies. [ABSTRACT FROM AUTHOR]

Introduction: Despite a decline in Sudden Unexpected Death in Infancy in the UK since 2004, inequalities have widened with higher rates among families from deprived backgrounds and those known to child protection services. Almost all cases involve parents who had engaged in unsafe sleeping practices despite awareness of safer sleeping advice. Objective: To understand the perspectives surrounding safer sleep of families supported by statutory child protection agencies, and use behavior change theory to inform how approaches to providing safer sleep advice to these families may be modified. Participants and setting: We interviewed 14 mothers, 2 fathers and one grandmother, who had recent contact with child protection services in northeast England. Methods: In-depth, semi-structured interviews, with purposive sampling. The COM-B model (Capability, Opportunity, and Motivation) structured our analysis. Results: Parents described how anxiety, sleep deprivation, settling infants, illness, and a desire to bond with infants influence their decision making about sleep. Parents valued credible, trusted sources and understanding how safer sleep practices protect infants. Responses to questions about 'out of routine' situations suggested social pressures surrounding routines and 'good parenting' may preclude parents from acknowledging risks and planning for these situations. Conclusion: Open conversations tailored to the needs of families, focused upon understanding why and when parent(s) do or do not follow safer sleep guidance seem a promising way of promoting safer sleep practices. Safer sleep discussions with these families are likely to be best delivered as part of wider infant care by professionals who have an established and continuing trusting relationship with parents. While advice and information should be provided by any professional in contact with the family with the necessary expertise, sensitive conversations around sleeping practices, particularly co-sleeping, may be more easily facilitated by professionals where the statutory responsibility for safeguarding is less apparent. [ABSTRACT FROM AUTHOR]

As ChatGPT emerges as a potential ally in healthcare decision-making, it is imperative to investigate how users leverage and perceive it. The repurposing of technology is innovative but brings risks, especially since AI's effectiveness depends on the data it's fed. In healthcare, ChatGPT might provide sound advice based on current medical knowledge, which could turn into misinformation if its data sources later include erroneous information. Our study assesses user perceptions of ChatGPT, particularly of those who used ChatGPT for healthcare-related queries. By examining factors such as competence, reliability, transparency, trustworthiness, security, and persuasiveness of ChatGPT, the research aimed to understand how users rely on ChatGPT for health-related decision-making. A web-based survey was distributed to U.S. adults using ChatGPT at least once a month. Bayesian Linear Regression was used to understand how much ChatGPT aids in informed decision-making. This analysis was conducted on subsets of respondents, both those who used ChatGPT for healthcare decisions and those who did not. Qualitative data from open-ended questions were analyzed using content analysis, with thematic coding to extract public opinions on urban environmental policies. Six hundred and seven individuals responded to the survey. Respondents were distributed across 306 US cities of which 20 participants were from rural cities. Of all the respondents, 44 used ChatGPT for health-related queries and decision-making. In the healthcare context, the most effective model highlights 'Competent + Trustworthy + ChatGPT for healthcare queries', underscoring the critical importance of perceived competence and trustworthiness specifically in the realm of healthcare applications of ChatGPT. On the other hand, the non-healthcare context reveals a broader spectrum of influential factors in its best model, which includes 'Trustworthy + Secure + Benefits outweigh risks + Satisfaction + Willing to take decisions + Intent to use + Persuasive'. In conclusion our study findings suggest a clear demarcation in user expectations and requirements from AI systems based on the context of their use. We advocate for a balanced approach where technological advancement and user readiness are harmonized. [ABSTRACT FROM AUTHOR]

Background: Family planning decision making is defined as women´s ability to determine the family planning methods that she wanted to use through the process of informed decision making. Despite the availability and accessibility of family planning methods, the utilization rate is not more than 41% in Ethiopia. Evidence and experts have consistently show that women decisions making ability on family planning method they desired to use is one of the possible reasons for this slow rate of family planning use increment. In consideration of this and further motives family planning use decision making has become one of the top sexual and reproductive health related sustainable development agendas. Hence, this study aimed at determining the level, trend and spatial distribution of family planning use decision making among married women and identify factors affecting it. Methods: This study was based on Performance Monitoring for Action (PMA) 2020 cross sectional national survey data. Married women who are currently using or recently used family planning method were included in this study. Frequency was computed to describe the study participants while chi-square statistics was computed to examine the overall association of independent variable with family planning use decision making. To identify predictors of family planning use decision making multinomial logistics regression was employed. Results were presented in the form of percentage and relative risk ratio with 95% CI. Candidate variables were selected using p value of 0.25. Significance was declared at p value 0.05. Results: This study revealed that one in two women (51.2%; 95% CI: 48.8%-53.6%) decide their family planning use by themselves while 37% (36.8%; 95% CI: 34.5%-39.2%) decide jointly with their husband and/or partner. Women alone family planning use decision making increased significantly 32.8% (95% CI: 29.4%, 36.4%) in 2014 to 51.2% (95% CI: 48.8%, 53.6%) in 2020. It also shows variation across regions from scanty in Afar and Somali to 63.6% in Amhara region and 61.5 Addis Ababa. Obtaining desired family planning method was found significantly to improve women alone and joint family planning use decision making. Women who have perceive control and feeling if they get pregnant now were found to be positively associated with women alone family planning use decision making. Discussion with husband, his feeling towards family planning were found positively to influence family planning use joint decision making. Moreover, women religion, was found reducing the likelihood of both women alone and joint family planning use decision making while experiencing side effect reduces the likelihood of joint family planning use decision making. Conclusion: Half of the women independently decide their family planning use which calls up on further improvement. Family planning use decision making ability is expected to be improved by efforts targeted on husbands' approval on wife's family planning use, discussion on family planning use with husband/partner, improving women psychosociological readiness and trust on her own to decide her desired family planning method; informing the possible side effects and what to do when they encountered during their family planning use visit. In addition, influencing women on the use of family planning via religious leader will help much in this regard. Monitoring and evaluating reproductive health policy 2021 to2025 and addressing bottlenecks which hinder women decision making health service use is hoped to improve women family planning use decision making. Further qualitative study to identify and address factors that contribute for the variation across regions also help much. [ABSTRACT FROM AUTHOR]

Coping capacity is a key aspect of driver-vehicle interaction when drivers observe and make decisions, and is of great importance for drivers. However, different drivers have different self-cognition and assess their driving abilities differently, especially for novice drivers. Based on questionnaire data, this study has investigated the coping capacities of drivers in both static environments and dynamic environments. With the ANOVA analysis method and the structural equation model (SEM), this study has verified the effects of gender and driving factors (driving years, driving frequency, driving time) on drivers' coping capacities based on drivers' self-assessment scores and mutual assessment scores. Drivers' self-assessment scores show significant effects of all factors on drivers' coping capacities, and drivers' mutual assessment scores show significant effects of all factors, excluding driving time, on drivers' coping capacities. Also, it has been found that all drivers in the driving year group have cognitive biases. It seems that first-year drivers are always overconfident with their driving skills, while drivers with a driving experience of more than three years usually score driving skills of themselves and other drivers most conservatively. With increased exposure to various traffic conditions, experienced drivers are more aware of their limitations in dealing with complex traffic situations, while novice drivers do not know their lack of capability to properly respond to any unexpected situation they could encounter. [ABSTRACT FROM AUTHOR]

Automated decision-making gains traction, prompting discussions on regulation with calls for human oversight. Understanding how human involvement affects the acceptance of algorithmic recommendations and the accuracy of resulting decisions is vital. In an online experiment (N = 292), for a prediction task, participants choose a recommendation stemming either from an algorithm or another participant. In a between-subject design, we varied if the prediction was delegated completely or if the recommendation could be adjusted. 66% of times, participants preferred to delegate the decision to an algorithm over an equally accurate human. The preference for an algorithm increased by 7 percentage points if participants could monitor and adjust the recommendations. Participants followed algorithmic recommendations more closely. Importantly, they were less likely to intervene with the least accurate recommendations. Hence, in our experiment the human-in-the-loop design increases the uptake but decreases the accuracy of the decisions. [ABSTRACT FROM AUTHOR]