Clinical relevance of follow-up blood cultures in patients with Streptococcus and Enterococcus species bacteremia.

Rhythmic sampling of decision alternatives through attention.

ABSTRACT Artificial intelligence (AI) and other digital technologies, such as machine learning, data visualization, and workflow automation, have transformed audit tasks, procedures, and, to an extent, the entire audit profession by providing predictive and intelligent audit capabilities to support auditors. However, to benefit from AI, auditors require cognitive flexibility, which captures the extent to which the collaborating actors (here, audit team members) listen attentively without distraction and are open to multiple perspectives and possibilities, engage in the free flow of ideas and opinions based on their conversations with peers and clients, and evaluate different decision alternatives. This study aimed to ascertain how AI impacts audit team performance and what role cognitive flexibility plays in this process. Data were collected from 191 respondents from India, and partial least squares structural equation modeling (PLS-SEM) was used to conduct a comprehensive analysis of the conceptual model, including testing for mediation effects, testing for heterogeneity utilizing the finite mixture partial least squares (FIMIX) procedure and entropy values, testing for endogeneity using the Gaussian copula term, importance—performance map analysis (IPMA), and PLSpredict modeling. The results showed that cognitive flexibility is significant in AI-enabled audit engagements. This study concludes that AI generates transactional value and that auditors require proficiency in AI to perform audits effectively using such technologies.

Alternative splicing of pre-mRNA is a fundamental step in human gene regulation, and aberrant splicing is frequently linked to complex diseases, including cancer. All splicing events, such as skipping or inclusion of an alternative exon (AE) and intron retention (IR), are regulated by a common molecular machinery, the spliceosome. However, it remains elusive how the spliceosome coordinates different splicing decisions. Here, we analyzed a large-scale mutagenesis screen and transcriptome-wide RNA sequencing data to show that intron retention adjacent to alternative exons most commonly occurs at intermediate inclusion levels. Using data-driven mathematical modeling, we revealed that multistep exon recognition mediated by spliceosome assembly and maturation explains the observed AE-IR dependency for cis-acting sequence mutations and upon knockdown of trans-acting RNA-binding proteins. Furthermore, we found that multistep exon recognition is commonly perturbed in a transcriptome-wide manner in cancer cells, which leads to the coordinated deregulation of IR and AE decisions. In total, our work suggests that stepwise alternative exon recognition by the spliceosome coordinates AE inclusion and the retention of flanking introns, which may facilitate the search for common molecular mechanisms for mis-splicing in cancer.

This research aims to optimize polylactic acid (PLA) materials with varying process parameter levels using the Taguchi method, compare tensile strengths, generate stress-strain curves, and achieve high-strength structures efficiently in a shorter production time while minimizing material use via fused deposition modeling (FDM). For this purpose, three factors were considered, and experiments were carried out using the Taguchi L9 orthogonal array. At the first stage, relationships between the measured tensile strength, the filament used, the production time, and the input factors were analyzed. The results revealed that tensile strength is predominantly influenced by wall line count (WLC), with a contribution of 56.2 %, followed by infill density (ID) at 33.5 % and print speed (PS) at 8.5 %. Conversely, ID emerges as the principal factor in material consumption, accounting for 95.7 % of the contribution margin. Similarly, printing time was largely determined by PS (71.5 %), followed by ID (27.7 %), indicating that significant reductions in production time can be achieved through PS optimization. At the second stage, a multi-criteria optimization and the VIKOR compromise solution, a multi-criteria decision-making (MCDM) method, were applied, with the trials as decision alternatives and the responses as criteria. As a result, the optimal combination was found to be WLC 6, ID 50 %, and PS 40 mm/s. Remarkably, the MCDM methodology has yet to be applied to Additive Manufacturing processes, with criteria importance determined through intercriteria correlation (CRITIC) approaches. This establishes a novel research pathway for multi-criteria optimization in 3D printing using PLA materials by addressing this gap through a unique optimization methodology.

Identifying utility maximizers and regret minimizers in zero-energy house adoption by using individual-specific heterogeneous alternative decision rules

Corporate Social Responsibility (CSR) has become a central element of organizational strategy, requiring decision-makers to evaluate complex trade-offs among economic, social, and environmental objectives under conditions of uncertainty and imperfect information. Traditional CSR assessment tools often rely on additive aggregation models or single-indicator approaches, which may inadequately capture hierarchical structures, interactions among criteria, and imprecise judgments commonly present in real-world evaluations. This paper proposes a novel framework for the evaluation of CSR strategies, integrating interacting criteria and interval-valued information within a multi-criteria decision analysis perspective. CSR performance is modeled through a hierarchical structure encompassing economic, social, environmental, and governance dimensions, decomposed into operational sub-criteria. Imperfect knowledge regarding criterion evaluations, weights, and decision thresholds is explicitly represented using interval numbers, allowing for a realistic treatment of expert judgment and stakeholder heterogeneity. The proposed approach is illustrated through case studies of organizations implementing alternative CSR strategies, where decision alternatives are evaluated and classified using hierarchical interval outranking relations. The method enables both global CSR assessments and partial evaluations at intermediate levels of the hierarchy, providing insights into organizational strengths and weaknesses across CSR dimensions.

The rapid diffusion of industry 4.0 technologies has substantially transformed the maritime transportation sectors by enabling data-driven operations, enhanced connectivity, and more intelligent decision-making processes. Digital technologies such as the Internet of Things (IoT), simulation systems, and advanced data analytics are increasingly reshaping operational structures in maritime logistics, positioning technological transformation as a strategic priority for firms. However, the weighting and prioritization of components emerging with industry 4.0 technologies remain an underexplored area in the literature. The primary motivation of this study is to determine the weights of these industry 4.0 components using the Bayesian Best Worst Method (BWM) and to reveal their corresponding credal ranking levels. In this context, the present study aims to evaluate and prioritize the critical industry 4.0 components influencing technological transformation processes using the Bayesian BWM. Bayesian BWM is preferred over alternative Multi Criteria Decision Making (MCDM) approaches due to its ability to explicitly model uncertainty within a probabilistic framework, generate more consistent weighting results, and flexibly incorporate decision-makers’ judgments. The findings reveal that safety and security (0.2945) constitute the most influential main component, underscoring the necessity of robust digital infrastructures and reliable systems within highly digitalized operational environments. Among the sub-components, data privacy (0.1301) demonstrates the highest global weight, highlighting the growing importance of safeguarding sensitive information in data-intensive digital systems. The results further indicate that autonomous operation and coordination play significant roles in facilitating efficient digital operations, particularly through real-time equipment monitoring and IoT-based operational visibility. Moreover, sustainability (0.1968) emerges as the second most important component, suggesting that organizations increasingly assess technological investments not only in terms of operational efficiency but also with respect to long-term resilience. Within this dimension, continuous training (0.0614) is identified as the most influential component, indicating that the success of digital transformation depends not only on technological infrastructure but also on the development of human capabilities. With the increasing digitalization of the maritime industry, protection against cyber threats has become essential for ensuring operational continuity and safeguarding data integrity. In this regard, adopting proactive cybersecurity strategies and continuously monitoring and updating systems are of critical importance. In the digital transformation of maritime transportation, integrating sustainability considerations is essential to ensure long-term operational efficiency and environmental responsibility. These practical implications are particularly relevant for policymakers, port authorities, and shipping companies seeking to enhance both digital capabilities and sustainable performance.

This study aims to analyze the application of differential cost analysis as a basis for managerial decision-making through a literature review approach using the PRISMA method. The increasingly competitive business environment requires management to make decisions effectively and efficiently, supported by relevant information. In this context, differential cost analysis serves as an important tool in management accounting, as it provides information regarding differences in costs and revenues among various decision alternatives. The research method employed is a systematic literature review using the PRISMA technique, which includes the stages of identification, screening, eligibility assessment, and inclusion of relevant scientific articles. A total of 11 articles that met the criteria were analyzed to obtain a comprehensive understanding of the application of differential cost analysis in managerial decision-making. The results indicate that differential cost analysis is effectively used in various types of decisions, such as accepting or rejecting special orders, make-or-buy decisions, investment decisions, and additional production decisions. This analysis helps management identify relevant costs, leading to more efficient and profitable decisions. However, its implementation remains suboptimal, particularly in small and medium enterprises, due to limited understanding and inadequate accounting information systems. Therefore, improving knowledge and the application of management accounting is necessary to maximize the use of differential cost analysis in supporting managerial decision-making.

Modern web applications increasingly adopt Single-Page Application (SPA) architectures to enhance the user experience through client-side rendering and dynamic content loading. However, these characteristics introduce significant challenges for automated end-to-end (E2E) testing, including asynchronous DOM manipulation, complex state management, and timing synchronization issues. This study presents a comprehensive empirical comparison of three prominent E2E testing frameworks—Selenium WebDriver, Cypress, and Playwright—across React and Vue-based SPAs. Using a quantitative experimental approach, 25 standardized test cases were executed 15 times each across Chrome, Firefox, and Edge, for a total of 270 testing sessions. Performance evaluation focused on four key metrics: execution time, success rate, CPU usage, and memory consumption. Results demonstrate that Playwright achieved the fastest execution time (56.25 seconds on React-Chrome), while Selenium exhibited superior resource efficiency with the lowest memory consumption (196.59 MB on Vue-Chrome). The Distance to Ideal Alternative (DIA) multi-criteria decision analysis method identified Playwright-Chrome as optimal for React applications (DIA score: 0.886715) and Selenium-Chrome for Vue applications (DIA score: 0.908237), indicating that framework selection should be context-dependent based on application characteristics and deployment requirements. This research supports the conclusion that no universal "best" testing framework exists, underscoring the importance of evidence-based, application-specific tool selection in software quality assurance.

This study develops an approach to intelligent support for monitoring transport and logistics services in integrated supply chains based on risk management. The relevance of the work is determined by the growing uncertainty and risk burden in transport and logistics processes, geopolitical instability, transformation of logistics routes, and increased competition between transport modes. Special attention is given to water transport, which is highly dependent on natural, infrastructural, organizational, and technological factors, increasing the likelihood of cascading effects within supply chains. The aim of the research is to form a concept for integrated risk monitoring of transport and logistics services and to develop an intelligent decision support system to ensure sustainable and safe functioning of integrated supply chains. The object of the research is transport and logistics services as part of integrated supply chains, while the subject is the methods and tools for monitoring and assessing risks arising during their implementation. The methodological basis includes risk management, system analysis, expert assessments, and methods for forming and analyzing management decision alternatives. The study describes procedures for obtaining relevant risk information, presents the structure of the intelligent support system, details the content of its semantic core, and explains the algorithm for selecting optimal management solutions. The results substantiate the expediency of using intelligent systems for monitoring risks in transport and logistics services, highlight the role of critical points in water transport for ensuring supply chain resilience, and emphasize the importance of data visualization in improving decision efficiency and validity. The proposed approach is applicable to various classes of transport and logistics services, including water transport, whose facilities may be considered critical points of integrated supply chains with increased risk exposure.

This paper investigates multi-expert multi-attribute decision making under linguistic uncertainty with incomplete assessments. Building on a linguistic formal context with fuzzy objects, we develop a multi-expert extension to characterize trust-level associations between decision alternatives and linguistic concepts across experts. To address missing evaluations in an incomplete context, a maximum-similarity-based completion strategy is proposed to obtain a complete set of multi-expert fuzzy linguistic information. We then construct a multi-expert fuzzy linguistic-concept decision matrix. When expert weights are completely unknown, an optimization model based on the deviation-maximization principle is established to derive objective expert weights. Furthermore, the classical TOPSIS framework is adapted to the proposed decision matrix by specifying the determination of the positive and negative ideal solutions and introducing a pseudo-distance and a relative closeness measure between alternatives and the ideals, thereby enabling the ranking of alternatives and the selection of the most satisfactory one. A numerical example demonstrates the feasibility and effectiveness of the proposed approach for handling incomplete multi-expert fuzzy linguistic assessments.

In recent years, many sellers have marketed products through third-party live streamers and shared revenue with them. During live streaming, streamers provide both informative content (e.g. product attributes) and non-informative content (e.g. social interactions) to entertain consumers, with gift-giving serving as an additional revenue source. This paper examines consumers' limited attention to these two types of content within a game-theoretic framework. We show that when gift-giving efficiency and consumers' attention to social-interaction content are sufficiently low, or when gift-giving efficiency is relatively low, greater consumer attention to social content induces the streamer to increase social-interaction effort while product demand declines. This occurs because reduced attention to product quality lowers consumers' perceived value from product information, which outweighs the increased value from social interactions. Moreover, we find that consumers' gift-giving behaviour can harm the streamer. When the effort cost coefficient is sufficiently low, gift giving induces the seller to raise the price by anticipating and free-riding on the streamer's higher effort response. If the gift income and increased commissions cannot offset this higher effort cost, the streamer's profit decreases. Finally, we consider three extensions: (1) endogenous commission rate negotiation; (2) optimisation of consumers' attention allocation; and (3) alternative decision sequences for effort and pricing.

Purpose Studies of wine choice in real or simulated retail environments typically assume that consumers are utility-maximizing individuals. However, choosing a bottle of wine from crowded shelves can be challenging and inherently risky. Describing consumers who use risk-reduction strategies as regret minimizers rather than utility maximizers is more in line with consumer psychology. Against this background, this study aims to introduce regret minimization as an alternative decision rule to explain wine purchase behavior. Design/methodology/approach This study evaluates the prevalence of decision-making rules – specifically, utility maximization and regret minimization – for regular wine buyers across different markets and consumer segments. Data from choice experiments conducted in a traditional wine market (Austria) and a non-traditional one (China) were analyzed using a hybrid random regret minimization-random utility maximization (RRM-RUM) model that links personal characteristics (wine knowledge, involvement and attitude toward price) with the probability of using regret- or utility-based decision rules. Findings The results indicate a high probability of regret-based decision-making among wine buyers in both countries, highlighting the need to account for diverse decision-making rules in future research. Notably, less knowledgeable and less involved wine buyers in both countries were more likely to use regret-based decision rules. These buyers relied on easily accessible cues and used risk reduction strategies by focusing on price-quality associations in Austria or reputation-signaling attributes in China. The managerial implications derived from the regret- and utility-based models differed considerably, highlighting the importance of considering both behavioral decision rules when tailoring wines and retail strategies. Originality/value To the best of the authors’ knowledge, this is the first application of a mixed random utility-random regret model to wine choice, and the first assessment of the generalizability of decision rules across countries.

Humans and other animals navigate decisions by sequentially attending to (sampling) subsets of the available information. The internal dynamics of the selective sampling of decision-relevant information remain unknown. Here we use magnetoencephalography recordings and neural decoding to track the spontaneous dynamics of the locus and strength of covert attention as human participants performed a three-alternative perceptual choice task. The strength of covert attention fluctuated rhythmically around 11 Hz. A shift of attention from one alternative to another tends to occur at the trough of this oscillation, presumably enabling comparisons. These shifts further reset the attentional oscillation. By contrast, at the peak of the oscillation, attention tends to increase the focus on the currently sampled alternative, presumably deepening processing of that alternative. We propose intrinsic attentional oscillations as a core mechanism governing the flexible sampling of decision alternatives.

Introduction The optimization of machining process decision-making remains a major challenge in intelligent manufacturing due to the uncertainty of process information, incompleteness of rule bases, and the tendency of traditional algorithms to converge to local optima. Therefore, enhancing the adaptability and robustness of decision-making systems is a crucial task for achieving efficient and reliable computer numerical control (CNC) process planning. Methods This study proposes a hybrid decision-making approach that integrates fuzzy theory with support vector machines (SVM) to address uncertainty and incomplete knowledge representation in CNC turning. An Analytic Hierarchy Process (AHP) is used to determine the relative importance of influencing factors, and trapezoidal membership functions are designed to determine the credibility of fuzzy reasoning rules. When the credibility value falls below a defined threshold, a linear-kernel SVM model is activated to provide alternative decisions which formed a fuzzy-SVM collaborative reasoning mechanism. Results Experimental validation demonstrates that the proposed hybrid fuzzy-SVM collaborative method achieves remarkable classification accuracy on the test dataset. The system maintains stable performance even under low-credibility or incomplete rule conditions. The SVM module effectively compensates for the limitations of the fuzzy reasoning process, thereby improving the robustness of decision inference compared to single-model approaches. Conclusion The proposed fuzzy-SVM collaborative reasoning framework enhances the adaptability, stability, and interpretability of CNC machining process decision-making. These findings offer a practical and scalable solution for intelligent process planning in complex and uncertain manufacturing environments.

By taking proactive mitigation actions, a community can improve its resilience against natural disasters, which entails reducing vulnerability, minimising social impacts, and facilitating faster recovery. Mitigation planning for the built environment involves making appropriate decisions regarding strengthening interdependent utility network systems, adding redundancies, and retrofitting buildings. Due to the presence of numerous stakeholders with diverse socio-economic characteristics, different risk attitudes, and propensity to take voluntary actions, the planning problem is challenging, especially when the hazards, magnitudes, occurrence times, and impacts on the community’s built environment and population are uncertain. An optimisation model, framed as a two-stage mean-risk stochastic programming model, was developed to address these challenges and support mitigation decisions. The optimisation model aims to minimise the costs and risks associated with hazards by integrating their impacts on the built environment and social functions. Under various incentive policies and risk preferences, the model helps generate decision alternatives and evaluate their effectiveness in achieving community resilience goals. A case study is presented using a community in Shelby County, Tennessee, subjected to earthquake hazards to demonstrate the model’s capability to develop alternative mitigation strategies under varied risk preferences and incentive policies.

Assessment is the process of giving or determining value to a particular object based on certain criteria (Sudjana, 2006). According to Merrens and Lehmans in Purwanto (2006) assessment is a process of planning, obtaining and completing information that is very necessary to make alternative decisions. Winkel (2009:535) argues that assessment is the determination of the level of quality of student achievement based on certain norms, benchmarks, or criteria. The objectives of the study are 1) to describe the steps in implementing student ability tests in solving TIMSS and PISA type mathematics problems in junior high schools, 2) to describe the results of the analysis of students' ability to solve TIMSS and PISA type mathematics problems, 3) to describe the supporting and inhibiting factors for students in solving TIMSS and PISA type mathematics problems. This study took place at SMP Negeri 1 Sidomulyo. The data sources were students of class IXC. The research techniques used were observation, interviews, and tests. The results of the study 1) the steps for implementing the student's ability test to solve TIMSS and PISA type Mathematics problems in students of SMP Negeri 1 Sidomulyo can be known by the stages of preparation, data collection, analysis and students' ability to work on TIMSS and PISA type Mathematics problems and analyzing abilities into categories based on literacy and levels that have been made, 2) the results of the analysis of students' ability to solve PISA type Mathematics problems include groups of mathematical content mastery abilities, mathematical process mastery abilities and mathematical context mastery abilities to solve TIMSS and PISA type Mathematics problems, 3) supporting factors for readiness, student abilities, PMRI approach and the material taught. Inhibiting factors, question variations, test implementation and selected materials.

Predicting wastewater influent is essential for reliable, energy-efficient operation in climate-sensitive, data-limited utilities. This study benchmarks monthly influent forecasts for major treatment plants in Kuwait using stepwise linear regression (SLR), ensemble trees (ET), support vector machines (SVM), kernel approximation, and penalized linear baseline (LASSO), with air temperature, relative humidity, municipal water consumption, and population as predictors. A five-fold cross-validation with a chronologically held-out test block is adopted. Performance is reported using RMSE, MAE, MSE, R2, and MAPE. LASSO achieved the lowest test errors while selecting a sparse specification; SLR/ET were close, and kernel methods underperformed. Model behavior was examined using SHAP summary and feature importance plots. Results indicate that low-complexity, transparent models, particularly penalized linear models, provide strong skill at low tuning cost, supporting operator trust and auditability. The framework offers actionable month-ahead guidance for load management, storage/reuse planning, and alternative water-supply decisions in hyper-arid utilities.

Considering today's geopolitical conditions, the risks of war and global transformation increasingly highlight the importance of intelligence. Intelligence diplomacy has become a critical component of national security and foreign policy processes. Intelligence diplomacy, an attempt to reduce conflicts and wars by shaping diplomacy through intelligence activities, rather than conflicts themselves, strives to utilize not only the social sciences but also all the instruments of the field. The primary objective of this study is to demonstrate the feasibility of this field, along with its first application, by conducting an application in the field of "intelligence engineering," a recently discussed need for which has not yet been implemented in practice. This study designed a game theory-based engineering problem by developing a successful decision algorithm to gain an advantage over adversaries in an intelligence-based diplomacy case. Engineering methods such as artificial intelligence, heuristic and metaheuristic algorithms, and pattern-based analysis were utilized in generating these decision alternatives. Furthermore, a simulation structure consisting of 1000 games, including 75,000 iterations determined through parameter optimization, was constructed and run to investigate which strategy, and therefore which engineering approach, yielded the most successful results. However, the real significance of the study is that it provides evidence in the literature that the concept of "intelligence engineering" is not a utopian approach but a feasible systematic approach, through the first academic application of engineering-oriented scientific and "objective" approaches in intelligence science, instead of traditional social science-oriented "subjective" approaches.