Search Results (143)

Truck congestion around international ports poses persistent challenges for safety, efficiency, environmental performance, and accessibility, particularly during container terminal disruptions when long queues of trucks accumulate. Traditional responses often address isolated components of the problem and fail to capture the interdependencies of sociotechnical systems, where multiple actors pursue partly conflicting goals. This study explores the usefulness of combining Sociotechnical Systems (STS) principles with the Systems-Theoretic Accident Model and Processes (STAMP) to analyze such complexity more holistically. Using the case of truck re-routing procedures during terminal closures at the Port of Rotterdam, structured interviews and document analyses were used to apply parallel STS and System-Theoretic Process Analyses (STPA). The STS analysis identified misalignments among procedures, actor intentions, infrastructure, and communication practices, clarifying why diversion protocols often fail in practice. The STPA complemented this diagnosis by modeling control relationships and feedback loops, identifying 92 unsafe control actions and 407 loss scenarios that informed 16 design recommendations. Together, the two approaches demonstrate how sociotechnical and control-theoretic perspectives can be combined to generate both diagnostic and prescriptive insights. The study highlights the potential of a combined STS–STPA framework as a transferable analytical tool for understanding and redesigning complex transport systems. Full article

Background: AMR causes a large global health burden, with approximately 4.95 million deaths linked to bacterial AMR in 2019, 1.27 million due to AMR directly. Although Brazil mandated prescriptions for systemic antibiotics in 2010/2011, self-medication and access without prescriptions continue, with community pharmacists playing a vital part in antimicrobial stewardship (AMS). This study examined antibiotic misuse and AMR in Brazil through community pharmacists’ perspectives, emphasising their dual role as professional actors and frontline observers of public behaviour. Methods: We conducted 20 semi-structured interviews with community pharmacists and performed reflexive thematic analysis of their accounts, repeating five independent analytic cycles to confirm thematic robustness. Results: Six themes were consistently identified as recounted by pharmacists in their practice contexts: Access and Self-Medication; Relationships with Healthcare Professionals; Knowledge and Beliefs about Antibiotics; Use and Adherence; Healthcare System Barriers; and Regulation and Enforcement. Pharmacists mentioned regular requests for antibiotics without prescriptions, drug reuse, and significant impact from community, i.e., from relatives, and peers. The common misunderstanding was that antibiotics treat viral illnesses. Structural issues, for instance GP appointment costs and long waits, made patients seek help from pharmacies. Due to regulation being applied inconsistently, pharmacies struggled to refuse unsuitable requests. Conclusions: Framed through pharmacists’ dual vantage as professionals and frontline observers, the findings highlight intertwined factors underpinning inappropriate antibiotic use in Brazil and support a multi-pronged intervention spanning health system strengthening, professional education, economic considerations, and community engagement. Full article

Currently, corporate social responsibility and environmental/social/governance topics are gaining more relevance in business and finance. Attention to corporate diversity in boards and the workforce is included in this trend. Although most studies focus on executive boards and objective scores, the perception of diversity by employees and its rankability are not fully understood or researched. In this paper, we analyze corporate diversity rankings from the perspective of predictive and prescriptive analytics. Inside predictive analytics, the perceived diversity of a sample of 350 European diversity leader companies is predicted by using three different feature sets (raw financial data, ratios and objective diversity variables) and three machine learning algorithms (K Nearest Neighbors, Logistic Regression, Decision Tree). The best performing algorithm is the Decision Tree, and all three feature sets outperform one random dummy algorithm; the best performing set is the financial ratios set. Inside prescriptive analytics, several rankings involving American companies are intersected and compared in three exercises (studying diversity categorization, ethnic origin and comparing diversity with other unrelated metrics). From these, global rankings were built to search for the best possible agreement among the rankings. These results with both predictive and prescriptive analytics encourage managers to strategize and include diversity in management, as well as employ new technologies in their decision-making processes. Full article

Background: Pharmacists’ perceptions and practices shape the real-world uptake of generic medicines. From a health-economics perspective, wider generic substitution reduces patient out-of-pocket spending and creates headroom in payer budgets for high-value interventions. We assessed attitudes toward the efficacy, safety, and use of generics and examined sociodemographic correlates among Polish pharmacists. Methods: Analytical cross-sectional survey of licensed pharmacists in Poland was used (June–August 2025). The questionnaire covered reasons for recommending generics in long-term and single-use therapy; doubts about efficacy; views on bioequivalence testing; patient-reported experiences; and Likert-scale opinions on innovation, safety, efficacy, access, and payer savings. Associations were tested with χ² and Mann–Whitney U (α = 0.05). Results: Of 342 respondents (67.5% women; 74.9% community pharmacists), cost was the leading reason to recommend generics in long-term therapy (91.0%), followed by efficacy (53.0%) and safety (51.5%); for single-use prescriptions, cost remained central (76.2%), with lower emphasis on efficacy (47.5%) and safety (45.0%). Pharmacists who never recommend generics were older and more experienced (p = 0.006; p = 0.012). Doubts about generic efficacy were reported by 36.2% overall and more often among women, hospital pharmacists, and those with a specialization; 53.5% of those with doubts would advise switching even to a costlier option. Nearly half supported conducting bioequivalence studies between generics (49.6%). Positive perceptions predominated: 82.9% agreed generics are as effective and 84.6% as safe as originators. Most endorsed system benefits, including payer savings enabling list expansion (73.6%) and improved patient access (92.5%); agreement on access was higher among community pharmacists (p = 0.004). Conclusions: Polish pharmacists largely view generics as clinically equivalent and system-enhancing, with cost the dominant driver of recommendation. Targeted education—especially for hospital settings and specialized pharmacists—and attention to patient-reported experiences may further strengthen confidence and appropriate use of generics. Full article

We developed a self-consistent double-null description of an evaporating white-hole spacetime by embedding the outgoing Vaidya solution in a coordinate system that remains regular across the future horizon. Starting from the radiation-coordinate form, we specialize in retarded time so that a monotonically decreasing mass function $M\left(u\right)$ encodes outgoing positive-energy flux. Expressing the metric in null coordinates $(u,v)$, Einstein’s equations for a single-directional null-dust stress–energy tensor, $T_{uu}=\rho \left(u\right)$, then reduce to one first-order PDE for the areal radius: ${\mathsf{\partial }}_{v}r=B\left(u\right)\left(1-2M\left(u\right)/r\right)$. Its integral, $r+2M\left(u\right)ln|r-2M(u\left)\right|=v-C\left(u\right)$, defines an implicit foliation of outgoing null cones. The metric coefficient follows algebraically as $f(u,v)=1-2M\left(u\right)/r$. Residual gauge freedom in $B\left(u\right)$ and $C\left(u\right)$ is fixed so that u matches the Bondi retarded time at null infinity, while v remains analytic at the apparent horizon, generalizing the Kruskal prescription to dynamical mass loss. In the limit $M\left(u\right)\to M$, the construction reduces to the familiar Eddington–Finkelstein and Kruskal forms. Our solution, therefore, provides a compact analytic framework for studying white-hole evaporation, Hawking-like energy fluxes, and back-reaction in spherically symmetric settings without encountering coordinate singularities. Full article

Making time-critical decisions with serious consequences is a daily aspect of work environments. To support the process of finding optimal actions, data-driven approaches are increasingly being used. The most advanced form of data-driven analytics is prescriptive analytics, which prescribes actionable recommendations for users. However, the produced recommendations rely on complex models and optimization techniques that are difficult to understand or justify to non-expert users. Currently, there is a lack of platforms that offer easy integration of domain-specific prescriptive analytics workflows into production environments. In particular, there is no centralized environment and standardized approach for implementing such prescriptive workflows. To address these challenges, large language models (LLMs) can be leveraged to improve interpretability by translating complex recommendations into clear, context-specific explanations, enabling non-experts to grasp the rationale behind the suggested actions. Nevertheless, we acknowledge the inherent black-box nature of LLMs, which may introduce limitations in transparency. To mitigate these limitations and to provide interpretable recommendations based on real user knowledge, a knowledge graph is integrated. In this paper, we present and validate a prescriptive analytics platform that integrates ontology-based graph retrieval-augmented generation (GraphRAG) to enhance decision making by delivering actionable and context-aware recommendations. For this purpose, a knowledge graph is created through a fully automated workflow based on an ontology, which serves as the backbone of the prescriptive platform. Data sources for the knowledge graph are standardized and classified according to the ontology by employing a zero-shot classifier. For user-friendly presentation, we critically examine the usability of GraphRAG in prescriptive analytics platforms. We validate our prescriptive platform in a customer clinic with industry experts in our IoT-Factory, a dedicated research environment. Full article

Background/Objectives: Understanding prescribing patterns for type 2 diabetes mellitus, a complex condition affecting over 10% of the global adult population, can optimise prescribing practices, guide policymakers in promoting evidence-based medicine, and help tailor first-line treatments to individual characteristics or specific subgroups, improving patient outcomes. This study aimed to identify factors influencing the prescription and non-prescription of metformin, the recommended first-line therapy in Western guidelines, and to evaluate whether these prescribing patterns align with evidence-based recommendations. It also explores factors associated with initial combination therapy, a more recent and controversial approach compared to stepwise therapy. Methods: We conducted a systematic search in PubMed, Scopus, and Web of Science on 25 August 2023, without language or time restrictions, to identify observational analytical studies assessing factors associated with the initiation of metformin or combination therapy in adults with type 2 diabetes mellitus who were naïve to antidiabetic medications. Studies involving pregnant or breastfeeding women were excluded. A narrative synthesis was conducted. Study quality was assessed using the Joanna Briggs Institute critical appraisal checklists (PROSPERO registration number CRD42023438313). Results: Thirty studies were included, evaluating 105 variables, most of which (62%) were assessed in one study. The 25 variables using combination therapy as the outcome were mostly (72%) evaluated also in one study. Initial metformin prescription was strongly and positively associated with younger age, lower glycated haemoglobin levels, higher body mass index, and absence of renal impairment. Initial combination therapy was associated with higher HbA1c levels and a lower burden of comorbidities. Findings also highlighted a discrepancy between clinical practice and evidence-based recommendations. However, concerns were raised regarding both the internal and external validity of the included studies. Conclusions: Our systematic review, which offers insights into real-world clinical practices, indicated that there is a misalignment between clinical practices and evidence-based recommendations, supporting the need for interventions in this field. Full article

Decision Support Systems (DSSs) are increasingly shaping high-performance sport by translating complex time series data into actionable insights for coaches and practitioners. This paper outlines a structured, five-stage DSS development pipeline, grounded in the Schelling and Robertson framework, and demonstrates its application in professional basketball. Using changepoint analysis, we present a novel approach to dynamically quantify Most Demanding Scenarios (MDSs) using high-resolution optical tracking data in this context. Unlike fixed-window methods, this approach adapts scenario duration to real performance, improving the ecological validity and practical interpretation of MDS metrics for athlete profiling, benchmarking, and training prescription. The system is realized as an interactive web dashboard, providing intuitive visualizations and individualized feedback by integrating validated workload metrics with contextual game information. Practitioners can rapidly distinguish normative from outlier performance periods, guiding recovery and conditioning strategies, and more accurately replicating game demands in training. While illustrated in basketball, the pipeline and principles are broadly transferable, offering a replicable blueprint for integrating context-aware analytics and enhancing data-driven decision-making in elite sport. Full article

Recent advances in the integration of artificial intelligence (AI) and microbiome analysis have expanded our understanding of gastrointestinal diseases, particularly in inflammatory bowel disease (IBD), colitis-associated colorectal cancer (CAC), and sporadic colorectal cancer (CRC). While IBD and CAC are mechanistically linked, recent evidence also implicates dysbiosis in sporadic CRC. The progression from IBD to CAC is mechanistically linked through chronic inflammation and microbial dysbiosis, whereas distinct dysbiotic patterns are also observed in sporadic CRC. In this review, we examined how machine learning (ML) and AI were applied to the microbiome and multi-omics data, which enabled the discovery of non-invasive microbial biomarkers, refined risk stratification, and prediction of treatment response. We highlighted how emerging computational frameworks, including explainable AI (xAI), graph-based models, and integrative multi-omics, were advancing the field from descriptive profiling toward predictive and prescriptive analytics. While emphasizing these innovations, we also critically assessed current limitations, including data variability, the lack of methodological standardization, and challenges in clinical translation. Collectively, these developments enabled AI-powered microbiome research as a driving force for precision medicine in IBD, CAC, and sporadic CRC. Full article

Background: Urban loneliness is rising worldwide and is a recognised public-health threat. Nature-Based Social Prescriptions (NBSPs), guided group activities in natural settings, are being piloted in six cities through the EU project RECETAS. However, in new contexts such as Marseille, its implementation is constrained by professionals’ limited knowledge of the concept. Objectives: (i) Exploring how professionals in Marseille (France) conceptualise NBSPs; (ii) Identifying perceived facilitators and barriers to implementing NBSPs among residents facing social isolation and loneliness. Methods: Twelve semi-structured interviews were conducted with health, social-care, and urban–environment professionals selected via network mapping and snowball sampling. Verbatim transcripts underwent inductive thematic analysis informed by Social Representation Theory, with double coding to enhance reliability. Results: Five analytic themes emerged: (1) a holistic health paradigm linking nature, community, and well-being; (2) stark ecological inequities with limited green-space access in deprived districts; (3) work challenges due to the urgent needs of individuals facing significant socio-economic challenges in demanding contexts; (4) a key tension between a perceived top-down process and a preference for participatory approaches; (5) drivers and obstacles: strong professional endorsement of NBSPs meets significant systemic and institutional constraints. Conclusions: Professionals endorse NBSPs as a promising approach against loneliness, provided programmes tackle structural inequities and adopt participatory governance. Results inform the Marseille RECETAS pilot and contribute to global discussions on environmentally anchored health promotion. Full article

This study addresses the shortcomings of traditional exercise volume assessment methods in dynamic modeling and individual adaptation by proposing a multi-modal data fusion framework based on a spatio-temporal attention-enhanced LSTM neural network for personalized exercise volume optimization in college basketball courses. By integrating physiological signals (heart rate), kinematic parameters (triaxial acceleration, step count), and environmental data collected from smart wearable devices, we constructed a dynamic weighted fusion mechanism and a personalized correction engine, establishing an evaluation model incorporating BMI correction factors and fitness-level compensation. Experimental data from 100 collegiate basketball trainees (60 males, 40 females; BMI 17.5–28.7) wearing Polar H10 and Xsens MVN devices were analyzed through an 8-week longitudinal study design. The framework integrates physiological monitoring (HR, HRV), kinematic analysis (3D acceleration at 100 Hz), and environmental sensing (SHT35 sensor). Experimental results demonstrate the following: (1) the LSTM-attention model achieves 85.3% accuracy in exercise intensity classification, outperforming traditional methods by 13.2%, with its spatio-temporal attention mechanism effectively capturing high-dynamic movement features such as basketball sudden stops and directional changes; (2) multi-modal data fusion reduces assessment errors by 15.2%, confirming the complementary value of heart rate and acceleration data; (3) the personalized correction mechanism significantly improves evaluation precision for overweight students (error reduction of 13.6%) and beginners (recognition rate increase of 18.5%). System implementation enhances exercise goal completion rates by 10.3% and increases moderate-to-vigorous training duration by 14.7%, providing a closed-loop “assessment-correction-feedback” solution for intelligent sports education. The research contributes methodological innovations in personalized modeling for exercise science and multi-modal time-series data processing. Full article

In today’s dynamic and highly uncertain environment, organizations, particularly in the aviation sector, face increasing challenges that demand resilient, flexible, and data-driven change management decisions. Responding to the growing need for structured approaches to managing complex uncertainties—geopolitical tensions, economic volatility, social shifts, rapid technological advancements, environmental pressures and regulatory changes—this research proposes a theoretical change management model for aviation service providers, such as airports. Integrating three analytical approaches, the model offers a robust, multi-method approach for supporting sustainable transformation under uncertainty. Normative analysis using Bayesian decision theory identifies influential external environmental factors, capturing probabilistic relationships, and revealing causal links under uncertainty. Prescriptive planning through scenario theory explores alternative future pathways and helps to identify possible predictions, offer descriptive evaluation employing fuzzy comprehensive evaluation, and assess decision quality under vagueness and complexity. The proposed four-stage model—observation, analysis, evaluation, and response—offers a methodology for continuous external environment monitoring, scenario development, and data-driven, proactive change management decision-making, including the impact assessment of change and development. The proposed model contributes to the theoretical advancement of the change management research area under uncertainty and offers practical guidance for aviation organizations (airports) facing a volatile external environment. This framework strengthens aviation organizations’ ability to anticipate, evaluate, and adapt to multifaceted external changes, supporting operational flexibility and adaptability and contributing to the sustainable development of aviation services. Supporting aviation organizations with tools to proactively manage systemic uncertainty, this research directly supports the integration of sustainability principles, such as resilience and adaptability, for long-term value creation through change management decision-making. Full article

Digital Twins (DTs) are transforming manufacturing by bridging the physical and digital worlds, enabling real-time insights, predictive analytics, and enhanced decision making. In Industry 4.0, DTs facilitate automation and data integration, while Industry 5.0 emphasizes human-centric, resilient, and sustainable production. However, implementing DTs in robotic metal additive manufacturing (AM) remains challenging because of the complexity of the wire-based laser metal deposition (LMD) process, the need for real-time monitoring, and the demand for advanced defect detection to ensure high-quality prints. This work proposes a structured DT architecture for a robotic wire-based LMD cell, following a standard framework. Three DT implementations were developed. First, a real-time 3D simulation in RoboDK, integrated with a 2D Node-RED dashboard, enabled motion validation and live process monitoring via MQTT (message queuing telemetry transport) telemetry, minimizing toolpath errors and collisions. Second, an Industrial IoT-based system using KUKA iiQoT (Industrial Internet of Things Quality of Things) facilitated predictive maintenance by analyzing motor loads, joint temperatures, and energy consumption, allowing early anomaly detection and reducing unplanned downtime. Third, the Meltio dashboard provided real-time insights into the laser temperature, wire tension, and deposition accuracy, ensuring adaptive control based on live telemetry. Additionally, a prescriptive analytics layer leveraging historical data in FireStore was integrated to optimize the process performance, enabling data-driven decision making. Full article

Antimicrobial resistance (AMR) is considered a global healthcare emergency in the 21st century. Although the evolution of microorganisms through Darwinian mechanisms and antibiotic misuse are established drivers, the structural socioeconomic factors of AMR remain insufficiently explored. This review takes on an analytical perspective, drawing upon a wide spectrum of evidence to examine the extent to which socioeconomic factors contribute to the global proliferation of AMR, with an emphasis on low- and middle-income countries (LMICs). The analytical review at hand was carried out through a search for relevant articles and reviews on PubMed, Google Scholar, the Centers for Disease Control and Prevention, and the World Health Organization database using combinations of the keywords “antimicrobial resistance,” “socioeconomic factors,” “low- and middle-income countries,” “surveillance,” “healthcare access,” and “agriculture.” Preference was given to systematic reviews, high-impact primary studies, and policy documents published in peer-reviewed journals or by reputable global health organizations. Our analysis identifies a complex interplay of systemic vulnerabilities that accelerate AMR in resource-limited settings. A lack of regulatory frameworks regarding non-prescription antibiotic use enables the proliferation of multi-drug-resistant microorganisms. Low sewer connectivity facilitates the environmental dissemination of resistance genes. Proper antibiotic selection is hindered by subpar healthcare systems and limited diagnostic capabilities to deliver appropriate treatment. Additionally, gender disparities, forced migration, and climate-driven zoonotic transmission compound the burden. During the COVID-19 pandemic, antimicrobial misuse surged, further amplifying resistance trends. AMR is not solely a biological phenomenon, but a manifestation of global inequity. Mitigation requires a transformation of policy directed toward a “One Health” strategy that incorporates socioeconomic, environmental, and health system reforms. Strengthening surveillance, investing in infrastructure, regulating pharmaceutical practices, and promoting health equity are essential to curb the rising tide of resistance. Full article

This article delves deeply into the moral rationalism advocated by R. M. Hare. Traditionally, the ultimate normativity of morality has been attributed to divine characteristics such as the abstract concepts of universality, transcendence, necessity, and ultimate authority, but Hare explicitly rejects any theological premises and seeks to base moral obligations on a pure structure of linguistic and rational consistency. However, this paper proposes that Hare’s secular rational system inevitably reproduces the functional structure of the divine moral order at its internal logical level. To demonstrate this, the key conceptual attributes involved in “divine normativity” are separated first, and an analytical framework is constructed. At the same time, this paper analyzes how the normative requirements, such as universality and prescriptiveness in the Hare system, relate to the attributes of divine norms. The results show that although Hare appears to maintain thorough secularism on the surface, the moral rationalism emphasis on consistency and universal applicability functionally reproduces a normative structure similar to divine commands. This finding reveals a profound philosophical paradox: even stripped of metaphysical assumptions, human attempts to pursue an objective moral order still tend to appeal to transcendent structures in an implicit way. This paper offers a critical examination of Hare’s theory, affirming both his ambition in the construction of secular moral thought and revealing the underlying tension within it that cannot completely break away from the framework of theological tradition. Full article