Search Results (2,469)

This paper investigates a multi-server retrial machine repair system that incorporates orbital search and a synchronous vacation mechanism. The system features standby units and examines two potential vacation scenarios for servers, reflecting real-world situations, such as technical staff in teaching hospitals taking periodic administrative or training vacations. We formulate a mathematical model using birth-and-death processes to establish the governing equation and propose a recursive matrix method to systematically derive the steady-state probabilities. Performance measures, including system availability, the expected number of failed units in the orbit, and expected waiting times, are derived. To address the conflicting objectives of minimizing total operating costs, minimizing expected waiting times, and maximizing system availability, we construct a tri-objective optimization problem. By implementing a multi-objective genetic algorithm, we identify a set of Pareto-optimal frontiers and reveal the explicit financial and operational trade-offs among these competing criteria. Numerical experiments and sensitivity analyses demonstrate that enhancing the automated retrial rate and managing the emergency repair rate are most critical to minimizing system downtime. Furthermore, the joint optimization of server capacities and vacation schedules effectively eliminates operational redundancy, showing that near-perfect equipment availability (up to 0.999) can be achieved with only marginal increases in cost. This research provides administrators with a robust decision-making framework to optimize technical resource management while ensuring near-perfect equipment availability in real-world environments. Full article

The intensive care unit (ICU) plays a crucial role in modern hospitals. ICU patients endure severe physical and mental conditions, making it essential to create a healing environment that reduces stress and promotes recovery. Among common environmental parameters, lighting conditions are particularly critical, as patients often face challenges with mobility and body positioning. Kinetic facades with adjustable external shading elements have gained attention for their ability to regulate sunlight effectively. However, their complexity poses challenges for design and implementation. This study proposes a reinforcement learning-based method, using Q-learning to handle discrete facade configurations and adaptive control under varying solar conditions for optimizing facade configurations in ICU rooms. The method aims to: (1) reduce direct sunlight glare and heat; and (2) maximize landscape views. A case study at Providence Alaska Medical Center demonstrates the method’s effectiveness, showing reduced glare and heat gain and improved landscape view availability through simulation. The results highlight the potential of reinforcement learning to address ICU-specific environmental challenges. Full article

Implantable medical device (IMD) and Internet of Medical Things (IoMT) environments need intrusion detection systems that learn across distributed hospitals without centralizing sensitive data, while controlling admission, protecting shared model artifacts, filtering unreliable contributors, and supporting post-run auditability. However, many secure federated learning designs treat identity, privacy, robustness, and evidence verification as separate layers, leaving a gap between privacy-preserving execution and public accountability. This paper presents an implemented zero-trust hierarchical federated learning-based intrusion detection system (FL-IDS) framework for IMD/IoMT security analytics. Hospital clients train eXtreme Gradient Boosting (XGBoost) detectors; self-sovereign identity gates participation; contribution-level differential privacy (DP) perturbs exported booster leaf weights; country aggregators apply adaptive Krum-inspired selection; and the global server performs trust-weighted prediction-level fusion. The evidence layer binds artifacts using Module-Lattice-Based Digital Signature Algorithm signatures, canonical hashes, Merkle roots, decentralized publication, Ethereum Sepolia anchoring, and standalone auditor verification. The framework is evaluated on WUSTL-EHMS-2020, ECU-IoHT, and CICIoMT2024 under paired DP-disabled and DP-enabled modes. Under DP-enabled execution, CICIoMT2024 achieved an F1-score of 0.998789 and area under the receiver operating characteristic curve (AUROC) of 0.999814, ECU-IoHT achieved an AUROC of 0.999337, and WUSTL-EHMS-2020 remained DP-sensitive with an F1-score of 0.422880 and AUROC of 0.776685. All paired evidence runs passed standalone auditor verification, demonstrating that privacy-preserving learning and public accountability can be integrated within a single experimental FL-IDS pipeline. Full article

Background: Healthcare institutions increasingly face sustainability challenges associated with environmental governance, operational efficiency, digital transformation, and social responsibility in the post-pandemic era. However, limited studies have comprehensively examined the organizational factors influencing ESG implementation and healthcare social sustainability practices among hospitals. Objective: This study aimed to investigate the organizational determinants of ESG implementation and healthcare social sustainability practices among Taiwanese hospitals and to explore how healthcare professionals and hospital administrators perceive sustainability implementation within post-pandemic healthcare environments. Methods: A convergent mixed methods design integrating quantitative surveys and qualitative interviews was employed. Exploratory factor analysis (EFA), correlation analysis, and multiple regression analysis were performed, and qualitative data were analyzed using thematic analysis. Results: A total of 135 valid questionnaires were analyzed, and qualitative data were obtained from three semi-structured interviews. ESG sustainability support demonstrated the strongest positive influence on healthcare social sustainability practices (β = 0.481, p < 0.001), followed by operational sustainability (β = 0.276, p < 0.01) and sustainability management capability (β = 0.214, p < 0.05). Organizational resource pressure did not significantly influence healthcare social sustainability practices. The qualitative findings converged with and expanded upon the quantitative results by highlighting the importance of leadership support, digital healthcare transformation, operational coordination, and community health promotion in facilitating ESG implementation and long-term healthcare sustainability. Conclusions: The integrated findings suggest that healthcare ESG implementation increasingly functions as a comprehensive sustainability governance strategy involving operational sustainability, digital healthcare transformation, healthcare accessibility, and social responsibility practices within post-pandemic healthcare environments. Strengthening sustainability support systems, governance capability, and operational resilience may facilitate long-term healthcare social sustainability implementation. Full article

Tourism destinations operate as multi-actor governance environments in which stakeholders interpret sustainability initiatives differently, reflecting their distinct institutional roles. This study applies a diagnostic system dynamics perspective to examine perception asymmetries among governance actors, tourism and hospitality professionals, and local community members across Greek tourism destinations. Drawing on survey data from 466 respondents, one-way Analysis of Variance (ANOVA) comparisons across four perception domains reveal a consistent pattern: stakeholder evaluations differ significantly for HR sustainability practices (F = 114.60, p < 0.001) and organisational support conditions (F = 21.29, p < 0.001), while remaining broadly aligned in assessments of overall sustainability outcomes (F = 0.15, p = 0.861). Interpreted through causal loop reasoning, this is consistent with divergence at the implementation level alongside shared strategic orientations. This combination may be interpreted as indicative of feedback asymmetry together with alignment in outcomes, and carries implications for coordination and institutional trust. The study positions stakeholder perception analysis within the problem-structuring stage of the system dynamics modelling cycle, showing how observed perception patterns may be used to identify areas warranting subsequent system dynamics modelling. In this way, it advances a diagnostic framework applicable to multi-actor governance contexts beyond tourism. Full article

The complex nature of the healthcare work environment, resource strains, and cultural diversity threaten human sustainability and exacerbate healthcare workers’ psychological disconnection from work. From the perspective of sustainable HRM and organizational sustainability, and in response to calls raised by the United Nations 2030 Agenda, our study examines how institutions can support human sustainability and healthy performance through sustainable HRM practices. Therefore, building upon the conservation of resources theory and person–environment fit theory, our study investigates the impact of grit and person–environment fit on work alienation, examining the mediating role of thriving at work, postulating that cultural intelligence moderates the effect of grit and person–environment fit on thriving at work. Data were collected from 335 healthcare workers working in private hospitals in Lebanon and analyzed via structural equation modeling with bootstrapping. The results indicated that grit, person–environment fit, and thriving at work have a significant negative effect on work alienation; meanwhile, grit and person–environment fit have a significant positive effect on thriving at work. Moreover, the results highlighted the mediating role of thriving at work between grit, person–environment fit, and work alienation. Additionally, the findings revealed that cultural intelligence moderates the effect of grit on thriving. However, the interaction between person–environment fit and cultural intelligence was significant in the opposite direction to the hypothesized effect. Theoretically, our research is one of the pioneering studies showing that thriving at work functions as a resource-gain mechanism linking personal resources, such as grit, and contextual resources, such as person–environment fit, to lower alienation. Practically, the findings suggest that management should strengthen person–environment fit, promote thriving, and develop cultural intelligence to enhance healthy performance and human-centered organizational sustainability. Full article

The co-circulation of respiratory viruses, including SARS-CoV-2, influenza A/B, and respiratory syncytial virus (RSV), represents a significant global health challenge that requires rapid, accurate, and differential diagnosis to support infection control and appropriate clinical decision-making. This narrative review summarizes emerging multimodal point-of-care testing (POCT) strategies for the detection and management of these respiratory viruses. Relevant studies were identified through literature searches of major scientific databases, including PubMed, Scopus, and Web of Science, focusing on recent advances in molecular diagnostics, biosensors, microfluidics, and digital health technologies. To improve clinical interpretation and comparative assessment, current POCT platforms were organized into four operational tiers based on infrastructure dependence, degree of portability, and level of decentralization of testing. Tier 1 (Professional Clinical Systems) includes fully integrated automated molecular diagnostic platforms designed for use in hospital and emergency care settings. Tier 2 (Field-Deployable Systems) comprises portable molecular and isothermal amplification technologies designed for use in decentralized or resource-limited environments. Tier 3 (Hardware-Lite Assays) includes simplified diagnostic approaches that minimize instrument requirements and are suitable for near-patient or low-infrastructure settings. Tier 4 (Consumer-Digital Diagnostics) encompasses emerging smartphone- and IoT-integrated diagnostic platforms that support user-driven testing and digital health connectivity. This tier-based framework reflects a proposed stratification of POCT technologies along a decentralization continuum and aims to facilitate comparison and selection of diagnostic strategies across diverse healthcare settings. Full article

By linking the technology–organization–environment (TOE) framework with dynamic capabilities, this research investigates the various organizational enablers for Industry 4.0 technology (I4T) adoption in the hotel industry, and the relationship between I4T and overall hotel performance during the supply chain disturbances. The proposed model was tested using data collected from hotel executives in Hong Kong and Shenzhen, China. Results revealed that perceived benefits, technology readiness, and supply chain partner pressure were significant predictors for I4T adoption by hotels. The direct positive relationship between I4T and overall hotel performance during disruptions was not found. This relationship was mediated by the hotel’s supply chain resilience (SCR) capability, highlighting the importance of I4T and SCR for improving performance during disruptions. This study provides both theoretical insights and practical implications regarding the adoption of I4T in the hospitality sector. Full article

Crisis management has become a critical strategic concern in hospitality, particularly following the COVID-19 pandemic, which exposed limitations in conventional approaches to preparedness, response, and recovery. Although existing hospitality crisis management research has generated valuable insights into individual crisis phases, a limited understanding exists regarding how preparedness, crisis response, and organizational learning interact as an integrated strategic process across the crisis lifecycle. Addressing this gap, this study examines strategic crisis management within Jakarta’s five-star hotel sector and develops an integrated understanding of crisis management across pre-crisis, during-crisis, and post-crisis phases. A constructivist grounded theory approach was employed based on in-depth semi-structured interviews with 25 senior hospitality leaders, including hotel executives, owners, and industry association representatives. The findings suggest that crisis management is best understood as an integrated strategic capability rather than a series of discrete emergency responses. Three interrelated theoretical dimensions emerged: Continuous Strategic Preparedness, characterized by adaptive readiness, environmental sensemaking, decision readiness, and financial preparedness; Coordinated Strategic Crisis Response, encompassing liquidity discipline, operational adaptation, workforce adaptability, leadership coordination, and cross-functional alignment; and Strategic Renewal Through Organizational Learning, involving the retention of effective crisis practices, capability strengthening, and strategic reorientation toward ongoing uncertainty. The study proposes an Integrated Strategic Crisis Management Framework for Hospitality Organizations that explains the mechanisms linking preparedness, response, and organizational learning as a cyclical capability-building process. By reconceptualizing crisis management as an integrated strategic capability rather than a phase-based operational response, the study contributes to hospitality crisis management scholarship and provides practical insights for hospitality organizations operating in increasingly uncertain environments. Full article

Background: Adverse drug reactions (ADRs) represent a major and often underestimated source of morbidity, hospitalization, and functional decline in older adults. The convergence of age-related pharmacokinetic and pharmacodynamic changes, multimorbidity, polypharmacy, and frailty creates a clinical environment in which ADR risk is not static but evolves along progressive trajectories—from mild, early manifestations toward severe, potentially irreversible outcomes. Understanding these trajectories is essential for rational geriatric prescribing. Methods: This narrative review synthesizes evidence from epidemiological studies, systematic reviews, Cochrane analyses, and clinical trials published between 2000 and 2025, focusing on adults aged 65 years and older with two or more chronic conditions. Sources were identified through a structured, non-systematic literature search of PubMed, EMBASE, Cochrane Library, Web of Science, and Scopus using the terms ‘adverse drug reactions’, ‘polypharmacy’, ‘multimorbidity’, ‘frailty’, ‘deprescribing’, and ‘pharmacokinetics’ in older adults, alone and in combination. Evidence quality was assessed narratively, distinguishing trial evidence from observational and expert consensus data. Results: ADRs in older adults are best classified using complementary frameworks—the augmented Type A to withdrawal Type E and failure-of-therapy Type F taxonomy (Types A–F), the Dose-Time-Susceptibility (DoTS) classification, and the EIDOS mechanistic scheme—which together capture the heterogeneity of drug-related harm in this population. Age-related pharmacokinetic changes (altered absorption, increased volume of distribution of lipophilic drugs, reduced hepatic and renal clearance) and pharmacodynamic shifts (heightened receptor sensitivity, baroreflex impairment, increased blood–brain barrier permeability) interact with polypharmacy and frailty to amplify ADR trajectories from mild to severe. Anticholinergic burden, prescribing cascades, and inappropriate polypharmacy function as structural accelerators of these trajectories. Medication review and deprescribing improve prescribing quality but evidence for hard outcome benefits remains of low to very low certainty. Emerging AI-enabled digital tools show promising accuracy for identifying frailty and pharmacological vulnerability, but this performance relates to frailty classification and has not yet been shown to prevent ADR trajectories; they require validation for routine clinical use. Conclusions: Recognizing ADRs in older adults as dynamic trajectories rather than isolated events repositions prescribing review and deprescribing from optional to essential clinical acts. An integrated approach combining pharmacological vigilance, comprehensive geriatric assessment, structured deprescribing, and emerging digital decision-support tools offers the most realistic pathway to reduce the trajectory-related burden of drug-related harm in complex older patients. Full article

Traditional dynamic community detection methods often struggle to simultaneously preserve local structural consistency, capture global topological relationships, and efficiently adapt to continuous graph updates in large-scale environments. To solve these limitations, this paper proposes a novel dynamic community detection framework called Embedded Clustering Boundary Refinement (ECBR). The proposed method integrates unsupervised GraphSAGE and Node2Vec embeddings to jointly capture local neighborhood aggregation patterns and global structural equivalence among nodes. The generated embeddings are fused through feature concatenation and z-score normalization to construct a unified latent representation space. Subsequently, Mini-Batch KMeans clustering is employed to efficiently generate the initial community structure while maintaining scalability for large-scale graphs. To further improve partition quality, ECBR introduces a boundary-aware refinement mechanism that identifies structurally ambiguous nodes using neighborhood consistency analysis and reassigns them according to embedding-space similarity. In addition, the framework incorporates an adaptive dynamic update strategy capable of distinguishing between major topological shifts and localized structural changes. Significant graph perturbations trigger complete model retraining, whereas minor modifications are handled through computationally efficient incremental updates on local subgraphs. Experimental evaluations were conducted on synthetic LFR benchmark networks and several real-world dynamic interaction datasets, including high school, workplace, and hospital contact networks. The results demonstrate that ECBR consistently outperforms several state-of-the-art methods, including QCA, DyPerm, DCDID, IncNSA, and DCDBFE, achieving better NMI and ARI scores across diverse network conditions. The experimental findings confirm that ECBR provides a scalable, robust, and highly effective solution for dynamic community detection in evolving large-scale social networks. Full article

The emergence and global dissemination of pandrug-resistant (PDR) Acinetobacter baumannii represents a critical public health crisis. This One Health study provides comprehensive surveillance and molecular characterization of carbapenem-resistant, extensively drug-resistant (XDR), and PDR A. baumannii isolates isolated from hospitalized patients and diseased chickens/environment in Egypt. We investigated cross-species clinical and pathological impacts, characterized resistance genes, and analyzed potential transmission links. Of 145 samples, 48 A. baumannii isolates were identified. Resistance profiling revealed an alarming prevalence, with PDR (56.3%) being the dominant phenotype, followed by XDR (43.7%), all exhibiting high multiple antibiotic resistance (MAR) indices (≥0.67). Chickens and humans infected with PDR A. baumannii suffered from increased neutrophilia, anemia, elevated inflammatory markers (CRP and procalcitonin), renal and liver impairment, and upregulation of MMP-9 and IL-8 response genes. Molecular analysis showed that all PDR isolates co-harbored multiple carbapenemase genes, including Class D beta-lactamases (bla_OXA-23 (most prevalent), bla_OXA-48, bla_OXA-58, bla_OXA-24) and Class B metallo-beta lactamase (bla_VIM, bla_IMP, bla_NDM). A substantial proportion also carried bla_KPC (44.4%) and the carO gene (81.48%). Genotyping using ERIC PCR and Multilocus Sequence Typing (MLST) identified a high diversity (23 ERIC types, DI = 0.986). Significantly, two ERIC types (ET19 and ET20) contained isolates from both human and chicken sources. MLST confirmed this interspecies correlation, with isolates from both hosts clustering into Sequence Types (STs) ST1410 and ST1828. These findings confirm the rapid and alarming spread of highly virulent, multi-carbapenemase-producing PDR A. baumannii strains across the human–animal interface in Egypt. The detection of shared STs between clinical and poultry isolates underscores a potential zoonotic or environmental transmission route, necessitating integrated One Health surveillance and urgent infection control interventions. Full article

This study claims that the Nigerian tourism and hospitality industry must shift from traditional business practices to innovative, knowledge-based approaches to remain competitive and achieve long-term wealth creation. Facing volatility, evolving consumer demands, technological change, and sustainability pressures, the sector cannot rely on outdated models. Through a quantitative survey of 391 staff and top-level managers from the Nigerian Tourism Development Corporation and tourism organizations in Southwest and North-Central Nigeria, this research tests how creativity, knowledge management, and innovation operations, defined as value creation, environment, and capability, drive wealth creation. Findings through the Partial Least Squares Structural Equation Modeling show that innovation and knowledge management both directly and, through the mediating role of innovation operations, significantly enhance wealth creation. By highlighting how these factors interact, the study advances understanding of wealth creation dynamics between emerging and developed economies and delivers actionable recommendations for managers and policymakers to institutionalize innovation for sustainable industry growth. Full article

Background/Objectives: Retention of healthcare professionals in the workforce, their employment, and the improvement of working conditions largely depend on identifying the factors that influence their departure and their health. The study was conducted during the period from January to June 2021. This study aimed to examine the association between specific work-related stressors and work ability. The initial hypothesis was that general and specific occupational stressors negatively associate with work ability among healthcare professionals in emergency medical intervention teams. Methods: The study was designed as a cross-sectional comparative study. It was conducted among nurses and physicians in pre-hospital emergency medical services, employed full-time in intervention teams, while the control group consisted of employees from dispatch and call-receiving units. The study was conducted on the 840 participants, representing 43.3% of all healthcare professionals employed in pre-hospital emergency medical services in the Republic of Croatia. In addition to questions on participants’ personal characteristics, the following instruments were used: 1. a validated Questionnaire on Workplace Stressors among hospital healthcare professionals; and 2. the international standardized Work Ability Index (WAI) questionnaire for assessing work ability. Participants completed the questionnaires in paper form. Results: On average, the participants demonstrated lower levels of stress compared to reference values, both for overall stress and for individual stress factors, while their work ability, assessed using the Work Ability Index (WAI), ranged from very good to excellent. The control group showed higher levels of stress across all factors and lower work ability. However, the control group was older on average, generally had lower levels of education, and consisted more often of women—personal characteristics that may influence the examined variables. Lower stress levels and better work ability were associated with job satisfaction, ambition, and the fact that participants were working in their desired profession. Frequent sick leave (absenteeism) was highly correlating with both higher stress levels and poorer work ability. Conclusions: Greater job satisfaction and higher motivation have a positive impact on stress levels and employees’ work ability. The study results can serve as a starting point for institutional management in designing feasible decisions aimed at improving satisfaction, health, the work environment, and the work ability of emergency medical service personnel, as well as making these institutions more attractive for recruitment and retention of employees both in their positions and within the profession. Full article

As global populations grow and technology advances, daily life is increasingly shaped by digital systems such as computers and smart devices. However, prolonged device use has contributed to increasing physical and mental health concerns, particularly those associated with poor sitting posture. Posture-related strain is frequently overlooked and contributes to musculoskeletal discomfort, including back, neck, shoulder, and wrist pain, and may also be associated with sleep disturbances and elevated stress levels. To the best of our knowledge and based on the existing literature, this is the first study to introduce a machine learning-based framework for advanced muscle strain severity classification using Internet of Things (IoT) devices that integrates posture monitoring and muscle strain detection into a unified low-cost framework ($23 hardware cost). The primary objective of this work is accurate classification of muscle strain severity, while real-time alerts serve as a secondary ergonomic feedback mechanism. Specifically, this study makes four major contributions. First, we created a novel dataset through real-time acquisition of electromyography (EMG) and posture signals from participants in hospital and industrial environments, capturing diverse muscle strain patterns validated against clinical assessment procedures. Second, we designed a two-part hardware architecture consisting of posture detection (PD) and strain detection (SD) modules using a NodeMCU ESP8266, HC-SR04 ultrasonic sensor, EMG sensor, and buzzer for real-time physiological monitoring, incorporating EMG-specific preprocessing including band-pass filtering, rectification, and RMS smoothing. Third, we proposed and evaluated a hybrid machine learning framework integrating Vision Transformer (ViT) and XGBoost to classify strain severity into three study-specific categories: baseline (EMG RMS < 40 µV), compensatory strain (40–59 µV), and overload (≥60 µV). These categories were used as reproducible severity proxies for machine learning annotation and should not be interpreted as universal biomarkers of structural tissue damage. Finally, the proposed framework achieved a classification accuracy of 99.0% (95% CI: 98.5–99.5%) with an inference latency of 15.2 ms. Full article