Search Results (397)

This study investigates the impact of green leadership on financial performance in the hospitality sector, focusing on the mediating roles of resource management and market positioning. Drawing upon the Resource-Based View and Stakeholder Theory, data were collected from 390 employees working in five-star hotels in Egypt and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The findings reveal that green leadership positively influences financial performance both directly and indirectly. Specifically, green leadership enhances resource management practices and strengthens market positioning, which in turn contribute to improved financial outcomes. The mediation analysis confirms that both resource management and market positioning serve as significant mechanisms through which green leadership translates sustainability-oriented strategies into economic benefits. These findings extend current knowledge on sustainable leadership by identifying key organizational pathways linking environmental responsibility with financial success. This study also provides practical implications for hospitality managers seeking to integrate sustainability initiatives into strategic and operational decision-making to achieve long-term competitive advantage. 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

Systematic logistics plays a key role in fostering profitable development in supply chains. An intelligent logistics model can help create a more agile, sustainable, and resilient supply chain. In recent years, several brain-inspired deep learning architectures, such as long short-term memory networks, graph neural networks, and convolutional neural networks, have been introduced for intelligent decision-making tasks. From a biomimetic perspective, these models are inspired by biological information-processing mechanisms. Convolutional neural networks reflect hierarchical procedures similar to those in the visual cortex, graph neural networks mimic communication among biological neurons, and LSTM networks are motivated by short-term and long-term memory mechanisms in the brain. Inspired by these biomimetic computational principles, this study proposes a novel hybrid deep learning strategy composed of LSTM, convolutional layers and GraphSAGE geometric layers for smart supply chain logistics management. This strategy enables leveraging information pertaining to LSTM-based long-term dependencies, convolutional local patterns and graph-related hidden connections of the supply chain dataset for intelligent decision-making. The GraphSAGE framework helps with scalable graph learning, which enhances predictive accuracy in the case of unseen data. The optimizer in the proposed methodology performs sequential optimization using the biomimetic particle swarm optimizer and the Adam approach (PSO-Adam), considering the hybrid cost function. The prediction of logistics parameters is investigated using five datasets, including DataCo, Shipping, Smart Logistics, Hospital Supply Chain, and Pharmaceutical Supply Chain. The average accuracies of 97.8%, 100%, 96.6%, 98.7% and 99.4% are obtained for practical multi-category logistics parameter forecasts. The evaluation metrics for ten logistics predictions confirm the effectiveness of the proposed intelligent logistics model and highlight the potential of biomimetic geometric networks for complex supply chain decision-making. The model is a cost-efficient approach with consideration of the prediction capabilities, helping to reduce the occurrence of logistics risks, increase the productivity of the supply chain and affect the supply chain visibility, customer satisfaction, and industry reputation. Full article

Circular economy principles are increasingly influencing tourism development strategies, particularly in rural destinations characterized by strong linkages between agriculture, gastronomy, and local economic systems. This study develops and empirically examines a conceptual model investigating the relationship between circular economy practices, gastronomy integration, perceived authenticity, and destination competitiveness within rural tourism economies. The research focuses on the role of gastronomy as a circular tourism resource capable of connecting local sourcing, sustainability, and experiential value creation. Data were collected through a stakeholder-based survey targeting tourism enterprises, local producers, destination management organizations, and hospitality providers operating in rural tourism destinations. The proposed relationships were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) implemented in SmartPLS 4 and bootstrapped mediation analysis. The findings indicate that circular economy practices positively influence gastronomy integration, while gastronomy integration significantly enhances perceived authenticity. Furthermore, authenticity demonstrates a strong positive effect on destination competitiveness. The mediation analysis confirms that gastronomy integration and perceived authenticity function as intermediary mechanisms through which circular economy practices contribute to competitiveness outcomes. The study contributes to tourism economics and destination competitiveness literature by developing and empirically testing a mediation-based framework linking circular gastronomy, authenticity, and rural tourism competitiveness. The findings provide theoretical and practical implications for destination managers and policymakers seeking to strengthen sustainability, local value creation, and competitiveness through circular gastronomy strategies. Full article

Autonomous robotic systems are being deployed in commercial, healthcare, logistics, and mixed-use built environments at a rate that significantly outpaces the adaptive capacity of existing building design and management paradigms. Buildings have historically been conceived exclusively for human occupants, and the resulting absence of a structured, scalable framework for evaluating or designing robot-ready facilities constitutes a critical gap in both research and professional practice. This article introduces the Robot-Friendly Buildings Level of Service (RFB-LOS) framework: a five-tier hierarchical classification system that characterises the degree to which a built environment supports autonomous robotic operations across six evaluative dimensions—building intelligence, active infrastructure, architectural planning, accessibility, observability, and safety. The framework spans a continuum from Robot Excluded (RFB-LOS-1), in which a building has no awareness of its robotic occupants, to Physical AI Robot Optimised (RFB-LOS-5), in which a Physical AI middleware layer assumes the highest command authority within a coordinated human–robot–building ecosystem. Drawing structural inspiration from the SAE J3016 Levels of Driving Automation, the EU Smart Readiness Indicator, HIMSS EMRAM, and BREEAM/LEED sustainability certification, the RFB-LOS framework is positioned as a foundational standard for the built environment and systems engineering community. Five real-world case studies spanning retail, hospitality, healthcare, and corporate sectors across four countries validate the framework’s tier assignments against observed operational outcomes. Full article

Intelligent sensors and Internet of Medical Things (IoMT) platforms are rapidly changing smart healthcare by enabling continuous capture of physiological, behavioral, and clinical events outside conventional hospital settings. Yet the value of connected sensing depends on more than signal acquisition alone. A practical remote-monitoring ecosystem must also convert sensor alerts, clinician-facing summaries, and historical electronic clinical records (ECRs) into ranked evidence that supports care decisions. This study reframes a large-AI clinical retrieval model as the intelligence layer of an edge–cloud IoMT architecture. The proposed framework combines Transformer-Based Sequence (TBS) encoding, BioBERT-driven representation learning, explicit retrieval, and domain-guided re-ranking to connect sensor-originated narratives, patient records, and clinician queries. The empirical evaluation is conducted on Medical Information Mart for Intensive Care III (MIMIC-III) and i2b2, two de-identified clinical text benchmarks that approximate the documentation layer of real-world remote patient monitoring. Compared with strong baselines, including DeepBio, UniT2T, Web4IR, A2A-API, CoLTiD, VLRG, ColBERT, DeepSDH, BiRex, and DL4BTM, the proposed model achieves the best overall performance, reaching F1/Pre/NDCG scores of 0.8399/0.8338/0.5235 on MIMIC-III and 0.8090/0.8100/0.5129 on i2b2. Ablation experiments confirm the importance of exploratory data adaptation, critical feature modeling, critical token learning, cross-disciplinary supervision, and data-driven regularization. Parameter sensitivity analysis shows stable behavior for beta values greater than or equal to 1, with the strongest results at beta = 5. The study concludes that large-AI retrieval can strengthen the clinical interpretation layer required for IoMT-enabled remote monitoring, while future work should validate the approach on live multimodal sensor streams and privacy-preserving deployments. Full article

Diabetic foot ulcers represent a severe complication of diabetes mellitus, affecting millions of adults worldwide and often leading to hospitalization and amputation. Diabetic neuropathy increases the risk of plantar injuries, while the lack of continuous monitoring and delayed detection contributes to the progression of these lesions. This study presents a low-cost smart insole system for continuous plantar pressure monitoring and screening of plantar pressure patterns associated with diabetic neuropathy. The system integrates piezoresistive sensors distributed across key regions of the foot, connected to a low-power ESP32 microcontroller for data acquisition. Measurements are transmitted via Bluetooth Low Energy to a mobile application that enables real-time visualization, user management, and storage in a MySQL database for historical data consultation. Data processing employs a convolutional neural network configured to classify plantar pressure patterns between non-diabetic individuals and diabetic patients presenting neuropathic alterations. System validation demonstrated 88% accuracy, 88% recall, and 87% F1-score in classifying plantar pressure patterns. The results confirm that the combination of low-cost hardware and open-source software constitutes a viable and scalable solution for screening biomechanical alterations associated with diabetic foot complications. Full article

This study examines smart technology adoption in foodservice microbusinesses by moving beyond intention-based explanations to examine how adoption and post-adoption unfold in practice. Hospitality technology research has largely emphasised attitudes and behavioural intentions, offering limited insight into how technologies become embedded in everyday operations, particularly in resource-constrained microbusiness contexts. Focusing on foodservice microbusinesses in Malaysia, this study goes beyond pre-adoption intention and examines the nuances of actual technology implementation, guided by adaptive training as the central adoption-enabling mechanism. Using an action research approach, this study implemented a one-month adaptive training intervention that enabled operators to engage in hands-on, experiential learning within their own business environments. The findings uniquely indicate that technology adoption is shaped by capability asymmetry, with differences in technological literacy, prior experience, and resources producing varied adoption pathways. These differences were addressed through adaptive training that aligned the pace and intensity of learning with operators’ capabilities. This study also identifies specialist mediation as a key mechanism supporting adoption, as guidance from a knowledgeable intermediary reduced complexity, facilitated learning, and enabled the transfer of trust. The findings suggest that smart technology adoption in microbusiness settings is not only a matter of intention but also a situated learning process shaped by unequal capabilities, adaptive training, and specialist-guided trust formation. Full article

Background: Although technology is rapidly transforming many industries, the healthcare industry remains comparatively conservative and slow to adopt new technologies due to patient safety concerns. Notwithstanding the abundance of research on technology acceptance, most studies overlook departmental variations, making it impossible to enhance technology adoption in the medical sector. Thus, the purpose of this study is to bridge this gap by concentrating on the emergency department (ED). Methods: This study examined the factors influencing Malaysian ED healthcare professionals’ acceptance of new medical technology by introducing organisational support and training with the Technology Acceptance Model (TAM). The study’s target population comprised ED healthcare professionals in Malaysian hospitals who were at least 25 to 60 years old. In total, 140 valid surveys were gathered by email and WhatsApp from Malaysian hospital EDs, and SPSS and SmartPLS were utilised for analysis. Results: Perceived usefulness and training have a significant impact on attitude towards use, whereas attitude towards use is the sole variable that directly influences behavioural intention to use and acts as a mediator in certain paths. Conclusions: Hospital administration should concentrate on the actual needs of ED healthcare professionals, improve their understanding of technology, and offer targeted training in order to promote its effective adoption and utilisation. In the meantime, technology providers should improve the innovation’s design to make it more accessible to EDs. These findings also show that incorporating organisational support and training enhances TAM’s explanatory power and reveals its flexibility in high-stress, fast-paced environments. Full article

Over the past 200 years (1820–2020), global life expectancy has nearly tripled, increasing from 26 to 72.91 years, due to factors such as poverty reduction and public health initiatives. Today, society faces different challenges than it did centuries ago. In patient care and healthcare system priorities, the goal is to develop smart, feasible, long-lasting, cost-effective, readily available, adverse-reaction-free, adaptable, and personalized solutions that minimize patient discomfort, reduce caregiver effort, and decrease hospitalization duration and costs. In this context, biomaterials serve as versatile tools capable of performing a wide range of diagnostic, therapeutic, and theragnostic functions. Thanks to their biocompatibility, biodegradability, surface chemistry, and responsiveness, biomaterials are currently addressing issues such as patient compliance (through controlled drug-delivery systems and smart wound dressings), long transplant waiting lists, transplant rejection, non-adaptable prosthetics (artificial organs), oncology treatment efficacy (nano-formulations for theragnostics and multiple tumor targeting), and inconsistent in vitro drug-testing models (organs-on-a-chip). In this review, we focus on biomaterials’ smartness, then explore databases for efficient product design, and finally highlight their applications in the biomedical field, especially in drug delivery, tissue engineering, and regenerative medicine. Full article

Digital Twins (DTs) are increasingly explored in tourism and hospitality as enabling technologies for smart destinations, service optimization, and data-driven decision-making. Yet these environments are inherently human-centered. Existing DT implementations, however, are largely technology-driven and focus mostly on infrastructures and operational processes. This study presents a systematic literature review of DT applications in tourism and hospitality. It combines a comparative taxonomy with a technological and data-oriented analysis to examine how these systems are currently conceptualized, implemented, and integrated. The review analyzes 42 studies, classifying them by application level, twin focus, architectural approach, and human integration. The results show a strong dominance of destination- and facility-level DTs, limited human-centered models, and a prevalent use of varied sensing technologies. There is limited attention to interoperability and semantic integration. Governance, socio-technical aspects, and real-time synchronization mechanisms are also mostly underexplored. Based on these findings, this study identifies key research gaps and calls for a shift towards Social Digital Twins (SDTs). SDTs integrate human actors, social interactions, and governance within unified modelling frameworks. This transition will require advances in semantic and ontology-driven architectures. Greater attention to privacy, trust, and user acceptance in data-intensive service environments is also needed. Full article

Human parainfluenza virus type 4 (hPIV4) remains poorly characterized compared with other hPIV serotypes and information on its genomic diversity is particularly limited for Russia and Eastern Europe. In this study, we report the first complete genome sequences of hPIV4 isolates from Russia and place them in the context of global hPIV4 genetic diversity. Eight hPIV4 viruses were isolated in cell culture from respiratory samples collected from hospitalized children in Saint Petersburg between 2017/2018 and 2023/2024. Complete viral genomes were recovered using a metagenomic whole-genome amplification approach based on SMART-9N technology. Phylogenetic analysis of 178 complete hPIV4 genomes showed clear separation into hPIV4a (n = 132) and hPIV4b (n = 46) subtypes. Based on genetic distance approach, hPIV4a formed two major clusters, with the dominant cluster B subdivided into four subclusters (B1–B4); and subcluster B4 further resolved into four genetic lineages. All Russian isolates belonged to the subcluster B4 and were distributed among multiple co-circulating lineages. In contrast, hPIV4b genomes segregated into three distinct clusters, reflecting structured genetic diversity within the subtype. Collectively, this study provides, to the best of our knowledge, the first p-distance-based framework for hPIV4 whole-genome classification and contributes new complete genome sequences for an underrepresented region. Full article

Background: Femoral cannulation is essential for minimally invasive mitral valve surgery (MIMVS). Our center transitioned from open femoral cut-down to ultrasound-guided percutaneous cannulation supported by smart venous cannulas, ThruPort arterial access, and the MANTA closure device. This study evaluates how this transition affects procedural efficiency, vascular safety, and postoperative outcomes. Methods: We retrospectively analyzed 575 consecutive MIMVS patients (2014–2025). Patients treated before 2021 formed the cut-down group, while those from 2021 onward underwent percutaneous cannulation. The outcomes included operative times, groin and lymphatic complications, MANTA performance, and 30-day mortality. Propensity score matching (PSM) was performed to adjust for baseline differences. Results: Of 575 patients, 393 (68.3%) underwent cut-down and 182 (31.7%) percutaneous access. Percutaneous access was associated with shorter cardiopulmonary bypass times (115 vs. 128 min, p < 0.0001), total operative times (210 vs. 242 min, p < 0.0001), ICU stays (2 vs. 3 days, p = 0.0267), and hospital stays (8 vs. 11 days, p < 0.0001). Lymph fistula occurred in 4.3% of cut-down cases and in 0% after the adoption of percutaneous access (p = 0.0004). Overall groin complication rates were comparable (2.8% vs. 4.9%, p = 0.51). MANTA closure had a 2.2% device-related complication rate (1.1% bleeding; 1.1% ischemia) with no documented long-term sequelae. Regarding 30-day mortality, this was 4.6% in the cut-down group and 1.6% in the percutaneous group (p = 0.096). In PSM (72 matched pairs), percutaneous access retained significantly shorter operative, bypass, and ICU times, with identical groin complication rates. Conclusions: Ultrasound-guided percutaneous femoral cannulation was associated with improved procedural efficiency and elimination of lymphatic morbidity, without increasing vascular risk or mortality. It represented a safe and effective standard strategy for contemporary MIMVS. Full article

Within the hospital sector, digitalization brings smarter, more resilient and more sustainable systems. Advancements in remote sensing technologies and building information modeling (BIM) are revolutionizing infrastructure design and construction. The aim of the study is to investigate the impact of digitalization on the spatial configuration of hospitals and its effects on operational efficiency and environmental sustainability, combining theoretical insights with an empirical survey of fourteen hospitals in Italy. The methodology adopted consisted of the following steps: (i) the conduct of a literature review; (ii) the analysis of international best practice; (iii) the definition of criteria to support the design of digital hospitals; (iv) the investigation on the Italian context through a survey; (v) data collection and analysis to support the formulation of strategies for smart hospital development. The findings highlight how the adoption of innovative solutions related to clinical and management sector can optimize hospital workflow, enhance management efficiency, and create safer and more functional and sustainable environments. However, the persistence of outdated infrastructures and the need for significant adaptation still represent major barriers: only 28.7% of hospitals have a fully centralized logistics hub, and just 7.1% have implemented a Digital Twin. In conclusion, this research provides a reference framework for designers, healthcare administrators, and policymakers, outlining strategies for the development of smart and sustainable hospitals. Full article

Internet-of-Things (IoT) sensing can improve traceability, safety, and efficiency of medical waste handling, yet many deployments remain fragmented, lack an end-to-end system architecture, and do not provide the structured data pipelines needed for artificial intelligence (AI) analytics. This paper presents a layered IoT-based system design for medical waste management that integrates: (i) Espressif Systems 32 (ESP32)-based edge devices for fill-level and Global Positioning System (GPS) telemetry; (ii) secure network communication; (iii) a cloud backend for data ingestion, storage, and analytics; and (iv) operator dashboards with event-driven alerting. The architecture extends our prior GPS-enabled tracking and route optimization by adding sensor-driven state monitoring, threshold-based decision support, and a time-series data pipeline designed for future AI-driven predictive analytics. In a 30-day pilot with five containers, the system collected one reading every 15 min (14,400 total readings). The backend demonstrated efficient processing with an average Application Programming Interface (API) response time of 45 ms, sub-50 ms database write latency, and high uptime; alerts were delivered promptly upon threshold violations. Compared with a fixed-schedule baseline, the system enabled condition-based collection scheduling with zero data loss. The proposed design emphasizes modularity, fault tolerance, and integration readiness for hospital information systems, providing a practical blueprint for scalable smart-healthcare waste logistics and a foundation for machine learning-based predictive waste management. Full article