Search Results (9,595)

Background/Objectives: Adequate nutrition in older adults is essential to maintaining health, functionality, and quality of life, particularly in long-term care hospitals (HACLEs). Previous studies suggest that dissatisfaction with hospital food is linked to longer stays, more complications, and negative perceptions of care. Given these concerns, this study aimed to assess patients’ experiences with hospital food over a 15-year period in a HACLE in Spain, identify key influencing factors, and validate an updated PREM (Patient Reported Experience Measure) tool for food services. Methods: A retrospective, observational, repeated cross-sectional study was conducted using annual PREM surveys administered between 2011 and 2025 to patients on oral diets. Psychometric validation of the updated 8-item version (2024) was conducted. Results: Out of 1618 surveys, 1540 were included in the final analysis. The updated PREM showed strong internal consistency (α = 0.85, ω = 0.87), a two-factor structure (food quality and service conditions), and adequate model fit. Perceptions worsened after a catering company change in 2022 but improved following the implementation of new food distribution carts in 2025. The PREM total score showed a strong positive association with the global satisfaction item, providing supportive evidence based on a closely related anchor measure (Spearman’s rho = 0.80, 95% CI 0.77–0.82; p < 0.001). Scores differed significantly by diet type: patients receiving a pureed diet reported the highest average satisfaction score, followed by those on a soft diet and a regular diet. The group on a soft diet excluding foods that pose a choking hazard had the lowest mean score. Conclusions: The validated PREM scale is a reliable tool to monitor patient experience with hospital food. It enables early detection of quality issues and supports targeted improvements. Routine use in long-term care settings may foster personalized, patient-centered nutrition strategies and enhance care quality. Full article

In active debris removal missions in low Earth orbit, the semi-major axis difference between a service platform and its target can be large. Analytical relative dynamics models used in formation-flying applications typically retain only the first-order expansion in the orbital element differences; at large separations, the discarded higher-order terms—particularly the power-law dependence on the semi-major axis—introduce systematic along-track drift that degrades the propagation accuracy. This paper derives the power-law truncation correction, a closed-form additive vector that exactly compensates the truncated semi-major-axis power-law remainder, together with a consistent Jacobian correction for the extended Kalman filter covariance prediction. The state dimension and state transition matrix structure remain unchanged. Propagation tests over semi-major axis differences of 36–146 km yield ten-revolution terminal position errors of 0.008–0.065 km for the corrected models, compared with tens to hundreds of kilometers for the uncorrected first-order models and 0.1–8 km for the second-order state transition tensor. In 500-run Monte Carlo angles-only filtering experiments, the corrected filter reduces the median terminal position error by one to nearly three orders of magnitude relative to the uncorrected model. A process noise sensitivity study confirms robustness to calibration uncertainty across two orders of magnitude at a lower computational cost and with simpler implementation than higher-order tensor methods. Full article

Ensuring DC bus voltage stability is a key enabler for the sustainable development of photovoltaic-storage DC microgrids (PV–storage DC MGs), which are regarded as critical infrastructure for high-penetration renewable energy utilization. However, the inherent randomness of PV power generation seriously threatens this stability. This paper proposes a novel model predictive control (MPC) scheme for the energy storage system (ESS) to mitigate voltage fluctuations and enhance system stability. To improve the model precision, a forgetting-factor-augmented recursive least squares (RLS) algorithm is employed for online identification and correction of the estimated equivalent impedance between the ESS and the DC bus. Rigorous Lyapunov stability analysis is performed to obtain the sufficient stability conditions and quantitative tuning rules for the weighting coefficients, which transforms the qualitative parameter selection into a theoretical constrained optimization. The state of charge (SOC) of the ESS is set as a security constraint to avoid excessive charge/discharge and extend battery service life. A distinguished advantage of the proposed strategy is that it generates ESS power commands solely based on local measurements, eliminating the dependence on external communication and improving system reliability. Simulation results on MATLAB R2021b/Simulink and hardware-in-the-loop experiments based on RT-Lab and DSP demonstrate that the proposed MPC method significantly reduces the DC bus voltage deviation, accelerates the dynamic recovery process, and maintains stable ESS operation under both normal PV fluctuations and sudden PV outage conditions. Full article

Background/Objectives: Previous research has shown that mental disorders are common in the general population in Latvia, while access to specialised psychiatric services is limited, particularly in rural areas. General practitioners, therefore, have a crucial role in the early detection and management of these conditions. Previous studies and national initiatives have highlighted an unmet need for continuing education in psychiatry tailored to the Latvian primary care context. In response, the first Latvian telementoring programme, the “ECHO School of Psychiatry” (Extension for Community Healthcare Outcomes, ECHO), was launched in 2023 to enhance general practitioners’ competencies and decision-making in mental healthcare. This study explored general practitioners’ experiences and perceptions of participation in the programme and its perceived impact on their practice, using a qualitative approach. Methods: Thirteen women general practitioners who had participated in the programme between October 2023 and February 2025 were recruited using voluntary response sampling, via email invitations from programme coordinators. Individual semi-structured interviews were conducted remotely between May and September 2025, audio-recorded, transcribed verbatim, and the resulting transcripts were analysed thematically using an inductive approach, supported by NVivo software. Data collection continued until no new themes emerged. Results: Four main themes emerged from the thematic analysis: (1) participants’ perceptions of the structure and educational value of the programme; (2) perceived impact of the programme on clinical practice and decision-making; (3) programme limitations in addressing professional isolation and fostering collaboration; (4) suggestions for programme improvement. Themes illustrate participants’ perceptions of the programme’s value, its impact on practice, and recommendations for further development. Conclusions: This study provides insights into the strengths and areas for improvement of the “ECHO School of Psychiatry” as perceived by general practitioners. It also acknowledges current challenges in primary care, such as limited access to specialists and professional isolation. Full article

Recreation is a key ecosystem service provided by mountainous areas. The European Alps are a main attraction for tourists due to their natural landscapes. Nature-based recreation and ecotourism are an opportunity for local communities in alpine valleys. However, tourism may also represent a threat to fragile mountain environments when large numbers of tourists are involved in touristic models based on heavy use of resources. This study aims to provide insights into how local communities in an alpine valley, the Comelico Valley, see the current tourism demand and its future changes. Comelico shares similar environmental and landscape characteristics with the surrounding valleys but is less developed from the touristic point of view. We used the Delphi method on a panel of nine local tourism experts from different areas of operations. The results about the forecast of future activities to be prioritized highlight the importance of diversification of tourism offer towards sustainable activities closer to the concept of nature-based tourism and ecotourism. Activities to be prioritized for development were hiking and thematic hiking, forest well-being initiatives and experience laboratories. These results suggest a potential need to change the present model of mountain tourism towards a more diversified and soft approach to mountain recreation. Full article

The integration of service function chains (SFCs) and unmanned aerial vehicles (UAVs) lays a crucial technological foundation for achieving efficient, reliable, and adaptive future airborne service networks. Service functions (SFs) in the SFC will be deployed on UAVs; this type of SFC is called unmanned aerial vehicle-based service function chains (USFCs). However, due to the combined effects of open hardware and software architectures, exposed communication links, and complex mission environments, UAVs have become ideal targets for attackers. Once a vulnerability is successfully injected into a UAV, data from the SFs running on it will be stolen, seriously threatening the dependability of the USFC. Therefore, it is necessary to conduct a quantitative evaluation of the USFC dependability to provide insights for further improving its dependability. This paper develops a USFC dependability evaluation model based on a semi-Markov process (SMP) to capture the dynamic interaction between attacker behavior and USFC system recovery behavior. The dependability of the USFC is comprehensively evaluated from two perspectives: availability and security. Extensive numerical analysis experiments are conducted, and the results not only demonstrate the changing trends of various dependability metrics under different parameters but also show parameter combinations for synergistic optimization among metrics. Full article

Promoting the green development of large public buildings is a crucial pathway toward environmental sustainability. As a type of public building characterized by both high energy consumption and high public engagement, green stadiums provide an important setting for examining whether building-embedded green features are visible, understandable, and valued by users. In this sense, green stadium consumption intention is treated in this study as a building-related outcome that reflects user acceptance of green building spaces and services rather than as a generic green marketing preference alone. This study examines the effects of Green Facility Visibility, Perceived Green Communication, and Green Interactive Experience on Millennials’ Green Stadium Consumption Intention, while investigating the parallel mediating roles of Green Self-Efficacy and Future Orientation. A sample of 976 millennial users was surveyed. The hypothesized model was tested using covariance-based structural equation modeling (CB-SEM), and Bootstrapping was employed to validate the significance of the mediating effects. Findings reveal that: (1) Green Facility Visibility and Perceived Green Communication significantly and positively influence Green Stadium Consumption Intention, whereas the direct effect of Green Interactive Experience is insignificant; (2) Green Self-Efficacy mediates the relationships between Green Facility Visibility, Perceived Green Communication, and consumption intention; and (3) Future Orientation similarly mediates the relationships between Green Facility Visibility, Perceived Green Communication, and consumption intention. Rather than proposing a major theoretical breakthrough, this study offers a context-specific extension of green consumption research by introducing Green Self-Efficacy and Future Orientation as parallel mediators in a stadium setting. The findings show how building-related green cues and user cognition jointly shape the acceptance of green stadiums, thereby providing evidence relevant to the design, operation, and evaluation of public-facing green buildings. Full article

Background: Chronic Obstructive Pulmonary Disease (COPD) remains a leading cause of emergency department (ED) presentation, hospitalisation, and preventable healthcare utilisation worldwide. Although guidelines advocate coordinated, preventative, and community-based management, care within ED settings often remains reactive and crisis-driven. Nurses occupy a central role in COPD management; however, the experiential dimensions of nursing practice and its contribution to improving patient outcomes are insufficiently understood. Objectives: To explore the lived experiences of patients, nurses and medical officers regarding COPD presentations to the ED, with particular focus on the nursing role in assessment, coordination, education, and identification of unmet and comorbid care needs. Methods: A qualitative phenomenological approach was undertaken across three regional Australian EDs. Purposive sampling recruited patients presenting with acute exacerbations of COPD and nursing and medical officers involved in their care. Semi-structured interviews were conducted and transcribed verbatim. Data were analysed using Braun and Clarke’s thematic analysis framework, supported by reflexive discussion and audit trails to enhance rigour. Results: Six interrelated themes were identified: (1) nursing within a “crisis first” model of care; (2) holistic assessment and translation of complexity; (3) education and care coordination as preventative nursing work; (4) relational care and therapeutic connection; (5) nurses as sentinels for undiagnosed comorbidities, particularly obstructive sleep apnoea; and (6) system pressures constraining optimal nursing practice. Participants consistently described nurses as the clinicians who stabilised acute episodes, interpreted contextual risks, coordinated services, and provided relational and educational support, yet whose preventative contributions were limited by time and organisational demands. Conclusions: ED nurses function as critical integrators between acute stabilisation and chronic disease management for patients with COPD. Formalising nurse-led assessment, education, coordination, and sleep-disordered breathing screening may reduce avoidable ED presentations and enhance patient-centred outcomes. Investment in structured nursing models represents a key opportunity for improving COPD care delivery. Full article

Background: Artificial intelligence (AI) is increasingly embedded in routine primary care, yet how the levels of integration might affect its acceptability is unknown, especially in relationship-based service models where patients expect visible human stewardship. Prior experimental studies often treat AI adoption as a binary condition, leaving the “intensity gradient” of automation and the role of model specialization under-explored. We examine whether increasing AI integration in the clinical encounter erodes patients’ intention to seek care from family doctors in China, and whether labeling the AI as a medical-specific model buffers such erosion. Methods: We conducted a nationwide online survey experiment in China (N = 2790). Participants were randomly assigned to vignettes that varied by (i) the level of AI integration (low, medium, high) and (ii) the AI type (general-purpose vs. medical-specific large language model), with a human-only care scenario as a reference. Care-seeking intention from family doctors was assessed immediately after exposure. We estimated treatment effects using OLS regression with heteroskedasticity-robust standard errors, and examined the buffering hypothesis through an interaction term between AI integration intensity and AI type. Results: Care-seeking intention declined steadily as AI integration increased (p < 0.001), with the sharpest drop under high-intensity AI integration where clinical decisions were delegated to the AI system. Across all intensity levels, framing the system as a medical-specific AI consistently resulted in higher care-seeking intention than a general-purpose model. However, the interaction between AI intensity and the AI type was not statistically significant (p = 0.508). Conclusions: Patient acceptance of AI in primary care depends not only on whether AI is involved, but on how deeply AI is positioned in the encounter. Medical-specific AI labeling may enhance acceptance across all AI integration levels. The findings underscore the need to preserve human clinical agency in AI-embedded primary care. The results contribute to research on healthcare systems, digital health, and AI–patient interaction. Full article

Background/Objectives: Continuous glucose monitoring (CGM) has transformed diabetes management by enabling high-resolution assessment of glucose dynamics, with well-established use in type 1 diabetes (T1D) and insulin-treated type 2 diabetes (T2D), and expanding applications across broader populations, including non-insulin-treated T2D and gestational diabetes. However, real-world implementation remains constrained by economic barriers, fragmented reimbursement, workflow challenges, and limited capacity for continuous data interpretation. This review examines key barriers to CGM implementation and synthesizes current evidence on pharmacist-integrated CGM care as an emerging model to support CGM adoption across clinical and community-based settings. Methods: A narrative literature review was conducted to synthesize evidence on pharmacist-integrated CGM services in diabetes care. Literature was identified through structured searches of PubMed, Embase, and the Cochrane Library, supplemented by Google Scholar and citation tracking, covering publications from January 2010 to December 2025. Studies were selected based on predefined criteria, including those reporting clinical outcomes, pharmacist involvement, or health system and implementation factors related to CGM use. Relevant survey-based and real-world studies were also considered to capture healthcare professionals’ perspectives and implementation experiences. Evidence was synthesized thematically across clinical, behavioral, and health system domains. Results: Available evidence suggests that pharmacist-integrated CGM care is associated with improvements in glycemic management, including increased time in range, reduced glycemic variability, and more timely pharmacotherapy optimization. Pharmacist involvement may also support patient education, self-management, and engagement with digital health technologies, and facilitate ongoing data interpretation and treatment adjustment between clinical encounters. However, evidence remains heterogeneous and geographically limited, with predominantly retrospective and pilot studies and few randomized trials, constraining the robustness and external validity of the findings. Further studies are needed to confirm its clinical effectiveness, comparative effectiveness, and economic value. Conclusions: Pharmacist-integrated CGM represents a promising and operationally feasible approach to supporting CGM use in routine diabetes care. While current evidence indicates potential benefits in glycemic management and care delivery processes, further research and implementation efforts are required to support its effective and sustainable adoption across diverse healthcare settings. Full article

Electric Vehicle Charging Systems (EVCSs) are increasingly connected with the Internet of Things (IoT) and smart grid infrastructure, yet they face growing cyber risks due to expanded attack interfaces. These systems are vulnerable to various attacks that potentially impact both charging operations and user privacy. Intrusion Detection Systems (IDSs) are essential for identifying suspicious activities and mitigating risks to protect EVCS networks, but conventional ML-based IDSs are often unable to achieve optimal performance due to imbalanced datasets, complex traffic distributions, and human design limitations. In practice, EVCS traffic is typically multi-class, imbalanced, and safety-critical, where both missed attacks and false alarms can lead to denial of charging, service interruption, unnecessary incident escalation, financial loss, and reduced user trust. Automated ML (AutoML) and Generative Artificial Intelligence (GAI) have emerged as promising solutions in cybersecurity. Existing GAI and augmentation methods are mostly class-frequency-driven, but this does not necessarily improve the error-prone regions where IDSs actually fail. In this paper, we propose a GAI and an AutoML-based IDS that incorporates a Conditional Generative Adversarial Network (cGAN) with the optimized XGBoost model to improve the effectiveness of intrusion detection in EVCS networks and IoT systems. The proposed framework involves two techniques: (1) a novel cGAN-based error-guided generative augmentation (EGGA) method that extracts misclassified samples and generates a more robust training set for IDS development, and (2) an optimized IDS model that automatically constructs an optimized XGBoost model based on Bayesian Optimization with Tree-structured Parzen Estimator (BO-TPE). The main algorithmic novelty lies in EGGA, which uses model errors to guide generative augmentation toward difficult decision regions, while the overall pipeline represents a practical system-level integration of EGGA, XGBoost, and BO-TPE. To the best of our knowledge, this is the first work that combines GAI and AutoML to specifically improve detection on hard samples, enabling more autonomous and reliable identification of diverse cyber attacks in EV charging networks and IoT systems. Experiments are conducted on two benchmark EVCS and cybersecurity datasets, CICEVSE2024 and CICIDS2017, demonstrating consistent and statistically meaningful improvements over state-of-the-art IDS models. This research highlights the importance of combining automation, generative balancing, and optimized learning to strengthen cybersecurity solutions for EV charging networks and IoT systems. Full article

During pregnancy, women are more inclined to modify their habits and lifestyle to find a new balance and promote well-being for themselves and the child-to-be. However, the availability of nutritional and psychological support is often limited by stigma, geographic barriers, and a lack of services. Digital health tools are emerging as possible solutions to cover these needs. This study explores the acceptability, feasibility, and user experience of Demetra, a virtual coach based on Acceptance and Commitment Therapy (ACT), designed to promote healthy lifestyles and mental well-being. Fifty pregnant women will be enrolled in the feasibility study of the intervention. It starts with an educational part on the foundations of healthy eating and suggestions about lifestyle habits, followed by a six-week psychoeducational module. Content is delivered through text, audio, and video formats. User experience and engagement will be measured through validated questionnaires and semi-structured interviews. Psychological well-being will be evaluated both before and after the program. The intervention is expected to be well-received, with high levels of satisfaction and engagement, leading to a greater awareness of healthy behaviors, improved psychological flexibility, and enhanced overall well-being. Demetra offers an accessible solution to support women through the transformative experience of motherhood with a multidisciplinary and innovative approach. Full article

Background: Artificial intelligence (AI) is increasingly integrated into healthcare systems, with important implications for nursing practice and clinical workflows. However, evidence regarding nurses’ digital skills, perceptions, and readiness to adopt AI-enabled technologies remains limited, particularly in national healthcare contexts such as Greece. Objectives: This study examined nurses’ digital skills, perceptions of AI, and readiness for AI adoption in clinical practice, and explored demographic and professional factors associated with these outcomes. Methods: A cross-sectional survey was conducted among 166 nurses working in two public hospitals in Greece. Results: Nurses reported moderate digital skills, with 59.1% indicating competence in email/video communication and 27.2% reporting adequate use of digital security tools, while exposure to AI remained limited (18.0% reported using AI products/services in daily life). Perceived professional impact of AI was moderate, whereas readiness for AI adoption was comparatively lower, with only 7.8% considering health professionals adequately prepared and 7.2% reporting adequate AI training. Statistical analyses indicated that educational level and computer literacy certification were positively associated with digital skills, whereas longer professional experience was negatively associated with readiness for AI adoption. Conclusions: These findings highlight a gap between general digital competence and preparedness for AI-driven healthcare applications and underline the need for targeted education and implementation strategies to support effective and ethical integration of AI in nursing practice. From a nursing workforce perspective, the results underscore the importance of integrating AI literacy into continuing professional education and aligning digital health implementation strategies with clinical nursing practice. Full article

The low participation of men in primary education is a persistent and structural phenomenon that cannot be adequately understood through homogeneous views of teachers’ motivations and experiences. This study is conducted in the Chilean context, which is characterized by a highly feminized teaching workforce and persistent challenges related to working conditions, social valuation of teaching, and teacher retention. It aims to analyze profiles of male primary school teachers, considering their motivations, perceptions, and the meanings they attribute to the teaching profession. A sequential explanatory mixed-methods design (QUAN → qual) was employed. First, 144 male in-service primary teachers completed the FIT-Choice scale and a latent class analysis was conducted. Subsequently, in-depth interviews were carried out with an intentionally selected subsample of 20 teachers, which were analyzed using qualitative content analysis. Three distinct motivational profiles were identified: altruistic, pragmatic, and critical. The qualitative findings complemented these profiles, highlighting the influence of personal trajectories and working conditions on teachers’ career choice and retention in the profession. Overall, the findings suggest that policies for training, support, and professional induction must recognize teacher heterogeneity and promote inclusive working environments, moving beyond approaches that focus exclusively on increasing the number of men in primary education. Implications for the design of policies aimed at attracting and retaining male primary school teachers are discussed. Full article