Search Results (633)

Rapid innovation-driven transitions have, across successive eras, been associated with increased psychological distress, substance use, and suicide when social and economic change outpaces the adaptive capacity of individuals, communities, institutions, and governments. The early artificial intelligence (AI) economy is emerging at an unprecedented pace, reshaping education, employment, and social organization in ways that may intensify these risks, particularly among young people. This perspective argues that school-based mental health nursing, working collaboratively with school psychologists, counsellors, physicians, and other interdisciplinary professionals, represents a practical upstream response to AI-era disruption. Drawing on historical evidence from the Industrial Revolution, population mental health research, and the contemporary mental health nursing literature, the paper examines the mechanisms through which rapid socio-economic change may contribute to psychological distress and substance-use problems. It further distinguishes AI as both a source of socio-economic disruption and a clinical tool that can support—but not replace—relationship-based nursing practice. Strengthening school-based mental health nursing within interdisciplinary systems offers a practical, prevention-oriented strategy to promote resilience, facilitate early intervention, and mitigate downstream mental health and substance-use harms during the AI transition. Full article

This study protocol presents a pre-specified framework for assessing whether Farm Machinery-as-a-Service (FMaaS) can support inclusive access to Agriculture 4.0 in fragmented agrarian systems. The aim of the protocol is to define the conceptual framework, research questions, hypotheses, target population, sampling strategy, data collection procedures and planned analytical approach for a future empirical study using Poland as a case of a fragmented, family-farm-based agricultural system in which many small and medium-sized farms face difficulties in keeping pace with Agriculture 4.0. The manuscript does not report empirical results or human-subject data. The planned study will use a sequential mixed-methods design in which each component has a specific role. Secondary data analysis will diagnose farm structural conditions, machinery-use patterns, productivity gaps and innovation gaps. Expert and stakeholder interviews will refine the typology of FMaaS arrangements and identify relevant service attributes. A farm survey and a discrete choice experiment will assess farmers’ willingness to use FMaaS, perceived transaction costs, trust, digital readiness and preferences for specific service characteristics. Farm-level economic modeling and Monte Carlo simulation will evaluate the viability of ownership-based and service-based modernization scenarios under uncertainty. Structural equation modeling will examine social-sustainability mechanisms, including adaptive capacity, perceived resilience, perceived inclusion in digital agriculture and the perceived ability to continue farming under technological change. The expected output of the protocol is an integrated analytical framework for evaluating the economic, institutional, resource-use and social conditions under which FMaaS may become a viable and socially acceptable pathway to Agriculture 4.0. By combining transaction-cost theory, market-design reasoning, stated-preference methods, farm-level modeling and social-sustainability assessment, the protocol provides a transparent basis for future empirical research on access-based digital mechanization. Full article

The popularity of electric vehicles (EVs) is growing at a fast pace, creating a need for the strategic deployment of charging stations (CSs) to provide enough coverage, cost effectiveness, and compliance with grid and urban planning regulations. The deployment of large-scale infrastructure under multiple, often conflicting constraints remains a challenging engineering decision-making problem. In this paper, we propose a hybrid optimization framework that combines greedy initialization with reinforcement learning to efficiently explore the charging station deployment problem. The proposed approach employs Q-learning and Deep Q-Network (DQN) agents to iteratively refine the initial deployment while simultaneously optimizing deployment cost, charging demand coverage, and operational utility under practical planning constraints. The constraints include grid capacity limitations, renewable energy utilization, and fairness considerations. The proposed framework is evaluated in realistic urban scenarios. The experimental results demonstrate that the reinforcement learning (RL) approach achieves superior trade-offs among competing objectives compared to baseline heuristic strategies, while maintaining computational scalability for large candidate location sets. The proposed framework demonstrates stable performance across three evaluated deployment scenarios, indicating its potential applicability to increasingly complex charging infrastructure planning problems. The proposed methodology is scalable to other complex engineering planning and resource allocation problems characterized by multi-objective trade-offs and dynamic constraints. Beyond improving optimization performance, the proposed framework contributes to sustainable transportation planning by supporting the efficient deployment of electric vehicle charging infrastructure. Optimized charging station placement promotes greater accessibility to charging services, encourages electric vehicle adoption, reduces unnecessary travel associated with charging activities, and contributes to lower greenhouse gas emissions. Consequently, the proposed methodology provides decision-makers with a scalable and intelligent planning tool that supports the transition toward more sustainable and energy-efficient urban mobility systems. Full article

Background: Hospital-wide centralized bed allocation, known in China as the “one-bed-for-the-whole-hospital” model, aims to improve inpatient access by pooling beds across specialties. Existing studies have mainly examined operational outcomes, process risks, or staff competence. Less is known about how bed redistribution interacts with clinical responsibility, professional roles, functional support, and shared governance. Objectives: This study explored healthcare professionals’ perspectives on the barriers, facilitators, and strategies for sustaining the hospital-wide “One Bed” model. It also examined how bed integration and care integration aligned or diverged across the dimensions of the Rainbow Model of Integrated Care. Methods: A single-center descriptive qualitative study was conducted in a Grade A tertiary hospital in Shanghai, China, where the model had operated across 11 pilot wards for approximately 48 months. Between December 2024 and March 2025, 25 healthcare professionals, including 14 clinical nurses, eight head nurses, and three physicians, completed face-to-face semi-structured interviews. Data were analyzed using inductive qualitative content analysis. After themes and subthemes were developed from participants’ accounts, the Rainbow Model of Integrated Care was used as an interpretive framework to map the findings across clinical, professional, organizational, system, functional, and normative integration. Results: Five themes were generated. Participants perceived centralized bed allocation as shortening waiting time and improving bed use, but also as intensifying ward workload and making single efficiency indicators insufficient. Patients could move to available wards before medical response, responsibility, and physician visibility were fully aligned. Cross-specialty case mixes exceeded what nurses could manage through temporary learning alone. Information, logistics, space, equipment, and supplies did not always move with patients, leaving nurses to maintain workflow through manual and often invisible coordination. Sustained bed sharing also depended on clearer boundaries for patient selection, specialty fit, severity, nursing workload, leadership authority, resources, and incentives. Together, these themes showed that bed resources were integrated faster than care processes, professional capability, functional support, and shared governance. Conclusions: Hospital-wide centralized bed allocation should be understood as an uneven process of integration rather than only as a bed-management strategy. These findings primarily reflect the perceptions and experiences of nurses and nursing managers, supplemented by limited physician input. The distinctive finding of this study is that beds may be pooled, and patients may move rapidly, while medical response, nursing competence, functional support, workload recognition, and governance arrangements do not always move at the same pace. Safe and sustainable implementation therefore requires bounded flexibility: bed allocation should be guided not only by bed vacancy but also by clinical suitability, specialty fit, nursing workload, timely medical response, functional systems that move with patients, and shared accountability. Full article

Vineyard monitoring requires efficient methods for detecting plant stress and disease while limiting flight time, data volume, and labour effort. This paper presents a satellite-enabled two-tier UAV workflow for semi-automated vineyard inspection. The proposed approach combines high-altitude multispectral scanning, NDVI-based point-of-interest identification, adaptive flight path planning, low-altitude RGB inspection, and downstream vision-based disease analysis. Processing tasks are offloaded to a remote server accessed through an emulated Low Earth Orbit satellite communication environment, allowing the UAV-side system to remain lightweight while receiving multispectral analysis results during the mission. A simulation framework was developed to evaluate mission behaviour under controlled and repeatable conditions, using both pseudo-random point generation and real multispectral vineyard images processed through the satellite emulation testbed. A flight with a real drone was also conducted to validate adaptive flight optimisation. Experimental results focus on the impact of path-adaptation strategies and communication bandwidth on mission efficiency. The results show that route optimisation can reduce mission time by up to $15\%$ when new low-altitude waypoints emerge, while bandwidth bottlenecks affect performance once image transmission can no longer keep pace with acquisition. The findings highlight the need to consider sensing, communication, and mission planning jointly in adaptive UAV-based crop monitoring. Full article

Syncope in older adults is common, heterogeneous, and frequently misclassified as an unexplained fall, leading to injury, fear of recurrence, and costly, low-yield testing and hospitalization. The 2018 European Society of Cardiology (ESC) Guidelines emphasize structured initial evaluation (history with witnesses, orthostatic blood pressure, and 12-lead ECG), risk-feature-guided disposition, and targeted second-line investigations rather than routine neuroimaging or carotid duplex ultrasound. We performed a structured narrative review anchored to the ESC 2018 Guidelines, searching MEDLINE/PubMed up to February 2026 for randomized trials, observational cohorts, and systematic reviews with substantial representation of adults aged ≥65 years and extracting geriatric-relevant outcomes including injury, falls, admission, arrhythmic diagnoses, and short-term serious events. Evidence since 2018 supports the enduring value of the ESC 2018 Guidelines’ framework but highlights the need for geriatric adaptation: repeated orthostatic phenotyping (including post-prandial and delayed hypotension), systematic deprescribing, and explicit integration of frailty, cognition, and trauma risk into disposition decisions. Post-2018 data strengthen mechanism-based strategies, including earlier implantable loop recorders with remote monitoring and selective pacing for tilt-induced asystolic reflex syncope, while cardioneuroablation remains investigational for most elders. We propose a pragmatic pathway prioritizing diagnostic yield, minimizing low-value testing, and aligning interventions with patient goals and functional outcomes to reduce recurrences and support safer transitions. Full article

The management of traumatic brain injury remains one of the greatest challenges in modern medicine. Determining whom to treat, when to intervene, and how aggressively to proceed and persist continues to define the central dilemma of neurotrauma care. While the injured brain possesses only limited regenerative capacity, substantial opportunity remains to mitigate the cascade of secondary brain injury. Previous technological and scientific advances have progressively challenged the therapeutic nihilism historically associated with severe neurotrauma, and the accelerating pace of innovation is invigorating efforts to push the boundaries of care. Contemporary neurotrauma research increasingly seeks to reduce the incidence of injury through prevention, enable ultra-early diagnosis and intervention, biologically subclassify heterogeneous injury patterns, and integrate multimodal neuromonitoring into precision-guided therapeutic strategies. Advances in artificial intelligence, computational modelling, imaging, biomarker science, and neurocritical care now build upon decades of foundational work established by prior generations of clinicians and scientists. Together, these developments may enable increasingly individualised approaches to neurotrauma management, with the aim of preserving meaningful neurological function, quality of life, and human potential on a global scale. Although many challenges remain, modern neurotrauma increasingly stands at the threshold of such transformation. This narrative review explores the emerging technologies, evolving paradigms, and future directions that may shape the next era of neurotrauma care. Full article

The decarbonisation of the economy has become one of the key challenges of sustainable development and energy transition, particularly in countries with a high dependence on fossil fuels. Although numerous studies evaluate decarbonisation using environmental indicators, relatively little attention has been paid to assessing the organisational maturity of enterprises and the dynamics of their climate transition. This study aims to assess the maturity of decarbonization processes in Polish enterprises using the Polish Economy Decarbonization Index (IDPG). The research is based on secondary analysis of data from four consecutive editions of the IDPG reports covering the years 2023–2025. To extend the analytical potential of the index, the study applies a multidimensional maturity assessment and proposes a two-dimensional decarbonisation maturity matrix combining the current maturity level with the change intensity across seven key dimensions. The results indicate that decarbonisation has become firmly embedded in corporate business strategies, while employee engagement, business environment pressure and carbon footprint management represent mature dimensions of climate transition. In contrast, public institutional support and regulatory requirements are undergoing the most intensive changes, whereas capital expenditures remain the principal barrier limiting the pace of corporate decarbonisation. The main contribution of this study is the development of a two-dimensional analytical framework that combines current maturity and change intensity and enables the identification of transformational leaders, emerging areas, established areas, and dimensions requiring additional support. The study is supplemented with examples of decarbonization initiatives implemented by selected companies in the Polish fuel and energy sector. The case evidence does not demonstrate that the companies participated in the IDPG surveys or represent the entire Polish energy sector. These serve only as illustrative cases demonstrating their activities’ alignment with the IDPG study results. The study contributes to the literature by extending existing decarbonisation assessment approaches beyond emission-based indicators and offering a practical framework for monitoring corporate climate transition. Full article

There is limited research examining how technology-enhanced assessment functions in nontraditional schools. The following research question guided our study: How are technology-enhanced formative assessment strategies perceived to support student learning in a flexible-paced high school environment? The study employed an educational design approach to co-develop and implement formative assessment strategies with teacher-participants during six professional learning sessions throughout the school year. Drawing on data from pre- and post-surveys administered to teachers (n = 36), researcher field notes with reflections during the professional learning sessions, and open-ended survey responses from students at the end of the school year (n = 43), we found: (a) a greater reported use of digital methods for formative assessment and a significant increase in the use of authentic, real world assessment criteria; (b) teacher and student perceptions that timely, constructive, and accessible digital feedback supported learning experiences; and (c) recognition that students could be more actively involved in determining assessment criteria. Students also reported benefits from receiving digital feedback, opportunities for self-assessment, and greater clarity in their learning progression. While technology can enrich feedback and efficiency, careful attention must be paid to sustaining equity and learner voice in assessment design. Full article

Background: Right-to-left shunting through a patent foramen ovale (PFO) is an underrecognized yet potentially reversible cause of refractory hypoxemia, particularly in patients with right ventricular dysfunction. This case report describes heart-rate-dependent right-to-left shunting through a PFO causing refractory hypoxemia in a patient with inflammatory cardiomyopathy and severe right ventricular dysfunction, presumably arising from biventricular output mismatch. Case Presentation: We report the case of a 41-year-old male with inflammatory cardiomyopathy and a recently implanted single-chamber pacemaker (VVI mode, lower rate limit 50 bpm), admitted for decompensated heart failure. After initial clinical improvement with guideline-directed therapy, the patient’s intrinsic heart rate declined, and ventricular pacing at 50 bpm became the dominant rhythm. He subsequently developed refractory hypoxemia unresponsive to mechanical ventilation. Systematic hemodynamic assessment was performed using transthoracic echocardiography and thoracic electrical bioimpedance (TEB) monitoring at different pacing rates. Results: Echocardiographic evaluation revealed dynamic interatrial shunting through a PFO with the following characteristics: left-to-right at a pacing rate of 80 bpm and right-to-left at 50 bpm. Hemodynamic and echocardiographic data suggested that bradycardia induced biventricular output mismatch—left ventricular outflow tract velocity–time integral (VTI) increased by approximately 38% (from 17.5 cm to 24.1 cm), whereas right ventricular outflow tract VTI increased by only approximately 4% (13.3 cm vs. 13.8 cm). This mismatch likely resulted in relative elevation of right atrial pressure, thereby driving right-to-left shunting through the PFO. Increasing the pacing rate to 80 bpm reversed the shunt direction, normalized oxygenation, and facilitated successful extubation. Conclusions: This case suggests that in patients with severe right ventricular dysfunction, bradycardia may induce biventricular output mismatch with substantially greater left than right ventricular stroke volume augmentation, and presumably relative elevation of right atrial pressure, potentially leading to dynamic right-to-left shunting through a PFO. For such patients with unexplained hypoxemia, the possibility of dynamic PFO shunting should be considered. Appropriately increasing the pacing rate may help restore biventricular output matching, reverse shunt direction, and improve oxygenation; individualized heart rate management strategies warrant clinical consideration. Full article

In an IRONMAN-distance triathlon, run performance is a particularly strong correlate of overall race outcome, alongside cycling. Although individual research areas such as pacing, performance prediction, and laboratory physiology have been reviewed separately, they have not been integrated to determine factors contributing to running performance decline or to identify key knowledge gaps. This scoping review aims to map the current evidence on running performance decline in IRONMAN-distance triathlon and identify key gaps, with a particular focus on age-group athletes. Following the Arksey and O’Malley framework and PRISMA-ScR guidelines, we searched PubMed and SPORTDiscus for studies reporting quantitative running performance outcomes in IRONMAN races or bike-to-run transition protocols. Of 896 deduplicated records, 70 studies were included. Findings were organised into five sub-questions addressing the extent of evidence, quantification of decline, physiological and biomechanical changes, underlying mechanisms, and inter-individual variability. Pacing patterns and performance predictors are well described, whereas mechanistic research relies almost entirely on short, controlled protocols. Critically, no study identified by this review quantified the “between-context decline,” defined as the difference between an athlete’s standalone marathon performance and their actual IRONMAN running performance. Addressing these gaps requires observational studies using real IRONMAN race data and individual-level analyses, particularly for age-group athletes. Full article

The rapid expansion of the global nutraceutical industry has heightened the urgency for rigorous yet efficient safety assessment of food-derived bioactives. Conventional toxicological methods, while indispensable, are often costly, time-consuming, and insufficient to keep pace with the rapid product innovation in this sector. In this context, in silico models for predicting Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET), alongside broader computational toxicology tools, are increasingly recognized as scalable and cost-effective strategies for early-stage safety evaluation of nutraceuticals. Techniques such as quantitative structure–activity relationship (QSAR) modeling, molecular docking, and physiologically based pharmacokinetic (PBPK) simulation enable early prioritization of candidate compounds, forecasting of potential enzyme- or receptor-mediated interactions, and prediction of bioactive metabolite profiles, prior to costly experimental or clinical evaluation. Yet, their application in the nutraceutical domain presents unique challenges. Food matrices are chemically complex, bioactive data remain sparse and unevenly curated, and long-term, low-dose effects are difficult to capture with existing computational frameworks. These limitations constrain predictive accuracy and raise questions about general applicability across diverse classes of compounds. Therefore, this review critically examines both the opportunities and limitations of computational safety assessment for nutraceuticals. It synthesizes representative case studies, evaluates current methodological limitations, and discusses the evolving regulatory landscape alongside emerging integrative frameworks such as New Approach Methodologies (NAMs) that may guide the future of nutraceutical safety assessment. Full article

Background and Objectives: Pacing behavior has been consistently associated with performance in long-distance running, yet its patterns across different race distances and population subgroups remain insufficiently understood. Therefore, this narrative review synthesizes and critically discusses available evidence on pacing in half-marathon and marathon running, considering both within- and between-race differences, with a particular focus on performance level, sex, and age. Materials and Methods: A structured, non-systematic search was conducted in PubMed, SPORTDiscus, Scopus, and Web of Science, with Google Scholar used as a supplementary source. Peer-reviewed English-language full-text studies reporting pacing-related outcomes were included, and findings from 37 studies published up to March 2026 were synthesized narratively. Results: Available observational evidence suggests differences in pacing profiles between race distances, with generally more even pacing in the half-marathon and more positive pacing in the marathon. Performance level showed the strongest association with pacing behavior: faster runners exhibited more stable pacing patterns, whereas slower runners showed greater variability and a more pronounced pace decline. Sex differences appeared more evident in marathon running, where women generally demonstrated more even pacing than men, whereas age-related differences were small and inconsistent. Comparisons between race distances suggest that pacing variability may increase with longer race duration and lower performance level. Conclusions: Overall, performance level showed the most consistent relationship with pacing behavior in long-distance running. In mass-participation events, observed pacing profiles may reflect fatigue-related changes and inaccurate pace selection rather than a deliberate strategy. More even or slightly negative pacing has generally been associated with more favorable performance outcomes, but this should be interpreted as an observed association rather than a direct prescriptive recommendation. The proposed practical reference points should be viewed as tentative, evidence-informed aids for interpreting pacing behavior according to performance level and race distance, while using sex and age as secondary contextual factors. Full article

Objectives: This study aimed to analyze the placebo effect of caffeine expectancy on interval running performance in moderately trained athletes. Methods: Participants were randomly allocated to one of two treatment sequences (placebo–control or control–placebo) and completed two 5 × 1000-m training sessions on an outdoor athletics track under two conditions: (1) ingestion of a placebo capsule described as containing caffeine (3 mg·kg⁻¹), and (2) a control condition (no ingestion). Running performance (primary outcome), pacing strategy, heart rate, kinematic variables, perceived exertion (RPE), and side effects were recorded. Results: Fourteen participants were randomized and all completed both study periods and were included in the analyses. No statistically significant differences were observed in total session time between conditions (1021.50 ± 114.01 vs. 1017.23 ± 109.08 s, control vs. placebo, respectively; p = 0.270; ES = 0.31). Likewise, no differences were found in pacing strategy, heart rate, rating of perceived exertion, or running kinematics (all p > 0.05). However, participants reported greater feelings of activeness and higher ratings of selected caffeine-related side effects, including increased urine production and sleep disturbances, under the placebo condition. Conclusions: Believing that caffeine had been consumed did not improve interval-running performance, pacing strategy, physiological responses, or running kinematics in trained runners. However, caffeine expectancy altered the perception of some expected caffeine-related side effects. These findings provide limited evidence for an ergogenic placebo effect of caffeine during interval training and highlight the need for further research to identify the conditions under which expectancy effects may influence exercise performance. Full article

Background: Aortic insufficiency (AI) during long-term left ventricular assist device (LVAD) support creates a maladaptive recirculatory loop that impairs forward flow, increases left ventricular filling pressures, leading to progressive congestion, and accelerates right ventricular dysfunction. Surgical correction carries high risk, making transcatheter aortic valve implantation (TAVI) an alternative strategy, although outcomes are challenged by complex annular geometry and the absence of calcific anchoring. Methods: We performed a retrospective single-center analysis of consecutive LVAD-supported patients undergoing TAVI for native AI. Annular sizing was area-based with a predefined oversizing strategy of approximately 15–30%, adjusted to annular geometry. Balloon-expandable valves were implanted using controlled deployment under rapid pacing and temporary LVAD flow reduction. Outcomes were assessed according to VARC-3 definitions. Results: Fourteen patients underwent TAVI. The cohort demonstrated high anatomical complexity, including annular eccentricity (median 0.21 [IQR 0.19–0.23]), horizontal aorta (42.9%), and flared left ventricular outflow tract (LVOT) morphology (50%). Median annular area was 528 mm² (IQR 487–574), with area-derived diameter 25.9 mm (IQR 24.9–27.0). Mean oversizing was 21.9% (median 20.0%; range 15.1–30.3%). Technical and VARC-3 device success were achieved in 71.4%. Second valve implantation occurred in 28.6%. Procedural mortality was 0%. In-hospital mortality (21.4%) was related to low cardiac output and multi-organ dysfunction rather than device failure. No survivor had moderate or greater residual AI at discharge. Conclusions: In anatomically complex LVAD patients, an anatomy-driven balloon-expandable TAVI strategy achieved elimination of clinically significant AI with zero procedural mortality. In our interpretation, mortality reflected the advanced stage of heart failure at the time of intervention rather than acute device-related failure. Full article