Search Results (1,795)

Background/Objectives: Hyperbaric Oxygen Therapy (HBOT) involves breathing oxygen at pressures greater than atmospheric levels and is used to treat diverse clinical conditions. However, little is known about the lived experiences and perceived needs of patients undergoing scheduled treatment in multiplace hyperbaric chambers, where nurses play a key role in support, safety, and communication. This study aimed to explore the perceptions, expectations, and needs of patients receiving scheduled HBOT sessions in a multiplace chamber in a hospital setting. Methods: A qualitative phenomenological design was used. Participants were recruited consecutively among adults who had completed at least 10 HBOT sessions and demonstrated adequate cognitive function. Individual semi-structured interviews were conducted between January and March 2023 in locations chosen by participants. Interviews were audio-recorded, transcribed, and validated by participants. Results: Twelve participants (eight men, four women; aged 25–84 years) were included. Four thematic areas emerged: (1) Biopsychosocial lived experiences, including initial uncertainty, physical discomfort such as ear pressure or mask-related issues, and progressive recognition of therapeutic benefits. (2) Interpersonal relationships, highlighting trust, security, and emotional support provided mainly by nurses. (3) Communication experiences, with participants expressing satisfaction but requesting clearer, earlier information on procedures, risks, and expected sensations. (4) Structural and organizational factors, where transportation logistics and treatment scheduling were significant sources of fatigue and discomfort. Conclusions: Patients valued HBOT and perceived notable health improvements, while identifying specific unmet informational and organizational needs. These findings suggest the importance of nurse-led educational interventions to enhance preparation, reduce anxiety, and optimize patient experience during HBOT. Full article

Oncosomes, a distinct subclass of extracellular vesicles released predominantly by tumor cells, have attracted increasing interest as potential carriers for targeted drug delivery in cancer research. Characterized by their large size (1–10 µm) and complex molecular cargo, including oncogenic proteins, nucleic acids, and lipids, oncosomes provide a biologically relevant platform for investigating tumor-associated communication and cargo transport. Preclinical studies suggest that oncosomes may enable tumor-associated delivery of therapeutic agents; however, evidence to date remains largely proof-of-concept and derived from in vitro and animal models. This review summarizes current knowledge on oncosome biogenesis and molecular composition; discusses their roles in cancer progression and metastasis; and critically evaluates existing methodologies for oncosome isolation, characterization, and cargo loading, including incubation, electroporation, sonication, freeze–thaw cycling, and transfection. Potential advantages such as cargo capacity and biological compatibility are discussed alongside key challenges, including vesicle heterogeneity, limited loading efficiency, large-scale manufacturing constraints, safety considerations, and regulatory uncertainty. Future perspectives focus on addressing these technical and translational barriers to support the systematic evaluation of engineered oncosomes as an experimental platform for personalized and precision-oriented cancer research. Full article

Autonomous Underwater Vehicles (AUVs) play a central role in marine observation, inspection, and monitoring missions, where effective trajectory planning is essential for ensuring safe operation, reliable sensing, and efficient data transfer. In realistic underwater environments, uneven seafloor geometry, limited acoustic communication, navigation uncertainty, and sensing visibility constraints significantly influence mission performance and challenge classical planar planning formulations. This survey reviews trajectory planning methods for AUVs operating in uneven environments, with a focus on two major classes of underwater sensing missions: underwater area coverage using onboard sensors and underwater sensor data collection within underwater acoustic sensor networks (UASNs) supporting the Internet of Underwater Things (IoUT). For area coverage, the survey examines the progression from classical planar coverage strategies to terrain-aware, occlusion-aware, multi-AUV, and online planning frameworks designed to address uneven terrain and sensing visibility. For underwater sensor data collection, it reviews mobile sink-based trajectory planning strategies, including energy-aware, channel-aware, and information-based formulations based on metrics such as Age of Information (AoI) and Value of Information (VoI), as well as cooperative architectures involving unmanned surface vehicles (USVs). By synthesizing these two bodies of literature, the survey clarifies current capabilities and limitations of trajectory planning methods for AUVs operating in uneven underwater environments. Full article

With the increasing deployment of grid-forming static var generators (GFM-SVG) in modern power systems, the reliability of the valve hall that houses the core power modules has become a critical concern. To overcome the limitations of conventional wired monitoring systems—complex cabling, poor scalability, and incomplete state perception—this paper proposes and implements a multi-source fusion wireless data acquisition system specifically designed for GFM-SVG valve halls. The system integrates acoustic, visual, and infrared sensing nodes into a wireless sensor network (WSN) to cooperatively capture thermoacoustic visual multi-physics information of key components. A dual-mode communication scheme, using Wireless Fidelity (Wi-Fi) as the primary link and Fourth-Generation Mobile Communication Network (4G) as a backup channel, is adopted together with data encryption, automatic reconnection, and retransmission-checking mechanisms to ensure reliable operation in strong electromagnetic interference environments. The main innovation lies in a multi-source information fusion algorithm based on an improved Dempster–Shafer (D–S) evidence theory, which is combined with the object detection capability of the You Only Look Once, Version 8 (YOLOv8) model to effectively handle the uncertainty and conflict of heterogeneous data sources. This enables accurate identification and early warning of multiple types of faults, including local overheating, abnormal acoustic signatures, and coolant leakage. Experimental results demonstrate that the proposed system achieves a fault-diagnosis accuracy of 98.5%, significantly outperforming single-sensor approaches, and thus provides an efficient and intelligent operation-and-maintenance solution for ensuring the safe and stable operation of GFM-SVG equipment. Full article

This study investigates the physical and cyber-physical resilience of smart grids with a high share of renewable energy sources (RESs) dominated by permanent magnet synchronous generators (PMSGs). The originality of this work lies in the development and unified evaluation of five integrated control strategies, the PLL with grid following, VSG with grid shaping, VSG+BESS, VSG+STATCOM, and VSG+BESS+STATCOM, implemented within a coherent simulation framework based on Python. Unlike previous works that analyze these methods in isolation, this study provides a comprehensive quantitative comparison of their dynamic characteristics, including frequency root mean square deviation, maximum deviation, and composite resilience index (RI). To extend the analysis beyond static conditions, a multi-generator (multi-PMSG) scenario with heterogeneous inertia constants and variable load profiles is introduced. This dynamic model allows the evaluation of natural inertia diversity and the effects of inter-generator coupling compared to the synthetic inertia emulation provided by VSG-based control. The combined VSG+BESS+STATCOM configuration achieves the highest synthetic resilience, improving frequency and voltage stability by up to 15%, while the multi-PMSG system demonstrates comparable or even higher RI values due to its inherent mechanical inertia and decentralized response behavior. In addition, a cyber-physical scenario is included to evaluate the effect of communication delays and false data injection (FDI) on VSG frequency control. The results show that a communication delay of 50 ms reduces RI by approximately 0.2%, confirming that even minor cyber disturbances can affect synchronization and transient recovery. However, hybrid control architectures with local energy buffering (BESS) show superior resilience under such conditions. The main technical contribution of this work is the establishment of an integrated analytical and simulation framework that enables the joint assessment of synthetic, natural, and cyber-physical resilience in converter-dominated smart grids. This framework provides a unified basis for the analysis of dynamic stability, hybrid control interaction, and the impact of cyber uncertainty, thereby supporting the design of low-inertia, resilient, and secure next-generation power systems. Full article

Background: This study examined how patients and caregivers perceive and experience AI-based care technologies through text mining analysis. The goal was to identify major themes, sentiments, and value-oriented interpretations embedded in their narratives and to understand how these perceptions align with key dimensions of patient-centered care. Methods: A corpus of publicly available narratives describing experiences with AI-based care was compiled from online communities. Natural language processing techniques were applied, including descriptive term analysis, topic modeling using Latent Dirichlet Allocation, and sentiment profiling based on a Korean lexicon. Emergent topics and emotional patterns were mapped onto domains of patient-centered care such as information quality, emotional support, autonomy, and continuity. Results: The analysis revealed a three-phase evolution of care values over time. In the early phase of AI-mediated care, patient narratives emphasized disruption of relational care, with negative themes such as reduced human connection, privacy concerns, safety uncertainties, and usability challenges, accompanied by emotions of fear and frustration. During the transitional phase, positive themes including convenience, improved access, and reassurance from diagnostic accuracy emerged alongside persistent emotional ambivalence, reflecting uncertainty regarding responsibility and control. In the final phase, care values were restored and strengthened, with sentiment patterns shifting toward trust and relief as AI functions became supportive of clinical care, while concerns related to depersonalization and surveillance diminished. Conclusions: Patients and caregivers experience AI-based care as both beneficial and unsettling. Perceptions improve when AI enhances efficiency and information flow without compromising relational aspects of care. Ensuring transparency, explainability, opportunities for human contact, and strong data protections is essential for aligning AI with principles of patient-centered care. Based on a small-scale qualitative dataset of patient narratives, this study offers an exploratory, value-oriented interpretation of how relational care evolves in AI-mediated healthcare contexts. In this study, care-ethics values are used as an analytical lens to operationalize key principles of patient-centered care within AI-mediated healthcare contexts. Full article

Sustainable service excellence in globalized industries requires organizations to develop workforce capabilities that support long-term relationship-building, cultural respect, and effective cross-cultural communication. This study examines how cultural intelligence functions as a mechanism for sustainable cross-cultural workforce development by investigating relationships among individual cultural values, cultural intelligence dimensions, and influence tactics among airline cabin crew members. Integrating Hofstede’s cultural dimensions framework, Ang and colleagues’ cultural intelligence model, and Yukl’s influence tactics taxonomy, we test a comprehensive mediation model using survey data from six hundred and sixty-three cabin crew members employed by international airlines operating in Turkey. The findings reveal that collectivism, long-term orientation, and uncertainty avoidance positively predict cultural intelligence development, creating foundations for sustainable cross-cultural competence. Cultural intelligence dimensions demonstrate differentiated effects on influence tactics, with metacognitive and behavioral cultural intelligence enhancing rational persuasion, behavioral cultural intelligence exclusively predicting relational tactics, and complex competitive mediation patterns for coercive tactics wherein motivational cultural intelligence reduces pressure-based influence while cognitive and behavioral dimensions increase strategic assertiveness. Cultural values directly influence tactics beyond cultural intelligence effects, with uncertainty avoidance most strongly predicting both rational and relational approaches that support relationship sustainability, while masculinity and power distance drive coercive tactics that may undermine long-term service relationships. These findings demonstrate that cultural intelligence functions as a multidimensional mediating mechanism with sometimes opposing effects, challenging assumptions that cross-cultural competencies uniformly produce sustainable outcomes. The research contributes to sustainable human resource management theory by illuminating how cultural socialization influences behavioral outcomes through complex psychological pathways, while offering practical guidance for aviation industry recruitment, training, and performance management systems seeking to build sustainable cross-cultural service capabilities. By revealing that certain cultural intelligence dimensions can enable both relationship-building and strategic coercion, the study highlights the importance of coupling cross-cultural skill development with ethical frameworks and motivational engagement to ensure that enhanced cultural capabilities support rather than undermine sustainable, respectful cross-cultural service relationships. Full article

Background: Financial sustainability remains a central challenge for U.S. hospitals as rising operating costs, shifting federal reimbursement, and policy uncertainty intensify economic pressures. This study estimates the prevalence and recent changes in financial distress among U.S. short-term acute care hospitals. Methods: We conducted a national longitudinal analysis of all U.S. short-term acute care hospitals from 2021 to 2023 using financial and operational data from Medicare cost reports linked with community-level data from the American Community Survey. Financial distress was measured using the Altman Z-score, with severe distress defined as Z ≤ 1.8. Logistic regression models were used to identify organizational, operational, and market characteristics associated with distress. Results: The proportion of hospitals classified as severely financially distressed increased from 18.6% in 2021 to 22.0% in 2023. Operating margins and returns on assets declined significantly over the study period, while mean Z-scores showed a modest but non-significant downward trend. In adjusted models, urban hospitals had higher odds of distress (OR 1.27, 95% CI 1.15–1.40, p < 0.001), as did hospitals with longer average lengths of stay (OR 1.07 per day, 95% CI 1.04–1.09, p < 0.001) and higher debt-to-equity ratios (OR 1.05 per unit, 95% CI 1.05–1.06, p < 0.001). Higher occupancy rates were protective (OR 0.31, 95% CI 0.25–0.40, p < 0.001). Larger market population was also associated with increased distress risk (OR 1.61, 95% CI 1.21–2.14, p = 0.001), while other market characteristics were not significant. Conclusions: Financial distress remains widespread and appears to be increasing among U.S. acute care hospitals. Operational efficiency, capital structure, and local market scale are key drivers of financial vulnerability, highlighting the need for targeted strategies to strengthen hospital resilience and preserve access to essential acute care services. Full article

Background: Cancer patients admitted to the bone marrow transplant (BMT) unit face life-threatening medical conditions. Consequently, their family members experience uncertainty, resulting in high levels of anxiety and depression (AD). Limited updates and communication from medical staff exacerbate these emotional burdens. To address these challenges, we developed a mobile health (mHealth) intervention, FamCarePlus, and evaluated its feasibility, usability, and efficacy. We hypothesized that the FamCarePlus application would demonstrate a high degree of feasibility and usability and would reduce AD compared to a control group relying solely on traditional communication through the nurses’ station. Methods: We employed a quasi-experimental pretest/posttest non-randomized, non-blinded self-report design over 3 weeks, with an experimental group (n = 10) using FamCarePlus and a control group (n = 9). We selected participants via convenience sampling using the electronic medical record to identify eligible patients and families, guided by inclusion and exclusion criteria. We used descriptive statistics and the Hospital Anxiety and Depression Scale (HADS) guidelines to analyze the data. Feasibility was defined by a retention rate > 80%, with usability testing using the System Usability Scale (SUS) and NASA Task Load Index (NASA-TLX) surveys. The HADS measured AD, comparing baseline to Week 3. Results: We met our feasibility criteria of >80%. All SUS and NASA scores were in the higher index, suggesting a significant degree of usability and low workload demand on participants. For efficacy, we compared baseline mean scores, with the experimental group reporting lower AD levels at Week 1 (41.9% and 27.8%, respectively) than the control group (55.2% and 34.2%, respectively). From Week 1 to Week 3, the percentage change showed an 8.6% decrease in anxiety in the experimental group, compared to a 12.8% decrease in anxiety in the control group. These results were consistent when analyzed according to HADS guidelines. Conclusions: The findings of this study provide preliminary evidence that the FamCarePlus intervention is feasible and usable, while also demonstrating that its use may be associated with a sustained reduction in AD levels among family members of patients admitted to the BMT unit. These outcomes underscore the potential of digital interventions to address disparities in patient health information access and psychosocial support. Full article

Despite existing protection limits established by different health agencies and regulatory bodies, chronic exposure to non-ionising electromagnetic field radiation (NIR) has raised concerns about its potential biological effects and its impact on human health. Exposure to NIR in urban environments is almost inevitable due to the density of devices and communication systems that emit these waves. Correctly measuring exposure levels among city residents is key to determining whether there is a relationship between these levels and potential health problems associated with NIR. Several factors, including the ubiquity of electromagnetic fields (EMFs) and people’s unawareness of their exposure, make the NIR assessment challenging. This paper proposes a standardised procedure for NIR testing and measurement for frequencies from 100 kHz to 3 GHz, designed explicitly for outdoor urban environments. The measurement procedure is intended for populated urban areas, a complex environment for signal propagation. The complete procedure, techniques, and equipment used for wideband and narrowband measurements are detailed, along with their corresponding overall uncertainty budgets. The data collected by this procedure are suitable and valuable for comparative epidemiological studies due to a systematic measurement protocol and rigorous control of measurement uncertainty. The proposed measurement procedure has been tested in two cities in central Spain, with a total population of 262,000. A total of 534 measurement points have been performed. The results can be used to verify compliance with exposure limits and to demonstrate levels below the applicable regulatory limits. Furthermore, it has been possible to test the validity of the hypothesis that urban environments can be characterised by NIR exposure, which was postulated in this work based on an ITU-R-inspired simplification that classifies urban outdoor areas into representative exposure categories. Full article

We consider a network of a residential heating system (RHS) composed of two types of agents: a prosumer and a consumer. Both are connected to a community heating system (CHS), which supplies non-intermittent thermal energy for space heating and domestic hot water. The prosumer utilizes a combination of solar thermal collectors and CHS heat, whereas the consumer depends entirely on the CHS. Any excess heat generated by the prosumer can either be stored on-site or fed back into the CHS. Weather conditions, modeled as a common noise term, affect both agents simultaneously. The prosumer’s objective is to minimize the expected discounted total cost, taking into account storage charging and discharging losses as well as uncertainties in future heat production and demand. This leads to a stochastic optimal control problem addressed through dynamic programming techniques. Scenario-based analyses are then performed to examine how different parameters influence both the value function and the resulting optimal control strategies. For a common noise coefficient ${\sigma }_0=0.4$, the prosumer incurs an approximate $16.08\%$ increase in the aggregated discounted cost from the case of no common noise. For a discharging efficiency ${\eta }_E={\displaystyle \frac{1}{0.9}}$, the maximum aggregated discounted cost increases by approximately $1.85\%$ as compared to the perfect discharging efficiency. Similarly, for a charging efficiency ${\eta }_E=0.9$, we observe an approximate $1.94\%$ increase in the aggregated discounted cost as compared to a perfect charging efficiency. Furthermore, we derive insights into the maximum expected discounted investment that a consumer would need to make in renewable technologies in order to transition into a prosumer. Full article

Thermodynamic entropy generation quantifies irreversibility in energy conversion processes, providing rigorous thermodynamic foundations for optimizing efficiency and sustainability in thermal and energy systems. This critical review synthesizes advances in computational entropy modeling across numerical methods, optimization strategies, and sustainable energy applications. Computational fluid dynamics, finite element methods, and lattice Boltzmann methods enable spatially resolved entropy analysis in convective, conjugate, and microscale systems, but exhibit varying maturity levels and accuracy–cost trade-offs. The minimization of entropy generation and the integration of artificial intelligence demonstrate quantifiable performance improvements in heat exchangers, renewable energy systems, and smart grids, with reported efficiency gains of 15 to 39% in specific applications under controlled conditions. While overall performance depends critically on system scale, operating regime, and baseline configuration, persistent limitations still constrain practical deployment. Systematic conflation between thermodynamic entropy (quantifying physical irreversibility) and information entropy (measuring statistical uncertainty) leads to inappropriate method selection; validation challenges arise from entropy’s status as a non-directly-measurable state function; high-order maximum entropy models achieve superior uncertainty quantification but require prohibitive computational resources; and standardized benchmarking protocols remain absent. Research fragmentation across thermodynamics, information theory, and machine learning communities limits integrated frameworks capable of addressing multi-scale, transient, multiphysics systems. This review provides structured, cross-method, application-aware synthesis identifying where computational entropy modeling achieves industrial readiness versus research-stage development, offering forward-looking insights on physics-informed machine learning, unified theoretical frameworks, and real-time entropy-aware control as critical directions for advancing sustainable energy system design. Full article

Nanofertilizers have attracted increasing attention as an approach to improve the low nutrient use efficiency of conventional fertilizers, in which only a limited fraction of applied nitrogen, phosphorus, and potassium is ultimately taken up by crops. Beyond their capacity to minimize nutrient losses, nanofertilizers have attracted increasing attention for their possible role in addressing environmental issues, including soil eutrophication and the contamination of groundwater systems. Owing to their nanoscale characteristics, including large specific surface area and enhanced adsorption capacity, these materials enable more precise nutrient delivery to the rhizosphere and sustained release over extended periods, while also influencing soil–plant–microbe interactions. In this review, nanofertilizers are classified into six major categories—macronutrient-based, micronutrient-based, organic, controlled-release, composite, and nano-enhanced formulations—and representative examples and preparation routes are summarized, including green synthesis approaches and conventional chemical methods. The agronomic mechanisms associated with nanofertilizer application are discussed, with emphasis on enhanced nutrient uptake, modification of soil physicochemical properties, and shifts in microbial community composition. Reported studies indicate that nanofertilizers can increase crop yield across different crop species and formulations, while also contributing to improved nutrient cycling. Despite these advantages, several limitations continue to restrict their broader adoption. These include uncertainties regarding long-term environmental behavior, relatively high production costs compared with conventional fertilizers, and the absence of well-defined regulatory and safety assessment frameworks in many regions. Overall, this review highlights both the opportunities and challenges associated with nanofertilizer application and points to the need for further development of cost-effective formulations and standardized evaluation systems that account for their distinct environmental interactions. Full article

Hydrogen-integrated decentralized energy systems (DIESs) promise communities higher renewable penetration, greater resilience, and sector coupling across electricity, heat, and mobility. AI supports forecasting, dispatch optimization, multi-asset coordination, and planning, yet designing AI-driven hydrogen communities is challenging because it spans physical infrastructure, cyber-control, and governance. This review (2020–2025) synthesizes design strategies for AI-enabled hydrogen DIESs, distilling architectural patterns, electricity–hydrogen co-optimization, uncertainty-aware operation, and digital-twin planning. It summarizes AI benefits (flexibility, efficiency, reduced curtailment) and recurring risks (forecast-optimization cascades, objective mismatch, data drift, safety and constraint breaches, digital-twin credibility gaps, cybersecurity and privacy issues, and weak reproducibility) and proposes a pragmatic roadmap prioritizing safety-aware control, standardized metrics, transparent assumptions, and community-appropriate governance. Full article

This qualitative, longitudinal study explores how student members’ sense of belonging changed over time in a Networked Improvement Community (NIC). Co-authored by researchers and student participants, this study uses the “students as partners” framework and four constructs of belonging (motivation, competencies, opportunities, and perceptions) to examine how sense of belonging manifested and developed for five student NIC members across their first and final semesters of participation in the NIC. Retrospective analysis of journal entries and interview data collected over two years revealed: student motivations shifted from individual goals to a collective investment in the NIC’s systemic improvement goals; students developed competencies in leadership, data-informed decision-making, and equity based on their lived experiences; and intentional NIC structures supported a shift in the student members’ opportunities for belonging from relationship-building and anonymous decision-making to taking on more responsibilities in NIC initiatives. As a result, student perceptions evolved from uncertainty about their roles to having a strong sense of agency, influence, and community within the department. The findings from this study offer evidence that intentionally structured NICs can function as spaces where motivations, competencies, opportunities, and perceptions grow together and position student members as contributors to inclusive systemic change in undergraduate STEM education. Full article