Search Results (7,798)

A living organism can be regarded as a dissipative, self-organizing physical system operating far from thermodynamic equilibrium. Such systems can be effectively described within the framework of Markov jump processes subjected to an external driving force that sustains the system away from equilibrium—leading, in the special case of stabilization, to a non-equilibrium steady state (NESS). By combining the Markov formalism with concepts from stochastic thermodynamics, we demonstrate the temporal evolution of entropy in such systems: entropy decreases during growth and development, stabilizes at maturity under NESS conditions, and subsequently increases during aging, death, and decomposition. This characteristic trajectory, which we term the entropy bathtub, highlights the universal thermodynamic structure of living systems. We further show that the system exhibits continuous yet time-dependent positive entropy production, in accordance with fundamental thermodynamic principles. Perturbations of the driving force—whether reversible or irreversible—naturally capture the impact of external stressors, providing a conceptual analogy to pathological processes in biological organisms. Although the model does not introduce fundamentally new elements to the physics of life, it offers a simple tool for exploring entropy-driven mechanisms in living matter. Full article

Agentic artificial intelligence (AI) is emerging as a paradigm for next-generation smart grids, enabling autonomous decision-making, adaptive coordination, and resilient control in complex cyber–physical environments. Unlike traditional AI models, which are typically static predictors or offline optimizers, agentic AI systems perceive grid states, reason about goals, plan multi-step actions, and interact with operators in real time. This review presents the latest advances in agentic AI for power systems, including architectures, multi-agent control strategies, reinforcement learning frameworks, digital twin optimization, and physics-based control approaches. The synthesis is based on new literature sources to provide an aggregate of techniques that fill the gap between theoretical development and practical implementation. The main application areas studied were voltage and frequency control, power quality improvement, fault detection and self-healing, coordination of distributed energy resources, electric vehicle aggregation, demand response, and grid restoration. We examine the most effective agentic AI techniques in each domain for achieving operational goals and enhancing system reliability. A systematic evaluation is proposed based on criteria such as stability, safety, interpretability, certification readiness, and interoperability for grid codes, as well as being ready to deploy in the field. This framework is designed to help researchers and practitioners evaluate agentic AI solutions holistically and identify areas in which more research and development are needed. The analysis identifies important opportunities, such as hierarchical architectures of autonomous control, constraint-aware learning paradigms, and explainable supervisory agents, as well as challenges such as developing methodologies for formal verification, the availability of benchmark data, robustness to uncertainty, and building human operator trust. This study aims to provide a common point of reference for scholars and grid operators alike, giving detailed information on design patterns, system architectures, and potential research directions for pursuing the implementation of agentic AI in modern power systems. Full article

Background/Objectives: Obesity has a high prevalence and is associated with many health risks. Minimal effects from behavioral obesity treatments might be linked to their atheoretical dependence on simply educating participants on healthy eating and increased physical activity/exercise, rather than evolving behavior-change methods through theory-based research. The use of pharmacologic interventions has recently overtaken bariatric surgery in medically based efforts to obtain greater weight losses than through behavioral means. Methods: The present longitudinal observational study aimed to extend earlier treatment-associated findings concerned with the order of emphasizing 3-, 6-, and 9-month changes in the theory-driven psychosocial constructs of self-regulation, self-efficacy, and mood associated with 12-month improvements in weight-loss behaviors. The “parent study” of women with obesity (N = 106) found significant paths through changes in self-regulation → mood → self-efficacy and mood → self-regulation → self-efficacy. In the present extension of that investigation, only those participants who did not already complete recommended physical activity amounts and consume recommended portions of fruits/vegetables at baseline were included (N = 73). Results: Only paths from changes in mood → self-regulation → self-efficacy were significantly associated with 12-month improvements in both physical activity and dietary behaviors. A mean weight change of −5.2 kg, or −5.5% from baseline weight, was found. Baseline scores in emotional eating and anxiety significantly moderated the mood–self-regulation change relationships within the significant paths. Conclusions: Findings suggested that future treatment contents focus on early physical activity-associated improvement in mood because of its association with increased usage of treatment-developed self-regulatory skills. Those skills should then be leveraged because of their association with feelings of ability (i.e., self-efficacy) to overcome lifestyle barriers to weight-loss behavior changes. Further improvements in behavioral obesity treatments should be reconsidered as either stand-alone modalities or, after appropriate testing, as an adjunct to medical means. Full article

To address the limitations of parameter drift in physical models and poor generalization in data-driven methods, this paper proposes a self-evolving digital twin framework for PMSM thermal safety. The framework integrates a dynamic-batch Bayesian calibration (DBBC) algorithm and a hierarchical physics-aware network (HPA-Net). First, the DBBC eliminates plant–model mismatch by robustly identifying stochastic parameters from operational data. Subsequently, the HPA-Net adopts a “physics-augmented” strategy, utilizing the calibrated physical model as a dynamic prior to directly infer high-fidelity temperature via a hierarchical training scheme. Furthermore, a real-time demagnetization safety margin (DSM) monitoring strategy is integrated to eliminate “false safe” zones. Experimental validation on a PMSM test bench confirms the superior performance of the proposed framework, which achieves a Root Mean Square Error (RMSE) of 0.919 °C for the stator winding and 1.603 °C for the permanent magnets. The proposed digital twin ensures robust thermal safety even under unseen operating conditions, transforming the monitoring system into a proactive safety guardian. Full article

Background: “Healthy Life Longevity” (a latent variable) is defined as the number of survival days, along with recommended subjective health and long-term care needs. This study aimed to clarify the structural relationships among several related factors. Methods: In September 2001, a postal survey using a self-administered questionnaire was conducted among 16,462 elderly residents of Tokyo. In a cohort study, 8162 individuals with confirmed survival after six years were examined. We analyzed data to evaluate the need for long-term care three years after the initial survey. Additionally, the number of days survived was calculated from the third year after the initial survey. Covariance structure analysis was used to explore the structural relationships. Results: The direct effects of lifestyle habits, including a healthy diet, dental care rather than physician care, and socioeconomic factors, were minimal in improving “Healthy Life Longevity.” However, a structural relationship was established: desirable lifestyles, including diet and dental care, were selected based on socioeconomic status, thereby influencing mental, physical, and social health and reducing disease incidence. This relationship ultimately enhanced “Healthy Life Longevity.” Socioeconomic factors were identified as confounders in the association between preferred lifestyle choices, including diet, and Healthy Life Longevity. The determination coefficient of “Healthy Life Longevity” is 83%. Conclusions: Although healthy longevity can be achieved by improving mental, physical, and social health, and reducing disease burden, the relevant structure is shaped by socioeconomic status. Additionally, socioeconomic status is associated with healthy longevity by facilitating the choice of a preferred lifestyle, including diet, and the selection of a dentist. Future randomized intervention studies focused on socioeconomic status should explore ways to promote healthy longevity. Full article

Curling presents a challenging continuous-control problem in which shot outcomes depend on long-horizon interactions between complex physical dynamics, strategic intent, and opponent responses. Despite recent progress in applying reinforcement learning (RL) to games and sports, curling lacks a unified environment that jointly supports stable, rule-consistent simulation, structured state abstraction, and scalable agent training. To address this gap, we introduce a comprehensive learning framework for curling AI, consisting of a full-sized simulation environment, a task-aligned Markov decision process (MDP) formulation, and a two-phase training strategy designed for stable long-horizon optimization. First, we propose a novel MDP formulation that incorporates stone configuration, game context, and dynamic scoring factors, enabling an RL agent to reason simultaneously about physical feasibility and strategic desirability. Second, we present a two-phase curriculum learning procedure that significantly improves sample efficiency: Phase 1 trains the agent to master delivery mechanics by rewarding accurate placement around the tee line, while Phase 2 transitions to strategic learning with score-based rewards that encourage offensive and defensive planning. This staged training stabilizes policy learning and reduces the difficulty of direct exploration in the full curling action space. We integrate this MDP and training procedure into a unified Curling RL Framework, built upon a custom simulator designed for stability, reproducibility, and efficient RL training and a self-play mechanism tailored for strategic decision-making. Agent policies are optimized using Soft Actor–Critic (SAC), an entropy-regularized off-policy algorithm designed for continuous control. As a case study, we compare the learned agent’s shot patterns with elite match records from the men’s division of the Le Gruyère AOP European Curling Championships 2023, using 6512 extracted shot images. Experimental results demonstrate that the proposed framework learns diverse, human-like curling shots and outperforms ablated variants across both learning curves and head-to-head evaluations. Beyond curling, our framework provides a principled template for developing RL agents in physics-driven, strategy-intensive sports environments. Full article

Background: Conservative treatment of chronic musculoskeletal pain includes exercise, manual therapy, medications, physical agents/modalities, and Therapeutic Patient Education (TPE). Research on TPE has predominantly focused on spinal pain, so we do not know the extent and scope of clinical research in other areas, particularly lower extremities. This review aimed to map current research on this topic. Methods: We searched PubMed, PEDro, CINAHL, PsycINFO, and Cochrane Library up to 1 April 2024. We included RCTs on adults with chronic lower limb musculoskeletal pain, written in English, French, Spanish, or Italian. Results: Fifty-two records concerning knee osteoarthritis (n.33), hip and knee osteoarthritis (n.8), hip osteoarthritis (n.3), chronic knee pain (n.3), patellofemoral pain (n.3), and gluteal tendinopathy (n.2) were included. TPE was delivered through self-management, disease-specific information, pain education, and the management of physical activity, load, diet, stress, and sleep. Interventions were both individual- and group-based; delivery methods included in-person intervention, telephone/video calls, and web tools/apps. TPE combined with exercise seemed to be more effective than exercise alone, information/little education, or usual care. The effects of TPE as a stand-alone intervention appeared uncertain. Conclusions: There is considerable variability in TPE in terms of teaching topics, providers, administration methods, and dosage of interventions. Future studies should investigate the effects of TPE in young adult populations and in ankle conditions. They should also investigate the effects of TPE on pain intensity versus pain interference with activities, by deepening TPE effects on disability and quality of life. Full article

Background: Among many deleterious effects on the well-being of children and youth, the COVID-19 pandemic contributed to a surge in youth mental health distress. This, coupled with pre-existing prolonged wait times for mental health care, highlighted the need for accessible community-based mental health supports. The Healthy Living Project (HELP) is a virtual lifestyle change support program aimed at promoting positive lifestyle changes and improved mental well-being among youth with mental distress. A pilot feasibility study explored youth engagement with HELP e-resources, and preliminary mental health and lifestyle measures over a 3-month period. Methods: Youth were enrolled in a 3-month pilot of the HELP e-resource. Feasibility metrics (recruitment, retention, and platform engagement) were documented, while exploratory self-reported data on emotional and behavioral difficulties, youth quality of life, sedentary behavior (screen time), sleep hygiene, and physical activity were assessed at baseline and 3 months. Results: Twenty-three youth (mean age 15.7 years, SD 1.7) completed baseline assessments and started the intervention, with ten participants retained by the end of the study. Compared with non-completers (n = 13), study completers (n = 10) tended to report higher quality of life and healthier habits (lower screen time, improved sleep hygiene, and higher activity). Ongoing access to HELP over 3 months was associated with suggestive trends toward improvement in emotional and behavioral difficulties and sleep hygiene. Engaged participants who received screen time education tended to report lower screen times as compared to unengaged counterparts. Conclusions: This study provides early insights into the implementation and acceptability of HELP e-resources among youth experiencing mental distress, with suggestive trends toward potential benefit. Low recruitment and high attrition preclude definitive conclusions, and the findings should be interpreted as exploratory. Lessons from this pilot will inform the design of a subsequent trial to more rigorously evaluate feasibility and the potential impact of HELP on youth with mental distress. Full article

Background: Health literacy can be defined as the ability to access, understand, evaluate, and apply health information in order to make informed health decisions in daily life. Despite its importance, little is known about health literacy in the domains of physical activity and nutrition among children with congenital heart disease. The aim of this study was to examine differences in health literacy related to physical activity and nutrition between children and adolescents with congenital heart disease and a healthy control group (Control). Methods: A total of 244 children and adolescents (age 8–18 years; mean age 13.4 ± 3.1 years, 53.3% girls) were enrolled, comprising 122 patients with congenital heart disease and 122 age- and sex-matched healthy controls. Patients were recruited during routine medical examinations at the German Heart Center, while controls were recruited from Munich schools. The Physical Activity Health Literacy Scale for Children (PAHL-C) and the Nutrition Health Literacy Scale for Children (NHL-C) were used to assess health literacy in the domains of physical activity and nutrition. Scores for both scales were calculated as additive sum scores and converted to a scale from 0 to 100, where higher scores indicate better health literacy. Physical activity levels were measured via self-reported frequency of days per week in which children engaged in at least one hour of physical activity. Results: Children with congenital heart disease showed lower PAHL-C scores (Patients: 65.9 ± 18.0 vs. Control: 72.9 ± 14.9, p = 0.001) and lower NHL-C scores (Patients: 63.6 ± 19.0 vs. Control: 69.3 ± 14.8, p = 0.009) than healthy controls. Boys reported significantly higher self-reported physical activity levels (p = 0.001) and had significantly higher PAHL-C scores than girls (p < 0.001). Patients with congenital heart disease reported significantly less physical activity compared to controls (Patients: 4.2 ± 1.7 days/week vs. Control: 5.2 ± 1.8 days/week, p < 0.001). Conclusions: Children with congenital heart disease demonstrate lower health literacy in the domains of physical activity and nutrition than their healthy counterparts and report being less active. This highlights the need for targeted interventions to address both physical activity and health literacy in children with congenital heart disease. Full article

Studying the origin of life requires identifying chemical and physical processes that could have supported early self-replicating and evolving molecular systems. Besides the requirement of information storage and transfer, an essential aspect is an energy source that could have thermodynamically driven the formation and replication of these molecular assemblies. Chemical energy sources such as cyclic trimetaphosphate are attractive because they could drive replication with relatively simple catalysts. Here, we focus on cyclic trimetaphosphate (cTmp), and compare its solubility in water to linear triphosphate, pyrophosphate, and phosphite when Mg²⁺ or Ca²⁺ are present. These solubilities are important for facilitating the reactions under prebiotically plausible conditions. The results showed that cTmp was soluble even at molar concentrations of Mg²⁺ and little precipitation with 200 mM Ca²⁺. In contrast, pyrophosphate and linear triphosphate precipitated efficiently even at low divalent metal ion concentrations. The precipitation of phosphate was pH-dependent, showing similar precipitation with Mg²⁺ and Ca²⁺ at a prebiotically plausible pH of 6.5. Phosphite was soluble at high Mg²⁺ concentrations but started precipitating with increasing Ca²⁺ concentration. At conditions that model Archaean seawater, cTmp was the most soluble of these compounds. Together, this experimental overview may help to identify promising conditions for lab-based investigations of phosphate-based energy metabolisms in early life forms. Full article

Background: Ultra-processed food (UPF) consumption has increased significantly in Latin America and Spain, impacting both health and environmental sustainability. To our knowledge, this is the first multicenter study to examine the association between UPF consumption and sustainable lifestyle behaviors in Latin America and Spain. Objective: To evaluate the association between UPF consumption and sustainable lifestyle behaviors in Latin America and Spain. Methods: This was an observational, analytical, multicenter, cross-sectional study. A validated, self-administered online questionnaire was distributed in 14 countries between March 2023 and January 2024. The survey collected sociodemographic data, UPF intake (classified using the NOVA system), body mass index and sustainable lifestyle behaviors (food, transport, environment). Multivariate linear regression models were applied to assess associations, adjusting for age, sex, smoking, physical activity and BMI. Results: Among 6009 adults (mean age: 34.98 ± 12.55; 79.5% women), those with the highest consumption of UPF (fast food, beverages and juices, salty snacks and sweet snacks) were significantly more likely to be in the least sustainable quartile compared to those who did not consume these food products ((OR = 2.51; 95% CI: 1.79–3.54), (OR = 1.82; 95% CI: 1.50–2.22), (OR = 1.51; 95% CI: 1.32–1.73) and (OR = 1.42; 95% CI: 1.20–1.67), respectively, with p values < 0.001). Conclusions: High consumption of ultra-processed foods (UPFs) is inversely associated with sustainable lifestyles. These findings position UPF consumption not only as a health problem but also as a key indicator of unsustainable lifestyles. Full article

Modern engineering systems require reliable and timely Fault Detection and Diagnosis (FDD) to ensure operational safety and resilience. Traditional model-based and rule-based approaches, although interpretable, exhibit limited scalability and adaptability in complex, data-intensive environments. This survey provides a systematic overview of recent studies exploring Machine Learning (ML) and Artificial Intelligence (AI) techniques for FDD across industrial, energy, Cyber-Physical Systems (CPS)/Internet of Things (IoT), and cybersecurity domains. Deep architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, and Graph Neural Networks (GNNs) are compared with unsupervised, hybrid, and physics-informed frameworks, emphasizing their respective strengths in adaptability, robustness, and interpretability. Quantitative synthesis and radar-based assessments suggest that AI-driven FDD approaches offer increased adaptability, scalability, and early fault detection capabilities compared to classical methods, while also introducing new challenges related to interpretability, robustness, and deployment. Emerging research directions include the development of foundation and multimodal models, federated learning (FL), and privacy-preserving learning, as well as physics-guided trustworthy AI. These trends indicate a paradigm shift toward self-adaptive, interpretable, and collaborative FDD systems capable of sustaining reliability, transparency, and autonomy across critical infrastructures. Full article

Introduction: Caregiving has been an emerging public health priority mainly due to the rapid pace of population aging, increase in chronic diseases and shortages of health professionals. In clinical settings, caregivers have a crucial role by providing support to patients. Consequently, they may experience physical and emotional burden mainly attributed to environmental, personal or family stressors. The aim of this study was to evaluate fatigue and the associated factors among caregivers of hospitalized patients in medical-surgical wards. Methods and Material: In the present study caregivers of hospitalized patients in medical and surgical wards were enrolled. Collection of data was performed with the following: a. Fatigue Assessment Scale (FAS), b. Zung Self-Rating Anxiety Scale (SAS) and c. Athens Insomnia Scale (AIS), which included patients’ characteristics. In this cross-sectional study, participants were selected using the method of convenience sampling. Results: Of the 142 participants, the majority were spouses (64.8%), female (64.8%) and over 60 years old (53.6%). The mean FAS score was 25.9 ± 9.3, the mean SAS was 38.1 ± 9.0, and the mean AIS score was 7.6 ± 4.7, indicating moderate, moderate to low and moderate levels of fatigue, anxiety and insomnia, respectively. Moreover, fatigue showed a positive linear correlation with both anxiety (Spearman’s rho = 0.713) and insomnia (Spearman’s rho = 0.671). The factors found to be statistically significantly associated with caregivers’ fatigue were the following: gender (p = 0.001), length of hospitalization (p = 0.013), experience of environmental stressors (p = 0.045), experience of financial stressors (p = 0.001), and unfamiliarity with the provision of care (p = 0.001). Conclusions: Provided that caregivers’ involvement in care not only enhances patient well-being but also supports clinical teams, it is widely comprehended that addressing their needs should be emphasized. Full article

This study aimed to examine the association between physical activity and individuals’ health status, healthcare utilization, socio-demographic characteristics, and health behaviors in a large representative sample from Northern Greece. A cross-sectional study was conducted involving 1227 participants (47.4% males, mean age 49.94 ± 14.87 years) from Thrace, Greece, selected through a two-stage stratified sampling method. According to the Greek version of IPAQ, participants were classified as inactive/insufficiently active, sufficiently and highly active. Data on socio-demographic, lifestyle, and health-related variables were collected through structured interviews. Multivariate logistic regression analysis was performed to determine the independent effect of physical activity on subjects’ characteristics using SPSS ver. 19. Half of the participants (49.8%) were inactive/insufficiently active, 418 participants (34.1%) were sufficiently active, and 198 participants (16.1%) were highly active. In univariate analysis, smoking (p < 0.001), higher coffee consumption (p = 0.002), higher adherence to Mediterranean diet (p < 0.001), napping during the day (p = 0.017) and short sleep duration (p < 0.001) were associated with lower prevalence of high activity. In adjusted analyses, sufficiently active participants had a lower risk for bad self-rated health (aOR = 0.63), hypertension (aOR = 0.41), dyslipidemia (aOR = 0.42), diabetes (aOR = 0.53), obesity (aOR = 0.61), cardiovascular diseases (aOR = 0.43), anxiety (aOR = 0.65), depression (aOR = 0.56), daily sleepiness (aOR = 0.62), poor sleep quality (aOR = 0.71), as well as for primary (aOR = 0.54) and secondary (aOR = 0.40) healthcare utilization compared to inactive participants. Higher-intensity physical activity did not enhance these beneficial effects of sufficient activity on subjects’ characteristics. Physical inactivity significantly compromises health across multiple domains. Promoting even moderate-intensity physical activity may reduce chronic disease burden and healthcare utilization. Full article

Selective mutism (SM) is an anxiety disorder that prevents speech in certain situations. Increasingly, it is reported that a proportion of those with SM may also be autistic and that physical freezing may be an important feature of SM. Information on speech and freezing behavior in children with a diagnosis of autism only (n = 20), SM only (n = 61), both autism and SM (n = 19), or neither diagnosis (n = 131) was collected via a self-selected cross-sectional online parent survey with an embedded child survey completed by a small subsection of the children (total n = 27: autism only n = 1, SM only n = 13, both autism and SM n = 3, neither diagnosis n = 10). Throat and body freezing were reported by children with SM, whether they were also autistic or not. The most common reasons given by the children that increased their difficulty in speaking were pressure to talk, worries about how they would be perceived, and fear of making mistakes. The Selective Mutism Questionnaire (SMQ) gave the lowest median score for children with both autism and SM, with median scores increasing in the order SM only, autism only, and neither diagnosis. Children who reported more freezing tended to have lower SMQ scores. Full article