Search Results (692)

Background: Age-related hormonal changes in older women accelerate bone and muscle loss, leading to postural dysfunction and chronic musculoskeletal pain. This study aimed to evaluate the short-term effects of a physical therapy program combining elastic band exercises and vigorous walking on pain, thoracic mobility, and functional capacity in older adult women. Methods: A multicenter randomized controlled trial was conducted older adult women (60–80 years) with chronic non-specific musculoskeletal pain, allocated to an elastic band plus vigorous walking group (EBWG), a vigorous walking group (VWG), or a control group (CG). A total of 91 participants completed all of the assessments. Outcomes included pressure pain threshold (PPT), self-reported pain (VAS), thoracic mobility (UPC, LWC), functional capacity (5XSTS), and perceived improvement (PGIC), evaluated at baseline, after a 4-week intervention, and at 4-week follow-up. Results: The EBWG demonstrated greater improvements in PPT (+0.66 kg/cm² at T2), upper chest expansion (+1.00 cm), and 5XSTS performance (−1.7 s) compared to the control group. The VWG showed significant reductions in overall pain (−0.9 points) and lumbar pain (−1.7 points). Improvements in PPT and thoracic mobility in the EBWG exceeded MDC/MCID thresholds, indicating clinically meaningful changes. Vigorous walking alone improved self-reported pain but was less effective than the multicomponent program. Conclusions: A 4-week combined program of elastic band exercises and vigorous walking produced clinically relevant improvements in pain threshold, thoracic mobility, functional capacity, and perceived change compared to walking alone or usual activity. These findings support the clinical utility of short, feasible, multicomponent interventions for managing chronic musculoskeletal pain in older women. Full article

Pain empathy plays an important role in both social bonding and defensive mechanisms, yet previous studies have mostly used non-predictive paradigms and rarely examined the effects of expectation. Using event-related potentials (ERPs), this study explored how pain expectation temporally modulates empathic responses and proposed an avoidance–approach dual-drive model. Behaviorally, participants responded faster and more accurately under pain-expectation conditions. At the neural level, greater N2 amplitudes were elicited by pain expectation, reflecting avoidance reactions driven by self-protection. In the P3 stage, two concurrent effects emerged: (1) overall P3 amplitudes decreased under pain expectation, suggesting reduced cognitive resource allocation due to avoidance; and (2) painful stimuli still evoked larger P3 amplitudes than neutral stimuli, indicating empathic engagement associated with approach motivation. These results suggest that pain empathy is not governed by a single mechanism but by a dynamic interplay between avoidance and approach motivations at different temporal stages, providing a neurophysiological framework that integrates defensive and affiliative needs in pain empathy. Full article

This study examined how narcissistic traits influence women’s cognitive processing of verbal violence. Using a lexical decision task, an emotional Stroop task, and event-related potentials, we analyzed neural responses to violent versus neutral words in 70 women. Behaviorally, while narcissism showed no significant impact on performance in the Lexical Decision Task, a specific interference effect emerged in the emotional Stroop task, where higher narcissistic vulnerability predicted reduced accuracy for violent words relative to neutral ones. Notably, ERP results revealed a consistent pattern across both tasks: higher PNI total scores significantly predicted reduced amplitudes of early components, specifically the N170 and P2. Furthermore, in the emotional Stroop task, the vulnerability dimension emerged as a significant predictor of reduced EPN and P2 amplitudes. These findings suggest that when exposed to verbal violence, narcissistic women exhibit attenuated early evaluation and attentional allocation. This reflects a preemptive cognitive avoidance strategy used to protect the self-concept, driven primarily by a general narcissistic defensive pattern that manifests most acutely in vulnerable traits under high-interference conditions. Full article

The integration of unmanned aerial vehicles (UAVs), autonomous vehicles, and advanced satellite systems in sixth-generation (6G) networks is poised to redefine next-generation communications as well as next-generation intelligent transportation systems. This paper examines the convergence of UAVs, CubeSats, and terrestrial infrastructures that comprise the framework of Space–Aerial–Ground Integrated Networks (SAGINs) as vital enablers of the International Mobile Telecommunications (IMT)-2030 standards. This paper examines the role of UAVs in providing flexible and quickly deployable airborne connectivity. It also discusses how CubeSats enhance global coverage through low-latency relaying and resilient backhaul links from low Earth orbit (LEO). Additionally, the paper highlights how terrestrial systems contribute high-capacity, densely concentrated communication layers that support various end-user applications. By examining their interoperability and coordinated resource allocation, the paper underscores that the seamless interaction of SAGIN nodes is essential for achieving the ultra-reliable, intelligent, and pervasive communication capabilities envisioned by IMT-2030. As 6G aims for ultra-low latency, high reliability, and massive connectivity, UAVs and CubeSats emerge as key enablers for extending coverage and capacity, particularly in remote and dense urban regions. Furthermore, the role of large language models (LLMs) is explored for intelligent network management and real-time data optimization, while quantum communication is analyzed for ensuring security and minimizing latency. The integration of LLMs into quantum-enhanced edge intelligence for SAGINs represents an emerging research frontier for adaptive, high-throughput, and context-aware decision-making. By exploiting quantum-assisted parallelism and entanglement-based optimization, LLMs enhance the processing efficiency of multimodal data across space, aerial, and terrestrial nodes. This paper further investigates distributed quantum inference and multimodal sensor data fusion to enable resilient, self-optimizing communication systems comprising a high volume of data traffic, which is a critical bottleneck in the global connectivity transition. LLMs are envisioned as cognitive control centers capable of generating semantic representations for mission-critical communications that enhance energy efficiency, reliability, and adaptive learning at the edge. The findings of the survey reveal that quantum-enhanced LLMs overcome challenges pertaining to bandwidth allocation, dynamic routing, and interoperability in existing classical communication systems. Overall, quantum-empowered LLMs significantly assist intelligent, autonomous, and immersive communications in SAGIN, while enabling secure, privacy-preserving communication. Full article

Energy Communities (ECs) have emerged as central legal instruments for decentralized renewable energy deployment across Europe; however, their long-term viability depends critically on financial sustainability mechanisms that remain inadequately understood. This study examines the economic foundations of ECs through a narrative literature review of revenue generation, cost allocation, and the capital mobilization pathways in three representative European markets (Germany, Spain, and Italy). A structured Scopus database search identified 280 peer-reviewed studies published between 2019 and 2025. Following systematic screening, 89 articles were selected for analysis through bibliometric mapping in R (Biblioshiny) and qualitative synthesis in NVivo. The analysis reveals that stable feed-in tariffs, tax incentives, and self-consumption remuneration schemes form the primary revenue mechanisms, while cost management effectiveness varies substantially across countries due to differing grid-charge structures and administrative frameworks. Capital access remains constrained for smaller communities despite hybrid financing innovations combining public grants, cooperative equity, and emerging crowdfunding mechanisms. Regulatory heterogeneity, high upfront investment requirements, and limited institutional credit availability continue to impede scalability. The findings emphasize that achieving widespread EC adoption requires harmonized policy frameworks, transparent cost-sharing arrangements, and diversified investment instruments that align local participation with national decarbonization objectives while ensuring equitable access across diverse socio-economic contexts. Full article

This article examines vulnerability to counterfeit currency fraud in Bulgaria by assessing citizens’ competence in recognizing genuine banknotes of the national currency (BGN) prior to the introduction of euro banknotes in 2026. Counterfeit banknotes represent a form of economic crime in which individual victims’ losses are closely tied to their ability to authenticate cash in everyday transactions. Drawing on level-1 security features and guidelines of the Bulgarian National Bank, we developed a structured questionnaire to operationalize knowledge of key authenticity checks (hologram, intaglio printing, watermark, security thread, see-through register). The survey was administered online and on paper over a 20-day period (22 August–11 September 2025) and completed by 371 respondents from across the country. Using descriptive statistics tools, we identify three distinct groups: (i) highly competent respondents who reliably distinguish genuine from counterfeit banknotes; (ii) individuals with high self-reported confidence but inconsistent performance; and (iii) a particularly vulnerable group with low knowledge of security features, limited awareness of official guidance and low self-confidence. Vulnerability is significantly associated with lower education, residence in smaller settlements, lack of prior exposure to counterfeit banknotes and absence of contact with institutional information campaigns. The findings have direct implications for crime prevention and criminal justice policy: they provide an evidence base for targeted public awareness initiatives and risk-based allocation of resources aimed at protecting high-risk groups from currency-related fraud in the context of the monetary transition. Full article

The cultivation of civilized rural customs—a core dimension of China’s rural revitalization strategy—is crucial for revitalizing the spirit of its rural communities. This study, grounded in the strategic context of China’s rural revitalization, examines the intricate interplay between government leadership and rural self-governance in the development of rural civilization. Using grounded theory to analyze 66 national model cases of “civilized rural culture development”, selected by China’s Ministry of Agriculture and Rural Affairs between 2019 and 2022, the study constructs a “top-down coordination and bottom-up coupling” model, systematically articulating the practical logic and underlying mechanisms of rural civilization development. The study reveals that the practical logic of rural civilization construction is manifested through three coupled adaptation mechanisms: authoritative coupling between Party–government coordination and village-level autonomy in resource allocation; interest coupling through multi-stakeholder participation in organizational mobilization; and value coupling by reconstructing public norms through shared values. These three coupling mechanisms collectively form a “resource revitalization–stakeholder symbiosis–rule reconstruction” pathway, thereby uncovering the collaborative governance logic between government leadership and rural autonomy within rural revitalization. This study offers a new theoretical perspective for understanding the interactive relationship between government and community in China’s rural governance. Full article

Conversational systems based on large language models (LLMs) are being increasingly used as advisors in mental health and self-regulation contexts, yet causal evidence remains limited about whether such guidance strengthens human agency rather than shifting responsibility to the system. We propose a dual framework in which the advice style reflects two dimensions, namely a structural stance (power distance) and a relational stance (psychological safety). In an online vignette experiment in China (N = 980), participants sought job search guidance from an LLM and read either a baseline reply or one of eight discourse variants, while holding the advice content constant. Relative to the baseline, a low power distance and a high psychological safety increased the self-efficacy, whereas a high power distance and a low psychological safety decreased it. Combination conditions revealed an asymmetric constraint: when the power distance was high, the self-efficacy declined even when the psychological safety was high, suggesting that authority allocation can override relational reassurance. Mediation analyses showed that the perceived self-control accounted for 26.3% of the low power distance effect and perceived belongingness accounted for 40.9% of the high psychological safety effect, with no cross-mediation. Although mental health outcomes were not directly measured, our results position conversational stances as actionable levers that shape self-efficacy and agency-related mechanisms, which are critical for persistence and adherence in mental health-relevant behavior change. Full article

Federated learning (FL) has emerged as a promising paradigm for privacy-preserving distributed intelligence in modern urban transportation systems, where vehicles collaboratively train global models without sharing raw data. However, the dynamic nature of vehicular environments introduces critical challenges, including unstable participation, data heterogeneity, and the potential for malicious behavior. Conventional FL frameworks lack effective trust management and adaptive incentive mechanisms capable of maintaining fairness and reliability under these fluctuating conditions. This paper presents a reputation-aware federated learning framework that integrates multi-dimensional reputation evaluation, dynamic incentive control, and malicious client detection through an adaptive feedback mechanism. Each vehicular client is assessed based on data quality, stability, and behavioral consistency, producing a reputation score that directly influences client selection and reward allocation. The proposed feedback controller self-tunes the incentive weights in real time, ensuring equitable participation and sustained convergence performance. In parallel, a penalty module leverages statistical anomaly detection to identify, isolate, and penalize untrustworthy clients without compromising benign contributors. Extensive simulations conducted on real-world datasets demonstrate that the proposed framework achieves higher model accuracy and greater robustness against poisoning and gradient manipulation attacks compared to existing baseline methods. The results confirm the potential of our trust-regulated incentive mechanism to enable reliable federated learning in smart cities transportation systems. Full article

The mental health of people living in farming communities has been identified as an important public health issue. Cumulative exposure to a range of situational factors contributes to heightened risk of poor mental health and suicide. Access to evidence-based psychological treatments is limited by the availability of skilled mental health professionals. The aim of this trial—co-designed by members of the farming community—is to establish the feasibility of conducting randomised controlled, trial-testing, peer-worker-delivered Behavioural Activation in the farming community. We will undertake a single-blind, parallel group, randomised controlled feasibility trial in rural Australia. People living in farming communities aged over 15 years and experiencing moderate to moderately severe depression symptoms will be included in the trial. Participants will be randomly allocated on a 1:1 ratio to 10 sessions of peer-worker-delivered behavioural activation (Back-on-Track) or a self-help workbook (Managing Stress on the Farm). Peer workers are members of the farming community that have completed training in behavioural activation and demonstrated competence. Feasibility outcomes include establishing recruitment rates, willingness to be randomised, dropout rate from trial, acceptability of peer delivered behavioural activation, and willingness to complete trial measures. The trial will contribute high quality evidence of the feasibility of undertaking a full-scale, randomised controlled trial of peer-delivered Behavioural Activation in farming communities in rural Australia. Full article

Limited study time constrains university EFL vocabulary learning, so efficiency should be evaluated alongside accuracy. A web-based multimodal environment was developed that uses a large language model for contextualized drills and tutoring, text-to-speech for pronunciation and listening rehearsal, and an interactive 3D mastery view for self-regulated tracking. Vocabulary knowledge is modeled as a discrete mastery state (m = 0–5), updated after each attempt, and an adaptive scheduler allocates practice across mastery strata. Learning ROI is defined as newly mastered words per hour and computed from logged study time and mastery transitions. In a three-month deployment (N = 171), learners achieved a mean ROI of 9.8 words/hour, about 60% higher than conventional estimates (5–6 words/hour); high-adherence users reached 17–21 words/hour. End-of-trial surprise review results indicated retention above 85%. For CET-4, the platform cohort obtained the highest mean score (457.66) and pass rate (74.24%) compared with Baicizhan (442.22; 64.81%) and traditional instruction (428.60; 53.70%). The results provide quantitative support for the hypothesis that multimodal personalization improves time-based vocabulary gains and their durability. Full article

Background: Functional impairments associated with bipolar disorder have a significant impact on daily life, including work, social relationships, and independent living. Bipolar disorder is treated with many approaches, with pharmacotherapy being the first choice; however, cases of relapse and side effects have been reported. The literature suggests that psychosocial interventions are effective in improving treatment adherence, recognizing early warning signs, enhancing self-management skills, and fostering open communication. The effects of interpersonal and social rhythm therapy (IPSRT) on circadian rhythm stability and social functioning in people with bipolar disorder remain uncertain. Therefore, this study is needed. Methods: This quasi-experimental study was conducted in the psychiatric outpatient clinic of a university hospital. Participants were recruited using convenience sampling from the psychiatric outpatient clinic. Eligible participants were then randomly allocated to either the intervention or control group using a coin-flip method. The dose of the intervention averaged 75 min per session with a weekly frequency over 12 weeks. Outcome measures included the Interpersonal Problem Areas Rating Scale, the Social Rhythm Metric Scale-II-5, and the Multnomah Community Ability Scale. Data were collected at baseline (week 0), post-intervention (week 12), and at follow-up (12 weeks post-intervention), and analyzed using repeated-measures ANOVA. Results: Participants in the IPSRT group demonstrated significant improvements in social rhythm regularity (SRM-II-5: 2.9 ± 1.3 at baseline, 3.7 ± 1.2 post-intervention, and 4.0 ± 1.5 at three-month follow-up; F = 18.5, p < 0.05, η² = 0.37). A significant between-group difference favoring IPSRT emerged at three months (t = 3.01, p < 0.05, d = 0.76). Social functioning also improved significantly in the intervention group (MCAS: 55.5 ± 7.4 at baseline, 63.7 ± 7.1 post-intervention, and 62.3 ± 6.9 at follow-up; F = 29.4, p < 0.05, η² = 0.49). Between-group differences were significant immediately post-intervention (t = 4.10, p < 0.001, d = 1.05) and at three-month follow-up (t = 2.73, p = 0.008, d = 0.72). Conclusions: IPSRT produced sustained improvements in social rhythm stability and social functioning, demonstrating its clinical value in the management of bipolar disorder. Full article

Within the cloud manufacturing paradigm, Cloud Manufacturing Service Composition (CMSC) is a core technology for intelligent resource orchestration in Cloud Manufacturing Platforms (CMP). However, existing research faces critical limitations in real-world CMP operations: single-task-centric optimization ignores resource sharing/competition among coexisting manufacturing tasks (MTs), causing performance degradation and resource “starvation”; traditional heuristics require full re-execution for new scenarios, failing to support real-time online decision-making; single-agent reinforcement learning (RL) lacks mechanisms to balance global efficiency and inter-task fairness, suffering from inherent fairness defects. To address these challenges, this paper proposes a fairness-aware multi-task CMSC method based on Multi-Agent Reinforcement Learning (MARL) under the Centralized Training with Decentralized Execution (CTDE) framework, targeting the symmetry-breaking issue of uneven resource allocation among MTs and aiming to achieve symmetry restoration by restoring relative balance in resource acquisition. The method constructs a multi-task CMSC model that captures real-world resource sharing/competition among concurrent MTs, and integrates a centralized global coordination agent into the MARL framework (with independent task agents per MT) to dynamically regulate resource selection probabilities, overcoming single-agent fairness defects while preserving distributed autonomy. Additionally, a two-layer attention mechanism is introduced—task-level self-attention for intra-task subtask correlations and global state self-attention for critical resource features—enabling precise synergy between local task characteristics and global resource states. Experiments verify that the proposed method significantly enhances inter-task fairness while maintaining superior global Quality of Service (QoS), demonstrating its effectiveness in balancing efficiency and fairness for dynamic multi-task CMSC. Full article

Background/Objectives: Obesity is a multifactorial condition and a major public health challenge. Conventional treatment centers on caloric restriction, which is often unsustainable and may cause stigma and psychoemotional harm. This study aimed to describe the methodology and assess the effects of a multicomponent nutritional intervention not focused on caloric restriction on psychoemotional outcomes. Women were selected as the target population because of the higher prevalence of obesity-related psychoemotional distress, body dissatisfaction, and weight-related stigma in this group, as well as their greater vulnerability to the psychosocial impacts of weight-focused interventions. Methods: This randomised, parallel, open-label trial included 89 obese women from primary care in Viçosa, Brazil. The participants were allocated into three groups: Group 1 (Control), which received a personalised hypocaloric diet (from 500 to 1000 kcal/day); Group 2 (NutrirCom (NutrirCom is a multicomponent, person-centred nutritional intervention protocol that is not focused on caloric restriction, conceived by a group of researchers at the Federal University of Viçosa for the care of women with obesity in Primary Health Care. It integrates nutritional, psychoemotional, behavioural, and social strategies, with an emphasis on promoting eating autonomy, mental health, and quality of life through a humanised, integrated, and sustainable approach, aiming to enhance the effectiveness of health care delivery and clinical practice)), which received 10 individual NutrirCom-based sessions; and Group 3 (NutrirCom + Social Support), which combined individual NutrirCom sessions with monthly group meetings for social support. Randomisation was stratified by body mass index via Excel^® with concealed allocation. The six-month intervention assessed changes in stress, anxiety, depression, and self-compassion, along with anthropometric and metabolic markers. Results: All groups presented reductions in waist circumference, fasting glucose, and total body fat, with increased lean mass. Anxiety remained unchanged in Group 1 but decreased significantly in Groups 2 (p = 0.002) and 3 (p = 0.005). Only Group 2 showed a significant reduction in depression symptoms (p = 0.023). Self-compassion improved significantly in groups 2 and 3. Conclusions: NutrirCom is a low-cost, scalable, and human-centered intervention that integrates emotional, social, and nutritional aspects of care. This approach shows promise as a sustainable strategy for obesity treatment in primary health care. Registration: Brazilian Registry of Clinical Trials (ReBEC) (no. RBR-87wb8x5). Full article

Urban rail transit networks in developing countries are rapidly expanding, entering a networked operational phase where accurate passenger flow forecasting is crucial for optimizing vehicle scheduling, resource allocation, and transportation efficiency. In the short term, accurate real-time forecasting enables the dynamic adjustment of train headways and crew deployment, reducing average passenger waiting times during peak hours and alleviating platform overcrowding; in the long term, reliable trend predictions support strategic planning, including capacity expansion, station retrofitting, and energy management. This paper proposes a novel hybrid forecasting model, EEMD-TiDE, that combines improved Ensemble Empirical Mode Decomposition (EEMD) with a Time Series Dense Encoder (TiDE) to enhance prediction accuracy. The EEMD algorithm effectively overcomes mode mixing issues in traditional EMD by incorporating white noise perturbations, decomposing raw passenger flow data into physically meaningful Intrinsic Mode Functions (IMFs). At the same time, the TiDE model, a linear encoder–decoder architecture, efficiently handles multi-scale features and covariates without the computational overhead of self-attention mechanisms. Experimental results using Xi’an Metro passenger flow data (2017–2019) demonstrate that EEMD-TiDE significantly outperforms baseline models. This study provides a robust solution for urban rail transit passenger flow forecasting, supporting sustainable urban development. Full article