Search Results (5,720)

Introduction: Myasthenia gravis (MG) is a chronic autoimmune disorder in which fatigue represents one of the most burdensome symptoms. This multidimensional manifestation extends beyond neuromuscular fatigability and has a substantial impact on daily functioning, mental health, and quality of life. The present study aimed to evaluate the perception of fatigue in patients with MG, with particular emphasis on its interference with everyday activities and the extent to which it is understood by others. Methods: The study included 67 MG patients (61.2% women, mean age 53 years) treated at the Neurology Outpatient Department of the University Clinical Center in Gdańsk. Data were collected using an author-developed survey and standardized instruments: Chalder Fatigue Scale (CFQ), MG-ADL, MG-QoL15, HADS-M, Mini-COPE, and ACDS. Results: More than 70% of patients reported constant or frequent fatigue. Higher fatigue severity was positively associated with functional impairment (MG-ADL) and lower quality of life (MG-QoL15). More than 70% of patients reported constant or frequent fatigue. Higher fatigue severity was moderately associated with greater functional impairment and poorer quality of life. The extent to which fatigue interfered with daily life was associated with higher levels of depressive symptoms, poorer self-rated health, and less favorable disease-related perceptions (acceptance and influence). In contrast, perceiving fatigue as being better understood by others was associated with lower anxiety and depression and more favorable disease-related perceptions (acceptance, control, understanding), while it was not significantly related to fatigue severity, functional status, or quality of life. Conclusions: Fatigue in myasthenia gravis is a prevalent symptom, closely related to functional impairment and reduced quality of life. Different aspects of fatigue perception show distinct psychosocial correlates, highlighting the importance of considering subjective and social dimensions of fatigue alongside its severity. These findings support the relevance of psychosocial factors in the comprehensive care of patients with MG. Full article

Self-rated health is a comprehensive indicator reflecting an individual’s subjective assessment of their overall health status. The health condition of students in elite universities is directly related to the quality of talent reserves and the long-term development of the country. However, the multiple challenges they face make them prone to subhealth issues. To understand and effectively intervene in the health dilemmas of this group from a psychological perspective, this study constructed a cross-lagged model to examine the potential bidirectional relationship between anxiety and self-rated health. We utilized two-wave longitudinal data from a sample of 896 undergraduate students (mean age 21.37 years, 60.27% male, 92.08% Han nationality) from five elite universities in Beijing, China. Anxiety was measured using the Depression Anxiety Stress Scales, while self-rated health was assessed via a single-item score. The study revealed that during the two survey periods, the anxiety levels of elite university students decreased (7.682/7.462), whereas their self-rated health scores increased (81.781/83.255). Higher levels of anxiety were significantly associated with lower levels of self-rated health in both the concurrent and cross-lagged analyses (r = −0.299~−0.173, p < 0.01). Prior anxiety could predict later self-rated health (β = −0.081, p < 0.05), but the reverse path from self-rated health to anxiety was not confirmed. Our findings indicate that anxiety among elite university students has a unidirectional prospective effect on self-rated health. On the basis of these findings, universities should integrate mental health services into their routine work systems, and students should also increase their sense of personal responsibility for their own health, actively seeking effective pathways to improve their physical and mental well-being. Full article

Autonomous navigation in unmanned systems increasingly relies on robust perception and mapping capabilities under large-scale, dynamic, and unstructured environments. Multi-camera simultaneous localization and mapping (MCSLAM) has emerged as a promising solution due to its improved field-of-view coverage, redundancy, and robustness compared to single-camera systems. However, the deployment of MCSLAM introduces several technical challenges that remain insufficiently addressed in existing literature. These challenges include the high-dimensional nature of multi-view visual data, the computational cost associated with multi-view geometry and large-scale bundle adjustment, and the strict requirements on camera calibration, temporal synchronization, and geometric consistency across heterogeneous viewpoints. This survey provides a comprehensive review of recent advances in MCSLAM for unmanned systems, categorizing existing approaches based on system configuration, field-of-view overlap, calibration strategies, and optimization frameworks. We further analyze common failure modes, evaluate representative algorithms, and identify emerging research trends toward scalable, real-time, and uncertainty-aware MCSLAM in complex operational environments. Full article

Clarifying the pathways through which public emergency response capability influences community responsibility holds positive implications for promoting public participation in community disaster prevention and mitigation efforts. Based on a large-scale community survey covering over 70 cities in China, this study obtained a sample of 1753 individuals through random sampling and employed Bootstrap methods for effect testing. Findings reveal the following: ① Public emergency response capability significantly correlates positively with sense of community responsibility, with both intrinsic cognitive emergency response capability and extrinsic skill-based emergency response capability demonstrating strong positive associations with community responsibility. ② Risk perception mediates the relationship between public emergency response capability and community responsibility, forming the associative pathway: “Enhanced public emergency response capability → Reduced risk perception → Strengthened sense of community responsibility”. ③ Community resilience moderates the “public emergency response capability → risk perception” pathway, with high-resilience communities significantly reducing public risk perception levels. Therefore, to fully leverage the role of public emergency response capability in enhancing community responsibility, efforts should focus on cultivating public risk prevention awareness, comprehensively disseminating safety and emergency knowledge, strengthening public emergency skills training, fostering a culture of neighborhood watch within communities, and optimizing public participation mechanisms for community disaster reduction. Full article

The ecological benefits of urban green spaces depend on their structure and ecological service function. Evaluation systems used to monitor these characteristics show distinct regional variations. This study analyzed China’s urban green spaces, developed a quantitative ecological benefit evaluation system, and comprehensively evaluated the ecological benefits of green spaces in Xi’an city. Suitable evaluation indexes for Xi’an were selected based on field survey data with large-scale samples and high-resolution remote sensing image data. The results showed that the ecological service function of urban green spaces in Xi’an has been substantially improved by ecological planning. Therefore, it is important to evaluate this function as part of the urban planning and design process. Furthermore, increasing the 3D Green Quantity through urban forests can effectively improve the ecological service function. Full article

Tumor mutational burden (TMB) is a key pan-cancer biomarker for immunotherapy selection, but its routine assessment by whole-exome sequencing (WES) or large next-generation sequencing (NGS) panels is costly, time-consuming, and constrained by tissue and DNA quality. In parallel, advances in computational pathology have enabled deep learning models to infer molecular biomarkers directly from hematoxylin and eosin (H&E) whole-slide images (WSIs), raising the prospect of a purely digital assay for TMB. In this comprehensive review, we surveyed PubMed and Scopus (2015–2025) to identify original studies that applied deep learning directly to H&E WSIs of human solid tumors for TMB estimation. Across the 17 eligible studies, deep learning models have been applied to predict TMB from H&E WSIs in a variety of tumors, achieving moderate to good discrimination for TMB-high versus TMB-low status. Multimodal architectures tended to outperform conventional CNN-based pipelines. However, heterogeneity in TMB cut-offs, small and imbalanced cohorts, limited external validation, and the black-box nature of these models limit clinical translation. Full article

Unmanned Aerial Vehicle Communication Networks (UAVCNs) have emerged as a transformative solution to enable resilient, scalable, and infrastructure-independent wireless communication in urban and remote environments. A key challenge in UAVCNs is the optimal placement of Unmanned Aerial Vehicle (UAV) nodes to maximize coverage, connectivity, and overall network performance while minimizing latency, energy consumption, and packet loss. As this node placement problem is NP-hard, numerous meta-heuristic algorithms (MHAs) have been proposed to find near-optimal solutions efficiently. Although research in this area has produced a wide range of meta-heuristic algorithmic solutions, most existing review articles focus on MANETs with terrestrial nodes, while comprehensive reviews dedicated to node placement in UAV communication networks are relatively scarce. This article presents a critical and comprehensive review of meta-heuristic algorithms for UAVCN node placement. Beyond surveying existing methods, it systematically analyzes algorithmic strengths, vulnerabilities, and future research directions, offering actionable insights for selecting effective strategies in diverse UAVCN deployment scenarios. To demonstrate practical applicability, selected hybrid algorithms are evaluated in a reproducible Python framework using computational time and coverage metrics, highlighting their ability to optimize multiple objectives and providing guidance for future UAVCN optimization studies. Full article

End-of-Line (EoL) quality control plays a crucial role in ensuring the reliability, safety, and performance of electric motors in modern industrial production. Increasing product complexity, tighter manufacturing tolerances, and rising production quantities have exposed the limitations of conventional EoL inspection systems, which rely primarily on manually crafted features, expert-defined thresholds, and rule-based decision logic. In recent years, artificial intelligence (AI) techniques, including machine learning (ML), deep learning (DL), and transfer learning (TL), have emerged as promising solutions to overcome these limitations by enabling data-driven, adaptive, and scalable quality inspection. This paper presents a comprehensive and structured review of the latest advances in intelligent EoL quality inspection for electric motor production. It systematically surveys the non-invasive measurement techniques that are commonly employed in industrial environments and examines the evolution from traditional signal processing-based inspection to AI-based approaches. ML methods for feature selection and classification, DL models for raw signal-based fault detection, and TL strategies for data-efficient model adaptation are critically analyzed in terms of their effectiveness, robustness, interpretability, and industrial applicability. Furthermore, this work identifies key challenges that prevent the widespread adoption of AI-based EoL inspection systems, including dependence on expert knowledge, limited availability of labeled fault data, generalization between motor variants and production condition, and the lack of standardized evaluation methodologies. Based on the identified research gaps, this review outlines research directions and emerging concepts for developing robust, interpretable, and data-efficient intelligent inspection systems suitable for real-world manufacturing environments. By synthesizing recent advances and highlighting open challenges, this review aims to support researchers and experts in designing next-generation intelligent EoL quality control systems that enhance production efficiency, reduce operational costs, and improve product reliability. Full article

Background/Objectives: There is still limited understanding of how psychotic symptoms and sexual risky behaviors (SRBs) are related to each other. Gaining more knowledge of the mechanisms involved in this relationship could inform interventions to reduce or prevent SRBs. This study aims to deepen comprehension of the relationship between psychotic experiences (PEs) and SRBs by examining the moderating effects of depression and childhood trauma. Methods: A web-based survey and a cross-sectional design were adopted to collect data from 466 young general population adults (aged 18–35 years) from Tunisia during the period January–March 2024. The snowball sampling technique was used to recruit participants. Results: Moderation analyses were adjusted over age, sex, household crowding index, marital status, and living situation. The interaction PEs by childhood trauma was significantly associated with SRB scores. At high and moderate levels of child abuse, higher PEs were significantly linked to higher SRBs. Furthermore, the interaction PEs by depression was significantly associated with SRB scores. At high, moderate, and low levels of depression, higher PEs were significantly associated with higher SRBs. Conclusions: Clinicians should consider including assessment of childhood trauma and depression in young adults with PEs who are engaged in sexual risk-taking behaviors. Findings may imply that strategies addressing these two factors can be effective in mitigating the association between PEs and SRBs. Full article

Cyberattacks in education are a serious concern (e.g., breaches and system intrusions) that teachers need to respond to by cultivating cybersecurity awareness, engaging in continuous professional development, and modeling safe digital practices in their daily work, while technical prevention and mitigation are primarily the responsibility of institutional IT services and system-level governance. Strengthening cybersecurity depends on fostering awareness of how information is collected, analyzed, and used, thereby enabling users to take proactive steps to protect data, which are key components of teachers’ professional media literacy, particularly in managing personal and student information across social media, email, and cloud platforms. This quantitative study was conducted in Hong Kong with 120 early childhood, primary, secondary, and tertiary education teachers (88.3% female, age range = 18–54, Mage = 23.76) via an online survey. The study focused on social media, email, and cloud storage, and administered the Perceived Severity, Perceived Vulnerability, and Self-Efficacy Scales; the Data Protection Strategies Scale; and the Data Fabrication Strategies Scale, along with questions assessing awareness of data protection. Results revealed significant positive relationships among data protection awareness, psychological factors, and use of protection strategies. Awareness and protection strategies were also moderately linked to data fabrication behaviors. The findings indicate concerning gaps in teachers’ awareness of cyberattacks and their limited understanding of media literacy concepts, highlighting the need to integrate comprehensive media literacy training into teacher education programs and also provide intensive, mandatory on-site training for in-service early childhood, primary, secondary, and tertiary education teachers. Full article

With the progressive release of data from numerous sky surveys, humanity has entered the era of astronomical big data. Multi-wavelength, multi-method research is playing an increasingly crucial role. Binaries account for a substantial fraction of all stellar systems, and research into binaries is of fundamental importance. The low-resolution spectra from Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) suggest that LAMOST J064137.77+045743.8 is a binary consisting of an A7-type subgiant star and a cool red dwarf star. LAMOST J064137.77+045743.8 has not yet been recorded in the SIMBAD astronomical database. We conducted a comprehensive analysis of the binary based on multi-wavelength and multi-method research. The spectral analysis suggests that the A7-type subgiant primary star has parameters of $T_{\mathrm{eff}}$ ∼ 7500 K and log g ∼ 3.9, and the red dwarf companion star is cool. Additional flux observations in the infrared bands further corroborate the presence of the red dwarf companion, and the near-infrared color index indicates a K4-type red dwarf. Astrometric data from Gaia support the binary speculation with a Renormalized Unit Weight Error metric value of 1.9. The i-band flare detected by the Zwicky Transient Facility (ZTF) photometric observations bolsters the interpretation of the M- or K-type red dwarf companion. Both the radial velocity variations in the H$\alpha$ lines from LAMOST medium-resolution spectra and the light curves from ZTF support the classification of the A7 subgiant as a pulsating star. No clear evidence of binary eclipses was detected in 1789 days of photometric observations from the ZTF. Future asteroseismology studies will enable us to further probe the internal physics of the A7 subgiant primary star. Full article

Extended reality (XR), encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR), is rapidly reshaping the landscape of digital interaction and immersive communication. As XR evolves toward ultra-realistic, real-time, and interactive experiences, it places unprecedented demands on wireless communication systems in terms of bandwidth, latency, and reliability. Conventional RF-based networks, constrained by limited spectrum and interference, struggle to meet these stringent requirements. In contrast, visible light communication (VLC) offers a compelling alternative by exploiting the vast unregulated visible spectrum to deliver high-speed, low-latency, and interference-free data transmission—making it particularly suitable for future XR environments. This paper presents a comprehensive survey on VLC-enabled XR communication systems. We first analyze XR technologies and their diverse quality-of-service (QoS) and quality-of-experience (QoE) requirements, identifying the unique challenges posed to existing wireless infrastructures. Building upon this, we explore the fundamentals, characteristics, and opportunities of VLC systems in supporting immersive XR applications. Furthermore, we elaborate on the key enabling techniques that empower VLC to fulfill XR’s stringent demands, including high-speed transmission technologies, hybrid VLC-RF architectures, dynamic beam control, and visible light sensing capabilities. Finally, we discuss future research directions, emphasizing AI-assisted network intelligence, cross-layer optimization, and collaborative multi-element transmission frameworks as vital enablers for the next-generation VLC–XR ecosystem. Full article

Background/Objectives: Missed Nursing Care is a global concern that affects nurses’ well-being and patients’ safety. Despite global recognition of Missed Nursing Care, there is limited synthesized evidence that determines its characteristics in a Middle Eastern context. The purpose of the study is to synthesize the existing evidence about the prevalence of Missed Nursing Care among nurses in hospitals, the types of care missed, and reasons for Missed Nursing Care in the Middle East. Methods: A systematic literature review is conducted by using a comprehensive search in CINAHL, Scopus, and ScienceDirect databases for studies published between 2020 and 2025 and utilizing the MISSCARE Survey. Results: 25 studies met the inclusion criteria. The reported prevalence of Missed Nursing Care ranged between 1.06 and 2.9 out of five, indicating a low to moderate level. Frequent missed care activities included ambulation, hygiene, mouth care, and patient teaching. Contributing factors were staffing shortages, heavy workload, resource limitations, and communication issues. Missed Nursing Care critically affected patients’ outcomes, reduced job satisfaction, and caused moral distress and a higher intent to leave the profession. Conclusions: Missed Nursing Care remains a significant, complex challenge in the Middle East. Therefore, understanding this phenomenon in the region is needed. Collaborative efforts among policymakers, administrators, and nursing leaders are essential to implement targeted interventions, supportive policies, and ongoing research to minimize Missed Nursing Care across the Middle East. Full article

The rapid integration of unmanned aerial vehicles (UAVs) into next-generation wireless systems demands seamless and reliable handover (HO) mechanisms to ensure continuous connectivity. However, frequent topology changes, high mobility, and dynamic channel variations make traditional HO schemes inadequate for UAV-assisted 6G networks. This paper presents a comprehensive review of existing HO optimization studies, emphasizing artificial intelligence (AI) and machine learning (ML) approaches as enablers of intelligent mobility management. The surveyed works are categorized into three main scenarios: non-UAV HOs, UAVs acting as aerial base stations, and UAVs operating as user equipment, each examined under traditional rule-based and AI/ML-based paradigms. Comparative insights reveal that while conventional methods remain effective for static or low-mobility environments, AI- and ML-driven approaches significantly enhance adaptability, prediction accuracy, and overall network robustness. Emerging techniques such as deep reinforcement learning and federated learning (FL) demonstrate strong potential for proactive, scalable, and energy-efficient HO decisions in future 6G ecosystems. The paper concludes by outlining key open issues and identifying future directions toward hybrid, distributed, and context-aware learning frameworks for resilient UAV-enabled HO management. Full article

In many developing countries, seismic vulnerability remains high due to the widespread presence of informally constructed buildings without professional design or technical supervision. In Colombia, where nearly 60% of structures are non-engineered, this issue is especially acute. The objective of this study is to design, implement, and quantitatively evaluate the Learning Experience for Earthquake Awareness Program (LEAP), an experiential educational strategy for young students that enhances seismic knowledge, promotes sustainable construction awareness, and contributes to disaster risk reduction as a component of social sustainability. To address this challenge, LEAP introduces students to basic principles of structural mechanics and seismic behavior through playful, hands-on activities combining theoretical instruction, practical experimentation, collaborative design, and the testing of model structures. An experimental design with pre- and post-surveys was implemented with 141 participants, including 80 secondary school students (grades 8th–11th) and 61 university students enrolled in engineering, architecture, and construction programs, using 3D-printed models, earthquake simulators, and interactive games. Statistical analysis using the Wilcoxon signed-rank test ($p<0.05$) revealed significant improvements in conceptual understanding and perception, including gains in distinguishing between the hypocenter and epicenter ($+45.39\%$, $p=5.10\times {10}^{-8}$, $r=0.50$), understanding seismic magnitude ($+39.01\%$, $p=1.67\times {10}^{-12}$, $r=0.71$), and visually identifying structural vulnerabilities ($+25.50\%$, $p=4.50\times {10}^{-2}$, $r=0.41$). Overall, LEAP contributes to disaster risk reduction and social sustainability by strengthening seismic awareness and responsible construction practices. The most significant results were observed among secondary school students, while university participants mainly reinforced applied and visual comprehension. Given its convenience sample, lack of control group, and immediate post-test, findings should be interpreted as exploratory and associative. Full article