Search Results (16,075)

Background/Objectives: Sleep quality, anxiety symptoms, physical activity, musculoskeletal pain, and multimorbidity are interrelated health domains, but evidence examining these factors together among adults aged ≥50 years in Saudi Arabia remains limited. This study described their prevalence and assessed cross-sectional associations among these domains. Methods: A cross-sectional online survey using convenience sampling was conducted in Saudi Arabia between December 2024 and April 2025. Participants completed measures of musculoskeletal pain, self-reported morbidity, physical activity (Global Physical Activity Questionnaire), sleep quality (Brief Version of the Pittsburgh Sleep Quality Index), and anxiety symptoms (Generalized Anxiety Disorder-7). Associations were examined using Spearman rank correlations and logistic regression with core adjustment for age, sex, and BMI. The Benjamini–Hochberg false discovery rate correction was applied to eight focal comparisons; Firth penalized logistic regression was applied as a sensitivity analysis for models in which multimorbidity was the outcome. Results: The sample included 298 adults (mean age 58.2 +/− 6.3 years; 75.5% male). Musculoskeletal pain was reported by 73.8%, multimorbidity by 8.4%, poor sleep by 43.3%, and moderate-to-severe anxiety symptoms by 23.8%. Better sleep quality was moderately associated with lower anxiety severity (Spearman rho = −0.45, p < 0.001). Poor sleep was associated with higher odds of moderate-to-severe anxiety symptoms (OR 6.49, 95% CI 3.45–12.20), and low total physical activity was associated with higher odds of both moderate-to-severe anxiety symptoms (OR 7.87, 95% CI 2.68–23.08) and poor sleep (OR 5.09, 95% CI 2.40–10.83). These focal associations remained significant after FDR correction. In an exploratory sensitivity analysis, the poor sleep–multimorbidity association remained comparable when Firth regression was applied (OR 2.71, 95% CI 1.12–7.06; p = 0.027). Conclusions: In this online convenience sample of adults aged ≥50 years, sleep quality, anxiety symptoms, and total physical activity showed consistent cross-sectional associations. Findings involving multimorbidity were exploratory because of the limited number of events. These results require confirmation in representative longitudinal studies. Full article

To address the limited generalization of lithium-ion battery state-of-health (SOH) estimation under unseen aging conditions, this study proposes a degradation-feature-constrained domain-difference gated method (DFC-DGGate). Cycle-level features are constructed from capacity, voltage, local statistics, and first-order degradation variations. Three branches, namely Local ET, Trend Ridge, and Robust Huber, are used to characterize local nonlinear mapping, global degradation trends, and robust estimation, respectively. Their outputs are fused by a condition-aware domain-difference gate and further smoothed to obtain continuous SOH estimates. Cell-wise and condition-wise validations are conducted on the XJTU dataset, and external testing is performed on the NASA dataset. Under XJTU condition-wise validation, DFC-DGGate achieves an RMSE of 14.3895%, while the best-performing baseline, SVR, achieves 5.7623%. The proposed framework remains more accurate than the global Trend Ridge branch (22.3915%) but does not outperform the strongest nonlinear baselines under the substantial Sim_satellite shift. In XJTU-to-NASA validation, the anchor-corrected external extension achieves RMSEs of 20.45% and 19.10% on NASA core and NASA clean, respectively, slightly outperforming ExtraTrees on both subsets. Full article

Coastal green tides may alter nearshore bioaerosols through coupled marine, atmospheric, and meteorological processes, yet their effects on airborne microbial communities remain poorly resolved. Atmospheric samples were collected in Aoshan Bay, Qingdao, China, during five phases of the Ulva prolifera green tide in 2019 (pre-bloom, 19 April; early bloom, 15 June; middle bloom, 15 July; late bloom, 6 August; post-bloom, 30 August); seawater samples were collected at one nearshore site on each of the five sampling dates, with microbial sequencing performed for the middle-bloom (15 July) and late-bloom (6 August) phases. Bacterial and fungal communities were characterized; although bioaerosols may also contain microalgae and viruses, this study profiled only the bacterial and fungal fractions, using bacterial 16S rRNA gene (V3-V4 region) and fungal internal transcribed spacer (ITS2) amplicon sequencing and evaluated together with meteorological variables, air-pollutant concentrations, and 72-h backward air-mass trajectories. Proteobacteria dominated the airborne bacterial assemblages (81.28–97.83%), with Sphingomonas as the most abundant genus (47.85–89.84%). Basidiomycota and Ascomycota dominated the fungal assemblages, whereas Cryptococcus and Alternaria were the major fungal genera. Community richness and composition varied across bloom phases. Chytridiomycota was undetected before the bloom (0%), appeared after bloom onset, and reached its highest relative abundance during the middle phase (8.19%). Spatial patterns indicated joint terrestrial and marine influences, although bacterial communities in seawater and air remained highly dissimilar. Temperature, relative humidity, particulate matter, ozone, and air-mass origin were associated with changes in microbial diversity and composition. These findings provide an observational baseline for coastal bioaerosol dynamics during a macroalgal green tide, extending the HAB–bioaerosol literature—which has focused predominantly on cyanobacterial blooms—to a large green macroalga. Bacteria and fungi showed contrasting environmental responses: bacterial richness increased with temperature, whereas fungal diversity declined. Greater compositional similarity between seawater and air for fungi than for bacteria suggests differential environmental filtering at the air–sea interface and implies that multiple source pathways—direct aerosolization, sea-surface release, and in-situ atmospheric production—may differentially shape the two domains. Given the single-date-per-phase sampling design, the absence of sequenced laboratory contamination controls, and the lack of absolute abundance data, these results should be regarded as preliminary and hypothesis-generating, underscoring the need for ASV-level source tracking, controlled chamber experiments, and replicated multi-year designs in future assessments of bloom–atmosphere interactions. Full article

High-altitude hypoxia is associated with sleep disruption, affective fluctuation, and changes in cognitive performance, yet prior findings remain markedly heterogeneous in both direction and magnitude. To clarify this variability, this structured narrative review synthesizes evidence from systematic reviews, meta-analyses, and key empirical studies on psychological and cognitive adaptation to high-altitude hypoxia, with a particular focus on personality and related traits. The review is organized along three axes: exposure context and characterization (real-altitude field/residential exposure, hypobaric chambers, normobaric hypoxia, expedition settings, and dose alignment), exposure duration and time course, and outcome domains spanning sleep/fatigue, affective adaptation, and cognitive performance. Across the literature, cognitive effects appear domain-specific and strongly conditioned by exposure dose, temporal stage, and task characteristics, whereas sleep disturbance during the early acclimatization period emerges as one of the more consistent findings and appears closely intertwined with anxiety-related responses. Although direct personality-related evidence remains limited and uneven, the available findings can be organized into a testable trait–process–outcome framework that distinguishes candidate associations with relatively more direct evidence from mechanism-informed hypotheses. Neuroticism/emotional stability and anxiety-related traits have received comparatively more direct empirical investigation than other personality domains, but the available evidence is insufficient to regard them as established predictors of high-altitude adaptation. The roles of conscientiousness, resilience/hardiness, extraversion, agreeableness, and openness remain more tentative and are better treated as targets for prospective testing. We therefore propose that personality-related dispositions may influence adaptation-relevant outcomes through candidate processes involving threat appraisal, coping, resource regulation, and social interaction. We conclude that future research should strengthen exposure dose characterization, specify the boundary conditions for generalizing across real-altitude, hypobaric-chamber, normobaric hypoxia, and expedition settings, and adopt longitudinal, multilevel, and ecologically valid designs to test personality-related pathways more rigorously. Full article

Protein tyrosine kinases (PTKs) and protein tyrosine phosphatases (PTPs) constitute two major superfamilies of signaling enzymes in mammals, displaying comparable genomic representation (~100 genes each) and numbers of catalytically active proteins (~80 enzymes each). Both families include receptor and non-receptor forms; however, their distributions differ substantially. PTKs comprise approximately 58 receptor tyrosine kinases (RTKs), whereas PTPs include only circa 21 receptor protein tyrosine phosphatases (RPTPs). Despite these differences, RTKs and RPTPs share a common structural organization consisting of (i) an extracellular domain responsible for ligand recognition; (ii) a single-pass transmembrane domain anchoring the receptor to the plasma membrane; and (iii) an intracellular catalytic domain containing either kinase or phosphatase activity. Signal transduction mediated by RTKs and RPTPs generally depends on ligand binding and receptor dimerization. Remarkably, although these receptor families regulate signaling through fundamentally opposite molecular mechanisms, both are essential for controlling cell proliferation, adhesion, migration, differentiation, development, and survival. RTKs have been more extensively characterized than RPTPs; nevertheless, both receptor classes function as critical regulators of intercellular and intracellular communication pathways. Moreover, their membrane-associated localization makes them attractive targets for therapy in multiple human diseases, particularly cancer and neurological disorders. Full article

The transition from high school to university is a critical developmental period marked by substantial academic, social, and lifestyle changes that may increase vulnerability to academic difficulties, disengagement, and dropout. Given the importance of academic achievement for student retention and long-term success and the growing recognition of sleep as a modifiable determinant of academic performance, this scoping review examined how the relationship between sleep and academic achievement has been investigated among first-year university students. Following PRISMA guidelines, a systematic search identified 20 records representing 21 studies published between 2013 and 2026. Sleep duration and sleep quality were the most frequently examined domains, although findings were heterogeneous. Nevertheless, shorter sleep duration, particularly below 6–7 h per night, was generally associated with poorer academic achievement. Poor sleep quality was also linked to lower academic performance in some studies, with affective factors influencing this relationship. Sleep problems, including chronic sleep deprivation and risk of sleep disorders, showed consistent associations with poorer academic outcomes. Similarly, an evening chronotype was generally associated with poorer academic performance and adjustment. These findings emphasize sleep as an essential target for future research and initiatives aimed at promoting academic success and student retention during the transition to higher education. Full article

Background/Objectives: Adults at high risk of type 2 diabetes frequently show concurrent dyslipidemia, insulin resistance, and central adiposity before overt diabetes develops. We evaluated cardiometabolic responses to standardized whole-grain staple replacement and examined grain-quality patterns in free-living and country-level settings. Methods: This secondary exploratory analysis included 144 participants (48 per group) from a 12-week, three-arm randomized dietary trial. Participants were analyzed according to their randomized assignments to 100 g/day, 50 g/day, or control. Lipid, anthropometric, body composition, and composite cardiometabolic phenotypes were evaluated. Longitudinal and time-weighted cumulative effects were estimated using linear mixed-effects and regression models, with Benjamini–Hochberg false discovery rate (FDR) correction applied within the corresponding multiplicity domains. Complementary analyses included 30,769 NHANES adults without diabetes, including 12,169 with prediabetes, and descriptive country-level data on whole-grain intake, high-FPG-attributable diabetes burden, and cereal supply. Results: Within the extended phenotype panel, HWG versus control time-weighted cumulative effects included triglycerides (β = −0.220, 95% CI −0.397 to −0.043; FDR-adjusted p = 0.047), the atherogenic index of plasma (β = −0.125, −0.191 to −0.060; FDR-adjusted p = 0.002), and the triglyceride–glucose index (β = −0.301, −0.429 to −0.173; FDR-adjusted p < 0.001). Conventional lipid components showed distinct temporal responses. In NHANES, higher whole-grain exposure and more favorable grain-quality ratios were associated with lower adiposity- and insulin resistance-related phenotypes, whereas refined-grain exposure generally showed the opposite pattern. Country-level analyses showed marked heterogeneity in whole-grain intake, diabetes burden, and cereal supply context. Conclusions: Standardized whole-grain staple replacement was associated with short-term changes across several cardiometabolic dimensions in adults at high risk of type 2 diabetes, with the clearest extended-phenotype signals involving triglyceride-related and glucose–triglyceride coupling measures. NHANES identified cross-sectional grain-quality associations in free-living adults; country-level analyses described heterogeneity in whole-grain intake, diabetes burden, and cereal supply context. Full article

Reliable crop-type mapping from satellite image time series is affected by distribution shifts across geographic regions, agricultural years, and heterogeneous label systems. To address this challenge, we propose Cross-Domain Mixup (CDMix), a supervised domain-adaptation method designed to leverage a larger labeled source dataset to improve performance on a smaller labeled target dataset under distribution shifts. We also introduce PixelSet-Slovakia, a new multi-year, parcel-level Sentinel-2 dataset covering three Slovak study regions and several growing seasons. Using a common backbone, we compare CDMix against three families of adaptation strategies: (i) no adaptation, (ii) weight transfer through fine-tuning and encoder freezing, and (iii) feature-space alignment using Maximum Mean Discrepancy (MMD) and Correlation Alignment (CORAL). All methods are evaluated in two scenarios: geographic supervised adaptation across datasets from two countries and temporal supervised adaptation across different growing seasons. Across both tested source–target settings, CDMix generally achieves competitive performance when initialized from pretrained representations. Under the region-held-out validation protocol, several pretrained adaptation strategies outperform training from scratch. Full article

Rapid advances in nanomaterials, wearable biosensors, multimodal acquisition, and artificial intelligence have enabled increasingly integrated health-monitoring systems, yet their progression toward health digital twins remains unclear. We conducted a protocol-driven scoping review of original studies combining nano-enabled multianalyte or multimodal sensing, AI-supported analysis, and health applications. PubMed/MEDLINE, Scopus, Web of Science Core Collection, and IEEE Xplore were searched using a publication cutoff of 10 July 2026; platform execution was completed on 13 July 2026. Two reviewers independently screened 528 unique records and assessed 20 full-text reports. A 79-item charting form was jointly verified for 12 included studies. Nine studies reported reference-method or matrix-relevant analytical validation, nine included human-sample or on-body evidence, and six acquired longitudinal or continuous data. Under the author-proposed, corpus-specific functional classification, six systems were L0, five L1, and one L2; none of the 12 met the L3 or L4 functional criteria. No included study combined dynamic individual-state assimilation with prospective prediction or simulation, and none reported external-site validation or formal predictive uncertainty quantification. Because eligibility required nano-enablement, multiple analytes or channels, AI integration, and selected clinical domains, these findings do not estimate the prevalence or maturity of health digital twins in the wider literature. Progress requires longitudinal multimodal data, validated state updating, external generalization, confidence-aware AI, and prospective evaluation of governed feedback. Full article

Arabic-speaking populations face disproportionate cybersecurity risks due to the predominantly English-centric design of existing awareness materials, which fail to accommodate Arabic dialectal diversity, script complexity, and culturally embedded communication patterns. These deficiencies impair users’ ability to interpret phishing messages, authentication requests, and security alerts, increasing susceptibility to social engineering, identity theft, and data breaches. This paper presents SECURE-A²RC, a rubric-constrained, Arabic-aware large language model framework designed to deliver scalable, interpretable, and culturally relevant cybersecurity education. The framework comprises two coupled components. The first, the Arabic-Aware Secure Communication Encoder (A-SCE), employs an instruction-tuned LLM to produce multidimensional encodings that capture three learner competencies: security intent comprehension; linguistic deception cue recognition encompassing urgency, authority impersonation, and incentive framing; and action-critical translation fidelity across Arabic dialectal registers and Arabic–English bilingual contexts. The second, the Rubric-Constrained Adaptive Feedback Generator (RCAFG), translates A-SCE encodings into personalized, expert-aligned instructional feedback and proficiency-calibrated adaptive tasks, ensuring pedagogical consistency, security correctness, and dialect awareness throughout the learning cycle. The framework is evaluated on three domain-relevant corpora: the English–Arabic Parallel Phishing Email Corpus, the Open MalSec dataset, and the Arabic Spam and Ham Tweets dataset. SECURE-A²RC achieves a 31% improvement in phishing identification accuracy and a 26% reduction in action-critical translation errors compared to conventional awareness materials. A comparative evaluation against SERENA, a Multi-Agent LLM, and the Arabic Multitask Learning Model confirms consistent superiority across detection accuracy, F1-score, dialectal robustness, and educational effectiveness metrics, affirming rubric-constrained LLM integration as a viable approach to equitable multilingual cybersecurity education. Full article

Cross-scene hyperspectral band selection must reduce spectral redundancy while retaining channels that remain useful beyond the source scene. We propose Adaptive Band Gating (ABG), a source-only selector that combines frequency-domain decoupling enhancement, global and sample-specific gating, source-side spectral, spatial, morphology-inspired, and sensor-noise perturbations, and a dual-head evaluator. The selector is trained with source data and frozen before downstream evaluation. Selected bands are assessed with a radial-basis-function support vector machine on Pavia Center and HyRANK under fixed band budgets and target-label fractions from 0% to 10%. At 5% target labels, 15 selected bands achieve 96.05% overall accuracy on Pavia Center, compared with 95.41% using all 102 bands; 20 selected bands achieve 81.49% on HyRANK, compared with 79.20% using all 176 bands. Across evaluated band budgets, ABG is comparable to XGBS on Pavia Center and provides stronger results on HyRANK. Ablation experiments show that adaptive gating, frequency-domain enhancement, and source-side expansion each contribute to performance. Together, these results demonstrate that ABG learns compact and traceable original-band subsets with strong downstream transfer utility across the evaluated cross-scene settings. Full article

The diverse application scenarios envisioned for sixth generation (6G) are characterized by the deep integration of sensing and ubiquitous connectivity, which imposes unprecedentedly stringent security requirements. However, due to the open nature of wireless channels, mobile communications systems always face severe information security threats such as falsification, spoofing, interception, and repudiation. Cryptography-based symmetric and asymmetric encryption techniques remain mainstream solutions for information protection. Symmetric encryption is efficient and secure for legitimate users but suffers from key-distribution difficulties over open wireless channels, whereas asymmetric encryption resolves this problem but faces increasing risks from quantum computing due to its reliance on structured mathematical hardness assumptions. In response to these limitations, physical layer security (PLS) has gained a great deal of research attention as a powerful security component that leverages the features of varying wireless channels. Existing PLS schemes can be broadly classified into two categories. The first one takes advantage of the legitimate link’s opportunistic channel-quality superiority over or different spatial-domain directions from the attacking link, which still faces many practical implementation difficulties. The second category is termed physical-layer key generation (PLKG). It extracts the unique features of the legitimate link’s channel variation, which is often reciprocal, as the source of secret key generation and therefore can naturally implement secure key distribution tasks. This advantage no doubt injects new vigor to symmetric encryption as a stronger protection approach. Following this trend, we in this paper concentrate on the PLKG techniques. Specifically, we present a comprehensive overview on existing PLKG schemes, discussing diverse secret key generation and reconciliation methods. We further investigate the model-driven and deep-learning-based approaches tailored for the scenario with imperfect channel reciprocity between the sender and receiver. After comprehensively reviewing major existing schemes, we further discuss a recently proposed PLKG design based on codeword reconstruction, which makes use of the strong error-correcting capability of the forward-error-correction (FEC) codes to effectively implement secure and consistent secret key generation between the legitimate sender and receiver. Finally, we share our opinions on the unsolved challenges and potential research directions dedicated to PLKG toward meeting the security requirements of 6G. Full article

Intelligent agents are emerging as an important system-level paradigm for smart agriculture. This review focuses on modern agricultural intelligent agents driven by large language models and related multimodal foundation models and examines how this emerging field is reshaping the organization of intelligent agricultural systems. It first clarifies the conceptual boundaries of agricultural intelligent agents and distinguishes them from traditional multi-agent systems, agent-based modeling, agricultural foundation models, and static retrieval-augmented question-answering systems. It then synthesizes their architectural foundations, key capabilities, application scenarios, deployment challenges, and future research directions. The reviewed literature indicates that agricultural intelligent agents are moving beyond isolated perception, prediction, and response generation toward the goal-oriented coordination of agricultural knowledge, dynamic data, external tools, and decision-making processes across agricultural task chains. They are beginning to support more integrated forms of knowledge services, crop monitoring and diagnosis, decision support, and farm-level collaborative management. Nevertheless, their transition from prototype systems to dependable and deployable agricultural systems remains constrained by context-aware knowledge grounding, heterogeneous data and tool integration, long-horizon reliability, the stability of multi-agent collaboration, and system security. This review further introduces an assessment perspective based on evidence reported in the original studies, comparing representative agricultural intelligent agents in terms of task decomposition, agronomic evidence applicability, tool-use validity, workflow reliability, multi-agent coordination, and deployment-related evidence. By distinguishing demonstrated capabilities from unevaluated dimensions, this review provides a structured framework for understanding the current status of agricultural intelligent agents and for guiding their future development toward reliable, deployable, and domain-oriented intelligent systems for smart agriculture. Full article

In large-scale multiple-input multiple-output (MIMO) systems, inadequate pilots can necessitate pilot reuse, reducing channel-estimation accuracy, while too few received pilot observations can also lead to inaccurate interference-plus-noise covariance estimates. These estimation errors can further degrade the performance of downstream interference suppression and data detection. Learning-based methods have been developed for pilot assignment, channel estimation, and receiver processing, but these methods are often studied separately. This survey organizes recent studies according to where learning-based methods are applied in the signal-processing chain: pilot-domain mitigation, intelligent channel estimation with contaminated or limited pilots, and intelligent receiver processing with contaminated or limited pilots. We also classify the studies by learning method and compare them using the same set of evaluation criteria. Across the surveyed papers, performance is evaluated using different metrics. Many studies also lack evaluations under changing channel or system conditions and do not fully report implementation costs such as computational complexity, memory usage, and latency. Among the studies that satisfy our selection criteria, none directly investigates learning-based estimation of the interference-plus-noise covariance matrix for interference rejection combining (IRC) receivers when only limited pilot observations are available. Based on these findings, we propose a minimum set of benchmarking requirements and identify lightweight online adaptation, joint processing, learning-based covariance estimation for IRC receivers, and robust processing for large-array architectures as future research directions for emerging sixth-generation (6G) systems. Full article

In the domain of rotating machinery fault diagnosis, challenges such as multi-operating condition distribution heterogeneity and the difficulty of distinguishing fault features within multi-scale temporal signals persist. To address these issues, this paper introduces the Time-Gated Multi-Expert Generative Adversarial Network (TGME-GAN), a fault diagnosis approach that integrates a multi-expert gated conditional generative adversarial network with a clustering structure-aware feature enhancement. This method combines unsupervised K-means clustering with supervised discriminative learning. The optimal number of clusters is selected adaptively using the silhouette coefficient, and the distance vector from each sample to the cluster centers serves as a topological prior feature. A spatial–temporal joint representation matrix is then formed by concatenating PCA principal components, differential features, cumulative statistical features, and standardized change rates, which together capture both abrupt mutations and progressive degradation in fault signals. In the model, the discriminator incorporates a multi-expert gated network. Each expert learns a feature subspace corresponding to a distinct operating condition, and the gated network dynamically assigns fusion weights, allowing the discriminator to capture heterogeneous distributions across industrial conditions. The generator extracts multi-scale local patterns with a three-layer one-dimensional convolutional network and models sequential dependencies with a two-layer LSTM, producing high-quality fault samples that preserve intrinsic consistency. At the engineering level, TGME-GAN is deployed for gearbox fault diagnosis in uneven, small-sample industrial settings. In two gearbox fault experiments, this method substantially outperforms current mainstream models. Full article