Search Results (176)

Background: Post-acute sequelae of COVID-19 (PASC), commonly known as long COVID, affects an estimated 10–30% of SARS-CoV-2 non-hospitalised and 50–70% of hospitalised survivors. This condition remains clinically heterogeneous, and the specific mechanisms driving the transition from acute infection to chronic sequelae remain poorly understood. We assessed independent risk factors, tracked the evolution of clinical features, defined distinct symptom-based phenotypes, and assessed the impact of different pandemic waves on the likelihood of developing long COVID in hospitalised survivors. Methods: We conducted a single-centre, retrospective cohort study at a university hospital in London. The population comprised 627 adults hospitalised with acute COVID-19 between February 2020 and December 2022. Baseline characteristics and outcomes were compared between long COVID and resolved cases using appropriate statistical tests for continuous and categorical variables. Multivariable logistic regression identified risk factors. McNemar’s test quantified the phenotypic shift from admission to follow-up. Latent Class Analysis (LCA) identified clinical subtypes based on symptom clusters. Results: Of 627 patients, 252 (40.2%) met long COVID criteria. Comorbidity burden was the strongest predictor; patients with a single condition had a 4-fold increase in odds (aOR 4.62, 95% CI 2.36–9.04). The ORs were also significantly elevated among patients with 2 (aOR 3.26), 3 (aOR 2.68), or 4 or more (aOR 3.24) comorbidities. Older age (aOR 1.04), acute disease severity (measured by length of hospital stay) (aOR 1.27 per log-day) and elevated admission fibrinogen (aOR 1.21 per g/L) were significant predictors. Temporal analysis revealed a precipitous decline in risk from Wild-type/Alpha (>50%) to Delta/Omicron (<21%). We observed a distinct phenotypic shift: while acute respiratory inflammation resolved, systemic fatigue increased fourfold (7.0% to 32.4%), and memory difficulties emerged in the post-acute phase. LCA identified two phenotypes: fatigue-dominant and multisystem phenotypes. Conclusions: Long COVID is a multifactorial syndrome driven by host susceptibility, acute severity, and persistent coagulopathy. Clinical management should move beyond a monolithic approach and favour phenotype-specific strategies. Full article

In satellite and space debris laser ranging, photon-counting time-of-flight sequences exhibit spatio-temporal echo coherence and deterministic orbital constraints. We propose an unsupervised framework exploiting this spatial-kinematic coupling to extract weak returns under high background noise. First, a fuzzy clustering regression employs a dynamic energy functional and cross-entropy-regularized photon attribution, guided by target motion priors. To enhance low signal-to-noise ratio sensitivity, we introduce a low-gradient sampling strategy that theoretically guarantees a signal-to-background ratio exceeding 1/2. Furthermore, a dual-stream autoencoder fuses orbital kinematic parameters and multi-scale echo densities via noise-adaptive latent gating. Validation on 88 satellite and 24 debris datasets achieves F1-scores of 0.73 and 0.93, respectively. The sampling strategy reduces computational latency by ∼5.2% with no loss in tracking precision. This label-free, physically grounded approach enables robust weak signal detection in ground-based photon-counting lidar. Full article

Electroencephalography is emerging as a promising conditioning modality for generative visual models. However, existing representation learning approaches often rely on high-capacity masked autoencoders and complex projection networks. When constrained to compact embedding dimensions to match vision-language models, these heavy transformer-based bottlenecks frequently suffer from representation collapse and lose critical signal dynamics. To address this, we propose a lightweight and projection-free autoencoder that directly outputs compact, Contrastive Language–Image Pre-training (CLIP)-scale latent vectors trained toward the CLIP embedding space. Our model adopts a U-Net-style architecture combining one-dimensional convolutional residual blocks for temporal dynamics and inter-channel attention modules for spatial dependencies, alongside skip connections to ensure stable reconstruction. Extensive experiments on visual perception datasets demonstrate that our approach successfully tracks complex signal amplitudes without collapsing. Under strict dimensional constraints, the proposed model achieves superior signal reconstruction fidelity across time and frequency domains using significantly fewer parameters than traditional masked autoencoder baselines. Furthermore, latent space visualizations and zero-shot retrieval tasks reveal that while the baseline collapses toward unstructured, near-chance representations, our architecture preserves emerging, partial semantic organization and retrieves several times above chance. This indicates that the proposed design preserves signal structure while exhibiting preliminary, above-chance semantic alignment, enabling integration into brain-driven generative pipelines. Full article

The sideslip angle is crucial for vehicle stability, especially for single-track vehicles. As it is difficult to measure this quantity directly, the use of virtual sensors (observers) is common practice in this field.Neural network-based virtual sensors are becoming increasingly popular due to their ability to handle nonlinear conditions and noise. This paper presents a rigorous methodological approach to selecting measured quantities and determining the appropriate sampling frequency for stable sideslip reconstruction using a nonlinear autoregressive neural network with exogenous inputs (NARX-NN) model. A literature review suggests that most studies on sideslip angle estimation focus solely on achieving superior accuracy, with little extensive discussion of the selected quantities or sampling conditions required for effective estimation. This paper uses an information theory approach combined with a qualitative approach to select suitable model input quantities, the optimal number of look-ahead steps (‘embedding’), and the optimal sampling frequency to maximise the ratio between the latent information provided to the model for state reconstruction and the reduced computational burden on the electronic control unit (ECU). The results show that the sampling and computing frequency can be reduced by up to 20 times compared to the common baseline. This enables the use of less powerful hardware for the same model, resulting in better resource utilisation. Full article

Social media has emerged as a crucial environment for examining public sentiment during disasters, providing immediate insights into collective emotions and urgent expectations. This research examines the emotional reactions expressed on Turkish posts shared on the X platform (formerly Twitter) following the 6 February 2023 earthquake by employing an integrated method that combines topic modeling and topic-based sentiment analysis. Data were collected between 10 February 2023 and 28 February 2023. A large dataset consisting of 305,000 tweets was compiled, and 296,836 tweets remained for analysis after preprocessing and filtering procedures. Latent Dirichlet Allocation (LDA), enhanced with term frequency-inverse document frequency weighting and bigram extraction techniques, was applied to identify prominent themes, including rescue operations, appeals for assistance, communication about missing persons, and disaster management. The sentiment polarity within each topic was determined using a hybrid deep learning model incorporating Bidirectional Encoder Representations from Transformers (BERT) embeddings Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM) layers, and FastText representations. This model reached a classification accuracy of 94%, with F1-scores of 0.91 and 0.95, recall values of 0.90 and 0.96, and precision values of 0.92 and 0.95, achieving higher performance than the evaluated baseline models. The findings indicate that supportive, solidarity-oriented, and resilience-related communication patterns were among the most frequently observed positive sentiment expressions, whereas negative sentiments appeared more frequently in discussions regarding delays in aid delivery and perceived shortcomings in institutional response. This study presents a scalable and flexible framework for analyzing sentiment in Turkish-language crisis communication, providing insights that may support disaster response monitoring and decision-making processes as well as the development of systems for tracking public reactions in real time. Full article

Tendon-driven bipedal robots exhibit complex time-varying dynamics due to elastic deformations, multi-joint coupling, and transmission delays. These characteristics lead to significant sim-to-real discrepancies and limit the robot’s performance in complex terrains. To address this issue, we propose a two-stage locomotion control training framework based on Real2Sim2Real (RSR). In the first stage, joint motion data collected from the real robot are used to train a torque refinement policy in simulation, implicitly modeling the time-varying dynamics of the tendon-driven system and reducing the body dynamics gap during sim-to-real transfer. In the second stage, we introduce a reinforcement learning approach that integrates explicit estimation with implicit representation. By explicitly estimating body linear velocity and local terrain information under the feet, and simultaneously learning task-relevant latent features through implicit representation, the robot’s adaptability to complex terrains is enhanced. Experimental results show that, for a forward velocity tracking task of 2.5 m/s, the proposed explicit–implicit learning method achieves a 15.9% reduction in velocity tracking error compared to the purely implicit representation baseline (IWM). When further combined with the torque refinement policy (RSR), the tracking error is further reduced by 86.4% compared to the explicit–implicit baseline (EIWM). Moreover, the proposed method enables stable locomotion across various complex terrains, demonstrating its effectiveness in improving sim-to-real transfer performance and terrain adaptability. Full article

High school is often an academically demanding environment that exposes adolescents to elevated emotional challenges. Yet the development of academic emotions across the full high school period, as well as the potential protective role of adaptive coping strategies such as shift-and-persist, remains insufficiently understood. To address this gap, the present study tracked 608 students (305 males; M_age = 15.56, SD = 0.58) from a public senior high school in Chongqing, China, over three consecutive years. Latent growth modeling showed that negative academic emotions increased steadily, whereas positive emotions remained relatively stable. Cross-lagged panel analyses further revealed that greater use of shift-and-persist strategies—a coping approach integrating cognitive reappraisal with optimistic persistence—was prospectively associated with more favorable emotional outcomes, reflected in higher positive and lower negative emotions. In contrast, academic emotions did not predict later use of S-P. These findings point to the cumulative emotional challenges adolescents face in competitive academic settings and highlight S-P as a stable psychological resource. The study extends the control-value framework by identifying a cognitive-affective pathway associated with emotional well-being and emphasizes the potential value of promoting S-P-related coping skills within educational settings. Full article

Kauri dieback, caused by Phytophthora agathidicida Weir, Beever, Pennycook & Bellgard, poses a major threat to the ecological and cultural significance of Aotearoa New Zealand’s kauri forests. Visitor behaviour, particularly boot-cleaning and adherence to track-use guidelines, is a key transmission pathway. Using the COM-B framework and audience segmentation, we surveyed 451 visitors to the Waitākere and Hunua Ranges to identify behavioural drivers, barriers, and segment-specific intervention needs. Stepwise regressions accounted for 52% of the variance in self-reported boot-cleaning compliance and 56% in track-use compliance within this sample (adjusted R²). Boot-cleaning compliance was enhanced by habit strength, worry about spreading the pathogen, awareness of correct procedures, and reliance on functional cleaning stations, while inconvenience and chemical aversion reduced compliance. Track-use compliance was lowered by perceived low likelihood of spread, doubts about mitigation effectiveness, time-cost concerns, and strong forest-use identity, whereas protection motivation and habitual rule-following increased compliance. Latent profile analyses produced three segments per behaviour: boot-cleaning—Conflicted, Receptive, Engaged; and track-use—Identity-Driven Forest Users, Uncommitted, Engaged—which differed systematically in knowledge, concern, and compliance. We outline potential intervention implications informed by these findings and prior literature. Full article

Background: Childhood malnutrition, manifesting as both underweight and obesity, is a global health concern with potential repercussions on neurodevelopment and metabolic health. Objective: To analyze the relationship between nutritional status, metabolic biomarkers, and cognitive performance in school-aged children. Methods: A cross-sectional study was conducted with 100 children between 6 and 12 years of age from a public elementary school in the municipality of Chiconcuac de Juárez, Mexico. Participants were categorized according to BMI: underweight (UW), normal weight (NW), overweight (OW), and obesity (OB). Anthropometric evaluation, serum biochemical markers, and three surrogate metabolic indices, namely the Triglyceride–Glucose (TyG), Triglyceride/high-density lipoprotein cholesterol (TG/HDL), and TyG-Body Mass Index (TyG-BMI), were calculated. Cognitive performance was assessed using the Wechsler Intelligence Scale for Children (WISC-IV). Results: The OB group children showed significantly higher levels of TG, TC and LDL-C, as well as elevated levels of TyG, TG/HDL and TyG-BMI indices (p < 0.05) and lower HDL-C concentration. While no significant differences were found in Full-Scale IQ (FSIQ), the NW group showed significantly higher performance in the PSI compared to all other groups outside the healthy weight range after FDR correction. Spearman’s correlation showed that surrogate metabolic indices exhibited exclusive negative correlations with the PSI in unadjusted bivariate models. Conclusions: The extremes of the nutritional status spectrum (UW and OB) are concurrently associated with early metabolic alterations and latent cardiovascular risk, while concurrently tracking with lower performance in selective fluid cognitive domains within unadjusted models. Furthermore, surrogate metabolic indices were shown to be valuable tools that co-vary with neurocognitive profiles. Full article

Shortwave infrared (SWIR) imagery plays an important role in land–water boundary delineation, coastal monitoring, and complex aquatic environment observation. However, the spatial resolution of SWIR bands is usually lower than that of visible bands, which limits their capability to represent fine-scale targets and boundary structures. To address this problem, this study proposes MLE-ResUNet, a SWIR image super-resolution method that integrates along-track oversampling with visible-light-guided deep learning. The proposed method first exploits dual-view SWIR observations with sub-pixel displacement generated by increasing the sampling line rate in the push-broom imaging process. A maximum likelihood estimation (MLE)-based physical prior module is then introduced to transform multi-view degraded observations into a physically consistent latent high-resolution prior. Finally, high-resolution visible images are used to provide edge, texture, and structural guidance, and a ResUNet-based network is employed for multi-source feature fusion and residual reconstruction. Based on multi-region measured data acquired by the LHRSI (Lightweight High-Resolution Spectral Imager) payload onboard the BlueCarbon-1A satellite, a SWIR super-resolution dataset covering typical urban, farmland, and coastal scenarios was constructed. Comparative experiments were conducted against PCA, BDSD, PanNet, GPPNN, and two additional lightweight-guided deep learning baselines, namely LGPConv and a CANConv-style visible-guided baseline. The results show that MLE-ResUNet achieves the best performance across different scenarios and consistently outperforms the comparison methods in terms of SSIM, SAM, ERGAS, and Q-index. The proposed method effectively enhances spatial detail recovery while maintaining favorable spectral consistency. Ablation experiments further demonstrate that both along-track oversampling information and the MLE-based physical prior contribute to improved reconstruction quality and more stable training convergence. These findings indicate that the proposed method can enhance fine-scale SWIR observation capability without substantially increasing hardware complexity, providing an effective technical solution for shoreline identification, land–water boundary extraction, and complex surface target monitoring. Full article

Accurate substation-level Short-Term Load Forecasting (STLF) is essential for secure day-ahead power-system operation, yet localized demand is often affected by meteorological variation and discrete calendar shifts such as statutory holidays and makeup workdays. At this spatial scale, end-to-end forecasting models may over-smooth abrupt local changes and fail to represent peaks and valleys accurately. To address this issue, this study proposes a Calendar-Aware Frequency-Decoupled Framework (CA-FDF) for 24 h ahead substation load forecasting. The load series is decomposed by the Discrete Wavelet Transform (DWT), and the low-frequency component is tracked by a regime-aware Recursive Least Squares (RLS) filter. The residuals are then refined through explicit calendar-state encoding and day-ahead weather forecasts. A Multi-Layer Perceptron (MLP) learns latent weather representations, while SHapley Additive exPlanations (SHAP) interpret calendar- and weather-related effects. Experiments on hourly operational data from 29 anonymized substations in East China show that CA-FDF achieves a Mean Absolute Percentage Error (MAPE) of 1.92% and outperforms representative baselines under the same day-ahead setting. The results indicate that frequency-decoupled residual refinement improves localized load forecasting, with calendar-aware correction contributing the largest gain. Full article

Robust quadruped locomotion in real-world environments remains challenging because external disturbances, sensor noise, and model uncertainties are coupled with intermittent foot–ground contact. Reinforcement learning has shown strong capability in generating agile locomotion, but many existing methods handle unobserved disturbances through implicit latent representations or domain randomization. This paper presents a disturbance-aware locomotion framework that integrates state and disturbance estimation with learning-based control. The core component is a deep generalized-momentum-based Kalman filter, which combines generalized momentum disturbance modeling with adaptive covariance inference to estimate the base velocity and external disturbance force. These physically meaningful estimates are incorporated into the policy observation space, reducing the gap between privileged simulation states and deployable onboard observations. The framework was evaluated in a simulation and on a quadruped robot platform under disturbance and outdoor locomotion scenarios. Compared with the baseline and ablated variants, the proposed method reduced estimation and tracking errors, limited impact-induced torque peaks, and improved locomotion success rates under the evaluated conditions. The results suggest that explicit disturbance estimation can complement a latent adaptation for quadruped locomotion under impact-rich conditions. Full article

Salinity stress remains a major bottleneck for agriculture in arid regions. While Opuntia ficus-indica is known for its resilience, its young cladodes maintain a misleadingly healthy visual appearance and stable biomass even under heavy saline pressure, making traditional vegetation indices and standard statistics unreliable for early diagnosis. The objective of this study was to develop a non-destructive phenotyping framework for the early detection of latent salinity stress in young Opuntia cladodes. Controlled experiments were conducted using hyperspectral data cubes (400–1000 nm) acquired from plants exposed to six distinct salinity levels ranging from 2 to 21 dS m⁻¹. Our methodology integrates these high-dimensional spatial–spectral data with a tailor-made 3D Convolutional Neural Network (3D-CNN). Seven physiological vegetation indices—NDVI, PRI, WI, PSRI, MCARI, SIPI, and NDRE were extracted to track sub-clinical shifts and processed as a volumetric depth dimension within the network to preserve spatial–spectral integrity. The optimized 3D-CNN framework achieved a validation accuracy of 99.7% and a weighted F1-score of 99.1%, delivering 100% precision at critical stress thresholds (13 and 21 dS m⁻¹). Spatial confidence maps (Softmax > 0.95) further confirmed the high reliability of the diagnostic output. Requiring a training duration of approximately 8 s, this framework provides a robust basis for precision early-warning irrigation systems to sustain Opuntia cultivation in challenging environments. Full article

Vaccination stands as a preeminent public health measure in the fight against infectious diseases, with a proven track record of significantly reducing morbidity and mortality rates. However, the presence of vaccine hesitancy and misinformation, particularly evident during the course of the pandemic, has emerged as a significant challenge. The present study analyzes public perceptions of vaccination by examining YouTube comments on 215 vaccine-related videos, which total over 94,000 comments. Employing advanced topic modeling techniques, such as Hierarchical Dirichlet Process (hLDA), Latent Semantic Analysis (LSA), and Non-Negative Matrix Factorization (NMF), the study identifies key themes, including vaccine safety, side effects, pharmaceutical ethics, and public trust in healthcare authorities. The findings indicate that debates frequently center on political, social, and scientific concepts. Vaccine hesitancy has emerged as a pervasive global phenomenon that transcends cultural boundaries. The dissemination of misinformation regarding the efficacy of vaccines and the safety of treatments, such as ivermectin, is a prevalent phenomenon on social media platforms. This poses significant challenges to public health efforts. The subjects of child vaccination and parental standpoints are also recurring topics of concern. This study underscores the pivotal function of digital platforms such as YouTube in influencing public attitudes regarding vaccination. This underscores the necessity for targeted communication strategies, advanced digital literacy, and proactive policies by social media platforms to address misinformation and promote evidence-based information. Such precautions are imperative to sustaining elevated vaccination rates and safeguarding public health in the digital age. Full article

Confined space operations are characterized by environmental complexity and latent hazards, where failures in human risk perception represent a primary precursor to industrial accidents, posing a significant challenge to sustainable occupational health and safety (OHS) management. This study investigates the mechanism by which individual traits (risk propensity) influence risk perception performance through cognitive processes (inattentional blindness). Utilizing psychological scales and eye-tracking technology, we quantitatively analyzed the visual search behaviors of participants with varying risk propensities across typical confined space hazard scenarios. The results indicate that individuals with high risk propensity tend to adopt a “random-exploratory superficial scanning strategy,” characterized by significantly delayed Time to First Fixation (TFF) and lower Fixation Count (FC) within critical hazard areas compared to the low-risk propensity group. Statistical analysis reveals that inattentional blindness exerts a full mediating effect between risk propensity and risk perception performance, accounting for 72.56% of perception failures. This research confirms that an imbalance in attentional resource allocation leads to higher cognitive omission of salient hazards among high-risk propensity individuals. These findings provide a theoretical foundation for cognitive reliability assessment and the design of sustainable safety training programs in high-risk industries, ultimately contributing to the social sustainability and well-being of industrial workforces. Full article