Search Results (210)

Introduction: Elasmobranchs play a vital role in marine food webs through top-down control and the structuring of ecosystem stability, yet more than one-third of species face extinction. The Maldives, a recognised Indian Ocean hotspot for shark and batoid diversity, designated its EEZ as a shark sanctuary in 2010, but multispecies elasmobranch occurrence patterns and environmental drivers remain poorly characterised in Lhaviyani Atoll in the central Maldives, which hosts two Important Shark and Ray Areas (ISRAs). Recreational SCUBA networks can turn routine dive activity into long-term conservation evidence, already informing nearly 10% of the western Indian Ocean ISRAs. Objective: To characterise spatiotemporal patterns of elasmobranch assemblages in Lhaviyani Atoll (2017–2024), quantify how environmental and geomorphic drivers shape relative abundance, diversity, and hotspots, and provide evidence for targeted elasmobranch conservation. Methodology: A seven-year opportunistic dive-log dataset of 12,732 SCUBA surveys and 142,994 elasmobranch records across 94 dive sites was analysed. Effort-standardised relative abundance and community metrics (Shannon diversity, Pielou’s evenness) were modelled against sea surface temperature (SST), salinity, dissolved oxygen, chlorophyll-a, zonal current velocity, substrate type, and reef geomorphology using generalised additive models (GAMs). Spatial analyses identified persistent northern-rim aggregation areas aligned with ISRAs. Results: Twenty-eight species (14 sharks, 14 batoids) were recorded, including 23 threatened on the IUCN Red List (4 Critically Endangered, 12 Endangered, 7 Vulnerable). Relative abundance and diversity peaked during the late southwest monsoon (August–September) and declined during the northeast monsoon (December–March). After 2021, diversity and evenness increased while overall abundance declined. Relative abundance was primarily driven by SST, salinity, and current velocity; for sharks, dissolved oxygen and chlorophyll-a were additionally significant, whereas batoid abundance was driven mainly by temperature, oxygen, and current velocity. Four persistent hotspots along the northern atoll rim were identified, with sharks concentrated along exposed slopes and channels, and batoids distributed broadly within lagoonal habitats. Conclusions: Long-term citizen science dive-log monitoring is cost-effective for elasmobranch conservation in remote tropical seascapes. These results show how dive-industry partnerships can inform conservation governance over a decade after sanctuary designation, supporting targeted, habitat-focused management as shark and batoid conservation frameworks continue to evolve. Full article

Wearable inertial sensing has considerable potential for process-level analysis of upper-limb function, but further evidence is needed to understand how it can be applied within ecologically structured immersive virtual reality (VR) tasks. Most VR-based functional assessments rely primarily on outcome-level indicators, such as task completion time, success rate, or error count, which may not fully capture how a task is executed. This exploratory study investigated whether wearable IMU signals collected during an immersive VR sushi-making task could support binary detection of a core upper-limb manipulation phase and provide additional information about task execution beyond global performance outcomes. A total of 45 participants contributed usable motion recordings for this study, with five Xsens DOT sensors placed on the hands, forearms, and waist. Three signal modalities were analysed, including acceleration (ACC), gyroscope angular velocity (GYR), and Euler angles. The downstream recognition problem was formulated as a binary classification task (Placing vs. Non-Placing), and a self-supervised learning (SSL) pretrain–fine-tune strategy was evaluated against conventional machine learning and from-scratch deep learning baselines using five subject-wise validation splits. The strongest overall performance was achieved with hand-mounted accelerometer signals, with LeftHand–ACC achieving a Macro-F1 of $0.712\pm 0.128$ and RightHand–ACC achieving $0.679\pm 0.118$. Under both hand-ACC settings, SSL fine-tuning showed higher mean Macro-F1 than the Balanced Random Forest baseline and the same deep architecture trained from scratch. Recognition performance varied substantially across sensor locations, signal modalities, and task segments, with distal upper-limb sensors generally outperforming waist-based configurations. Cross-age analyses further showed that within-cohort and cross-cohort performance did not fully align, indicating sensitivity to age-related distribution shift. Beyond classification, Log Dimensionless Jerk (LDLJ) derived from the Placing action showed a significant positive association with Cognitron motor control time cost ($r=0.636$, $p<0.001$). These findings suggest that wearable IMU sensing can provide preliminary process-level information during immersive VR functional tasks, including task-phase detection, sensing-configuration comparison, cross-cohort generalisation assessment, and exploratory motion-quality analysis. The results should be interpreted as evidence of feasibility rather than as a mature biomechanical or clinical assessment model. Full article

Background: Celiac disease (CeD) requires lifelong adherence to a strict gluten-free (GF) diet. During the COVID-19 pandemic, the prevailing clinical assumption was that food supply disruptions and dietary management difficulties would be the primary sources of patient distress. This exploratory cross-sectional study directly tested this assumption in an Italian CeD cohort. Methods: COVIMPACT is an exploratory observational, web-based study conducted in Italy (data collected: July–September 2024; participants retrospectively reported their experiences during the COVID-19 pandemic period 2020–2022). Participants with a confirmed CeD diagnosis were recruited through patient associations and online networks. A structured 26-item questionnaire addressed socio-demographic, nutritional, psychological, and healthcare-access domains. Descriptive statistics, chi-square bivariate analyses (Cramér’s V as effect size), and binary logistic regression were performed using R (v4.1) and Python. Results: Among 118 participants (78% female; median age 36 years; IQR 12–42), 27% reported self-perceived psychological distress. Against expectation, difficulties in accessing GF products and changes in gluten consumption showed no clear associations with distress. Instead, social exclusion showed the strongest association (Firth OR = 5.55, 95% CI: 1.80–17.09, p = 0.003), while reduced physical activity (Firth OR = 5.28, 95% CI: 1.86–14.99, p = 0.002, full model; Firth OR = 5.54, p = 0.001, reduced model) and negative economic impact (Firth OR = 3.77, 95% CI: 0.89–15.97, p = 0.071, trend) were additional associated factors. Female sex showed a non-significant trend (Firth OR = 4.21, p = 0.082). All estimates carry wide confidence intervals (EPV = 4.1) and should be treated as hypothesis-generating. Conclusions: These preliminary findings suggest that social exclusion and physical inactivity may be more strongly associated with self-perceived distress than dietary challenges in contexts where GF food access is structurally protected. Results are exploratory, hypothesis-generating, and should not be generalised beyond this selected Italian cohort. Full article

Sleep disturbance is a central feature of depression and a proposed pathway through which Bright-Light Therapy (BLT) exerts antidepressant effects. However, little is known about how sleep reorganises day by day during BLT or whether these dynamics relate to symptom improvement. We analysed daily sleep diaries from 66 patients with depression undergoing three weeks of BLT in routine outpatient care. Generalised Additive Mixed Models characterised daily trajectories in sleep timing, continuity, duration, and Subjective Sleep Quality, and weekly changes in sleep regularity were assessed using Root Mean Square of the Successive Differences. Structural Equation Modelling examined whether within-person deviations in sleep parameters mediated changes in depressive symptoms. Sleep timing showed gradual adjustment across treatment, with a progressive 48 min advance in weekday sleep onset. Sleep regularity improved from Week 1 to Week 2 before partially reversing, and the probability of nocturnal awakenings followed a non-linear trajectory. Other sleep parameters showed weaker directional trends. Improvements in Subjective Sleep Quality accounted for a modest portion of the association between treatment progression and reductions in depressive symptoms, whereas changes in sleep timing and regularity were not associated with symptom change. These findings indicate that sleep reorganises gradually during outpatient BLT, with different sleep dimensions evolving on distinct timescales and Subjective Sleep Quality emerging as one observable component linked to symptom improvement. More broadly, the results highlight the value of day-to-day modelling for understanding sleep–mood dynamics during real-world chronotherapy. Full article

The growing adoption of Large Language Models (LLMs) in software engineering has generated considerable interest in their potential for Requirements Engineering (RE) tasks. However, evaluating LLM performance in RE contexts presents a fundamental challenge: the absence of established ground truth, compounded by the free-form nature of LLM outputs that resist automated comparison. This paper proposes a general methodology for systematically evaluating LLMs in RE tasks in the absence of traditional ground truth with three complementary strategies: replacing traditional ground truth with literature-based knowledge extraction to create reference standards; decomposing complex prompts into discrete closed-form questions that enable quantitative assessment; and optionally employing synthetic data generation for controlled parameter variation. The methodology is conceived to generalise across diverse RE evaluation contexts, providing researchers and practitioners with a systematic approach for assessing LLM capabilities in RE tasks where traditional ground truth is unavailable. We illustrate the methodology through several examples. In addition, to validate the reliability of decomposing complex tasks into closed-form prompts, we conducted a comparative analysis to verify if the strategy provides a quantitatively reliable assessment. Full article

Sepsis is a prominent cause of death in intensive care units, and delayed diagnosis greatly worsens fatal outcomes due to the complex, irregular, and uneven character of clinical time-series data. Hence we proposed an interpretable and uncertainty-aware deep learning architecture that solves data quality, temporal irregularity, and clinical explainability restrictions, which are frequently addressed separately by existing models. The suggested method combines Bidirectional Recurrent Imputation for Time Series (BRITS)-based imputation, hybrid Conditional Tabular Generative Adversarial Network-Synthetic Minority Over-sampling Technique (CTGAN-SMOTE) data augmentation, a Temporal Convolutional Networks (TCN)-Attention architecture, and continuous-time neural Ordinary Differential Equations (ODEs), along with SHapley Additive exPlanations (SHAP)-based feature attribution and uncertainty quantification. The experimental evaluation on a large ICU dataset reveals greater predictive accuracy, with an AUROC of 0.926 and accurate early warnings up to six hours before clinical onset, all while maintaining strong interpretability and calibration. The proposed framework demonstrates strong predictive performance and provides early warnings up to six hours before clinical onset, while maintaining interpretability and calibration. While the results are promising, further validation across multiple clinical settings is required to confirm its generalisability and real-world applicability. Full article

Reliable tracking of multi-pollutant air-quality progress is essential for assessing policy effectiveness and health risks, yet most assessments still focus on single pollutants. We analysed population-weighted exposures to fine particulate matter (PM_2.5), nitrogen dioxide (NO₂) and household air pollution (HAP) for Australia and the global average over 1990–2019, using harmonised estimates from a Global Burden of Disease–type framework. Non-parametric LOESS and semi-parametric generalised additive models were applied to characterise long-term trends, and a composite clean-air progress index (CAPI; 1990 = 1) was constructed to summarise joint changes in the three pollutants. Statistical and Monte Carlo methods were used to propagate reported exposure uncertainty into both pollutant-specific trends and the composite index. Globally, exposures to PM_2.5, NO₂ and HAP all declined, and the CAPI fell to around 0.7 by 2019, indicating substantial multi-pollutant improvement relative to 1990. In Australia, NO₂ decreased more rapidly than the global mean, but PM_2.5 showed little long-term decline and the HAP-related metric increased more than three-fold. As a result, Australia’s CAPI rose to approximately 1.6–1.7, with Monte Carlo uncertainty envelopes remaining well above 1 from the early 2000s onwards. Correlation analyses revealed that pollutants improved together at the global scale, but were partially decoupled in Australia, implying that source-specific gains have not translated into aggregate clean-air progress. These findings demonstrate that single-pollutant assessments can obscure important trade-offs and that multi-pollutant, uncertainty-aware indices such as CAPI provide a more informative basis for benchmarking national trajectories against global experience and for guiding integrated clean-air policy. Full article

Deployable structures offer new solutions in space, and among them, tubular origami-inspired space structures have proven to be a robust solution for packaging problems. This study focuses on the analysis of the Kresling origami pattern, which theoretically offers bi-stability during its folding process. The bi-stability of this pattern is a well-known property for paper models. However, it cannot be generalised for any material or geometry, as this property can be traced back to the manufacturing process and the materials being used. Consequently, we propose and test additive manufacturing models implementing different geometry parameters with the materials of interest. In parallel, a parametrised numerical model was developed in the commercial software Abaqus, replicating the structural behaviour of these test specimens under displacement-controlled compression. The aim is to obtain a final validated numerical model from where the entire behaviour and energetic response of each sample and, thus, their stability can be tested. Combining experimental and numerical results paints a whole picture of bi-stability, verifying this useful property for different space materials and configurations. Full article

Ramadan fasting substantially alters meal timing, sleep patterns, and daily activity, thereby increasing the risk of hypoglycaemia in adults with type 1 diabetes (T1D). Although continuous glucose monitoring (CGM) systems provide real-time alerts, these are largely reactive or limited to short prediction horizons, offering insufficient warning under fasting-related behavioural and circadian disruption. This study aims to evaluate whether behaviour-aware, temporally enriched recurrent deep learning models, leveraging multimodal CGM and wearable-derived signals, can forecast hypoglycaemia one hour ahead during Ramadan and the post-fasting period. In an observational, free-living cohort study conducted in Qatar, 33 adults with T1D were monitored using CGM and a wrist-worn wearable during Ramadan 2023 and the subsequent month. Multimodal data were aggregated into hourly features and organised into rolling 36 h sequences. In addition to physiological signals, explicit temporal and circadian proxy features were engineered, including cyclic time encodings, day–night indicators, and Ramadan-specific behavioural windows (e.g., pre-iftar, iftar, post-iftar, and fasting phases). Recurrent models, including LSTM and BiLSTM architectures, were trained using patient-wise, leak-free splits, with focal loss applied to address class imbalance. Model performance was evaluated on a held-out, naturally imbalanced test set using ROC AUC, precision–recall AUC, recall, and probability calibration, alongside cross-phase evaluation between Ramadan and post-fasting periods. Following quality control, 1164 participant-days were retained, with hypoglycaemia accounting for approximately 4% of hourly observations. Temporal feature enrichment and the use of a 36 h lookback window improved both discrimination and calibration, with performance stabilizing beyond this horizon. On the imbalanced test set, the best-performing multimodal model achieved an ROC AUC of 0.867 and a precision–recall AUC of 0.341, identifying 77% of next-hour hypoglycaemic events at a sensitivity-focused operating point (precision = 0.14). The selected BiLSTM model demonstrated good probability calibration (Brier score ≈ 0.03). Models trained using wearable-derived inputs alone achieved comparable discrimination and, in some configurations, higher precision–recall AUC than CGM-only baselines. Notably, models trained on the original imbalanced data outperformed resampled variants, suggesting that temporal and behavioural features provided sufficient discriminatory signal without requiring aggressive class balancing. Cross-phase evaluation indicated robust generalisation, particularly for the BiLSTM model. Overall, behaviour-aware, temporally enriched multimodal models can provide calibrated, hour-ahead hypoglycaemia risk estimates during Ramadan fasting in adults with T1D, enabling proactive intervention beyond reactive CGM alerts. Explicit modelling of circadian and behavioural dynamics enhances predictive performance under real-world class imbalance. Furthermore, integrating wearable-derived behavioural and physiological signals adds predictive value beyond CGM alone, supporting robustness across varying levels of contextual data availability. External validation and prospective clinical evaluation are required prior to deployment. Full article

Introduction: Workplace mobbing is a psychosocial risk factor associated with adverse mental health outcomes, including depression, anxiety, and suicidal ideation. Accurate assessment of this phenomenon is essential for both research and applied settings; however, validated brief instruments in Spanish remain limited. The Luxembourg Workplace Mobbing Scale (LWMS) is a short measure with sound psychometric properties that allows efficient evaluation of exposure to workplace mobbing. Objective: Translation and validation of the LWMS into Spanish in adults. Methods: A total of 345 adults (51.3% women) participated, completing a sociodemographic questionnaire and psychological instruments. Statistical analyses were conducted using structural equation modelling and item response theory. Results: The LWMS demonstrated adequate content validity; a unidimensional structure (CFI = 0.99, RMSEA = 0.04 [90% CI: 0.001, 0.092], SRMR = 0.02); and reliability (ω = 0.79, H = 0.86 and r_xx = 0.78). In addition, significant associations were found with depressive symptoms (r = 0.37, p = 0.001), generalised anxiety (r = 0.38, p = 0.001), and suicidal ideation (r = 0.27, p = 0.001). Item 2 showed the highest discrimination and information, and the scale proved to be accurate at higher levels of workplace mobbing. Conclusions: The Spanish version of the LWMS shows solid evidence of validity and reliability, supporting its use as a brief and precise instrument for assessing workplace mobbing in adult populations. Its strong psychometric performance and clinical relevance make it suitable for research, screening, and preventive interventions in occupational settings. Full article

There have been significant advancements in object detection using extensive labelled datasets. However, existing learning-based approaches remain constrained in industrial environments, primarily due to the limited diversity in training datasets; the lack of generalisation of close-set detectors to unseen asset categories; and the inherent spatial and geometric complexity of mechanical, electrical, and plumbing (MEP) assets. To address this challenge, we propose a new approach that leverages pre-trained vision language models and close-set object detectors to detect unseen MEP assets using unlabelled data. Experimental results reveal the superior performance of Grounding DINO using Swin B transformer in open-vocabulary MEP asset detection, achieving the mean intersection over union (mIoU) of 0.6586 for valve detection and 0.4883 for pump detection. In addition, the combination of Grounding DINO (Swin B) and YOLOv8 outperforms other configurations in MEP asset detection, attaining the highest performance for both valve detection, with mean average precision at IoU = 0.5 (mAP50) of 0.928 and mean average precision over IoU threshold from 0.5 to 0.95 (mAP50:95) of 0.889, and pump detection, with corresponding values of 0.778 and 0.662, respectively. The quantitative and qualitative results of our approach were evaluated against fine-tuned Grounding DINO and fully supervised close-set object detectors. Full article

For Unmanned Aerial Vehicles (UAVs), optical sensing for reliable landing and the detection of the landing area is a crucial element. In low-light conditions, at night, and in foggy weather, where optical sensing is not feasible, thermal imaging can be utilised. Although this situation has been widely researched, most UAV landing approaches rely on GNSS assistance or single-mode detection, which limits their robustness and scalability in real-world operations. This study proposes an actively heated thermal helicopter landing pad designed using electrically powered resistive heating elements and a high-emissivity surface coating. Furthermore, optical and thermal images collected during actual UAV flight experiments under daytime and night-time conditions were processed using image fusion techniques with AVGF, DWTF, GPF, LPF, MPF, and HWTF fusions, and their performance in deep learning models was compared. The obtained optical, thermal, and fused datasets are used to train and evaluate deep learning-based helicopter landing pad detection models based on the YOLOv8 architecture. Experimental results show that models trained with single-mode data exhibit limited cross-domain generalisation, while fusion-based learning significantly improves detection robustness in optical and thermal domains. Among the evaluated methods, LPF, MPF and HWTF provide the most consistent performance improvements. The findings indicate that electrically heated thermal helicopter landing pads, when combined with image fusion and deep learning-based detection, can increase the landing detectability of UAVs at night and in low-visibility conditions. This detection-focused approach contributes to UAV flight safety by enhancing the visibility of the landing area without relying on active infrared markers or additional navigation infrastructure. Full article

This study develops generalised equations to predict arsenic removal efficiency during adsorption using synthetic sand, based on two key factors: adsorbent dose and temperature. Previous experimental investigations demonstrated that iron oxide coated sand (IOCS), aluminium oxide coated sand (AOCS), and their mixtures are highly effective for arsenic removal. Best-fit equations were first derived for IOCS and AOCS at discrete temperatures as functions of dose concentration, and these were subsequently unified into single predictive equations capable of estimating removal efficiency across a wide range of temperatures and doses. The resulting models closely replicate experimental results, with correlation coefficients exceeding 0.99 for both IOCS and AOCS. Using the same methodology, an additional equation was developed for a 50:50 mixture of IOCS and AOCS, yielding a slightly lower but still strong correlation coefficient of 0.97. In contrast, linear proportioning of the individual IOCS and AOCS equations failed to accurately predict the removal efficiency of the mixed adsorbent, indicating that simple linear scaling is inadequate for representing the combined adsorption behaviour. Full article

Accurate forecasting of local weather patterns is essential for climate resilience and sustainable planning. This study analysed 35 years (1990–2025) of hourly temperature and precipitation data from Thohoyandou, South Africa, to assess the impacts of climate change and improve anomaly prediction. Exploratory analysis and Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST) decomposition revealed accelerated warming trends of 0.025 °C per year in temperature anomalies, alongside highly irregular rainfall patterns characterised by extreme events rather than systematic changes. Three models, Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, and a Generalised Additive Model (GAM), were evaluated for anomaly forecasting, with feature selection guided by LASSO regression. For temperature, the LSTM performed better than the ANN and GAM, with MSE = 0.458, MAE = 0.457, MBE = 0.087, and MASE = 0.510. For temperature anomalies, the LSTM model performed best, followed by the GAM and ANN models. For precipitation anomalies, the LSTM model also achieved the lowest prediction error, with MSE = 0.187, MAE = 0.111, MBE = −0.009, and MASE = 1.873; however, MASE values above 1 indicate that rainfall forecasting remains challenging. These results show the LSTM model’s ability to handle temperature anomalies and the difficulty of modelling rainfall. GAM performed less accurately but steadily in modelling precipitation. Full article

Artificial Intelligence (AI)-based crack detection in buildings uses computer vision and deep learning to automatically identify structural cracks from inspection images. In recent years, many studies have explored this topic, but the overall development of the field, its methodological practices, and the remaining challenges are still not fully clear. Unlike most previous reviews that focus mainly on technical methods, this study combines a large-scale scientometric mapping of the research field with a focused technical analysis of recent AI-based crack detection methods specifically applied to building structures. This study therefore provides a dual-layer review covering research published between 2015 and 2025. A total of 146 Scopus-indexed publications were analysed using Visualization of Similarities viewer (VOSviewer) to examine publication growth, thematic evolution, collaboration patterns, and citation structures. In addition, a focused technical review of 36 highly relevant studies was carried out to analyse task formulations, model families, datasets, evaluation protocols, and methodological practices. The results show a rapid increase in research activity after 2020, largely driven by advances in deep-learning and Unmanned Aerial Vehicle (UAV)-based inspections. At the same time, collaboration networks remain uneven, and citation influence is concentrated in a limited number of research communities. The technical review further shows that most studies focus on detection-level tasks, particularly You Only Look Once (YOLO)-based models, while predictive diagnostics, automated inspection reporting, and decision-oriented Structural Health Monitoring (SHM) are still rarely addressed. Current datasets and evaluation protocols also remain mostly perception-oriented, which makes it difficult to assess robustness, generalisability and long-term predictive capability. Full article