Search Results (1,618)

Polycyclic aromatic hydrocarbons (PAHs) are recognized carcinogens that enter the food chain through pre-existing environmental contamination (air, water, soil), and their formation and accumulation during food preparation and processing involve high temperatures. We established a modified QuEChERS GC-MS method that couples n-hexane-saturated acetonitrile containing 1% toluene with a freeze-out step. Compared to the previously reported ACN QuEChERS protocol, this method enhanced PAH desorption and suppressed lipid interference across four matrices. The method linearity (R² ≥ 0.99), limit of detection (LOD, from 0.03 to 0.20 μg/kg), limit of quantitation (LOQ, from 0.10 to 0.60 μg/kg), and intra-/inter-day precision (≤5.7% RSD) all satisfied AOAC criteria. The modified QuEChERS reduced solvent consumption and shortened preparation time compared to other conventional extraction methods. The developed method was applied to 302 retail food samples, and Kezuribushi was found to have the highest concentration of the 4PAHs, reaching 22.0 µg/kg. Risk assessment based on EFSA’s margin-of-exposure (MOE) approach identified grilled chicken feet (MOE = 7604) as a potential health concern, as this value falls below EFSA’s threshold of 10,000 for potential risk characterization. The validated method enables sensitive and scalable monitoring of PAHs in complex food matrices within the tested matrices and conditions. Full article

This study presents a novel cyber-resilient, data-driven optimisation framework for real-time energy management in electric vehicle (EV)-integrated smart grids. The proposed framework integrates a hybrid optimisation engine—combining genetic algorithms and reinforcement learning—with a real-time analytics module to enable adaptive scheduling under uncertainty. It accounts for dynamic electricity pricing, EV mobility patterns, and grid load fluctuations, dynamically reallocating charging demand in response to evolving grid conditions. Unlike existing GA/RL schedulers, this framework uniquely integrates adaptive optimisation with resilient forecasting under incomplete data and lightweight blockchain-inspired cyber-defence, thereby addressing efficiency, accuracy, and security simultaneously. To ensure secure and trustworthy EV–grid communication, a lightweight blockchain-inspired protocol is incorporated, supported by an intrusion detection system (IDS) for cyber-attack mitigation. Empirical evaluation using European smart grid datasets demonstrates a daily peak demand reduction of 9.6% (from 33 kWh to 29.8 kWh), with a 27% decrease in energy delivered at the original peak hour and a redistribution of demand that increases delivery at 19:00 h by nearly 25%. Station utilisation became more balanced, with weekly peak normalised utilisation falling from 1.0 to 0.7. The forecasting module achieved a mean absolute error (MAE) of 0.25 kWh and a mean absolute percentage error (MAPE) below 20% even with up to 25% missing data. Among tested models, CatBoost outperformed LightGBM and XGBoost with an RMSE of 0.853 kWh and $R^2$ of 0.416. The IDS achieved 94.1% accuracy, an AUC of 0.97, and detected attacks within 50–300 ms, maintaining over 74% detection accuracy under 50% novel attack scenarios. The optimisation runtime remained below 0.4 s even at five times the nominal dataset scale. Additionally, the study outlines a conceptual extension to support location-based planning of charging infrastructure. This proposes the alignment of infrastructure roll-out with forecasted demand to enhance spatial deployment efficiency. While not implemented in the current framework, this forward-looking integration highlights opportunities for synchronising infrastructure development with dynamic usage patterns. Collectively, the findings confirm that the proposed approach is technically robust, operationally feasible, and adaptable to the evolving demands of intelligent EV–smart grid systems. Full article

This study develops a data-driven framework to prevent traffic isolation on snow-affected highways by analyzing vehicle detection system (VDS) data collected over the past decade in the Yeongdong region of the Republic of Korea. Specifically, we used hourly traffic volume and average travel speed between interchange to interchange (IC-IC) segments on days with cumulative snowfall exceeding 30 cm, enabling the identification of critical thresholds that trigger congestion and isolation under extreme snow conditions. By examining the correlation between hourly snowfall intensity, traffic volume, and travel speed, we identified critical thresholds that signal the onset of traffic congestion and isolation, where traffic congestion refers to temporary flow deterioration with average speeds falling below 40 km/h, and traffic isolation denotes and operational breakdown characterized by average travel speeds falling below 20 km/h and prolonged loss of roadway functionality. Results indicated that when snowfall intensity exceeded 2 cm per hour, traffic congestion generally emerged once hourly volumes surpassed 1500 vehicles, whereas traffic isolation became likely when volumes exceeded 2200 vehicles per hour. Building on these findings, this study proposes adaptive traffic control measures that can be proactively implemented during snowstorm conditions. The proposed framework further provides a basis for determining the optimal timing of intervention before isolation occurs, thereby preventing operational breakdowns and enhancing both the resilience and sustainability of winter highway operations. Full article

Rheumatoid arthritis (RA) is a highly prevalent chronic inflammatory rheumatic disorder leading to functional impairment and sequels. The search for new biomarkers helping in detecting RA subjects of high risk of functional disability is required. Studies showing high follistatin levels in RA have been described; however, none of them have placed focus on the role of follistatin as marker of deteriorated functionality. We aim to identify whether follistatin concentrations could be a potential biomarker of physical disability and disease activity in RA patients. Fifty-seven female RA subjects and 20 age–gender-matched controls were included in a cross-sectional evaluation. An assessment of clinical characteristics, grip strength, gait speed, and muscle mass was conducted. In RA subjects, disability was assessed using HAQ-DI and active disease using the DAS28-ESR. Follistatin levels were measured by ELISA. We compared (a) RA + functional disability and (b) RA + preserved physical function. Serum follistatin levels were increased in RA subjects compared to controls (175 ± 119 vs. 133 ± 47; p = 0.030). Follistatin levels correlated with deteriorated physical function levels (r = 0.491; p < 0.001) and severe activity (r = 0.344; p = 0.009). The RA + functional disability group, as compared to the RA + preserved physical function group, had higher serum follistatin levels (218 ± 159 vs. 141 ± 59; p = 0.030), lower grip strength (7.9 ± 4.6 vs. 14.5 ± 5.1; p < 0.001), reduced gait speed (0.77 ± 0.20 vs. 0.92 ± 0.20; p = 0.010), as well as higher proportions of tender joints ≥4 (48% vs. 16%; p = 0.008), and higher disease activity scores (3.8 ± 1.5 vs. 2.8 ± 1.2; p = 0.008). We concluded that higher follistatin levels are associated with physical functional impairment and the severity of disease activity in women with RA. Future studies are required to evaluate whether these follistatin levels can be related to other outcomes such as labor disability, hospitalization, and falls. Full article

Methane concentration anomalies during coal mining operations are identified as important factors triggering major safety accidents. This study aimed to address the key issues of insufficient adaptability of existing detection methods in dynamic and complex underground environments and limited characterization capabilities for non-uniform sampling data. Specifically, an intelligent diagnostic model was proposed by integrating the improved Dung Beetle Optimization Algorithm (SGDBO) with Transformer-SVM. A dual-path feature fusion architecture was innovatively constructed. First, the original sequence length of samples was unified by interpolation algorithms to adapt to deep learning model inputs. Meanwhile, statistical features of samples (such as kurtosis and differential standard deviation) were extracted to deeply characterize local mutation characteristics. Then, the Transformer network was utilized to automatically capture the temporal dependencies of concentration time series. Additionally, the output features were concatenated with manual statistical features and input into the LSSVM classifier to form a complementary enhancement diagnostic mechanism. Sine chaotic mapping initialization and a golden sine search mechanism were integrated into DBO. Subsequently, the SGDBO algorithm was employed to optimize the hyperparameters of the Transformer-LSSVM hybrid model, breaking through the bottleneck of traditional parameter optimization falling into local optima. Experiments reveal that this model can significantly improve the classification accuracy and robustness of anomaly curve discrimination. Furthermore, core technical support can be provided to construct coal mine safety monitoring systems, demonstrating critical practical value for ensuring national energy security production. Full article

Since 2007, white-nose syndrome (WNS), caused by the fungus Pseudogymnoascus destructans, has killed millions of bats across North America by disrupting hibernation cycles, causing premature fat depletion and starvation. Little brown bats (Myotis lucifugus) from some populations persisting after WNS store larger pre-hibernation fat reserves than bats did before WNS, which may help bats survive winter starvation and mount an immune response to Pd in spring. MicroRNAs (miRNAs) are highly conserved, small, non-coding RNA molecules that regulate gene expression post-transcriptionally. Aberrant miRNA expression can affect metabolic pathways in mammals and has been linked to various diseases. If fat reserves and immune mechanisms influence survival from WNS, then miRNAs regulating metabolic and immune-related genes might affect WNS pathogenesis and bat survival. A previous study identified 43 miRNAs differentially expressed in bats with WNS. We analyzed these miRNAs for their roles in metabolism and immune-related pathways, using DIANA Tools and KEGG analysis, to determine a subset that could serve as biomarkers of pathophysiology or survival in WNS-affected bats. We identified miR-543, miR-27a, miR-92b, and miR-328 as particularly important because they regulate multiple pathways likely important for WNS (i.e., immune response, lipogenesis, insulin signaling, and FOXO signaling). As proof-of-concept, we used reverse transcription quantitative real-time PCR (RT-qPCR) to quantify the prevalence of these miRNAs in plasma samples of bats (n = 11) collected from a post-WNS population during fall fattening. All the selected miRNAs were detectable in at least some bats during fall fattening although prevalence varied among miRNAs. Future in vivo validation studies would help confirm functional roles and biomarker utility of these miRNAs for WNS-affected bats. Full article

The widespread adoption of smartphones and smartwatches has enabled non-intrusive fall detection through built-in sensors and on-device computation. While these devices are widely used by older adults, existing systems still struggle to accurately detect soft falls in real-world settings. There is a notable drop in performance when fall-detection models trained offline on labeled accelerometer data are deployed and tested in real-world conditions using streaming, real-time data. To address this, our experimental study investigates whether incorporating additional sensor modalities, specifically gyroscope data with accelerometer data from wrist and hip locations, can help bridge this performance gap. Through systematic experimentation, we demonstrated that combining accelerometer data from the hip and the wrist yields a model capable of achieving an F1-score of 88% using a Transformer-based neural network in offline evaluation, which is an improvement of 8% over a model trained solely on wrist accelerometer data. However, when it is deployed in an uncontrolled home environment with streaming real-time data, this model produced a high number of false positives. To address this, we retrained the model using feedback data that comprised both false positives and true positives and was collected from ten participants during real-time testing. This refinement yielded an F1-sore of 92% and significantly reduced false positives while maintaining comparable accuracy in detecting true falls in real-world settings. Furthermore, we demonstrated that the improved model generalizes well to older adults’ movement patterns, with minimal false-positive detections. Full article

The escalating scale and psychological harm of cyberbullying across digital platforms present a critical social challenge, demanding the urgent development of highly accurate and reliable automated detection systems. Standard fine-tuned transformer models, while powerful, often fall short in capturing the nuanced, context-dependent nature of online harassment. This paper introduces a novel hybrid deep learning model called Robustly Optimized Bidirectional Encoder Representations from the Transformers with the Bidirectional Long Short-Term Memory-based Attention model (RoBERTa-BiLSTM), specifically designed to address this challenge. To maximize its effectiveness, the model was systematically optimized using the Optuna framework and rigorously benchmarked against eight state-of-the-art transformer baseline models on a large cyberbullying dataset. Our proposed model achieves state-of-the-art performance, outperforming BERT-base, RoBERTa-base, RoBERTa-large, DistilBERT, ALBERT-xxlarge, XLNet-large, ELECTRA-base, DeBERTa-v3-small with an accuracy of 94.8%, precision of 96.4%, recall of 95.3%, F1-score of 95.8%, and an AUC of 98.5%. Significantly, it demonstrates a substantial improvement in F1-score over the strongest baseline and reduces critical false negative errors by 43%, all while maintaining moderate computational efficiency. Furthermore, our efficiency analysis indicates that this superior performance is achieved with a moderate computational complexity. The results validate our hypothesis that a specialized hybrid architecture, which synergizes contextual embedding with sequential processing and attention mechanism, offers a more robust and practical solution for real-world social media applications. Full article

Background: The ageing population demands effective health solutions for the elderly. Wearable devices offer real-time monitoring and early alerts, but a comprehensive review of research in this field is lacking. This study uses bibliometric methods to analyse trends and advances in wearable devices for the elderly. Methods: Literature from 2014 to 2024 was retrieved from the Web of Science Core Collection using keywords related to the elderly and wearable devices. A total of 1015 English-language papers were analysed using tools including CiteSpace, VOSviewer, and R-Bibliometrix. Results: The annual growth rate of publications was 7.65%, with research increasing from 4 in 2014 to 1015 in 2024. Major contributors were the United States and China, with key authors including Bijan Najafi and Lynn Rochester. Research shifted from fall detection and activity monitoring to heart rate variability, balance, and AI integration. Key themes included “digital health”, “wearable technology”, and “cardiac health monitoring”. Conclusions: Research on wearable devices for the elderly is growing rapidly. Future studies should focus on multimodal sensor fusion, AI-enhanced analytics and personalised health interventions, and long-term, real-world validation of wearable solutions to improve elderly health management. Full article

The ageing of the global population, especially in developed countries, is driving significant societal changes. In Portugal, demographic data reflect a marked increase in the ageing index. Understanding gait alterations associated with ageing is essential for the early detection of mobility decline and fall risk. This study aimed to analyse gait patterns in older adults to contribute to a biomechanical ageing profile. Thirty-six community-dwelling older adults (29 female, 7 male; mean age: 74 years) participated. Gait data were collected using the Xsens full-body motion capture system, which combines inertial sensors with biomechanical modelling and sensor fusion. Spatiotemporal and kinematic parameters were analysed using descriptive statistics. Compared to younger adult norms, participants showed increased stance and double support phases, reduced swing phase, and lower gait speed, stride length, and cadence, with greater step width. Kinematic data showed reduced peak plantar flexion, knee flexion, and hip extension, but increased dorsiflexion peaks—adaptations aimed at stability. Despite a limited sample size and lack of clinical subgroups, results align with age-related gait literature. Findings support the utility of wearable systems like Xsens in capturing clinically relevant gait changes, contributing to normative biomechanical profiling and future mobility interventions. Full article

Training fall detection systems is challenging due to the scarcity of real-world fall data, particularly from elderly individuals. To address this, we explore the potential of Large Language Models (LLMs) for generating synthetic fall data. This study evaluates text-to-motion (T2M, SATO, and ParCo) and text-to-text models (GPT4o, GPT4, and Gemini) in simulating realistic fall scenarios. We generate synthetic datasets and integrate them with four real-world baseline datasets to assess their impact on fall detection performance using a Long Short-Term Memory (LSTM) model. Additionally, we compare LLM-generated synthetic data with a diffusion-based method to evaluate their alignment with real accelerometer distributions. Results indicate that dataset characteristics significantly influence the effectiveness of synthetic data, with LLM-generated data performing best in low-frequency settings (e.g., 20 Hz) while showing instability in high-frequency datasets (e.g., 200 Hz). While text-to-motion models produce more realistic biomechanical data than text-to-text models, their impact on fall detection varies. Diffusion-based synthetic data demonstrates the closest alignment to real data but does not consistently enhance model performance. An ablation study further confirms that the effectiveness of synthetic data depends on sensor placement and fall representation. These findings provide insights into optimizing synthetic data generation for fall detection models. Full article

Background: Freezing of gait (FoG) is one of the most debilitating motor symptoms in Parkinson’s disease (PD). It often leads to falls and reduces quality of life due to the risk of injury and loss of independence. Several types of wearable sensors have emerged as promising tools for the detection of FoG in clinical and real-life settings. Objective: The main objective of this systematic review was to critically evaluate the current usability of wearable sensor technologies for FoG detection in PD patients. The focus of the study is on sensor types, sensor combinations, placement on the body and the applications of such detection systems in a naturalistic environment. Methods: PubMed, IEEE Explore and ACM digital library were searched using a search string of Boolean operators that yielded 328 results, which were screened by title and abstract. After the screening process, 43 articles were included in the review. In addition to the year of publication, authorship and demographic data, sensor types and combinations, sensor locations, ON/OFF medication states of patients, gait tasks, performance metrics and algorithms used to process the data were extracted and analyzed. Results: The number of patients in the reviewed studies ranged from a single PD patient to 205 PD patients, and just over 65% of studies have solely focused on FoG + PD patients. The accelerometer was identified as the most frequently utilized wearable sensor, appearing in more than 90% of studies, often in combination with gyroscopes (25.5%) or gyroscopes and magnetometers (20.9%). The best overall sensor configuration reported was the accelerometer and gyroscope setup, achieving nearly 100% sensitivity and specificity for FoG detection. The most common sensor placement sites on the body were the waist, ankles, shanks and feet, but the current literature lacks the overall standardization of optimum sensor locations. Real-life context for FoG detection was the focus of only nine studies that reported promising results but much less consistent performance due to increased signal noise and unexpected patient activity. Conclusions: Current accelerometer-based FoG detection systems along with adaptive machine learning algorithms can reliably and consistently detect FoG in PD patients in controlled laboratory environments. The transition of detection systems towards a natural environment, however, remains a challenge to be explored. The development of standardized sensor placement guidelines along with robust and adaptive FoG detection systems that can maintain accuracy in a real-life environment would significantly improve the usefulness of these systems. Full article

This study delves into the response characteristics of core grounding current signals in power transformers across different operating conditions, aiming to enhance the accuracy of transformer condition assessment. Existing detection technologies often rely on single-parameter methods, which fall short in providing a comprehensive evaluation of transformer conditions. To address this limitation, this research develops a wideband circuit model based on multi-conductor transmission line theory and backed by experimental validation. The model systematically investigates the response mechanisms of core grounding current to various electrical stresses, including impulse voltages, power-frequency harmonics, and partial discharges. The findings reveal distinct response characteristics of core grounding current under different stresses. Under impulse voltage excitation, the core current exhibits high-frequency oscillatory decay with characteristics linked to voltage waveform parameters. In harmonic conditions, the current spectrum shows linear correspondence with excitation voltages, with no resonance below 1 kHz. Partial discharges induce high-frequency oscillations in the grounding current due to multi-resonant networks formed by distributed winding-core parameters. This study establishes a new theoretical framework for transformer condition assessment based on core grounding current analysis, offering critical insights for optimizing detection technologies and overcoming the limitations of traditional methods. Full article

This study examines the impact of spoken data collection techniques and language background on falling, level, and rising tones. Elicited data from a Discourse Completion Task (DCT), structured speech from a collaborative oral assessment task, and naturalistic speech from a comprehensive corpus of inner-circle and Hong Kong English were analyzed for Discourse Intonation features, resulting in 2756 tone choices by 184 speakers. Multinomial logistic regression indicates that structured speech by L2 English learners and naturalistic speech by both inner circle and Hong Kong English speakers exhibited similar tone choice patterns. However, DCT responses by L2 English learners contained significantly fewer level tones and more rising tones. Qualitative analyses suggest that contrary to naturalistic studies, L2 learners use rising tones to focus their attention on the speaker during a request. L1 users, on the other hand, used a variety of tone choices that focus on language and mitigate directness. Overall, these results add further evidence that DCTs do not elicit speech that generalizes to naturalistic discourse. Structured tasks in which two L2 speakers interact mirror the rates of the inner circle and Hong Kong English speakers detected in this study. Full article

The metastrongyloid nematodes Angiostrongylus vasorum, Aelurostrongylus abstrusus, and Crenosoma vulpis can cause severe cardiopulmonary and respiratory symptoms in domestic dogs and cats and free-ranging canids and felids (e.g., foxes, wolves, wild cats, lynxes). Recent data on the prevalence of A. vasorum infections in dogs and foxes and on the prevalence of Ae. abstrusus and Troglostrongylus brevior infections in free-ranging lynxes and wild cats revealed several endemic and hyperendemic foci in Germany. Nonetheless, long-term investigations on the prevalence of metastrongyloid larvae infecting gastropod intermediate hosts are still scarce for Germany. To fill this gap, we conducted an epidemiological survey on native slugs and snails in a selected meadow close to Obrigheim, previously identified as a hyperendemic focus for canine angiostrongylosis. To re-evaluate this location as a ‘hotspot’ of canine angiostrongylosis, terrestrial slugs and snails (n = 533) were collected in all seasons, artificially digested, and microscopically and molecularly analyzed for the presence of metastrongyloid lungworm larvae. Here, the prevalence ranged greatly between seasons. In summer, 27.46% (59/215) of gastropods were infected with metastrongyloid larvae. In fall, the prevalence dropped to 10.00% (16/160) and lowest infection rates were observed in both winter (5.65%) and spring (1.47%). In total, A. vasorum was detected in 12.01% (64/533), Crenosoma sp. in 0.94% (5/533), and Ae. abstrusus in 0.38% (2/533) of gastropod samples. Even though total A. vasorum infection levels were revealed to be considerably lower than in the prior study, this epidemiological survey in principle reconfirms Obrigheim as a stable hyperendemic focus and thereby as a location with high metastrongyloid infection risk for domestic dogs, cats, and wildlife throughout the year. These results call for continuous epidemiological studies on gastropod populations to better understand metastrongyloid lungworm spread and infection dynamics over the years. Full article