Search Results (9,112)

The rapid growth of large-scale Internet of Things (IoT) deployments in urban environments requires accurate and energy-efficient localization methods for low-power wireless devices. In long-range wide-area networks (LoRaWAN), traditional GPS-based positioning is often impractical due to energy consumption constraints and signal propagation challenges in urban areas. This study proposes a hybrid localization system that integrates weighted centroid localization (WCL) with a machine learning (ML) regression model to improve outdoor positioning accuracy. The proposed approach first estimates approximate transmitter coordinates using a physically grounded WCL method based on received signal strength indicator (RSSI) measurements. These initial estimates are subsequently refined by ML models trained to learn nonlinear residual corrections. In addition to random partitioning, a spatial data splitting strategy is proposed and evaluated using a publicly available LoRaWAN dataset. The experimental results demonstrate that the hybrid WCL framework combined with a multilayer perceptron (MLP) significantly outperforms other ML models. The proposed method achieves a mean localization error of 160.47 m and a median error of 73.78 m. Compared to the baseline model, the integration of WCL reduces the mean localization error by approximately 29%, highlighting the effectiveness of incorporating physically interpretable priors into localization models. Full article

Cloud-based intrusion detection systems (IDSs) increasingly rely on deep learning classifiers to identify malicious traffic; however, this reliance exposes them to adversarial evasion attacks in which adversaries craft near-imperceptible perturbations to bypass detection. Existing defenses based on conventional adversarial training often recover robustness against known perturbation patterns at the cost of degraded detection accuracy on canonical attack categories—a robustness–accuracy trade-off that remains an open challenge in the field. In this paper, we propose GT-CSAT (Game-Theoretic Cost-Sensitive Adversarial Training), a novel defense framework tailored for cloud security environments. GT-CSAT couples an improved Wasserstein GAN with Gradient Penalty (WGAN-GP) threat generator—conditioned on attack semantics to simulate functionally consistent and highly covert traffic variants—with a minimax adversarial training loop governed by a game-theoretic cost-sensitive loss function. The proposed loss function assigns asymmetric misclassification penalties derived from a two-player zero-sum payoff matrix, enabling the detector to maintain vigilance over both novel adversarial variants and well-characterized conventional threats simultaneously. Specifically, misclassifying an adversarially perturbed attack as benign incurs a strictly higher penalty than the symmetric cross-entropy baseline, while the cost weights are dynamically adapted via a Nash equilibrium-inspired update rule during training. We conduct comprehensive experiments on the Cloud Vulnerabilities Dataset (CVD), CICIDS-2017, and UNSW-NB15, which encompass diverse cloud-specific attack scenarios including denial-of-service, port scanning, brute-force, and SQL injection traffic. Under six representative evasion strategies—FGSM, PGD, C&W, BIM, DeepFool, and IDSGAN-style black-box perturbations—GT-CSAT achieves an average robust accuracy of 94.3%, surpassing standard adversarial training by 6.8 percentage points and the undefended baseline by 21.4 percentage points, while preserving clean-traffic detection at 97.1%. These results confirm that the game-theoretic cost structure effectively decouples robustness from accuracy, yielding a Pareto-superior detection profile relative to competing baselines across all evaluated threat models. The source code and experimental configurations have been publicly released to facilitate reproducibility. Full article

Shape optimization of arch dams is essential for balancing structural safety and economic efficiency, yet remains computationally intensive due to costly finite element analyses and strict geometric constraints. Conventional sampling techniques often yield infeasible designs that undermine surrogate model fidelity. This study proposes an integrated framework combining Stratified Conditional Latin Hypercube Sampling (SC-LHS), automated modeling, and Gaussian Process (GP) surrogate models. SC-LHS incorporates hierarchical constraints to eliminate infeasible samples during generation, while a Python-driven workflow automates the process from parameterization to simulation. Coupling the GP surrogate with NSGA-II enables efficient Pareto front exploration. The results indicate that SC-LHS is superior to standard LHS, Constrained LHS, and Sobol sequences with rejection in terms of feasibility rate and space-filling metrics. The optimal compromise solution reduces dam volume by 10.4% and tensile zone volume by 15.2% compared to the initial design. This framework effectively reconciles economic and safety objectives, offering a robust methodology for complex hydraulic structure design. Full article

Objectives: The objective of this study was to investigate metabolic dysfunction-associated steatotic liver disease (MASLD)-related awareness, health literacy (HL), and illness perception among patients at risk of MASLD in European primary care settings. Methods: Participants aged ≥50 years with either obesity, metabolic syndrome (MetS), or type 2 diabetes mellitus (T2DM), and attending general practices (GPs) in Greece, Spain, or The Netherlands were included in the study. The participants completed surveys to collect data on their socio-demographic characteristics and health habits, including the European Health Literacy Survey (HLS-E-Q16), the Brief Illness Perception Questionnaire [B-IPQ], and the Public Awareness of NAFLD Questionnaire. Results: Overall, 234 patients participated in the study (mean age: 66.5 ± 9.5 years; 45.7% were male). Among the participants, 64.5%, 66.2%, and 59.8% had a diagnosis of diabetes, obesity, and MetS, respectively. Almost one-third (27.9%) had never heard about MASLD or discussed MASLD with their GP. Twelve percent (12.1%) had never heard about cirrhosis, and 20.5% were unaware that liver disorders may cause serious health problems. Overall, 43.6% of the patients had a sufficient level of HL (score >13) with a mean score of 11.5 ± 3.3. Illness perception (B-IPQ score) was low at 41.6 ± 11.6. Significantly higher B-IPQ scores were documented for female compared to male respondents (43.1 vs. 39.8; p < 0.01). Multivariate analysis found that knowledge about MASLD was associated with higher HLS-E-Q16 (p = 0.017) and B-IPQ (p = 0.028) scores. Conclusions: Despite being at risk, a significant proportion of the study participants were unaware of MASLD, its risk factors, and their personal susceptibility. This study underscores the importance of enhancing patient HL and promoting prevention and risk reduction, particularly among high-risk patient populations. Full article

The continued advancement of small unmanned aircraft systems (UASs) has resulted in growing concerns regarding the potential threat that UASs present. To deal with harmful or disruptive drones, techniques that can be performed using affordable, widely distributed sensor platforms would provide an immense benefit. One such sensor platform is Android smartphones, which continue to see improved sensor quality and orientation estimation while being prevalent worldwide. In this work, the results of crowdsourced drone localization experiments using a custom-built Android smartphone app will be presented. Using GPS positions and angular measurements collected from human-operated smartphones, the ability to localize a static and dynamic target will be demonstrated, as the positions of these targets are estimated from the intersection of line-of-sight vectors. The results from these tests show that the position of these targets can be computed to below 10 m using correction techniques to alleviate measurement errors introduced by environmental or human factors. The results from these tests validate the potential of using readily available smartphones as sensor platforms as an alternative to specially designed localization technology. The inclusion of environmental and human errors can significantly influence the resulting solution, but steps can be taken to alleviate their impact. Full article

Background: Tirzepatide is a novel therapeutic option for the management of metabolic disorders which has started to be implemented in routine practice. The study aimed to analyze the effectiveness of tirzepatide use and patient education in the field of healthy eating and weight loss, based on real-life data from the practice of a primary care physician, in metabolic syndrome (MetSyn) patients during a one-year follow-up period. Methods: This is a retrospective study based on real-life data of 118 MetSyn patients who were under the supervision of a general practitioner (GP). Analysis was conducted on 62 patients supported by trizepatide (2.5 mg for 4 weeks, then 5 mg for 4 weeks and 7 mg for 46 weeks) with dietary education and 56 patients that underwent dietary education with motivation only. Lipid profile, glucose level and blood pressure were assessed. Body Mass Index (BMI), waist-to-height ratio (WHtR), A Body Shape Index (ABSI), Lipid Accumulation Product (LAP), Visceral Adiposity Index (VAI) and Body Roundness Index (BRI) were calculated. The KomPAN^® questionnaire was used for dietary assessment and WHO Quality of Life-BREF for the quality of life assessment at 52 weeks. Results: Patients from both groups significantly reduced their body weight and WC and the values of the following indices: BMI, WHtR, ABSI, LAP and BRI. A significant increase in LDL cholesterol and triglyceride values was observed in both groups and a significant decrease in glucose level only in the group with tirzepatide combined with dietary modification. Energy value, energy density of food and nutrient intake did not differ between groups, while the intensity of beneficial nutritional features (pHDI-10) was low. Significant differences in patients’ QoL were observed, especially in the domain related to mental health (higher in trizepatide + diet group). Conclusions: Support in primary care by a physician was successful from a long-term perspective in the group using tirzepatide in combination with diet modification as well as in the group based on dietary modification only. The data do not indicate a significant advantage of any one approach for patients, prioritizing an individualized approach to treatment. Full article

To explore the appropriate supplementation rate of yeast culture (YC) in Kazakh sheep during fattening, the effects of different YC supplementation rates on rumen fermentation parameters and microbial community were studied through in vitro rumen fluid fermentation experiments. A 0.40 g high-concentrate diet was used as the fermentation substrate, and five groups were added with YC at 0% (CK), 1.25% (YC1), 2.5% (YC2), 3.75% (YC3) and 5% (YC4) of dietary dry matter, respectively. Anaerobic fermentation was carried out for 48 h in 60 mL fermentation broth. The results showed that the 48 h GP and microbial crude protein (MCP) concentration in all YC supplementation groups were significantly higher than those in the CK group (p < 0.05). The concentrations of total volatile fatty acids (TVFA) and propionate in the YC1 and YC2 groups were significantly increased and the A/P ratio in the two groups was significantly decreased (p < 0.05). The Multi-factor Comprehensive Evaluation Index (MFAEI) calculation indicated that 1.25% was appropriate. The YC1 and YC2 groups significantly increased the richness and diversity of rumen bacterial communities (Chao1 and Shannon indices, p < 0.05), and significantly increased the relative abundance of Bacteroidota and NK4A214_group (p < 0.05), while significantly decreasing the relative abundance of the potential pathogenic bacterium Campylobacter (p < 0.05). Ustilago abundance was significantly suppressed in all the YC-supplemented groups (p < 0.05). The most effective YC supplementation rate among the tested doses was 1.25% according to the MFAEI and key microbial indicators. The results suggest that dietary supplementation of 1.25% YC (dry matter basis) may beneficially modulate rumen fermentation parameters under in vitro conditions, providing a reference for further in vivo studies on its application in fattening Kazakh sheep. Full article

Mobility is key to independent living, and life-space mobility (LSM) describes the extent to which a person moves within and beyond their home. Restricted LSM may limit participation in life. Parkinson’s disease (PD) can influence LSM through changes in physical function and social engagement. This study investigated LSM in people with mild-to-moderate PD using self-reported and objective measures, examining agreement between measures and relationships with physical activity and quality of life. The participants completed the Life-Space Assessment (LSA) and wore an accelerometer and GPS monitor for seven days. The accelerometer and GPS data were processed to derive GPS life-space and physical activity variables. The participants (n = 11) demonstrated variability in GPS-derived measures of LSM despite high levels of self-reported LSM (median 82). The participants spent approximately two thirds of wear time in close proximity to the home, where most activity time was sedentary. Higher levels of moderate-to-vigorous physical activity and step counts were observed beyond the home. Self-reported health-related quality of life showed weak associations with LSM. These findings suggest that while the LSA captures overall mobility, GPS data provides complementary insight into location-specific activity and individual movement patterns. Full article

Gas concentration series in coal mining faces are jointly affected by multiple coupled factors, including geological conditions, mining disturbances, ventilation organization, and gas drainage intensity, and therefore exhibit pronounced nonstationarity, strong fluctuations, spatiotemporal correlations across multiple monitoring points, and occasional abrupt spikes. To address these challenges, this study proposes a gas concentration prediction and early-warning method that integrates CEEMDAN–SST with GraphPINN-TimesFM (Graph Physics-Informed Neural Network–Time Series Foundation Model). First, based on multi-source monitoring data such as wind speed, gas concentrations at multiple monitoring points, and equipment operating status, anomaly removal, operating-condition segmentation, and change-point detection are performed to construct stable operating-state labels. Feature selection is then conducted by combining optimal time-lag correlation, Shapley value contribution, and dynamic time warping. Second, WGAN-GP is employed to augment samples from minority operating conditions, while CEEMDAN–SST is used to decompose and reconstruct the target series so as to reduce the interference of nonstationary noise and enhance sequence predictability. On this basis, TimesFM is adopted as the backbone for long-sequence forecasting to capture long-term dependency features in gas concentration evolution. Furthermore, GraphPINN is introduced to embed the topological associations among monitoring points, airflow transmission delays, and convection–diffusion mechanisms into the training process, thereby enabling collaborative modeling that integrates data-driven learning with physical constraints. Finally, the predictive performance, early-warning capability, and interpretability of the proposed model are systematically evaluated through regression forecasting, warning discrimination, and Shapley-based interpretability analysis. The results demonstrate that the proposed method can effectively improve the accuracy, robustness, and physical consistency of gas concentration prediction under complex operating conditions, thereby providing a new technical pathway for gas over-limit early warning and safety regulation in coal mining faces. Full article

Search and rescue (SAR) missions in collapsed or underground environments remain challenging due to GPS unavailability, which hinders localization and autonomous navigation. Systems that rely on single-sensor inputs or structured settings often degrade under smoke, dust, or dynamic clutter. This paper presents an autonomous ground robot for GPS-denied SAR that integrates low-cost thermal, visual, inertial, and acoustic cues within a unified, computation-efficient architecture. The stack combines Thermal–Visual Odometry (TV–VO) with Zero-Velocity Updates (ZUPT) for drift-resistant localization, RescueGraph for multimodal survivor detection, and a Proximal Policy Optimization (PPO) planner for adaptive navigation under uncertainty. Across simulated disaster scenarios and benchmark corridor runs, the system shows embedded-feasible runtime behavior and supports return to base without external beacons under the evaluated conditions. Quantitatively, TV–VO+ZUPT reduces drift in short internal evaluations, while RescueGraph attains an F1-score of 0.6923 and an area under the ROC curve (AUC) of 0.976 for survivor detection. At the system level, the integrated navigation stack achieves full mission completion in the reported SAR-style trials, while the separate A*/PPO comparison highlights a trade-off between completion rate, traversal time, and collisions. Overall, the results support the practical promise of a low-cost sensor-fusion and learning-assisted navigation framework for GPS-denied SAR robotics. Full article

With increasing port automation and operational intensity, the gearboxes of gantry cranes widely used in bulk cargo terminals are prone to bearing and gear failures under prolonged heavy loads, intense vibrations, and complex operating conditions. Since fault samples often exhibit imbalanced distributions, this imposes two higher requirements on diagnostic methods—first, the ability to effectively address sample imbalance and, second, the capability to simultaneously identify multiple fault categories. To address these challenges, this paper proposes a joint diagnostic method integrating an improved Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP) and Multi-Task Learning (MTL). First, the modified CWGAN-GP performs conditional augmentation for minority fault classes, evaluating synthetic sample authenticity and diversity through multiple metrics. Subsequently, a multi-channel diagnostic network is constructed, in which vibration signals are fed into two parallel sub-networks: time–frequency features are extracted from the Short-Time Fourier Transform (STFT)-based time–frequency representations via a residual-block Convolutional Neural Network (CNN), while temporal features are captured from the raw time-domain signal using a Bidirectional Long Short-Term Memory (Bi-LSTM) with an attention mechanism. An attention fusion layer then integrates these two feature types, enabling joint classification of bearings and gears within a multi-task learning framework. Experimental validation on public gearbox datasets and port gantry crane gearbox datasets demonstrates that this method achieves an average diagnostic accuracy exceeding 97%. The proposed method reduces the impact of class imbalance, thereby improving the accuracy and stability of multi-task fault identification. Full article

Drug resistance remains a major challenge in epilepsy, and overexpression of ATP-binding cassette transporters, particularly P-glycoprotein (P-gp), at the blood–brain barrier (BBB) has been consistently implicated in limiting central nervous system drug exposure. Genetic experimental models suitable for investigating molecular regulation and functional alterations of P-gp in epilepsy remain scarce. This study evaluated P-gp expression and functional alterations in the Wistar Audiogenic Rat (WAR), a genetic model of epilepsy exhibiting phenotypic heterogeneity. WAR animals were classified into refractory epilepsy (WAR-RE) or temporal lobe epilepsy (WAR-TLE) phenotypes and compared with non-epileptic Wistar controls. Fexofenadine, a well-established in vivo P-gp probe substrate, was administered orally, and plasma pharmacokinetic parameters were determined. P-gp expression at the BBB was assessed by immunohistochemistry in hippocampal regions. WAR-RE animals exhibited significantly increased systemic exposure to fexofenadine, characterized by higher area under the curve and prolonged half-life, alongside reduced apparent clearance, compared with control animals (p < 0.05). In contrast, WAR-TLE animals showed greater interindividual variability without statistically significant differences. Immunohistochemical analysis revealed increased P-gp expression in hippocampal microvessels in both WAR phenotypes. These findings demonstrate that the WAR model displays molecular upregulation of P-gp at the BBB, accompanied by functional alterations in the disposition of a prototypical P-gp substrate. Although direct brain drug concentrations were not assessed, the integration of systemic pharmacokinetics with transporter expression supports the use of WAR as a genetic proof-of-concept model for studying P-gp regulation and transporter-mediated drug disposition in epilepsy. This model provides a valuable molecular framework for future investigations addressing transporter modulation and mechanisms underlying pharmacoresistance. Full article

Background: Hungary operated a public health-focused primary care model programme with expanded preventive and community-based services between 2013 and 2017 in four disadvantaged regions. This study aimed at assessing the association of this programme in one region with the patterns of emergency ambulance care before (2012) the programme and 3 years later when all services were available (2016). Methods: Patients in the selected region who received emergency ambulance care in the hospital catchment area were included. De-identified demographic data, reason for emergency service, on-site and hospital diagnosis, and treatment outcomes were entered into an electronic database from paper-based records. Diagnoses were assigned separate codes at GBD 1 and 3 levels. Results: The proportion of patients in emergency ambulance care showed a significant, 0.85% increase (p = 0.013) from 2012 to 2016. The proportion of female/male patients was roughly equal, but males needed emergency ambulance care significantly, 7 years younger than females in both years. Among patients with GPs in the model programme, 3.41% fewer needed emergency ambulance care due to non-communicable diseases, and 1.98% fewer were referred to other institutions from the hospital A&ED compared to those whose GPs did not participate (p < 0.001 for all). Conclusions: Utilisation of emergency ambulance services rose in the region in line with global trends suggesting that expanding primary care services alone may not be sufficient to reduce demand for emergency ambulance services. Further research is warranted to identify individual and systemic factors with major influence on emergency care use, including patient-level differences in the use of acute and preventive primary care services, and the availability of primary care after work hours. Full article

Unmanned aerial vehicles (UAVs) are increasingly deployed in missions that require high autonomy and reliable decision-making; however, many operational concepts still assume access to GNSS and stable communication with a human operator. In contested environments, this assumption may no longer hold because GNSS degradation, radio-frequency interference, and intentional jamming can disrupt positioning and communication, thereby reducing mission effectiveness and safety. Recent surveys show that operation in GNSS-denied environments remains a major challenge and often requires alternative perception, localization, and control strategies. In response, this article investigates a reinforcement learning (RL)-based decision-support system for the autonomous control of a quadrotor UAV in a three-dimensional simulated environment. Rather than following pre-programmed waypoints, the UAV learns a control policy through interaction with the environment and reward-driven adaptation. The proposed system is designed for mission execution under uncertainty, limited external guidance, and partial observability. Two policy-gradient approaches are implemented and compared: classical REINFORCE and Proximal Policy Optimization (PPO) with an Actor–Critic architecture. The study presents the simulation environment, state and action representation, reward formulation, staged training procedure, and comparative evaluation. The results indicate that, within the considered unseen test scenario, the PPO-based configuration achieved higher mission effectiveness than REINFORCE in the final unseen test scenario, supporting the practical relevance of structured deep reinforcement learning for UAV operation in GPS-denied and communication-constrained environments. Full article

Background/Objectives: The aim of this study was to describe seasonal trends in match-average External Load (EL) variables across three (2022/23, 2023/24, 2024/25) consecutive competitive seasons in a Hungarian professional second-division soccer team (Gyirmót FC Győr), using the Catapult Vector S7 Global Navigation Satellite System (GNSS). Specifically, Average Distance (AD; m), Average Player Load^TM (PL; AU), and Acceleration–Deceleration Efforts (>2 m·s⁻²) (ADE) were examined. The study aimed to provide descriptive reference values and characterize seasonal variation in match EL demands within a professional second-division context. Methods: A descriptive seasonal comparison was conducted based exclusively on aggregated match average EL values. The unit of analysis was the match, with each match contributing one aggregated value per variable derived from players who completed the full match. A total of 94 matches were included (2022/23: N = 38; 2023/24: N = 29; 2024/25: N = 27); matches with red cards were excluded. EL data were collected using a 10 Hz Catapult Vector S7 GNSS. Results: The median AD decreased continuously from the 2022/23 season (10.210 m) to the 2024/25 season (9.795 m). The median PL decreased from 1002 (2022/23 and 2023/24) to 846 in the 2024/25 season. The median ADE decreased from 220.8 (2022/23) to 199.0 (2024/25). Conclusions: Lower values were observed across match EL variables, with the most pronounced reduction in PL. These findings provide descriptive reference values and may support the interpretation of seasonal variation in match EL demands in professional second-division soccer. Full article