Search Results (9,033)

Seed aging is a major constraint for plant establishment in arid and semi-arid ecosystems, where poor seed vigor directly limits species persistence and restoration success. Desert species are particularly vulnerable to storage- and stress-induced deterioration, yet practical strategies to recover germination capacity in aged seeds remain limited. This study aimed to quantify aging-induced losses in germination performance and to evaluate whether exogenous gibberellic acid (GA₃) can partially restore seed vigor through physiological, biochemical, and hormonal regulation. Fresh seeds (FS), naturally aged (NA), and artificially aged (AA) seeds of four desert species (Salsola affinis C.A.Mey., Trigonella arcuata C.A.Mey., Ceratocarpus arenarius L., and Alyssum desertorum Stapf) were exposed to graded GA₃ concentrations (0–500 mg L⁻¹). Germination indices (GP, GR, GI, VI), antioxidant enzymes (SOD, POD, CAT), lipid peroxidation (MDA), phytohormones (IAA, ABA, cytokinins), and multivariate trait relationships were assessed. Without GA₃, NA reduced germination potential by 22.8–33.6%, while AA caused more severe losses of 42.4–67.8%, depending on species. Germination rate declined by 15.7–32.5% under NA and 36.4–65.2% under AA. GA₃ application improved all germination indices up to 200 mg L⁻¹ (GA200), which increased GP by 22.8–32.0% and vitality index by 17.0–28.5% compared with GA₀, whereas GA500 showed diminishing returns. Aging suppressed antioxidant enzymes by 15–20% (NA) and 30–45% (AA) and increased MDA by up to 50%, while GA200 enhanced SOD, POD, and CAT and reduced MDA by 8–18%. Aging also reduced IAA and cytokinins (~28–50%) and increased ABA (27.7–77.4%), with GA200 partially restoring hormonal balance. In conclusion, GA₃ at an optimal dose (200 mg L⁻¹) partially reverses aging-induced physiological and hormonal constraints, improving germination and vigor, although recovery remains limited under advanced deterioration. Full article

Ebola virus disease remains one of the most serious viral infections with no approved small-molecule treatments. The Ebola virus glycoprotein (EBOV-GP), which enables the virus’s entry to host cells, is a promising target for drug discovery. In this study, a multistage computer-aided drug discovery approach was used to identify new specific EBOV-GP inhibitors. A reliable QSAR model was built using 55 terpenoid derivatives. This model was able to predict the activity of newly designed compounds with good accuracy and validated statistical metrics ($R_{tr}^2$= 0.70; $R_{ext}^2$ = 0.73). It was subsequently applied to screen over 15,500 newly generated compounds from three lead molecules by fragment-based design tools. Predicted activity, binding affinity toward EBOV-GP, and good ADMET drug-like properties prioritized the eleven most promising hits. Through 150 ns molecular dynamics simulations, these compounds remained stable in the EBOV-GP binding site. Further binding free energy analysis (MM/PBSA) showed strong binding affinities, especially for the compounds L-60, L-832, M-1618, and L-1366. This study showed how combining QSAR, fragment-based design, docking, ADMET, and molecular dynamics could help in identifying potent and safe small molecules against the EBOV-GP. The top compounds are ready for further experimental and in vitro biological testing. Full article

Due to high intraclass variability and subtle intercharacter differences, automatic Arabic handwriting recognition remains a challenging task, particularly for children’s handwriting. This study proposes a hybrid framework that combines class-conditional Wasserstein generative adversarial networks with gradient penalty (CWGAN-GP) for data augmentation and a convolutional neural network (CNN) enhanced with squeeze-and-excitation (SE) blocks for improved feature discrimination. Experiments were restricted to disconnected (isolated) characters from the Hijja dataset, which comprised 12,355 samples divided as follows: 80% for training (9884), 10% for validation (1236), and 10% for testing (1235). Training the CNN on real data alone yielded an accuracy of 93.47%, while incorporating CWGAN-GP-generated samples improved performance to 96.27%. Notably, the proposed SE-CNN trained with the CWGAN-GP-augmented data achieved the highest accuracy of 99.27%. This result demonstrates that the combination of advanced generative data augmentation and architectural refinement significantly enhances Arabic handwritten character recognition performance. Full article

Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) poses a serious threat to the swine industry in China. As a major pig-producing province, Shandong requires continuous epidemiological monitoring of PRRSV. To elucidate the molecular epidemiology of the virus, 1621 clinical samples were collected from suspected cases across different regions of Shandong Province between 2023 and 2025, primarily from Tai’an, Linyi, Jining, and Liaocheng. RT-qPCR detection showed that the positive rate for PRRSV-2 was 20.05% (325/1621). Genetic analysis based on ORF5 and NSP2 genes indicated that Sublineage L1C (NADC30-like) was the dominant strain for 38.38% of ORF5 gene and 72.73% of NSP2 sequencing results. This was followed by Sublineage L8E and L1A and L5A strains. Key virulence-related mutations were identified at residues R13 and R151 in the GP5 protein, which are associated with enhanced pathogenicity. Additionally, variations in neutralizing epitope and the number of N-glycosylation sites (ranging from 2 to 5 per strain) suggested potential immune evasion. Notably, 26.79% (15/56) of sequenced samples showed discordant ORF5 and NSP2 genotyping results, indicating widespread recombination among PRRSV strains in Shandong Province. These finding demonstrated that the genetic diversity, high recombination frequency, and key amino acid variations in circulating PRRSV strains collectively undermine vaccine effectiveness. This study highlights the need to optimize vaccination strategies, enhance biosecurity measures, and implement effective disease control and elimination programs to reduce the impact of PRRSV in Shandong Province. Full article

Does where an incident happens affect how quickly first responders arrive? Timely emergency responses are important to urban safety. However, the combined influence of street-level environments, operational conditions, and neighborhood contexts on dispatch performance remains unclear. We examined such geographical complexity by modeling geographic predictors for whether emergency vehicles successfully arrived at incidents in the city of Dallas within the city’s eight-minute benchmark. Using 250,647 incidents and 56 million GPS points along emergency dispatch routes in 2016, we compiled fourteen spatial and operational variables for every incident to train a Bayesian-optimized random forest classifier. The fourteen variables characterized street network topology, roadway attributes, land use, and socioeconomic status, and the model achieved an accuracy of 77.26% in predicting whether emergency response arrived at an incident within eight minutes. A longer distance to dispatch stations, dispatching from non-nearest stations, and low street–network integration were the strongest predictors of unsuccessful responses. Higher-income areas showed slightly elevated unsuccessful rates linked to frequent construction-related disruptions. These findings highlight emergency response as a coupled spatial–operational–temporal process and underscore the need for context-sensitive dispatch strategies and coordinated urban planning. Full article

Older adults often need guidance when visiting new buildings for the first time. However, indoor navigation remains challenging due to the lack of Global Positioning System (GPS) availability, visually repetitive corridors, and frequent location failures. This article presents a multimodal indoor navigation assistant that combines graph-based route planning with visual landmark verification to provide step-by-step guidance. The environment is modelled as a directed graph whose nodes are annotated with semantic landmarks, and the graph is constructed primarily from a video of the building, reducing the need for 3D scanners, beacons, or other specialised instruments. Routes are calculated using Dijkstra’s shortest-path algorithm over the semantic graph. During navigation, camera frames are analysed using a restricted vision-language recognition strategy that only considers candidate landmarks from the current and next nodes, reducing false detections and improving interpretability. To increase robustness, a temporary voting mechanism was introduced to confirm node transitions, as well as a hierarchical redirection strategy with local and global recovery. The system is implemented in two modes: handheld mode with visual cues using augmented reality arrows, mini map and voice instructions, and hands-free mode with front camera using voice instructions and keywords. Evaluation involved preliminary technical testing in the United Kingdom followed by formal user validation in Spain. During these trials, participants reported high usability, strong confidence and safety, and increased perceived independence. Full article

Healthcare fraud poses significant risks to insurance systems, undermining both financial sustainability and equitable access to care. Accurate detection of fraudulent claims is therefore critical to ensuring the integrity of healthcare insurance operations. However, the increasing sophistication of fraud techniques and limited data availability have undermined the performance of traditional detection approaches. To address these challenges, this paper proposes WCGAN-GA-RF, an integrated fraud detection framework that synergistically combines Wasserstein Conditional Generative Adversarial Network with gradient penalty (WCGAN-GP) for synthetic data generation, genetic algorithm-based feature selection (GA-RF) for dimensionality reduction, and Random Forest (RF) for classification. The proposed framework was empirically validated on a real-world dataset of 16,000 healthcare insurance claims from a Chinese healthcare technology firm, characterized by a 16:1 class imbalance ratio ($5.9\%$ fraudulent samples) and 118 original features. Using a stratified $80/20$ train–test split with results averaged over five independent runs, the WCGAN-GA-RF framework achieved a precision of $96.47\pm 0.5\%$, a recall of $97.05\pm 0.4\%$, and an F1-score of $96.26\pm 0.4\%$. Notably, the GA-RF component achieved a $65\%$ feature reduction (from 80 to 28 features) while maintaining competitive detection accuracy. Comparative experiments demonstrate that the proposed approach outperforms conventional oversampling methods, including Random Oversampling (ROS), Synthetic Minority Oversampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN), particularly in handling high-dimensional, severely imbalanced healthcare fraud data. Full article

Human T-cell leukemia virus type-1 (HTLV-1) is endemic to numerous regions worldwide, including Central Australia. The Australo-Melanesian subtype-C is endemic within Australia and Oceania, whereas subtype-A is the most widely distributed subtype globally. The lack of an approved vaccine highlights HTLV-1 as a neglected public health issue. To inform the development of HTLV-1 Envelope (Env)-based vaccines, we assessed anti-Env antibodies in an HTLV-1c+ cohort of First Nations individuals in Central Australia. Of the 62 plasma samples from patients with confirmed HTLV-1 serological diagnosis, 76% were positive for Env binding in ELISA, but 90% neutralized HTLV-1c pseudovirus (PSV) infection. Neutralization breadth with the capability of blocking both subtype-A and subtype-C PSV infection was identified in 100% of samples tested. Proviral load was positively associated with anti-Env response, with binding epitopes mapping to the proline-rich region of gp46-SU. Env-directed IgG showed the capacity to engage Fcγ receptors key to inducing antibody-dependent cellular cytotoxicity/phagocytosis responses. Serological response was not associated with comorbidities linked to HTLV-1c in this population (bronchiectasis, chronic kidney disease, diabetes). These findings demonstrate that potent humoral immunity arises and is sustained during HTLV-1 infection, suggesting that an Env-based vaccine displaying authentically native epitopes will be capable of recapitulating these neutralizing responses. Full article

Modern-day urban warehouses face exploding large inventory and tight spaces requiring fast, accurate, and safe stocktaking in a narrow aisle in a GPS-denied environment. This paper proposes a complete UAV-enabled framework performing real-time QR code scanning with inventory synchronization through a safety-aware trajectory generation for obtaining collision-free motion. A novel hybrid workflow integrating MATLAB/Simulink R2024b and Unreal Engine is used for dynamics and photorealistic rendering, alongside a real-time warehouse setup using drone cameras and 3D LiDAR coupled with a ground control station and live dashboard. The system in this paper was evaluated by testing with single and multi-UAV models across high-fidelity simulations and experiments. Results demonstrate simulated QR accuracy of approximately 95 to 96%, with experimental validation achieving between 86 and 90.5% due to real-world environmental factors. In experimental and simulation analysis, mean end-to-end latency remained under half a second, trajectory error range between 8 and 10 cm, and safety margins were consistently maintained throughout the test. It was further observed that multi-UAV coordination halved mission time compared to single-drone tests while keeping duplicate reads negligible, indicating a scalable and safe pipeline for industry application. Full article

A deeper understanding of the relationships between the local and landscape scales in herbivore foraging should place the management of rangeland production systems on a firmer footing. The objective was to test whether local-scale landscape features modulate the coupling between locomotion and eating, thereby altering the pattern of landscape-scale grazing pressure. We studied shepherded small-ruminant herds on hilly semiarid rangeland by integrating acoustic monitoring to detect jaw movements, GPS to track location and movement, and GIS to link location to landscape attributes. Based on 69 one-day foraging routes, minutely rate of jaw movement (RJM) as a function of time-into-foraging-route showed a unimodal concave shape but did not respond to path angle. Minutely movement velocity responded convexly to time-into-foraging-route, and the quadratic term for path angle was negative and highly significant. The response to path angle was concave and symmetrical for uphill and downhill travel. Based on the empirical evidence that increasing path angle reduces velocity but not RJM and a set of reasonable associated assumptions, it is inferred that more jaw movements are performed per unit area scanned by the animal. It is further inferred abductively that more bites are removed per unit area and that more mass is removed per unit area, and hence, grazing pressure is more intense on sloping terrain than on level areas. For a given duration of foraging route, an increase in density of bite placement at the local behavioral scale implies a contraction in the surface area of the daily herd footprint at the landscape scale. This has implications for how carrying capacity of such areas should be defined. Full article

The investigation aimed to determine if live feedback of team- and player-specific global positioning system (GPS) running performance data between bouts of hurling small-sided games (SSGs) altered the physical and physiological responses during subsequent bouts of SSGs during a 6-week hurling pre-season period. Twenty-four (n = 24) hurling players (age 25.5 ± 3.2 years; height 177.9 ± 3.2 cm; body mass 83.5 ± 4.5 kg) received either feedback or no feedback during hurling-specific SSGs across a 6-week pre-season period. Teams were assigned to two specific groups, a) GPS live feedback or b) no GPS live feedback (control) for each session, with feedback provided during the SSG rest interval. Running performance (10-Hz, STATSports, Apex, Northern Ireland), heart rate (Polar T31 coded, Polar Electro, Finland), and rating of perceived exertion (RPE) were measured. Data was analyzed using linear mixed-effect models with the effect size (Cohen’s d) used to determine the size of the effect between feedback and non-feedback conditions. Trivial-o-small differences at all time points were observed in heart rate and RPE measures during SSGs, respectively. Trivial-to-moderate effects were observed between feedback and non-feedback conditions for total distance (p = 0.04; ES = 0.25; small) high-speed running (p = 0.043; ES = 0.59; moderate), maximal speed (p = 0.345; ES = 0.11; trivial) and accelerations (p = 0.03; ES = 0.55; moderate). The current data suggests that coaches and applied practitioners may use live GPS feedback to alter the running and physiological performance within hurling-specific SSGs during a pre-season period. Full article

Visual localization plays a critical role for mobile robots to estimate their position and orientation in GPS-denied environments. However, its efficiency, robustness, and generalization are fundamentally undermined by severe viewpoint changes and dramatic appearance variations, which present persistent challenges for image-based feature representation and pose estimation under real-world conditions. Recently, map-free visual relocalization (MFVR) has emerged as a promising paradigm for lightweight deployment and privacy isolation on edge devices, while how to learn compact and invariant image tokens without relying on structural 3D maps still remains a core problem, particularly in highly dynamic or long-term scenarios. In this paper, we propose the Debiased Multiplex Tokenizer as a novel method (termed as DMT-Loc) for efficient and versatile MFVR to address these issues. Specifically, DMT-Loc is built upon a pretrained vision Mamba encoder and integrates three key modules for relative pose regression: First, Multiplex Interactive Tokenization yields robust image tokens with non-local affinities and cross-domain descriptions. Second, Debiased Anchor Registration facilitates anchor token matching through proximity graph retrieval and autoregressive pointer attribution. Third, Geometry-Informed Pose Regression empowers multi-layer perceptrons with a symmetric swap gating mechanism operating inside each decoupled regression head to support accurate and flexible pose prediction in both pair-wise and multi-view modes. Extensive evaluations across seven public datasets demonstrate that DMT-Loc substantially outperforms existing baselines and ablation variants in diverse indoor and outdoor environments. Full article

Internet-of-Things (IoT) sensing can improve traceability, safety, and efficiency of medical waste handling, yet many deployments remain fragmented, lack an end-to-end system architecture, and do not provide the structured data pipelines needed for artificial intelligence (AI) analytics. This paper presents a layered IoT-based system design for medical waste management that integrates: (i) Espressif Systems 32 (ESP32)-based edge devices for fill-level and Global Positioning System (GPS) telemetry; (ii) secure network communication; (iii) a cloud backend for data ingestion, storage, and analytics; and (iv) operator dashboards with event-driven alerting. The architecture extends our prior GPS-enabled tracking and route optimization by adding sensor-driven state monitoring, threshold-based decision support, and a time-series data pipeline designed for future AI-driven predictive analytics. In a 30-day pilot with five containers, the system collected one reading every 15 min (14,400 total readings). The backend demonstrated efficient processing with an average Application Programming Interface (API) response time of 45 ms, sub-50 ms database write latency, and high uptime; alerts were delivered promptly upon threshold violations. Compared with a fixed-schedule baseline, the system enabled condition-based collection scheduling with zero data loss. The proposed design emphasizes modularity, fault tolerance, and integration readiness for hospital information systems, providing a practical blueprint for scalable smart-healthcare waste logistics and a foundation for machine learning-based predictive waste management. Full article

Ongoing health system reforms in Kazakhstan have transformed the working environment of primary health care (PHC) staff and may increase workload and psychosocial stress. This study aimed to assess perceived stress among PHC workers in Almaty and its associations with socio-demographic characteristics and health-related behaviors. A cross-sectional survey was conducted in October–November 2023 across all 36 state-funded PHC facilities in Almaty. General practitioners (GPs) and family nurses employed in these facilities were invited to participate. In total, 1484 respondents completed a standardized questionnaire in Kazakh or Russian administered electronically via Google Forms. Perceived stress was assessed using PSS-10, physical activity using IPAQ-SF, alcohol consumption using AUDIT-C, and tobacco use through items aligned with STEPS/GATS. Statistical analyses were performed using SAS. Associations between variables were evaluated using χ² and Fisher’s exact tests, and multivariable logistic regression models were applied. Statistical significance was set at p < 0.05. Higher stress levels were more common among GPs than nurses (OR = 2.58; p < 0.0001) and less common in younger workers (18–29 vs. 50–59: OR = 0.504; p = 0.017) and alcohol abstainers (OR = 0.587; p = 0.0004). Kazakh ethnicity showed a borderline protective association (OR = 0.472; p = 0.057), while physical activity was not a significant predictor. Perceived stress is highly prevalent in Almaty PHC and disproportionately affects GPs; younger age and alcohol abstinence are protective. The findings support prioritizing organizational measures to reduce role-related burden and maladaptive coping behaviors. Full article

This study evaluated the potential of Sentinel-2-derived NDVI (Normalized Difference Vegetation Index) for predicting within-field variability of winter wheat grain yield in central Lithuania during the 2024 growing season. Reliable within-field yield prediction remains challenging in regions with heterogeneous soils and limited region-specific models, particularly in northeastern Europe. Grain yield data were obtained from combine harvesters equipped with GPS yield monitoring across 13 fields with a total area of 283.6 ha. NDVI values were calculated for four half-monthly periods from March to May, corresponding to key phenological stages (from tillering to spike emergence). Spatial and temporal variability in NDVI–yield relationships was observed, with early May consistently showing the strongest correlations (r up to 0.49), particularly in lower-fertility fields, indicating its critical role in yield prediction. Machine learning models (Random Forest, XGBoost, and Deep Neural Networks), along with linear regression, were applied to predict yields based on NDVI from four growth stages. Random Forest achieved the highest predictive accuracy (MAE = 0.951 t/ha), outperforming the other models. The model also showed the highest correlation with observed yields (Pearson r = 0.717), indicating strong agreement between predicted and measured values. Feature importance analysis confirmed NDVI from 1 to 15 May as the most influential predictor across all models. Predicted yield maps closely matched observed patterns, with the largest discrepancies near field edges due to combine harvester effects. These findings highlight the utility of mid-season NDVI for precise estimation of within-field grain yield variability and demonstrate that Random Forest models can effectively capture the NDVI–yield relationship, particularly under heterogeneous field conditions. Full article