Search Results (3,671)

Mobile networks have become targets of sophisticated cyber attacks. Critical vulnerabilities persist in the General Packet Radio Service Tunneling Protocol (GTP). Signature-based Intrusion Detection Systems (IDS) are inadequate against zero day exploits and novel attack patterns, necessitating more adaptive approaches. We propose GTP-AEGIS (Adaptive Ensemble with Gated Input Selection), a hybrid IDS that integrates signature-based detection with a CatBoost gradient boosting classifier via a Selective Heterogeneous Ensemble (SHE) framework. An Input-Dependent Confidence Gate (IDCG) applies a per-sample priority rule over CatBoost, a NearestCentroid Rule Engine (NCRE), and a signature pathway. A real Suricata engine detects attacks with 100% precision but only 69.2% binary recall when run standalone; within the ensemble, the signature role is played by an idealized Signature-Detection Surrogate (SDS), so the reported ensemble gains are upper bounds. On the evaluated GTP-U dataset, GTP-AEGIS reaches accuracy above 90% with 10% of the training data and raises recall for the rare invalid-TEID class from 48.9% to 64.4%; this improvement comes from the signature pathway rather than the NCRE, and the aggregate accuracy gain is not statistically significant after correction for multiple comparisons. All accuracies are obtained under a packet-level split, which a group-aware comparison shows to be optimistic by approximately 19 percentage points. The model flags 81 to 100% of packets from unseen attack families as non-normal, although this does not constitute unknown-class recognition. We report the limits of signature-only detection and of packet-level evaluation alongside the gains. Full article

Beyond 5G and future 6G services require radio access networks to support heterogeneous applications with diverse latency, reliability, throughput, mobility, and computing requirements. These challenges are particularly pronounced in vehicle-to-network (V2N) communications because of high mobility, dynamic channel conditions, frequent handovers, and heterogeneous service requirements. Conventional traffic-steering methods primarily rely on radio-side indicators, while computing-resource availability and traffic-specific computation demands are often considered separately. To address this limitation, this paper proposes a coordinated communication and computing resource management framework for O-RAN-based V2N communications. The framework integrates a traffic-management rApp (TM-rApp) in the non-real-time RIC with a traffic-steering xApp (TS-xApp) in the near-real-time RIC to enable policy-based closed-loop control. Candidate cells are ranked using communication quality, computing-resource capability and availability, predicted throughput, mobility characteristics, and traffic-class priority. As a proof-of-concept supporting component, proactive throughput forecasting is evaluated using standalone LSTM and stacked ensemble (S-LSTM) models based on lagged radio, mobility, load, and throughput features. The S-LSTM provides an adaptive mechanism for combining base learners but does not achieve a statistically significant improvement over the standalone LSTM; moreover, the forecasting evaluation uses fixed, non-optimized hyperparameters and a single chronological train–test split without cross-validation. Accordingly, the prediction results are interpreted as preliminary evidence of forecasting feasibility rather than as a definitive predictive-performance contribution. The framework further incorporates O-RAN-compatible traffic-steering policies, a minimum dwell-time constraint, and priority-aware resource allocation. Evaluation using a real-world corridor based on Al Haramain Expressway Road in Jeddah and a synthetic straight-highway scenario shows that the proposed method improves SLA compliance over RSS and HHAARC, achieves the highest computing-resource satisfaction, and reduces handovers relative to RSS. The results demonstrate a balanced trade-off among SLA compliance, computing-resource satisfaction, delay, throughput, and mobility robustness, while also showing that load-aware steering can provide higher aggregate SLA compliance under specific traffic distributions. Full article

Kneeosteoarthritis (KOA) is a common degenerative joint disease that causes pain, stiffness, and impaired mobility. Automated Kellgren–Lawrence (KL) grading from knee X-ray images facilitates efficient screening and follow-up assessment. However, models trained on a single-source dataset frequently suffer performance degradation when applied to external cohorts, due to heterogeneities in image quality, acquisition protocols, class distributions, and annotation patterns. Furthermore, conventional domain adaptation approaches typically treat all source samples uniformly, making them vulnerable to negative transfer induced by ambiguous or distributionally divergent instances. To overcome these limitations, the present study develops a supervised contrastive domain adaptation framework designed for robust KOA severity grading under domain shift. The framework incorporates two task-specific modules: (1) a source-domain sample screening module that dynamically allocates class-wise quotas based on transferability and identifies high-value source samples by evaluating target intra-class affinity, inter-class separability, and source-class compactness; and (2) a target-balanced ordinal contrastive learning module that aligns the screened source samples with target features and imposes stronger constraints on negative pairs with larger KL-grade distances. The framework was evaluated bidirectionally on KneeKL (8260 images) and MedicalExpert-I (1650 images), two public knee radiograph datasets for KOA grading. With ResNet-18, it achieved a Quadratic Weighted Kappa (QWK) of 0.8557 for KneeKL-to-MedicalExpert-I transfer, exceeding source-only training and direct source–target merging by 0.2652 and 0.0468, respectively. Comparisons with representative existing methods and multiple experimental analyses further validate the competitiveness of the proposed framework. Full article

Global olive oil consumption continues to grow, and Spain, particularly Andalusia, holds the largest cultivation area within the EU, covering 1.68 million hectares, which expanded by 7.7% between 2013 and 2023. However, precise data on the planting systems used are lacking; this information is essential for assessing crop intensification and its environmental, economic, and social implications. This study applies Deep Learning (DL) techniques to classify the planting system of olive parcels previously identified in a national cadastral database, using high-resolution RGB imagery acquired from a nationwide public aerial orthophotography program. Five DL algorithms (VGG19, InceptionV3, MobileNet, ResNet50, and Xception) were compared across different planting-system classification schemes and spatial resolutions, using a dataset of 8000 images (2000 per class) divided into training (70%), validation (20%), and testing (10%) subsets. The best results were obtained with a three-class scheme (traditional, intensive, and super-intensive) at 0.5 m/pixel resolution, reaching precision values of 99.00% with MobileNet and 98.66% with VGG19. These results support extending the approach to larger areas for regional-scale monitoring of planting systems, including other Mediterranean olive-growing regions, and provide a basis for further research on the water use and labor demand associated with each planting system. Full article

Background: Mobile phones are widely used by healthcare students during clinical training and may act as potential reservoirs for pathogenic microorganisms. Objectives: This study aimed to investigate and evaluate the role of mobile phones as epidemiological factors in the transmission of microorganisms in medical and dental practice. Methods: A cross-sectional epidemiological and microbiological study was conducted between September 2025 and April 2026. A total of 125 mobile phones belonging to fourth-year dental students and fifth-year medical students were examined. Samples were collected from the entire phone surface and cultured on blood agar, eosin–methylene blue agar, and Candida chromogenic agar. Participants also completed a structured questionnaire assessing demographic characteristics, clinical experience, hand hygiene practices, and mobile phone use and cleaning habits. Results: No microbial growth was detected in 52.8% of samples on blood agar, while 44.8% showed growth below 10⁵ CFU of coagulase-negative staphylococci and 2.4% showed growth ≥10⁵ CFU. Minimal growth was detected on eosin–methylene blue agar (0.8%). Fungal contamination was identified in 2.4% of samples, exclusively among students who reported touching their phones with contaminated gloves. Statistically significant associations were observed between microbiological growth and students’ specialty, duration of clinical experience, and contact with mobile phones while wearing contaminated gloves (p < 0.05). Conclusions: Touching mobile phones with contaminated gloves was significantly associated with microbial growth. These findings emphasize the need for targeted infection control education and stricter guidelines regarding mobile device use during clinical training. Full article

Reliable defect detection in motor production lines is important for maintaining manufacturing quality. This study investigates the feasibility of a deep learning-based airborne acoustic screening approach for controlled no-load end-of-line induction motor inspection. Acoustic signals collected under controlled no-load test conditions were used to evaluate Feedforward Neural Networks (FNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and pre-trained models including ResNet50, MobileNetV2, and EfficientNetB0 for detecting rotor unbalance, assembly-induced bearing abnormalities, and their combination. Experimental results show that several models achieved up to 96% classification accuracy, depending on the selected architecture and feature representation. In cross-motor external validation using an unseen motor from the same manufacturer, the CNN with MFCC features achieved the best performance with 96% accuracy and 97% precision, recall, and F1-score, while ResNet50 with spectrogram inputs achieved 93% accuracy and 92% F1-score. These results demonstrate that airborne motor acoustic signals contain discriminative defect-related information under controlled no-load conditions and support the feasibility of low-cost, non-contact airborne acoustic sensing as a complementary screening approach for rapid end-of-line motor inspection. Full article

Continuous, non-invasive fish monitoring supports aquatic animal management, biodiversity assessment, and sustainable aquaculture, but embedded deployment requires a careful balance among accuracy, speed, memory, and computation under visually degraded underwater conditions. We developed ULFD-YOLO, an ultra-lightweight detector derived from YOLOv11n through coordinated redesign of the backbone, neck, and detection head. The model combines a custom convolutional MobileNetV4-tiny backbone, a hypergraph-based multi-scale fusion neck, and a lightweight MBConv head with channel attention. Experiments were conducted on Fish-BJ, an in-house dataset of 3402 images covering 21 species-informed aquarium-fish detection categories, and on a deliberately difficult 1180-image WildFish subset after dataset-specific training. On Fish-BJ, ULFD-YOLO achieved 0.960 mAP@0.5 and 0.732 mAP@0.5:0.95 with 1.3 M parameters, 2.6 GFLOPs, and a 3.0 MB model file, reducing parameters and computation by 50.0% and 58.7% relative to YOLOv11n. Bootstrap resampling yielded 95% confidence intervals of 0.946–0.973 and 0.638–0.821 for the two metrics, respectively. The model achieved 0.803 mAP@0.5 on WildFish and 19–24 FPS at 448 × 640 on a Jetson Orin Nano under its 15 W nvpmodel power mode. These results establish a practical accuracy–efficiency trade-off for embedded fish monitoring rather than peak localization accuracy. Full article

Agile project monitoring commonly relies on numerical metrics and visual artifacts, such as Burndown charts, to assess Sprint progress and identify potential deviations. However, most automated approaches focus on structured data, while the visual patterns contained in agile charts remain underexplored. This paper presents an exploratory study on visual-based Sprint evaluation using convolutional neural networks and agile project metrics. The proposed approach uses Burndown and Completed vs. Uncompleted Work chart (TTvsNT) images to classify Sprint performance into four categories: Poor, Regular, Good, and Excellent. A transfer-learning strategy based on MobileNetV2 was applied, including image preprocessing, Sprint-level data partitioning, two-phase training, and multiclass evaluation. The model achieved an overall accuracy of 70.33% on the evaluation set. Class-level results showed better performance for the Poor and Excellent categories, while the intermediate classes presented greater ambiguity. The main contribution of this study lies in evaluating Sprint monitoring charts as a complementary visual representation to traditional metric-based models. The findings provide preliminary evidence that these images contain useful performance-related patterns; however, the limited dataset size and current accuracy do not support production-level deployment. Further research with larger datasets, additional architectures, and multimodal approaches is required. Full article

This study assessed the feasibility of a technology-supported high-intensity interval training (HIIT) program for occupational drivers. A multiphase feasibility study incorporating participant feedback was conducted in Flanders, Belgium, including consultation, pilot, and intervention phases. Occupational drivers were randomized to a HIIT group, an active control group (mindfulness program), or a passive control group (no intervention). The 16-week, equipment-free HIIT program consisted of two 15-min sessions per week, delivered via a technology-supported mobile application, preceded by a 2-week baseline. Primary feasibility outcomes included program completion, session adherence, intervention fidelity, adverse events, and participant experiences, while changes in health-related outcomes were assessed as secondary exploratory outcomes using validated questionnaires. During the consultation (n = 11) and pilot (n = 2) phases, participants reported positive attitudes toward the intervention. Key barriers, including scheduling constraints (n = 5) and bicycle transportation (n = 4), informed refinements such as an equipment-free, technology-supported protocol and flexible scheduling. Of the 23 randomized participants across all groups, 16 participants initiated their assigned study protocol, and 10 completed all assessment points and were included in the final analysis. Of the nine participants allocated to the HIIT group, four initiated the intervention (44.4%), and three completed the program (33.3% of those allocated; 75% of those who initiated). Among the three completers, mean session attendance was 80.2%. Session fidelity was well maintained, but exercise-intensity fidelity could not be objectively confirmed. No exercise-related adverse events were reported. Participant feedback indicated generally positive usability but mixed acceptability of the technology-supported HIIT program among the three participants who completed the intervention. Exploratory individual-level changes in health-related outcomes were heterogeneous, with lower mean scores for depression, anxiety, stress, and fatigue. A technology-supported HIIT program was feasible among the small subgroup of occupational drivers who initiated it, with high session attendance. However, considerable pre-initiation attrition and low retention limit its feasibility for the broader target population. Observed health-related outcomes were heterogeneous and should be interpreted descriptively, warranting evaluation in a fully powered randomized controlled trial. Full article

This paper presents a pilot educational workflow that couples 3D remote sensing with heritage-driven pedagogy by engaging architecture master’s students in the documentation and digital archiving of Transylvanian cultural sites. Using terrestrial and mobile 3D scanning, students documented multiple typologies—wooden churches (Târgușor, Tioltiur), historical ensembles (Mociu, Coplean), industrial sites (1 Mai–Luduș, Vânătorilor–Luduș), and an urban street segment (Potaissa)—to generate dense point clouds that served as the basis for geometric reconstruction, semantic interpretation, and condition assessment. The study describes how the characteristics of different construction systems (timber, brick, stone, mixed structures) relate to point-cloud quality, survey coverage, and subsequent CAD/BIM drafting, with attention to the qualitative reading of minor deformations in wooden churches and of degradation patterns in masonry and industrial buildings. We also consider how artefacts in the data (noise, occlusions, registration errors) affect scene understanding and the interpretation of derived observations relevant to condition assessment and, prospectively, to monitoring. For the Tioltiur dual-sensor case, the TLS and SLAM datasets were compared through an internal CloudCompare registration check (final RMS 0.1121 on 50,000 points, fixed scale 1.0 and theoretical overlap 100%), surface-density displays (r = 0.005 for the Z+F dataset and for the GeoSLAM dataset), fitted-wall-plane readings (dip values around 89 deg. and 85 deg.) and a longitudinal section documenting roof/vault deformation. Beyond technical performance, the paper examines the self-reported formative impact on students’ digital skills and their understanding of cultural values, arguing that participation in 3D data acquisition, processing, and interpretation positions them as co-creators of a living digital archive. Pre- and post-workshop questionnaires (n = 13 each) are analysed descriptively—counts, percentages and medians with interquartile ranges—because the two instruments are unmatched and carry no shared identifier, so no paired test is applied; post-workshop self-ratings of technical competence, heritage understanding, archival awareness and collaboration were consistently high (medians 4–5), with uneven access to VR the main gap. By connecting point-cloud-based documentation workflows with heritage education, the project outlines a transferable, monitoring-ready baseline model in which 3D remote sensing supports both careful documentation and the transmission of regional identity and cultural meaning in architectural training. As an exploratory pilot with a small, self-reported sample, the study reports descriptive and qualitative findings rather than validated metric or statistical results. Full article

Plant disease symptoms combine local texture changes with patterns distributed across a leaf, while practical recognition models must remain compact. We introduce DC-FEN, a MobileNetV3-based design that models spatial-token relations and channel interactions in parallel and injects them through gated residual fusion. We also examine output-distribution, direct-feature, and token-relation transfer under same-backbone and heterogeneous teachers. PlantVillage and Plant Pathology 2021 (FGVC8) are evaluated with duplicate-audited, group-aware 70/15/15 splits, an explicit unresolved-leaf sensitivity check, validation-only selection, five training seeds, class-sensitive metrics, and paired seed-wise descriptive summaries. On PlantVillage, the no-additional-attention student, DC-FEN teacher, and DC-FEN joint student obtain macro F1 scores of $96.46\pm 0.91\%$, $96.90\pm 0.40\%$, and $96.55\pm 0.25\%$. On FGVC8, the corresponding scores are $87.29\pm 0.63\%$, $87.14\pm 0.52\%$, and $87.20\pm 0.26\%$. At the prespecified FGVC8 threshold of 0.5, DCAB changed sample-wise F1 by $-0.02\pm 0.55$ percentage points relative to the unmodified backbone; validation-selected global and label-specific thresholds changed this contrast to $+0.28\pm 0.55$ and $+0.55\pm 0.29$ points, while threshold-free macro mAP remained essentially unchanged. A duplicate-audited PlantDoc pressure test reduced frozen-checkpoint accuracy to $30.34\pm 1.10\%$ and $29.57\pm 1.10\%$, showing that external generalization remains unestablished. A ResNet50 teacher gives logit-only students $97.42\pm 0.51\%$ macro F1 on PlantVillage and $89.82\pm 0.43\%$ sample-wise F1 on FGVC8. After separately weighting the direct and relation terms, the corresponding joint students obtain $97.37\pm 0.56\%$ and $89.94\pm 0.27\%$, recovering the degradation seen with unit internal weights while remaining close to logit-only transfer. Thus, the study evaluates the benefits and limits of explicit spatial–channel interaction and shows that adding intermediate transfer constraints does not guarantee a stronger student. Full article

Physical activity is widely recognized as a fundamental component of health promotion and chronic disease prevention. Historically the focus has primarily been on aerobic exercise, while resistance training has received comparatively less attention. This imbalance underestimates the critical role of muscular performance in supporting physical health, functional capacity, long-term mobility, and overall quality of life. Current physical activity guidelines for Americans recommend that adults engage regularly in both aerobic and muscle-strengthening activities. However, only approximately one-quarter of adults in the United States meet these recommendations, despite nearly half achieving recommended levels of aerobic activity alone. Although any form of physical activity can confer health benefits, participation in both aerobic exercise and muscle-strengthening, such as resistance training, produces more comprehensive and substantial health outcomes. As research on resistance training has evolved beyond its traditional focus on athletic performance, a growing body of evidence has demonstrated its effectiveness in the prevention and management of numerous chronic conditions. This narrative review summarizes the role of resistance training in the prevention and management of several prevalent lifestyle-related chronic conditions, including type 2 diabetes, obesity, hypertension, cardiovascular disease, dyslipidemia, and osteoporosis. Full article

Shrimp farming is a strategic productive activity for Ecuador; however, pond inspection still substantially depends on manual observation and fragmented visual records. This study describes a prototype mobile/web architecture for image capture, storage, and result visualization and, as a separate experiment, evaluates supervised multiclass instance segmentation on public proxy data. A unified dataset of 4508 images was constructed from three external sources using the classes foam, floater, and shrimp. YOLOv8s-seg was used as the internal reference baseline and YOLOv11s-seg as the comparison candidate; both were trained under the same configuration and evaluated on 445 test images. YOLOv8s-seg achieved mAP50 values of 0.678 for BOX and 0.621 for MASK, whereas YOLOv11s-seg achieved 0.677 and 0.616, respectively. Their isolated GPU inference times were 10.4 and 10.2 ms/img. A weighted global experimental performance index comprising 90% predictive quality and 10% inference efficiency reached 0.596 and 0.593, respectively. Both models performed strongly for floater and shrimp, whereas foam showed low recall and zero MASK mAP50 because of the small number of positive test images and heterogeneous annotations. The evaluated task is supervised segmentation of proxy visual categories rather than anomaly detection in its conventional methodological sense, and no general improvement or practical superiority of one architecture was demonstrated. The study does not calibrate an operational confidence threshold or alert-persistence rule, implement adaptive or online learning, validate images from Ecuadorian production ponds, deploy the unified detector within the API, or demonstrate end-to-end real-time monitoring. Consequently, the results constitute a controlled experimental baseline and must not be interpreted as evidence of operational performance or local-domain generalization. Full article

Accurate morphological analysis of blood smears is vital for hematological diagnosis, yet manual examination is labor-intensive and subjective. While deep learning offers automation, its black-box nature and computational demands often hinder clinical trust and deployment. We propose XHIC-Net, an Explainable Hybrid Involution–Convolution Network designed for efficient and transparent cell classification. By integrating spatially adaptive involution operations with convolutional layers within a residual framework, XHIC-Net captures both contextual and fine-grained features efficiently. To enhance interpretability, a Grad-CAM-based explainable AI (XAI) module visualizes the cellular regions driving model predictions. The proposed framework was evaluated on a dataset comprising 12,879 microscopic blood smear images belonging to 12 morphological cell categories. Experimental results demonstrate that XHIC-Net achieves an overall accuracy of 98.88%, precision of 98.89%, recall of 98.87%, F1-score of 0.9887, and Cohen’s Kappa score of 0.9887. It outperformed established models, including DL models such as EfficientNetV2S, MobileNet family, DenseNet family, and VGG16, while using fewer parameters and requiring shorter training times. Furthermore, the XAI maps consistently highlighted biologically relevant structures, validating the model’s decision-making process. XHIC-Net is a strong, effective, and clear research model for automated hematology. With future clinical validation, it has the potential to be modified for point-of-care diagnostics in healthcare settings with limited resources. Full article

Reliable aboveground biomass (AGB) estimates for woody crops are essential for carbon accounting and for Measurement, Reporting and Verification (MRV) frameworks. However, it remains unclear how LiDAR modality and sampling geometry influence plot-scale and tree-scale AGB predictions in intensively managed Mediterranean orchards. In this study, we benchmarked four LiDAR modalities, namely open national airborne laser scanning from the Spanish National Aerial Orthophotography Plan (PNOA/ALS), a dedicated Riegl airborne laser scanner (ALS), unmanned laser scanning (ULS) and mobile laser scanning (MLS), across three woody-crop sites in Córdoba (southern Spain): IFAPA, Doña María, and Villaseca. Plot-level LiDAR metrics (mean height, 95th height percentile, maximum height, and canopy-cover proxies) were extracted from normalized point clouds and related to field AGB using Random Forest and XGBoost regression models, together with an ensemble predictor, under an 80/20 train–test split. In parallel, TreeQSM-based Quantitative Structure Models (QSMs) were evaluated as an independent tree-level three-dimensional reconstruction approach. XGBoost achieved the lowest errors at IFAPA (RMSE = 0.400 Mg ha⁻¹; R² = 0.994) and Villaseca (RMSE = 0.872 Mg ha⁻¹; R² = 0.995), whereas PNOA/ALS was competitive at Doña María (RMSE = 0.725 Mg ha⁻¹; R² = 0.994). TreeQSM closely matched the field inventory at the low-biomass IFAPA site but tended to overestimate biomass at Doña María and Villaseca, and only 28% of scanned trees yielded usable reconstructions. The results support the use of cross-platform LiDAR for orchard AGB and carbon mapping and identify the conditions under which open national LiDAR can enable scalable MRV of Mediterranean woody crops. Full article