Search Results (1,100)

Background: Whole-blood transcriptomic profiling can capture systemic immune-response alterations associated with COVID-19 and may support host-response-based classification. However, evidence regarding the discriminatory value of targeted immune-gene panels remains limited, and in small, high-dimensional datasets, the timing of feature selection may substantially affect model performance and interpretation. Aim: This study aimed to evaluate whether NanoString Human Immunology Panel profiles could distinguish COVID-19 from healthy-control measurements and to compare full-dataset feature selection (FDFS) with leave-one-out cross-validation (LOOCV)-embedded feature selection (LEFS). Methods: Publicly available E-MTAB-8871 data comprising 579 genes and 32 whole-blood transcriptomic profiles were analyzed. The dataset included 22 longitudinal COVID-19 measurements obtained from three participants and 10 measurements obtained from 10 healthy controls. Elastic Net regularization was used for feature selection. Random Forest, XGBoost, and LightGBM classifiers were evaluated using sample-level LOOCV. Model performance was assessed using threshold-dependent, discrimination, and probability-based metrics. A separate exploratory LightGBM model was analyzed using SHapley Additive exPlanations (SHAP) to characterize feature contributions. Results: FDFS identified a fixed 40-gene set, whereas LEFS selected a mean of 42 genes per fold (range: 40–47). LightGBM correctly classified all 32 measurement-level profiles (derived from 13 unique participants: 10 healthy controls and three longitudinally sampled COVID-19 participants) in both frameworks, achieving area under the receiver operating characteristic curve (ROC-AUC) and area under the precision–recall curve (PR-AUC) values of 1.000 and Brier scores of 0.005 and 0.006 in the FDFS and LEFS frameworks, respectively. Random Forest achieved accuracies of 0.969 and 1.000, whereas XGBoost achieved an accuracy of 0.969 in both frameworks. SHAP analyses consistently identified AICDA as the dominant contributor to model predictions, followed by ARHGDIB. Conclusions: This exploratory analysis showed that targeted NanoString immune-response profiles contained a compact transcriptomic signal capable of distinguishing COVID-19 from healthy-control measurements within the analyzed dataset. These findings provide proof-of-concept evidence of internal measurement-level discrimination. However, because the COVID-19 profiles consisted of repeated measurements from only three participants, sample-level LOOCV did not constitute independent participant-level validation. External validation in larger cohorts comprising independently sampled participants is required. Given that the COVID-19 arm comprised only three independent participants, these biological findings should be regarded as hypothesis-generating and require validation in substantially larger independent cohorts. Full article

Bridge surface cracks are important early indicators of structural performance degradation. However, affected by complex environmental interferences and irregular morphologies, existing models still fall short in micro-crack recognition, accurate bounding-box localization, and lightweight. To address these challenges, this study proposes a multi-module collaborative lightweight model (MCL-YOLO) based on YOLOv12. Specifically, the existing ADown module from YOLOv9 is incorporated into the YOLOv12 architecture to reduce computational complexity while preserving critical information during feature downsampling. To enhance the representation of slender, curved, and branched crack patterns, a C3k2-RFAConv module is designed by integrating a receptive-field attention mechanism. Furthermore, an iEMA module is embedded before the high-resolution detection branch to strengthen the semantic response to weak-texture cracks. A bridge crack dataset containing 4029 images was constructed to evaluate the proposed model. Experimental results show that MCL-YOLO achieves Precision, Recall, mAP@50, and mAP@50:95 values of 0.891, 0.757, 0.844, and 0.675, with 5.5 GFLOPs, 2.240 M parameters, and a model-file size of 4.689 M. Compared with the YOLOv12n baseline, MCL-YOLO improves the four detection metrics by 2.30%, 4.56%, 4.07%, and 3.21%, while reducing GFLOPs and parameter count (Params) by 12.70% and 12.77%, respectively. Ablation experiments, model version comparisons, attention mechanism comparisons, and qualitative detection results collectively verify the effectiveness of the integrated architectural modifications. Full article

Digital technologies are increasingly used to understand and influence consumer food waste behavior, yet evidence on their applications and effectiveness remains fragmented. This systematic literature review synthesizes research at the intersection of consumer behavior, technological innovation, and food waste. Peer-reviewed articles published in English were identified through searches of Scopus and Web of Science, resulting in a final sample of 22 studies. Thematic analyses were conducted to examine publication patterns, research contexts, methodological approaches, and emerging research gaps. The literature shows a shift from predominantly theoretical and exploratory work towards more applied, technology-centered research. Scientific production is concentrated in Australia, China, Italy, the Netherlands, and the United Kingdom, while the selected studies are mainly published in journals focused on environmental sustainability, food-chain management, and consumer psychology. Two broad research approaches were identified: quantitative studies using cross-sectional surveys and statistical modeling, in which technology is treated as an explanatory factor, and experimental or design-oriented studies that employ technology as a direct intervention. However, the evidence base is constrained by extensive reliance on self-reported data, recall and social-desirability bias, and cross-sectional designs that limit causal and long-term conclusions. Future research should therefore prioritize longitudinal, mixed-methods, and objective measurement approaches, while examining emotional and psychological responses to food-waste-prevention technologies. The implementation of such technologies also requires attention to usability, user fatigue, privacy and ethical concerns, and psychological and cultural barriers to adoption. Overall, the findings indicate that digital technologies offer promising but insufficiently validated opportunities to support food waste reduction and that stronger empirical evidence is needed to guide their effective and responsible development. Full article

To support the early identification of potential drowning-risk states and improve rescue response efficiency, this paper proposes a UAV-oriented dual-scale detection-pose cascade for frame-level drowning-risk recognition. The framework first performs high-recall preliminary detection on wide-field input images to identify potential drowning targets. The detected target regions are subsequently extracted and resized to construct localized inputs for the second-stage pose-based verification. This software-based target-region refinement simulates the localized high-resolution observation that could be provided by a telephoto camera in a future physical dual-camera UAV implementation. By focusing subsequent analysis on the localized target regions, the second-stage pose model can exploit finer-scale human structural information for drowning-risk state verification, thereby providing decision support for potential drowning detection. To address the challenges of small target scales and severe background interference in wide-field images, a lightweight YOLOv8n-based detection model is developed. An enhanced edge-feature-guided residual convolutional block attention module (EGRCBAM) is introduced, together with a recall-oriented FPIoU loss function designed for hard sample optimization, improving the recall of the drowning category by 17%. For localized target verification, an enhanced YOLOv8n-Pose model is constructed by incorporating a coordinate-aware pose head, spatial attention mechanism, and skeletal structure constraints to enhance human-region localization and pose-based drowning-versus-swimming recognition. The model improves Box mAP@0.5 from 0.768 to 0.816. Comparative experiments on the self-collected dataset demonstrate the effectiveness of the proposed detection and pose-based verification framework. The proposed framework provides a lightweight vision-based solution or UAV-oriented frame-level drowning-risk recognition and offers a potential algorithmic basis for future integration with physical dual-camera UAV platforms. Full article

BOOLE is a four-face educational platform integrating analog, combinational-logic, and sequential-logic activities with optional AI-assisted component identification and datasheet support. The system was developed through requirements translation, circuit simulation, two-layer PCB design, mechanical review, fabrication, assembly, functional verification, and iterative refinement. A Raspberry Pi 5, Camera Module 3 NoIR, and touchscreen support image capture and local interaction, while an Arduino Mega provides deterministic control of the physical learning faces. Segmented power energizes only the selected face and activity, and removable boards improve maintenance and fault isolation. Hardware demonstrations reproduced the intended voltage-divider, diode threshold/polarity, counter, and sequential-logic states. Ten one-versus-rest classifiers were fine-tuned from a pretrained ViT-Base model using 2000 original photographs, with 200 images for each of ten categories. The dataset was partitioned class-wise into mutually exclusive 80/10/10 training, validation, and final-test sets before augmentation, which was applied only to training data. Final-test accuracy ranged from 91.0% to 99.5%, with precision, recall, F1-score, specificity, balanced accuracy, and confusion matrices also evaluated. A 73-student pilot produced 89–96% positive (Yes) responses across six binary survey items, providing preliminary evidence of learner-perceived effectiveness, engagement, usability, and theory-to-practice support. Overall, BOOLE demonstrates a feasible, serviceable architecture for progressive electronics education. Full article

Immersive Virtual Reality (VR) environments are increasingly adopted in clinical psychology and stress-management contexts; however, their therapeutic effectiveness depends on the system’s ability to understand and dynamically respond to users’ affective states. Emotional regulation plays a key role in psychological resilience, directly influencing stress coping mechanisms, cognitive performance, and overall mental well-being. Despite recent advances, automatic recognition of scenario-associated affective conditions in VR remains challenging because head-mounted displays occlude facial features. This study proposes a VR-based serious game for affective training and regulation, in which users interact with goal-oriented scenarios targeting fear, anger, and joy. We introduce a multimodal affective computing model to objectively assess users’ emotional responses by integrating electrocardiogram (ECG) signals and posture-based features extracted through computer vision. An early-fusion architecture combined with a Long Short-Term Memory (LSTM) network captures temporal dependencies in synchronized multimodal data. We established a controlled experimental framework using immersive VR scenarios, enabling the collection of synchronized physiological and behavioral data from a cohort of 20 healthy adult participants. The proposed model was evaluated under a strict Leave-One-Subject-Out (LOSO) cross-validation scheme across independent subjects, achieving a robust inter-subject accuracy of $78.94\%\pm 10.77\%$ and a global macro F1-score of 0.635, demonstrating strong generalization to entirely unseen users without data leakage. Furthermore, the system maintained an outstanding balance in detecting active emotional states (recall > 80.0% for fear, anger, and joy). Additionally, subjective evaluations using the PANAS and SGU questionnaires confirmed the coherence between detected and perceived emotional states, as well as the system’s high usability. The results suggest the potential viability of combining immersive environments and multimodal affective computing to explore the technical feasibility of adaptive frameworks that could eventually translate into healthcare contexts. This work may contribute to the development of intelligent digital health technologies by providing a foundation for responsive VR systems that can monitor emotional regulation and are fully aligned with sustainable well-being ecosystems (SDG 3: Good Health and Well-being and SDG 10: Reduced Inequalities). Full article

Accurate perception of eggplant fruits and stems remains challenging for greenhouse harvesting robots because illumination changes, foliage occlusion, fruit overlap, and background branches can degrade target visibility, particularly for small and curved stems. To improve joint fruit-and-stem detection under these conditions, this study develops an enhanced YOLOv8n model using a greenhouse dataset collected across different illumination levels, viewpoints, occlusion degrees, and fruit-overlap situations. The baseline network was modified in three aspects. Selected conventional convolutions in the backbone and neck were replaced by Omni-Dimensional Dynamic Convolution (ODConv) to improve feature adaptation to targets with different scales and shapes. Efficient Multi-Scale Attention (EMA) was placed after the SPPF module to emphasize informative responses from fruit and stem regions while reducing background interference. In addition, C2f_MSBlock was incorporated into the neck to strengthen multi-scale feature representation and fusion. The resulting model achieved 96.4% precision, 97.2% recall, 99.0% mAP@0.5, and 86.0% mAP@0.5:0.95, with 3.74 M parameters, 6.5 GFLOPs, and a model size of 7.9 MB. Relative to the original YOLOv8n, these four detection metrics increased by 2.2, 0.3, 0.5, and 2.9 percentage points, respectively, while GFLOPs decreased by 16.7%. These results indicate that the modified model improves detection robustness in complex greenhouse scenes while maintaining moderate computational requirements, providing a feasible visual perception approach for eggplant fruit recognition and stem localization in robotic harvesting. Full article

Landslides are frequent and destructive geological disasters. Accurate landslide identification is essential for post-disaster reconstruction and preventing secondary disasters. Deep learning has shown considerable potential for recognizing landslide objects from remote-sensing images; however, existing models still suffer from insufficient detection accuracy in scenarios with complex backgrounds, blurred boundary localization, sample-class imbalance, and difficulty in balancing detection speed and segmentation accuracy. To address these issues, this study investigates landslide identification in the Great Bend of the Yarlung Zangbo River region using improved deep learning models and heterogeneous optical remote-sensing imagery. (1) By introducing the convolutional block attention module (CBAM) into YOLOv8, 82.79% precision was achieved, and the recall improved by 9.56% compared to the original model, reaching a mean average precision of 75.27% while maintaining computational efficiency, outperforming YOLOv5 and the original YOLOv8. (2) Replacing the cross-entropy loss with Focal Loss in DeepLabV3+ improved the landslide edge segmentation by dynamically adjusting the weights of difficult and easy samples. Compared with the original DeepLabV3+, the precision and recall of the DeepLabV3+-FL semantic-segmentation model were improved by 0.26% and 1.93%, respectively, with the mean pixel accuracy and mean intersection over union reaching 81.76% and 62.59%, respectively. Overall, the two improved models enhanced the accuracy of landslide identification and resistance to interference, demonstrating potential for landslide monitoring and emergency response. Full article

Visible-light photovoltaic (PV) inspection is affected by periodic grid-line backgrounds, low-contrast cracks, irregular crack topology, and the extreme aspect ratios of slender defects. To address these difficulties, a frequency-domain hybrid deformation and geometry-regression YOLO network, termed FHDG-YOLO, is proposed in this study. The method introduces frequency-domain dynamic decoupled convolution to attenuate periodic background responses, incorporates a high-resolution P2 detection head and efficient multi-scale attention to retain and recalibrate shallow spatial details, embeds DCNv2 to adapt convolutional sampling to irregular defect boundaries, and replaces the original regression loss with MicroShape-IoU for geometry-sensitive localization. Experiments are conducted on a reorganized two-class visible-light PV dataset containing 6493 images, comprising 6262 screened public images and 231 field images collected by the authors. On the 1300-image validation split, FHDG-YOLO obtains a Precision of 0.954, Recall of 0.943, mAP@0.5 of 0.971, and mAP@0.5:0.95 of 0.861. Compared with YOLOv8n, mAP@0.5 and mAP@0.5:0.95 increase by 3.6 and 6.9 percentage points, respectively. On the held-out 649-image test split, the corresponding mAP values are 0.970 and 0.860, compared with 0.931 and 0.785 for YOLOv8n. Under the original four-class Panel Solar validation protocol, FHDG-YOLO obtains mAP@0.5 and mAP@0.5:0.95 values of 0.954 and 0.843, compared with 0.929 and 0.780 for YOLOv8n. Full article

Retrieval-augmented generation (RAG) systems for scientific literature require evidence-based choices of document segmentation, representation, retrieval, and generation components, particularly when the source collection varies in topical specificity and document structure. This study addresses the lack of an end-to-end, component-level comparison of these choices for soil science question answering. A three-stage evaluation was conducted across general, domain-specific, and geospatial soil science corpora. The corpus combines foundational soil science books, peer-reviewed research articles, European soil monitoring material, and geospatial mapping publications, thereby covering both broad disciplinary concepts and specialized scientific evidence. The study compares four chunking strategies, three embedding models, five retrieval methods, and five large language models. In Experiment 1, semantic chunking with text-embedding-3-large achieved the highest aggregate retrieval scores (recall@1 = 0.824; MRR = 0.819), whereas text-embedding-3-small delivered practically comparable performance at lower cost. In Experiment 2, hybrid reciprocal rank fusion achieved recall@5 values of 0.957, 0.960, and 0.647 for the general, domain-specific, and geospatial corpora, respectively; the cross-encoder reranker showed weaker rank quality on scientific content. In Experiment 3, model responses attained BERTScore values of 0.909–0.927 and faithfulness of at least 0.993; these automated measures indicate low contradiction with retrieved context but do not establish answer completeness or human-perceived correctness. The study provides a reproducible component-level evaluation design, characterizes the effect of corpus specificity on RAG retrieval, and identifies a practical configuration for soil science literature retrieval. Among the models retained for direct aggregate comparison, Llama 3.1 8B offered the most favorable observed balance of answer quality, latency, cost, and model openness. Full article

Accurate discrimination among static eccentricity fault (SEFs), dynamic eccentricity fault (DEFs), and mixed eccentricity fault (MEFs) in permanent-magnet-synchronous motors remains difficult because these conditions produce similar stator-current patterns. This paper proposes an STFT–CNN–LSTM-based Eccentricity Fault Diagnosis System (STFT-CL-EFDS) for classifying Normal, SEF, DEF, and MEF conditions using only three-phase stator currents. Six signal transformations were initially compared, after which the three leading representations—STFT, DWT, and CWT—were evaluated with seven neural-network architectures. Sensitivity and ablation analyses selected a 500-sample observation window and a compact log-magnitude STFT representation over the nominal 0–1 kHz band. Following full-schedule retraining, the proposed model achieved 99.57% accuracy and a 99.57% weighted F1-score, with recalls of 100.00%, 98.66%, 100.00%, and 99.53% for Normal, SEF, DEF, and MEF, respectively. It exceeded the strongest machine-learning benchmark, STFT–MLP, by 8.42 percentage points in accuracy and 8.52 percentage points in weighted F1-score. On an independent unseen test bench, the proposed model provided the most balanced response across the three fault types and ultimately converged to the correct class in every case, although temporary DEF–MEF confusion remained. These results demonstrate the effectiveness of STFT-CL-EFDS for current-only multiclass PMSM eccentricity diagnosis. The main contributions of this study are as follows: (1) a systematic comparison of six signal-transformation methods, namely Fast Fourier Transform (FFT), STFT, Discrete Wavelet Transform (DWT), Continuous Wavelet Transform (CWT), Hilbert–Huang Transform (HHT), and Variational Mode Decomposition (VMD), to determine their suitability for eccentricity fault diagnosis; (2) a comparative evaluation of seven neural-network architectures, including CNN, LSTM, CNN–LSTM, DNN, TCN, ModernTCN, and TimesNet, using the three best-performing transformation methods, namely STFT, CWT, and DWT; and (3) the development of a unified current-only STFT-CL-EFDS that combines STFT-based time–frequency representation with convolutional feature extraction and temporal-sequence learning for the classification of Normal, SEF, DEF, and MEF conditions. Full article

Automated essay scoring models trained on imbalanced examination data often compress predictions toward the score mean, disproportionately harming low-proficiency responses. We investigate whether synthetic tail data can mitigate this failure in Chinese second-language writing assessment. Using 10,067 HSK writing samples, we introduce Error-Profile-Conditioned Synthesis (EPCS), which transfers a complete error profile and examiner score from a real low-band donor to an expert-corrected mid-band substrate, uses authentic error exemplars, and verifies the generated manifest. We evaluate three method versions using surface-feature tests, real-versus-synthetic discrimination, and downstream MacBERT scoring under full, 25%, and 10% low-band retention. Surface similarity did not predict downstream utility: the least detectable version transferred poorly, whereas content-degraded versions were more useful. With only 49 real low-band training samples, EPCS with reweighting reduced low-band mean absolute error from 14.91 to 12.66 and increased below-60 recall from 0.10 to 0.33; recall improved in all five paired seeds and low-band error in four. However, overall QWK changed from 0.506 to 0.497, precision decreased from 0.78 to 0.56, and the false-positive rate increased from 0.015 to 0.106. The pattern replicated across two further pretrained encoders and a feature-based scorer and survived a $\pm 5$-point stress test of the inherited labels. EPCS therefore acts as a scarcity-conditioned operating-point intervention, improving tail detection at the cost of more false alarms and high-band under-scoring. Full article

Background: Omega-3 fatty acids may be relevant to mental health, but evidence among university students remains limited. This small exploratory cross-sectional study examined associations between estimated dietary omega-3 intake and anxiety and depressive symptoms among university students in the UK. Methods: Data were collected between February and May 2025. Habitual dietary EPA + DHA and alpha-linolenic acid (ALA) intakes were estimated using an adapted omega-3 food-frequency questionnaire, while 24-h dietary recall was used to estimate energy intake. Anxiety and depressive symptoms were assessed using the GAD-7 and PHQ-9. The primary association between estimated dietary EPA + DHA intake and GAD-7 score was examined using linear regression adjusted for age, sex, energy intake, and alcohol use. Analyses involving ALA and PHQ-9 were exploratory. Results: Of 96 students recruited, 50 (52.1%) were analysed after 46 exclusions, most commonly for omega-3 supplement use (n = 21) or diagnosed mental-health conditions (n = 16). Mean GAD-7 and PHQ-9 scores were 6.66 ± 4.73 and 7.02 ± 4.76, respectively. In the fully adjusted model, each additional 100 mg/day of estimated dietary EPA + DHA intake was associated with a 0.325-point lower GAD-7 score, but the association was not statistically significant (B = −0.325; 95% CI: −0.725 to 0.074; p = 0.108). Exploratory analyses found no significant associations of estimated dietary EPA + DHA or ALA intake with anxiety or depressive symptoms. The inverse coefficient lost statistical significance after full adjustment, and the log-transformed coefficient reversed direction and remained non-significant, arguing against a consistent dose–response relationship. Conclusions: Estimated dietary EPA + DHA intake showed an inverse but inconclusive association with anxiety symptoms in this small exploratory sample. These preliminary findings are hypothesis-generating and require confirmation in larger longitudinal studies using repeated dietary assessments and biomarkers of omega-3 status. Full article

Security Operations Centers increasingly use artificial intelligence to rank alerts, summarize evidence, and automate repetitive response actions. However, AI-enabled security operations can also create new risks for incident response and digital forensic reliability, including false-negative prioritization, model drift, hallucinated explanations, prompt injection, automation bias, unsafe SOAR actions, and evidence contamination. This article proposes a risk-driven deployment and forensic readiness framework for AI-enabled Security Operations Centers. The framework combines Monte Carlo loss simulation, detector threshold analysis, analyst queueing, model-drift monitoring, and evidence-preserving governance controls. It explicitly separates evidence, recommendation, and action so that AI can accelerate triage while preserving source artifacts, provenance, audit trails, and chain-of-custody information needed for incident reconstruction. Illustrative simulation results show a mean annualized loss expectancy of USD 1.70 M, a 95% Value-at-Risk of USD 3.85 M and a 99% Value-at-Risk of USD 6.29 M, a detector ROC-AUC of 0.942 and PR-AUC of 0.750 with precision 0.719, recall 0.650 and F1 0.683 at the selected decision threshold of 1.873, a manual triage workload reduction of about 38%, and a reduction in mean time to respond (MTTR, defined throughout as mean time to respond rather than mean time to resolution) from about 44 to 26 min under controlled assumptions. The results are demonstration outputs rather than universal benchmarks. The main contribution is a reproducible governance method for deciding when AI reduces SOC risk, when it transfers risk, and when forensic readiness requires human approval, evidence preservation, or automation rollback. Full article

Reliable joint detection of mango fruits and stems is an essential upstream perception task for robotic harvesting, but remains challenging because stems are small, slender, frequently occluded, and visually degraded by illumination variation. This study proposes MangoNET, a YOLOv11n-based framework for joint mango fruit and stem detection in complex orchard environments. A P2 high-resolution detection head preserves fine spatial information for small targets, while SPPF-ELAN aggregates local and contextual features for partially visible objects. SENet recalibrates channel responses under illumination variation, and WIoU v3 regulates bounding-box samples with different localization qualities. A dataset containing 1782 original images of Tainong and Jinhuang mangoes was collected from two orchards and data augmentation was applied only to the training set, increasing its size from 1172 to 2886 images through rotation, contrast adjustment, and Gaussian noise addition. MangoNET achieved fruit and stem F1-scores of 0.920 and 0.916, respectively, with mAP50 and mAP50–95 values of 0.941 and 0.690. Compared with YOLOv11n, mAP50 and mAP50–95 increased by 1.6 and 2.9 percentage points, respectively, while stem recall increased from 0.877 to 0.906. Source-image-independent five-fold cross-validation yielded mean mAP50 and mAP50–95 values of 0.944 and 0.711, respectively. Pilot evaluations using images acquired by a UAV and an RGB-D camera in a geographically distinct orchard suggested that MangoNET could maintain detection performance in a different orchard environment. MangoNET supplies fruit and stem candidate regions for subsequent association, harvesting-point localization, and robotic manipulation. Full article