Search Results (9,797)

In recent years, deep learning models have exhibited exceptional performance across several tasks. However, the substantial computational and storage demands impede implementation on edge devices with constrained resources. Online Knowledge Distillation (OKD) has arisen as an effective model compression strategy that removes the reliance on pre-trained teachers characteristic of conventional distillation approaches. Nonetheless, OKD persists in facing challenges, including substantial performance variances within student networks, insufficient learning capacity for difficult data, and network homogeneity. To address those issues, this paper proposes an Elite-Student-Enhanced Adaptive Online Knowledge Distillation (ESAKD) algorithm. ESAKD introduces a patience factor-based adaptive temperature scheduling mechanism to dynamically balance knowledge clarity and richness during knowledge transfer. This mechanism utilizes the performance benefits of elite-students, particularly during initial training phases, to offer superior supervision that successfully transcends the learning capacity limitations of current OKD approaches. This method promotes swift convergence and substantially enhances the ultimate precision of the standard-student models. Furthermore, a confidence-weighted ensemble student model is designed to improve collective decision-making. Experimental assessments indicate that ESAKD provides substantial performance improvements over supervised learning without distillation and other leading online distillation techniques. On the CIFAR-100 dataset, ESAKD improves the test accuracy of various models by 1.49–6% over the undistilled baselines, and by 0.27–2.18% compared to advanced online distillation algorithms. Moreover, it exhibits enhanced performance on the Tiny-ImageNet-200 dataset as well. Full article

This study addresses the critical need for accurate, continuous monitoring of surface water quality parameters (SWQPs) using remote sensing, overcoming limitations in existing models that often rely on pre-trained networks ill-suited for complex aquatic environments. We present a customized convolutional neural network (CNN) architecture, implemented in the MATLAB environment, designed to simultaneously predict optically active (Total Organic Carbon, TOC) and non-optically active (Dissolved Oxygen, DO) parameters from eighteen Sentinel-2 Level-2A satellite images, acquired between 2023 and 2024. Our approach integrates spatial and spectral data through a customized CNN with three convolutional layers and two dense layers, optimized via adaptive learning strategies, data augmentation, and rigorous regularization to enhance predictive performance and prevent overfitting. The models were trained and validated on fused datasets of satellite imagery and in situ measurements, organized into comprehensive four-dimensional arrays capturing spectral, spatial, and sample dimensions. The results demonstrated high accuracy, with coefficient of determination (R²) values exceeding 0.97 and low root mean square error (RMSE) across training, validation, and testing subsets. Spatial prediction maps generated at high resolution revealed realistic ecological and hydrological patterns consistent with known regional water quality dynamics in New Brunswick. Our contribution, accessible to users with MATLAB, lies in the development of a transparent, adaptable, and reproducible CNN framework tailored for multiparameter water quality estimation, which extends beyond traditional empirical, site-specific regression models by enabling non-invasive, cost-effective, and continuous monitoring from satellite platforms over a large, heterogeneous province-scale domain. Additionally, model interpretability was enhanced through SHapley Additive exPlanations (SHAP) analysis, which identified key spectral bands influencing predictions and provided ecological insights, offering guidance for future sensor design and data reduction strategies. This study addresses a significant research gap by providing a dual-parameter focused, end-to-end deep learning solution optimized for province-scale remote sensing data, facilitating more informed environmental management. This study can support water managers and agencies by providing province-wide DO and TOC maps derived from freely available Sentinel-2 imagery, reducing reliance on sparse field sampling alone and helping to identify areas of low oxygen or high organic carbon. Future work will extend this framework temporally and spatially and explore hybrid CNN architectures incorporating temporal dependencies for improved generalization and accuracy. Full article

Background/Objectives: Active surveillance (AS) has emerged as a safe alternative to primary therapy for low- and select intermediate-risk prostate cancer (PCa), but optimal patient selection and surveillance strategies remain challenging due to limited risk stratification tools enabling early detection of lesions with high potential for histopathological progression. This study presents an integrated method for predicting prostate cancer progression within 12 months, aiming to improve AS patient selection by categorizing patients into two risk groups: rapid progressors who would benefit from immediate treatment and slow progressors suitable for AS. Methods: The risk assessment platform combines convolutional neural networks for automatic segmentation of prostate and suspicious-for-cancer lesions on multiparametric MRI (mpMRI) with logistic regression to estimate progression risk. The networks were trained on annotated lesions from radical prostatectomy specimen mapped to mpMRI. The prediction model incorporated pre-biopsy clinical variables (age, PSA, PI-RADS) and MRI-derived intratumoral radiomic features from 163 participants of a prospective clinical trial, using histopathological progression within 12 months as endpoint. Results: The clinical-radiomics model achieved an AUC of 0.84 in distinguishing rapid from slow progressors, using non-invasive monitoring techniques. In an independent test set, the model significantly improved AS patient selection, increasing negative predictive value by 18.5% compared to current standard-of-care (p < 0.001). Conclusions: The risk assessment platform shows promise for use during annual follow-up visits to reliably differentiate suitable AS candidates with stable disease from PCa patients who are likely to experience early progression. Full article

Alzheimer’s disease (AD), the most common form of dementia, is a progressive and irreversible neurodegenerative disorder. Structural MRI is widely used for diagnosis, revealing brain changes associated with AD. However, these alterations are often subtle and difficult to detect manually, particularly at early stages. Early intervention during prodromal stages, such as mild cognitive impairment (MCI), can help slow disease progression, highlighting the need for reliable automated methods. In this work, we introduce a dual-level evaluation framework comparing fifteen deep learning architectures, including convolutional neural networks (CNNs), Transformers, and hybrid models, for classifying AD, MCI, and cognitively normal (CN) subjects using the ADNI dataset. A central focus of our work is the impact of robust and standardized preprocessing pipelines, which we identified as a critical yet underexplored factor influencing model reliability. By evaluating performance at both slice-level and scan-level, we reveal that multi-slice aggregation affects architectures asymmetrically. By systematically optimizing preprocessing steps to reduce data variability and enhance feature consistency, we established preprocessing quality as an essential determinant of deep learning performance in neuroimaging. Experimental results show that CNNs and hybrid pre-trained models outperform Transformer-based models in both slice-level and scan-level classification. ConvNeXtV2-L achieved the best scan-level performance (91.07%), EfficientNetV2-L the highest slice-level accuracy (86.84%), and VGG19 balanced results (86.07%/88.52%). ConvNeXtV2-L and SwinV1-L exhibited scan-level improvements of 7.60% and 9.04% respectively, while EfficientNetV2-L experienced degradation of 2.66%, demonstrating that architectural selection and aggregation strategy are interdependent factors. These findings suggest that carefully designed preprocessing not only improves classification accuracy but may also serve as a foundation for more reproducible and interpretable Alzheimer’s disease detection pipelines. Full article

Retrieval-Augmented Generation (RAG) has been observed to encounter challenges in heterogeneous query scenarios characterised by varying evidence requirements and reasoning depths. In order to address this limitation, the present paper puts forward a proposal for an Adaptive Multi-Source RAG framework (AMSRAG) that integrates query complexity awareness with confidence-aware fusion. The framework performs query complexity classification with a pretrained language model, calibrates the classification confidence to guide the dynamic scheduling of retrieval paths and the adjustment of fusion weights, and enables a controllable balance between answer quality and retrieval efficiency through hierarchical path selection and cross-source weighting. The experiments conducted on multiple open-domain question-answering datasets demonstrate that the query complexity classifier achieves an accuracy of 85.9% and a Macro-F1 score of 85.4%. These outcomes indicate the potential for the classifier to generate a reliable decision signal, which can subsequently be utilised to guide the process of adaptive retrieval and fusion. The proposed framework demonstrates a marked improvement in terms of both answer accuracy and retrieval relevance when compared to the fixed-pipeline RAG. In scenarios involving high-confidence queries, the system has been shown to effectively avoid redundant retrieval, thereby reducing the average number of retrievals. In instances of low-confidence complex queries, the system has been shown to enhance evidence coverage and completeness of answers through multi-source retrieval and confidence-weighted fusion. This study proposes a novel methodology for enhancing the adaptability and resource efficiency of RAG systems in response to heterogeneous query conditions. Full article

The palm swamp landscapes, particularly the Vereda wetlands and their associated swamp gallery forests (VED.SGF), comprise essential yet threatened ecosystems within the Brazilian Cerrado. In addition to supporting significant portions of biodiversity, they provide critical ecosystem services such as storing and filtering excess rainwater and serving as major carbon reservoirs in organic soils. These wetlands are directly linked to the drainage systems of the headwaters of the main Cerrado river basins, which together account for about two-thirds of Brazil’s hydrographic basins. Mapping and managing VED.SGF ecosystems through remote sensing present major challenges addressed in this first study. Their narrow, dendritic, and complex tabular spatial pattern, often elongated along watersheds on scales of hundreds of kilometers, suffering distortions due to human impact, and the limited amount of annotated data make segmentation particularly challenging. Existing deep learning (DL) methods, typically pre-trained on natural images, struggle to capture the spectral and spatial intricacies of these ecosystems. This study introduces a trained-from-scratch U-Net model supported by field-based experimental procedures to ensure high-quality wetland annotations. The resulting dataset covers approximately 7300 km² in western Bahia and provides domain-specific weights tailored to remote sensing applications. Using high-resolution (4.6 m) RGB mosaics, the model was trained, validated, and tested to establish a reproducible and scalable pipeline. The proposed method achieved robust results in an independent test area of 8040 km², with a mean IoU of 0.728, F1-score of 0.843, and Cohen’s Kappa of 0.837. These results demonstrate consistent performance and strong generalization to new areas, establishing a scientifically reliable baseline that situates the model competitively within the current state of the art. By releasing both the model weights and annotated dataset, this study provides valuable resources to advance future research on mapping and monitoring these unique and strategic wetland ecosystems. Full article

Education for Sustainable Development places increasing demands on teachers’ ability to integrate technology into STEM teaching in meaningful and sustainable ways. However, many STEM pre-service teachers struggle to develop transferable technology integration competencies. To address this challenge, this study designed and evaluated a project-based learning and graphical programming training module aimed at enhancing STEM pre-service teachers’ technology integration competencies. Grounded in the Technological Pedagogical Content Knowledge framework, the training module supports practice-oriented learning in authentic instructional contexts. A mixed-methods research design combining design-based research and a quasi-experimental approach was employed. Following the iterative development of the training module, a quasi-experimental study involving 114 STEM pre-service teachers was conducted to examine its effectiveness. The results of independent-samples and paired-samples t-tests indicate statistically significant improvements in most dimensions of technology integration competencies, including pedagogical knowledge, technological knowledge, and overall TPACK. Effect size analysis revealed a moderate effect on technology integration competencies. These findings suggest that embedding graphical programming within project-based learning can effectively support the development of transferable and sustainable technology integration competencies among STEM pre-service teachers, thereby contributing to sustainable STEM teacher education. Full article

The present paper provides a human activity-level classification model based on the patient’s lifelog collected from wearable devices. During about two months, the heart rate, step count, and calorie consumption for a total of 182 patients were collected from a wearable device. Using the lifelog data, the machine learning models were developed to classify the physical activity status of patients into five levels. Three types of wearable data with heart rate, step count, and calorie consumption were pre-processed as integrated data in time series. A total of 80% of the integrated data was used as the training dataset, and the remaining 20% was used as the test dataset. Sixteen algorithms were evaluated, including 12 traditional machine learning models (SVM, KNN, RF, etc.) and 4 deep learning models (CNN, RNN, etc.), and cross-validation was performed by dividing the training dataset into 5 folds. By changing the parameters required for training, the models with optimal parameters were derived. The performance of the final models with the new patient lifelog data was evaluated, and it was shown that the classification for human activity level based on heart rate and step count can be performed with high accuracy. Full article

Artificial intelligence (AI) is rapidly transforming virology by strengthening pandemic preparedness, enhancing our molecular understanding of virus–host interactions, and accelerating the discovery and development of novel antiviral therapies. Yet, the same technologies also pose urgent biosecurity risks, particularly by enabling the development of bioweapons or identifying strategies that maximize harm. This paper presents a critical content analysis of current and emerging AI applications in virology, including tools used to detect synthetic alterations in viral genomes, assess the severity of new variants, and design clinical vectors for gene therapy. It also highlights the potential for misuse, whether intentional or due to poor data quality and flawed model training. Drawing on case studies, public databases, and documented applications from research institutions and biotechnology firms, the analysis shows that AI can integrate large datasets to reduce reliance on animal testing in drug development, improve therapeutic precision, and allocate resources more effectively during outbreaks. However, the increasing accessibility of AI tools and genomic data also creates vulnerabilities, especially as models become capable of autonomously interpreting the scientific literature and mining bioinformatics databases. To address this dual-use dilemma, the paper proposes targeted and adaptable policy recommendations for governments, research institutions, and commercial biotech firms, emphasizing pre-emptive oversight, responsible innovation, and ethical AI deployment. These recommendations are designed for immediate relevance yet flexible enough to evolve alongside the expanding role of AI in global health. Full article

Inorganic nitrate (NO₃⁻) has demonstrated therapeutic efficacy in several populations characterised by cardiovascular risk. However, it is unknown whether increasing nitric oxide (NO) bioavailability affects vascular and cardiovascular responses in men with androgenic–anabolic steroid (AAS) abuse. Objective: To investigate the effects of dietary NO₃⁻ on cardiovascular, autonomic, and strength performance in men with AAS abuse. Methods: In this double-blind, randomised, placebo-controlled crossover trial, participants consumed beetroot juice (12.8 mmol [800 mg] NO₃⁻) or a placebo (0.3 mmol NO₃⁻). After two hours, assessments included saliva collection, endothelial function, heart rate, and systolic (SBP) and diastolic (DBP) blood pressure at rest, during, and after an isometric handgrip test. Results: Thirteen resistance-trained males [mean (standard deviation) age: 31 (9) y; body mass index (BMI): 30 (4) kg/m²; SBP: 132 (3) mmHg; DBP: 70 (2) mmHg] completed the protocol. NO₃⁻-rich juice significantly increased salivary NO₃⁻ (40.6 μM, p < 0.001) and nitrite (NO₂⁻) (3.1 μM, p = 0.002) versus placebo. Flow-mediated dilation was greater with NO₃⁻ both at pre-exercise (2.37%, p = 0.02) and post-exercise (2.57%, p = 0.01). No between-group differences were observed in isometric strength (0.02 kgf, p = 0.99) or systolic/diastolic blood pressure across conditions. Conclusions: Dietary NO₃⁻ enhanced salivary NO₂⁻ and NO₃⁻ concentrations and modestly improved endothelial function but did not reduce the elevated blood pressure or alter cardiac autonomic responses associated with AAS abuse. Full article

Ginger (Zingiber officinale) is an essential crop that is widely cultivated for its medical and culinary value. In 2023, ginger was considered one of the highest value herbs, with approximately 9089.85 tons produced in Malaysia. However, the ginger cultivation suffers from plant diseases, which lead to plant death and eventually cause crop losses. Furthermore, the lack of studies in ginger leaf disease detection using deep learning techniques is a limitation that hinders the early diagnosis and management of ginger diseases. To address this limitation, we collected 968 ginger plant images cropped into single leaf images and labelled into 4 classes: leaf blight, dehydrated, damaged pest, and healthy, using the Encordplatform. The generated dataset consisted of 4033 leaf images. Through data augmentation, the dataset was expanded into 10,910 leaf images to improve the model’s generalization. As deep learning techniques are popular in plant disease detection, we evaluated several popular pre-trained models using TensorFlow and PyTorch libraries and compared the performance with that of other models. For all of these models, the same settings were applied with minimal modification to the model’s layers. Among the compared models, EfficientNetB3 achieved the highest accuracy of 94.3% in detecting ginger leaf diseases. It surpassed other models and exceeded the next-best model in this experiment, MobileNetV2, which achieved 89.66% accuracy, by 4.64%. Full article

Background: Sodium bicarbonate (SB) supplementation can enhance performance in short, high-intensity movements. However, its effectiveness in team sports such as rugby remains insufficiently explored. Methods: In this double-blind, parallel, controlled trial, 17 male professional rugby players ingested SB (0.3 g/kg) or a placebo 90 min before a high-intensity, rugby-specific training session monitored via GPS. The training session was conducted under real-world conditions to enhance ecological validity. Physical performance (countermovement jump, CMJ), fatigue markers (capillary lactate and ratings of perceived exertion, RPE), and gastrointestinal (GI) symptoms were assessed pre- and post-exercise. Results: No significant pre–post changes were observed in CMJ performance in either group. Lactate concentrations increased from pre- to post-exercise in both groups (both p < 0.001). The SB group showed higher GI symptom severity before, during and after exercise versus placebo, with several symptoms increasing over time solely in the SB group (p < 0.05). RPE increased similarly in both groups (SB: p = 0.012; PLA: p = 0.008). Due to the small sample size, only moderate-to-large within-group effects and very large between-group differences could be detected; therefore, the study was powered to detect moderate-to-large within-group effects but underpowered for detecting between-group differences. Conclusions: Acute SB ingestion at 0.3 g/kg did not result in detectable improvements in performance or fatigue markers during rugby-specific high-intensity training and was associated with a greater incidence of GI discomfort; however, the study was underpowered to detect small between-group differences. This study was registered on 23 May 2025 on ClinicalTrials.gov (NCT07017582). Full article

Maintaining consistent part quality remains a critical challenge in industrial additive manufacturing, particularly in laser-based powder bed fusion of polymers (PBF-LB/P), where crystallization-driven thermal instabilities, governed by isothermal crystallization within a narrow sintering window, precipitate defects such as curling, warping, and delamination. In contrast to metal-based systems dominated by melt-pool hydrodynamics, polymer PBF-LB/P requires monitoring strategies capable of resolving subtle spatio-temporal thermal deviations under realistic industrial operating conditions. Although machine learning, particularly convolutional neural networks (CNNs), has demonstrated efficacy in defect detection, a structured evaluation of heterogeneous modeling paradigms and their deployment feasibility in polymer PBF-LB/P remains limited. This study presents a systematic cross-paradigm assessment of unsupervised anomaly detection (autoencoders and generative adversarial networks), supervised CNN classifiers (VGG-16, ResNet50, and Xception), hybrid CNN-LSTM architectures, and physics-informed neural networks (PINNs) using 76,450 synchronized thermal and RGB images acquired from a commercial industrial system operating under closed control constraints. CNN-based models enable frame- and sequence-level defect classification, whereas the PINN component complements detection by providing physically consistent thermal-field regression. The results reveal quantifiable trade-offs between detection performance, temporal robustness, physical consistency, and algorithmic complexity. Pre-trained CNNs achieve up to 99.09% frame-level accuracy but impose a substantial computational burden for edge deployment. The PINN model attains an RMSE of approximately 27 K under quasi-isothermal process conditions, supporting trend-level thermal monitoring. A lightweight hybrid CNN achieves 99.7% validation accuracy with 1860 parameters and a CPU-benchmarked forward-pass inference time of 1.6 ms (excluding sensor acquisition latency). Collectively, this study establishes a rigorously benchmarked, scalable, and resource-efficient deep-learning framework tailored to crystallization-dominated polymer PBF-LB/P, providing a technically grounded basis for real-time industrial quality monitoring. Full article

Background: The modernization of Traditional Chinese Medicine (TCM) is hindered by a “structure-blind” bottleneck: establishing molecular mechanisms for complex formulations with uncharacterized chemical constituents. Conventional computational screening fails in these scenarios due to a heavy reliance on pre-determined structures. We developed NovelHTI, an inductive graph-based framework designed to reverse-engineer protein targets directly from standardized clinical symptom profiles. Methods: NovelHTI implements a “Phenotype-to-Target” paradigm by integrating heterogeneous graph neural networks with systemic pathway constraints. Unlike traditional transductive models, NovelHTI leverages multi-view feature fusion of symptom semantics and biological pathways to enable de novo prediction for unseen herbs. The framework was evaluated across 698 herbs and 7854 targets, benchmarking against advanced GNNs (HAN) and non-graph classifiers (XGBoost) under strict cold-start and knowledge erosion simulations. Results: NovelHTI maintains high precision (>$84\%$) and balanced performance (F1-score $>77\%$), outperforming baselines by over 33% (ROC-AUC) in realistic imbalanced screening, where traditional models typically fail (AUC ≈ 0.51). Robustness analysis confirmed stable performance (>$0.83$ AUC) despite 30% structural data incompleteness. Notably, retrospective validation successfully “rediscovered” emerging mechanisms (e.g., the Artemisinin-GPX4 ferroptosis axis) elucidated in 2021–2024 literature, which were entirely latent in the training data. Conclusions: NovelHTI provides a robust computational prioritization filter that effectively bridges macroscopic phenotypes and microscopic pharmacology. By enabling mechanism-driven target de-risking, this framework optimizes resource allocation for downstream experimental validation and accelerates TCM-based drug discovery. Full article

Background/Objectives: Lung cancer (LC) is the leading cause of cancer-related mortality worldwide, making early and accurate diagnosis crucial for improving patient outcomes. Although chest computed tomography (CT) enables detailed assessment of lung abnormalities, manual interpretation is time-consuming, requires expert expertise, and is prone to diagnostic variability. To address these challenges, this study proposes DE-SAMNet, a hybrid deep learning framework for automated multi-class LC classification from CT scans. Methods: The model integrates two pre-trained convolutional neural networks—DenseNet121 and EfficientNetB0—operating in parallel to extract complementary multi-scale features. A Spatial Attention Module (SAM) is applied to each feature stream to emphasize clinically important regions. Final classification is performed through a compact fusion mechanism involving global average pooling, batch normalization, and a fully connected layer. DE-SAMNet was evaluated on two datasets: a public dataset (IQ-OTH/NCCD) with benign, malignant, and normal cases, and a private clinical dataset including benign, malignant, cystic, and healthy cases. Results: On the public dataset, the model achieved a 99.00% F1-score, 98.41% recall, 99.64% precision, and 99.54% accuracy. On the private dataset, it obtained 95.96% accuracy, 95.99% precision, 96.04% F1-score, and 96.21% recall, outperforming existing approaches. To enhance reliability, explainable AI (XAI) techniques such as Grad-CAM were used to visualize the model’s decision rationale. The resulting heatmaps effectively highlight lesion-specific regions, offering transparency and supporting clinical interpretability. Conclusions: This explainability strengthens trust in automated predictions and demonstrates the clinical potential of the proposed system. Overall, DE-SAMNet delivers a highly accurate and interpretable solution for early LC detection. Full article