Search Results (6,017)

Background: Accurate brain tumor segmentation from Magnetic Resonance Imaging (MRI) depends on the fusion of multiple complementary modalities. However, clinical practice often faces incomplete modality sets due to acquisition failures, patient contraindications, or protocol variations. Current methods either treat each modality feature extractor in isolation or depend on computationally expensive teacher networks for cross-modal knowledge transfer. Objective: This paper presents Hierarchical Adaptive Group Self-Support Learning with Boundary-Aware Calibration (HAGSS), a framework that overcomes three key limitations of existing group self-support methods: static group formation that ignores temporal prediction quality, uniform treatment of boundary and interior voxels, and distribution mismatch across heterogeneous modality logits. Methods: We propose a hierarchical adaptive group formation mechanism that reassigns group leader roles at each epoch based on voxel-level prediction confidence scores instead of fixed sensitivity priors. We also introduce a boundary-aware calibration module that applies spatially varied distillation weights with greater emphasis on tumor boundary regions. In addition, we design a cross-scale consistency regularization term that enforces agreement between multi-resolution predictions to stabilize the self-support target. Results: Experiments on BraTS2020, BraTS2018, and BraTS2021 datasets show that HAGSS achieves consistent improvements over state-of-the-art baselines. The average Dice gains across the whole tumor, tumor core, and enhancing tumor regions reach 1.30% on BraTS2020 and 1.61% on BraTS2021 compared to existing methods. All improvements are statistically significant ($p<0.05$). Conclusions: HAGSS operates exclusively during training, adds no parameters or inference cost, and can be applied as a plug-in module to any multi-encoder incomplete multi-modal segmentation architecture. Code is publicly available at GitHub. Full article

Modulation and signal classification are two highly correlated core tasks in wireless communications and are the core foundation of intelligent spectrum management in Future Internet and 6G networks. Although their objectives differ, the two tasks often share a substantial amount of underlying information in the feature space. However, focusing solely on their commonalities while neglecting their intrinsic differences may lead to suboptimal model performance. Therefore, by taking into account both the correlation and inherent differences between the two tasks, we propose TAMTNet, a task-adaptive multi-task network for edge deployment in Future Internet. TAMTNet introduces Extremely Efficient Spatial Pyramid (EESP) into the shared layer to efficiently extract multi-scale temporal information. In addition, a multi-gate mixture-of-experts (MMoE) mechanism is employed after the shared layer to enhance the modeling capability of task-specific features. Furthermore, to address the difficulty of deploying deep models on resource-constrained edge devices, a joint lightweight framework combining quantization-aware training and knowledge distillation is proposed, which significantly reduces model complexity while maintaining performance. Extensive experiments conducted on the simulation and real-world over-the-air transmission datasets demonstrate that the TAMTNet model achieves excellent performance on both modulation and signal classification tasks across a wide range of signal-to-noise ratios and radio transmit gain conditions. Meanwhile, the low-bitwidth lightweight models are able to maintain classification performance comparable to the full-precision model while significantly reducing model storage and computational complexity. Full article

Modeling historically situated gender ideology remains challenging for language models, as contemporary embeddings struggle to reflect temporally specific semantic structures beyond surface lexical patterns. Although large language models exhibit extensive general-purpose performance, their direct use with history-specific semantic analysis is limited by the distributional mismatch between contemporary training data and historical linguistic patterns. These constraints encourage the distillation of temporally based semantic knowledge into small student architectures. To solve this issue, we propose Temporally Informed Distillation of Embedding Semantics (TIDES), which integrates continued pretraining on temporally specific corpora with feature-level distillation from large embedding teachers. We evaluate TIDES across teacher architectures with distinct pretraining objectives. While continued pretraining provides lexical and syntactic adaptation, our results show that improvements in ideological modeling cannot be attributed to additional training exposure alone. Rather, teacher–student structural alignment is also critical to transfer effectiveness. Contrastive, encoder-aligned teachers yield substantially more stable preservation of fine-grained, historically situated semantic distinctions. These findings suggest that temporal ideology transfer is representation-dependent: ideological meaning can be shaped by the geometry and training objectives of embedding spaces. By introducing TIDES and providing evidence that architectural compatibility can influence ideological inheritance, this study advances a representation-centered account of modeling ideology in temporally grounded semantic research. Full article

Tomato (Solanum lycopersicum) cultivation is highly vulnerable to fungal, bacterial, and viral leaf diseases that can significantly reduce crop yield and fruit quality when not detected at early stages. Although recent deep learning approaches have achieved remarkable performance in plant disease classification, many state-of-the-art architectures remain computationally expensive and therefore difficult to deploy on resource-constrained edge devices commonly used in smart agriculture environments. To address this challenge, this paper introduces EcoTomHybridNet, an adaptive resource-aware CNN–Transformer framework designed for efficient tomato leaf disease classification under edge-computing constraints. The proposed architecture combines a lightweight convolutional backbone with a dual-branch inference mechanism composed of a fast convolutional branch for computationally efficient prediction and a Transformer-enhanced branch with local self-attention for richer contextual feature extraction. Unlike conventional lightweight hybrid models relying on static inference pipelines, EcoTomHybridNet integrates a lightweight policy-guided routing mechanism that dynamically allocates inputs between the fast convolutional branch and the Transformer-enhanced branch according to input complexity. This adaptive inference strategy dynamically reduces unnecessary Transformer computations for simpler samples while preserving strong predictive performance on more challenging inputs through policy-guided branch allocation. To further improve representation capability without significantly increasing computational complexity, the proposed student network is trained using knowledge distillation from a ViT-Tiny teacher model. Experimental results on the PlantVillage tomato dataset demonstrate that EcoTomHybridNet achieves 99.42% test accuracy and 99.0% validation accuracy under the full hybrid inference configuration. Additional validation strategies, including 5-fold cross-validation and robustness evaluation under Gaussian noise and motion blur perturbations, indicate stable performance across different data splits and moderate image degradations, suggesting improved generalization capability beyond simple dataset memorization. Furthermore, adaptive routing experiments using a lightweight threshold-based policy mechanism achieved 99.20% test accuracy while reducing computational complexity from 0.36 GFLOPs to 0.25 GFLOPs per image, corresponding to approximately 30% computational savings. These results demonstrate the effectiveness of policy-guided adaptive inference for balancing predictive performance and computational efficiency in edge-oriented plant disease classification. Overall, EcoTomHybridNet provides an efficient and adaptive framework for intelligent plant disease monitoring in IoT-enabled smart agriculture systems. Full article

The systematic influence of signal electrode width on electro-optic bandwidth and insertion loss in L-type traveling-wave lithium niobate modulators has not yet been comprehensively quantified, limiting the parametric engineering design of this device configuration. This study presents a full-band systematic simulation sweep of signal electrode width and three auxiliary geometric parameters in an L-type traveling-wave lithium niobate Mach–Zehnder modulator, combined with optical mode simulation to establish joint microwave–optical optimization constraints. The study reveals the coupled modulating effect of signal electrode width on characteristic impedance, velocity mismatch, and transmission loss; it elucidates the competition mechanism underlying non-monotonic high-frequency loss behavior; and it identifies the complete impedance-neutral characteristic of the electrode–waveguide contact width as an independent loss-tuning degree of freedom decoupled from the impedance constraint. Full-system validation confirms that the final design simultaneously satisfies broadband impedance matching, low insertion loss, and high electro-optic bandwidth. The results are distilled into four quantitative design rules that provide simulation-driven guidance directly applicable to the engineering design of L-type thin-film lithium niobate modulators, advancing the systematic establishment of a parametric design methodology for this device configuration. Full article

Currently, there is a lack of sufficient research regarding spirituality in the lives of young people in youth care contexts. In this study, youth care refers to various forms of either voluntary or mandatory support and care for young people (children and teenagers) and their educators for growing-up problems, parenting problems, and psychological, psychosocial, and behavioral problems or intellectual disabilities. The available research is not systematically gathered in an overview. Against this background, this article presents a systematic literature review based on the following main research question: What insights can be distilled from scholarly peer-reviewed journal articles published from January 2000–July 2025 regarding spiritual formation in youth care? The results of the review study were based on 41 journal articles. Half of these articles thematize the foster care context. The other articles are spread over other youth care contexts, including psychiatric care, child and youth welfare, residential care, social work, and services for unaccompanied minors. Most of the articles presented empirical research. Three major themes can be defined that connect most articles: (a) the discussion of religion and spirituality as naturally present in the lives of children and the need or right to recognize that dimension and to facilitate continuity in it; (b) the question or the hypothesis that religion and spirituality can promote well-being, including the finding that this does not always appear unambiguous, up to and including attention to the harmful effects of religion and spirituality; and (c) the question of whether and how religion and spirituality can be used more instrumentally in youth care services to provide the best possible care to young people. The article discusses these findings, and recommendations for youth care professionals and follow-up research are presented. Full article

Fatty alcohols and aliphatic hydrocarbons occur abundantly in nature and serve as critical feedstocks for the surfactant and fuel industries, respectively. However, their industrial-scale separation and purification are significantly hampered by high boiling points and the formation of complex azeotropes. To address these challenges, this study explores a five-column high-vacuum pressure-swing distillation (HVPSD-5C) strategy. Vapor–liquid equilibrium (VLE) analysis of the key components (n-hexanol, n-octanol, n-dodecane, and n-tridecane) validated the thermodynamic viability of the process and established optimal operating conditions. To further enhance efficiency, a heat-pump-integrated configuration (HPI-HVPSD-5C) featuring vapor recompression and heat integration was designed, optimized, and evaluated. Comparison with the baseline HVPSD-5C process demonstrates that the HPI-HVPSD-5C configuration significantly improves sustainability and economics, reducing the total annual cost (TAC) by 17.48%, CO₂ emissions by 16.09%, and energy consumption cost by 12.79%. These findings provide a robust framework for the efficient separation of fatty alcohols from aliphatic hydrocarbons, offering a valuable reference for the purification of other pressure-sensitive azeotropic mixtures. Full article

In recent years, accelerated industrialization has made water pollution a major challenge, bisphenol pollutants being one of the most typical examples. Advanced oxidation processes (AOPs) based on peroxymonosulfate (PMS) activation have been applied in environmental remediation due to their broad applicability and high pollutant removal efficiency. The key to AOPs lies in developing low-cost, highly active catalysts. This study utilized waste biomass of baijiu distillers’ grains (DSGs) as precursor to prepare biochar materials for bisphenol pollutant removal. Through high-temperature pyrolysis at 900 °C for 2 h in the presence of NaCl and KCl as activator, biochar-based materials (BC-x) were prepared, which possessed advantageous features of large specific surface area and high nitrogen doping content. When applied for typical bisphenol pollutant removal, the selected BC-900 biochar exhibited almost 100% bisphenol AF (BPAF) removal efficiency after a 30 min adsorption and following a 5 min PMS activation process under reaction conditions of 200 mg L⁻¹ of BC-900, 200 mg L⁻¹ of PMS, and 20 mg L⁻¹ of BPAF. Reactive species of sulfate radicals (SO₄⦁⁻), hydroxyl radicals (⦁OH) and singlet oxygen (¹O₂) were responsible for BPAF degradation, among which ¹O₂ played the major role. Further toxicity prediction of the BPAF degradation intermediate products implied the low ecological risk of the constructed BC-900/PMS catalytic system for BPAF removal. The findings in this study may provide useful guidance for waste biomass conversion and organic contamination remediation in water. Full article

To address the pronounced degradation in detection accuracy of encrypted traffic intrusion models after incremental updates, which is primarily caused by catastrophic forgetting and task-level overfitting during incremental learning, this paper proposes a novel incremental intrusion detection method for encrypted traffic based on dual augmentation and fusion knowledge distillation, termed IID-DAKD. First, both known and previously unseen attacks identified during detection are leveraged to update a representative sample set. An encrypted traffic representative sample augmentation strategy based on Gaussian noise is then devised to reduce storage requirements and classifier bias, thereby effectively mitigating catastrophic forgetting. Second, a self-supervised learning framework driven by encrypted traffic class augmentation is constructed to alleviate representation bias and suppress task-level overfitting. Finally, three complementary knowledge distillation strategies are jointly employed to extract and transfer attack classification knowledge from the old model to the updated model, further improving detection accuracy and robustness while enhancing training efficiency. Extensive experimental results demonstrate that the proposed IID-DAKD approach alleviates catastrophic forgetting and task-level overfitting while maintaining symmetrical knowledge transfer during incremental learning, enabling efficient model updates and high detection accuracy for encrypted traffic intrusion detection. Full article

Backround: The aim of this study was to evaluate the effects of probiotic yogurt containing Lactobacillus rhamnosus (LGG) on the microhardness and surface roughness of restorative dental materials commonly used in pediatric dentistry. Methods: Three materials were tested: conventional glass ionomer cement Fuji II, high-viscosity glass ionomer cement Fuji IX, and microhybrid composite resin Te Econom. The samples were prepared according to the manufacturers’ instructions, initially stored in distilled water, and subsequently immersed in probiotic yogurt. Microhardness was measured by the Vickers hardness test, and surface roughness was assessed by 3D profilometers. Results: Statistical analysis was performed using the Wilcoxon signed-rank test and the Kruskal–Wallis test. Exposure to probiotic yogurt resulted in increased microhardness for the resin-modified and high-viscosity glass ionomer cements, whereas the microhardness of the microhybrid composite resin decreased. The surface roughness increased for all the tested materials, with statistically significant differences observed in most groups (p < 0.05). Conclusions: These findings indicate that probiotic yogurt can alter the physical properties of restorative dental materials and highlight the importance of careful selection of preventive agents in pediatric dental practice. Further research is needed to clarify the long-term effects of probiotic preparations on dental restorations. Full article

Membrane distillation (MD) is an attractive complementary technology to conventional desalination systems. Yet commercial uptake remains limited by membrane pore wetting, temperature polarisation, and material trade-offs. This review critically examines polymeric membranes and demonstrates that reported performance gains cannot be attributed to individual polymers or fillers alone, but rather to optimised structure–property interactions governing wetting resistance, mass transfer, and mechanical integrity. Through a comparative analysis of benchmark metrics (water flux, contact angle, and liquid entry pressure), we identify recurring failure mechanisms, including nanoparticle agglomeration, coating instability, and hydrophobicity-driven compromises in liquid entry pressure and durability. Moving beyond a descriptive summary of materials, this review introduces a predictive structure–property–performance framework that systematically links dominant operational limitations and targeted modification strategies. The analysis reveals that surface-localised, adhesion-controlled modifications outperform bulk approaches by preserving pore architecture while mitigating fouling and wetting risks. Key research priorities include validation under high-salinity conditions relevant to brine management, standardised environmental and leaching assessments of nanomaterials, scalable fabrication protocols supported by techno-economic considerations, and developments on bioinspired materials. By shifting focus from material novelty toward rational design principles, this review establishes actionable selection criteria to accelerate the translation of MD membranes from laboratory concepts to industrially viable desalination technologies. Full article

Early-exit cascades over a frozen convolutional backbone enable adaptive inference but suffer from three sources of train–inference mismatch: branches train on samples they will never see at inference; their per-class precision thresholds are calibrated on the wrong distribution; the standard cross-entropy target on backbone argmax labels discards the backbone’s uncertainty signal. We close all three gaps with CalexNet (cascade-aligned early exits), a training-recipe-only modification: branches train under continuously weighted importance sampling that matches the cascade-survivor distribution; per-class precision thresholds are calibrated on the actual cascade-survivor subset of the validation set; the classification head is trained against the backbone’s full softmax via a temperature-scaled KL objective. Combined with an augmented prototype-pooling branch head, CalexNet is evaluated on ResNet18 and ResNet50 backbones across CIFAR-100 (20-supe-class coarse, the harder primary setting) and CINIC-10 (10-class, the easier cross-validation counterpart). On the accuracy–FLOPs Pareto frontier, CalexNet matches or exceeds three published baselines (PTEEnet, ZTW, BoostNet) and a within-paper “no-alignment, no-KD” reference. The largest gains appear in the practically relevant 30–70% FLOPs-reduction regime and show consistent trends across $n=3$ training seeds. CalexNet requires no inference-time architectural change and is a drop-in for any frozen-backbone early-exit cascade. Full article

This study explores the use of metallurgical slag extracts as a liquid mineral fertilizer for maize cultivation. Slag samples were obtained from the former lead smelter in Shymkent and the Zhezkent Mining and Processing Plant. Elemental analysis identified the slag from the second storage area of the Shymkent smelter as the least contaminated with potentially toxic elements and enriched in macro- and micronutrients. Slag extraction was conducted via chemical leaching using potassium sulfate and ammonia solutions in a hydrogen peroxide medium, yielding Cu²⁺ and Zn²⁺ concentrations of 423.751 mg/L and 86.649 mg/L, respectively. The resulting extracts were diluted with distilled water at a ratio of 1:10 (potassium sulfate extract) and 1:200 (ammonia extract) and applied to assess early seed development and subsequent maize yield. Seed germination rates were comparable to those of the control group (100%). After 90 days of growth, maize plants treated with the ammonia-based extract showed positive effects on root system development, stem growth, and cob formation. The concentration of potentially toxic elements in the dry plant biomass remained within permissible limits. These findings demonstrate the potential for the safe agricultural use of these extracts while ensuring the rational utilization of industrial waste. Full article

Automated optical coherence tomography (OCT) diagnosis is clinically important for retinal disease assessment, but image-level classification can be limited by class imbalance and localized pathological patterns. Standard image-level classifiers may compress small pathological regions together with large areas of normal retinal tissue, reducing the contribution of subtle structural biomarkers during global pooling. To address this limitation, we propose Saliency Mask-Guided Local-context Consistency (SMGLC), a training framework that uses saliency maps from a frozen teacher proxy to extract lesion-focused local crops and align their feature representations with the corresponding whole-scan representations. This consistency objective encourages a lightweight student backbone to preserve local pathological cues while retaining a standalone inference pathway. We evaluate SMGLC on OCTDL and OCT2017 against four representative baseline architectures and same-student ablations under a fixed 8:1:1 evaluation protocol. On the OCTDL test split, SMGLC achieves an accuracy of 95.88%, an F1 score of 88.92%, and an AUC of 99.09%. On the OCT2017 test split, it reaches an accuracy of 95.58%, an F1 score of 92.93%, and an AUC of 99.37%. These results show that train-time local-context supervision remains competitive under the revised protocol without requiring an additional saliency branch at inference time. Full article

Ultra-wide field (UWF) fundus image classification is an important part of the entire process of medical screening and decision support. However, the discrimination of various retinal disease classes is difficult due to the similarity between classes, class imbalance, and the indeterminacy of visual patterns. In our research, an explainable neutrosophic knowledge distillation (NKD) model for UWF fundus image classification is proposed. In the proposed model, the teacher model is a ResNet50 architecture that provides the student model with supervisory information that is aware of the indeterminacy of predictions. The proposed model combines the CLAHE-based preprocessing method with the neutrosophic distillation method to enable the student model to learn from the hard labels as well as the teacher model. The experimental results were evaluated using the 5-fold cross-validation method with an additional hold-out evaluation. The experimental results show that the proposed NKD model has a mean accuracy of 84.00%, specificity of 97.33%, precision of 84.99%, recall of 84.00%, and F1-score of 84.02%. The proposed model also has an accuracy of 87.86% with specificity of 97.48% and AUC of 97.48% in the ablation-based full model evaluation. It outperformed classical machine learning baselines based on Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), and LBP + HOG features with Support Vector Machines (SVM) classifiers, as well as the baseline student, fuzzy student, and teacher Convolutional Neural Network (CNN) models. For improved interpretability, the Grad-CAM++ technique was used to analyze the proposed NKD model. This analysis showed that the network attended to relevant retinal regions during classification. These results suggest that the proposed model can be an effective tool for UWF fundus image classification. Full article