Search Results (2,709)

Postoperative facial scars frequently remain aesthetically problematic despite advances in laser-based treatments, as residual inflammation and disorganized dermal remodeling often limit clinical outcomes. Exosome-based formulations have gained attention as biologically active adjuncts capable of influencing key wound-healing pathways, including inflammatory regulation, neovascularization, and extracellular matrix modulation. This randomized, controlled clinical study aimed to evaluate the short-term clinical effect of laser-assisted delivery of exosome skin boosters for postoperative facial scars and facial rejuvenation. Seventy-five patients with postoperative facial scars were randomly allocated to receive fractional non-ablative Nd:YAG laser treatment alone or in combination with either human-derived or plant-derived exosome skin boosters. All participants completed five treatment sessions at two-week intervals. Clinical outcomes were evaluated using validated scar assessment tools, including the modified Vancouver Scar Scale and the Patient and Observer Scar Assessment Scale, along with objective imaging analyses using Mark-Vu and ImageJ software. Compared with laser monotherapy, adjunctive exosome treatment was associated with numerically greater short-term improvements in scar appearance and reductions in grayscale intensity. Improvements in additional skin quality parameters, such as pigmentation uniformity, erythema, pore size, and fine wrinkles, were also observed in the exosome-treated groups. Clinical responses were comparable between human- and plant-derived exosome formulations, and no serious adverse events were reported. These findings indicate that exosome-based skin boosters may serve as a safe and well-tolerated biological complement to laser therapy for short-term improvement of postoperative facial scars and skin quality. Larger studies with longer follow-up are warranted to determine long-term efficacy and clinical durability. Full article

Conventional transformer protection schemes are limited by the difficulty in distinguishing inrush currents from internal and external faults, which restricts operational accuracy to below 70%. Existing solutions are constrained by a trade-off: sensitivity is compromised when setting values are increased, while speed is sacrificed when time delays are introduced. To address these limitations, a novel deep learning-based method for transformer fault identification is proposed. First, a feature model is constructed utilizing the time-domain sum of voltage and current fault components alongside current polarity characteristics. Subsequently, a channel attention-based Capsule Network (SE-CapsuleNet) is employed to automatically extract deep features across normal operation, inrush currents, and fault types. Simulation results demonstrate that inrush conditions are accurately differentiated from fault states. Robustness is maintained under high fault resistance (400 Ω) and 20 dB noise interference, while immunity to current transformer (CT) saturation and core residual magnetism is exhibited. The proposed protection relay simultaneously meets the requirements of rapid response, high sensitivity, and safety stability. Full article

Microbial components are part of the composition of all waste, including lignocellulosic biomass (e.g., agricultural, domestic, industrial, and municipal wastes) generated via human activities. If little attention is given to these wastes or if they are not adequately managed, they tend to end up in the environment (soil, water, and farmland), decomposing naturally through microbial activities, producing greenhouse gases, causing eutrophication, preventing sunlight penetration, and depleting oxygen in the water. Several treatment methods are applicable to these wastes. However, anaerobic digestion is presented as the best option to properly treat the waste. It is regarded as the best technique to achieve sustainable energy development in both developing and developed countries. During anaerobic digestion, the organic matter in the waste is converted via the concerted activities of microbes belonging to different trophic levels, in the absence of oxygen, to yield biogas (renewable energy), bio-fertiliser, and sanitisation of the waste, rendering it better and safer for human handling. Varying levels of loss of bacterial viability and their antibiotic-resistance genes are observed with this process, as bacteria differ in susceptibility to temperature, pH, nutrient scarcity, and the presence of antimicrobials. Anaerobic digestion of agricultural residues and the immediate processing (post-treatment) of the digestate help to stabilise the digestate, making it safe for land applications, tackling waste management, and protecting food chains from contamination, in addition to the environment. This review focuses on the anaerobic digestion of lignocellulosic biomass, yielding biogas as energy, alongside sanitising the wastes by inactivating microbial components found therein, therefore reducing the contamination potential of the effluent or digestate discharged from the biodigester following the process. Several findings registered by different researchers through different studies performed in different countries under different scenarios while employing varying methods have been assembled in a chronological fashion to emphasise similarities and divergences or variations that deepen knowledge pertaining to the significance of the anaerobic digestion process in terms of the microbial interactions responsible for producing energy, addressing sanitisation and hygiene crisis, and the post-treatment of the digestate to ensure its use as biofertiliser. In other words, it is a comprehensive review that synthesises knowledge from multiple fields covering comparative aspects of anaerobic digestion in terms of sanitation, hygiene, and energy production and consolidates it in a single document to present and address the problem of waste management through anaerobic digestion technology. Full article

The rapid increase in organic waste generation poses significant environmental challenges and highlights the limitations of conventional waste management practices. In this context, black soldier fly (Hermetia illucens) larvae (BSFL) have emerged as a promising biological tool for valorizing organic residues within circular bioeconomy frameworks. This review provides an integrated analysis of BSFL-based bioconversion systems, focusing on the biological characteristics of BSFL, suitable organic waste streams, and the key process parameters influencing waste reduction efficiency, larval biomass production, and frass (the residual material from larval bioconversion) yield. The performance of BSFL in converting organic waste is assessed with emphasis on substrate characteristics, environmental conditions, larval density, and harvesting strategies. Environmental and economic implications are discussed in comparison with conventional treatments such as landfilling, composting, and anaerobic digestion. Special attention is given to the nutritional composition of BSFL and the valorization of larvae as sustainable protein and lipid sources for animal feed and emerging human food applications, while frass is highlighted as a nutrient-rich organic fertilizer and soil amendment. Finally, current challenges related to scalability, safety, regulation, and social acceptance are highlighted. By linking waste management, resource recovery, and sustainable protein production, this review clarifies the role of BSFL and frass in resilient and resource-efficient food and waste management systems. Full article

To reduce reliability testing time, experiments are often terminated at a predetermined time, producing right-censored lifetime data. Alternatively, when test samples are inspected at fixed intervals, failures are only observed within these intervals, resulting in interval-censored lifetime data. Although quality control methods for right-censored data are well established, relatively little attention has been given to interval-censored observations. Motivated by the success of residual control charts based on convolutional neural network (CNN) for right-censored data, this study extends the chart for monitoring normally distributed interval-censored lifetime data. Simulation results based on average run length (ARL) indicate that the proposed method outperforms the traditional exponentially weighted moving average (EWMA) chart in detecting decreases in mean lifetime. The findings also highlight the practical benefits of employing high- or low-order autoregressive CNN models depending on the magnitude of process shifts. Full article

Disulfiram (DSF) is a well-established inhibitor of aldehyde dehydrogenases (ALDHs) and an FDA-approved drug for chronic alcoholism. DSF has gained attention as a versatile scaffold for drug repurposing. Its metabolite, diethyldithiocarbamate (DDTC), mediates multiple biological effects via metal chelation and covalent modification of key cysteine residues. Beyond its established anticancer properties, DSF modulates cancer stem cells, reactive oxygen species, proteasome function, and drug-resistance pathways. It also shows promise in metabolic disorders, including type 2 diabetes and obesity, by targeting enzymes such as fructose-1,6-bisphosphatase and α-glucosidase, and influences energy expenditure and autophagy. DSF exhibits antimicrobial and antiparasitic activity, enhances antibiotic efficacy against multidrug-resistant bacteria, and demonstrates antischistosomal and anti-Trichomonas effects, while also providing radioprotective benefits. The clinical translation of DSF is limited by poor solubility, rapid metabolism, and off-target effects; consequently, the development of DSF analogs has become a major focus. Structural optimization has yielded derivatives with improved selectivity, stability, solubility, and target specificity, enabling precise modulation of key enzymes while reducing adverse effects. A key structure-based strategy involves introducing bulkier substituents to exploit differences in ALDH active-site architecture and achieve target selectivity. This concept is exemplified by compounds (1) and (2), in which bulky substituents confer selective inhibition of ALDH1A1 while sparing ALDH2. This review provides a comprehensive overview of DSF analogs, their molecular mechanisms, and therapeutic potential, highlighting their promise as multifunctional agents for cancer, metabolic disorders, infectious diseases, and radioprotection. Full article

Precise control of feeding rates is critically important in intensive shrimp farming for cost reduction, optimization of farming strategies, and protection of the aquatic environment. However, current assessment of residual feed in feeding trays relies predominantly on manual visual inspection, which is inefficient, highly subjective, and difficult to standardize. The residual feed particles typically exhibit characteristics such as small size, high density, irregular shapes, and mutual occlusion, posing significant challenges for automated visual detection. To address these issues, this study proposes a lightweight detection model named YOLO-Shrimp. To enhance the network’s capability in extracting features from small and dense targets, a novel attention mechanism termed EnSimAM is designed. Building upon the SimAM structure, EnSimAM incorporates local variance and edge response to achieve multi-scale feature perception. Furthermore, to improve localization accuracy for small objects, an enhanced weighted intersection over union loss function, EnWIoU, is introduced. Additionally, the lightweight RepGhost module is adopted as the backbone of the model, significantly reducing both the number of parameters and computational complexity while maintaining detection accuracy. Evaluated on a real-world aquaculture dataset containing 3461 images, YOLO-Shrimp achieves mAP@0.5 and mAP@0.5:0.95 scores of 70.01% and 28.01%, respectively, while reducing the parameter count by 19.7% and GFLOPs by 14.6% compared to the baseline model. Full article

Current agricultural irrigation management practices are often extensive, and traditional soil moisture content (SMC) monitoring methods are inefficient, so there is a pressing need for innovative approaches in precision irrigation. This study proposes a Multi-Feature Fusion Network (MFF-Net) for SMC inversion. The model uses a designed Channel-Changeable Residual Block (ResBlockCC) to construct a multi-branch feature extraction and fusion architecture. Integrating the Channel Squeeze and Spatial Excitation (sSE) attention module with U-Net-like skip connections, MFF-Net inverts root-zone SMC from summer maize leaf images. Field experiments were conducted in Zhengzhou, Henan Province, China, from 2024 to 2025, under three irrigation treatments: 60–70% θ_fc, 70–90% θ_fc, and 60–90% θ_fc (θ_fc denotes field capacity). This study shows that (1) MFF-Net achieved its smallest inversion error under the 60–70% θ_fc treatment, suggesting the inversion was most effective when SMC variation was small and relatively low; (2) MFF-Net demonstrated superior performance to several benchmark models, achieving an R² of 0.84; and (3) the ablation study confirmed that each feature branch and the sSE attention module contributed positively to model performance. MFF-Net thus offers a technological reference for real-time precision irrigation and shows promise for field SMC inversion in summer maize. Full article

Prostate cancer is diagnosed through a comprehensive study of histopathology slides, which takes time and requires professional interpretation. To minimize this load, we developed a semi-supervised learning technique that combines transformer-based representation learning and a custom TransUNet classifier. To capture a wide range of morphological structures without manual annotation, our method pretrains DINOv2 on 10,000 unlabeled prostate tissue patches. After receiving the transformer-derived features, a bespoke CNN-based decoder uses residual upsampling and carefully constructed skip connections to merge data from many spatial scales. Expert pathologists identified only 20% of the patches in the whole dataset; the remaining unlabeled samples were contributed by using a consistency-driven learning method that promoted reliable predictions across various augmentations. The model received precision and recall scores of 91.81% and 89.02%, respectively, and an accuracy of 93.78% on an additional test set. These results exceed the performance of a conventional U-Net and a baseline encoder–decoder network. All things considered, the localized CNN (Convolutional Neural Network) decoding and global transformer attention provide a reliable method for prostate cancer classification in situations with little annotated data. Full article

Reliable monitoring of pesticide residues is essential for ensuring food safety. Conventional chromatographic and spectrometric techniques remain labor-intensive, time-consuming, and costly. Recent progress in Machine Learning (ML) provides computational tools that improve the precision and efficiency of pesticide residue detection in diverse food matrices. This review presents a comprehensive analysis of current ML-based approaches for pesticide analysis, with particular attention to supervised learning algorithms such as support vector machines, random forests, boosting methods, and deep neural networks. These models have been integrated with chromatographic, spectroscopic, and electrochemical platforms to achieve enhanced signal interpretation and more reliable prediction from existing analytical data, and more robust data processing in complex food systems. The review also discusses methodologies for feature extraction, model validation, and the management of heterogeneous datasets, while examining ongoing challenges that include limited training data, matrix variability, and regulatory constraints. Emerging advances in deep learning architectures, transfer learning strategies, and portable sensing technologies are expected to support the development of real-time, field-ready monitoring systems. The findings highlight the potential of ML to advance food quality assurance and strengthen public health protection through more efficient and accurate pesticide residue detection. Full article

The time-scale mismatch between rapid influent fluctuations and slow biochemical responses hinders the stability of wastewater treatment plants (WWTPs). Existing models often fail to capture shock signals due to noise interference (“signal pollution”). To address this, we propose the HD-MAED-LSTM model, which employs a “decompose-and-conquer” strategy. Targeting the dynamic characteristics of different components, this study innovatively designs heterogeneous attention mechanisms: utilizing Long-term Dependency Attention to capture the global evolution of the trend component, employing Multi-scale Periodic Attention to reinforce the cyclic patterns of the seasonal component, and using Gated Anomaly Attention to keenly capture sudden shocks in the residual component. In a case study, the effectiveness of the proposed model was validated based on one year of operational data from a large-scale industrial WWTP. HD-MAED-LSTM outperformed baseline models such as Transformer and LSTM in the medium-to-long-term (10-h) prediction of COD, TN, and TP, clearly demonstrating the positive role of differentiated modeling. Notably, in the core task of shock load early warning, the model achieved an F1-Score of 0.83 (superior to Transformer’s 0.77 and LSTM’s 0.67), and a Mean Directional Accuracy (MDA) as high as 0.93. Ablation studies confirm that the specialized attention mechanism is the key performance driver, reducing the Mean Absolute Error (MAE) by 56.7%. This framework provides precise support for shifting WWTPs from passive response to proactive control. Full article

Accurately identifying drug off-targets is essential for reducing toxicity and improving the success rate of pharmaceutical discovery pipelines. However, current deep learning approaches often struggle to fuse chemical structure, protein biology, and multi-target context. Here, we introduce HDPC-LGT (Hybrid Dual-Prompt Cross-Attention Ligand–Protein Graph Transformer), a framework designed to predict ligand binding across sixteen human translation-related proteins clinically associated with antibiotic toxicity. HDPC-LGT combines graph-based chemical reasoning with protein language model embeddings and structural priors to capture biologically meaningful ligand–protein interactions. The model was trained on 216,482 experimentally validated ligand–protein pairs from the Chemical Database of Bioactive Molecules (ChEMBL) and the Protein–Ligand Binding Database (BindingDB) and evaluated using scaffold-level, protein-level, and combined holdout strategies. HDPC-LGT achieves a macro receiver operating characteristic–area under the curve (macro ROC–AUC) of 0.996 and a micro F1-score (micro F1) of 0.989, outperforming Deep Drug–Target Affinity Model (DeepDTA), Graph-based Drug–Target Affinity Model (GraphDTA), Molecule–Protein Interaction Transformer (MolTrans), Cross-Attention Transformer for Drug–Target Interaction (CAT–DTI), and Heterogeneous Graph Transformer for Drug–Target Affinity (HGT–DTA) by 3–7%. External validation using the Papyrus universal bioactivity resource (Papyrus), the Protein Data Bank binding subset (PDBbind), and the benchmark Yamanishi dataset confirms strong generalisation to unseen chemotypes and proteins. HDPC-LGT also provides biologically interpretable outputs: cross-attention maps, Integrated Gradients (IG), and Gradient-weighted Class Activation Mapping (Grad-CAM) highlight catalytic residues in aminoacyl-tRNA synthetases (aaRSs), ribosomal tunnel regions, and pharmacophoric interaction patterns, aligning with known biochemical mechanisms. By integrating multimodal biochemical information with deep learning, HDPC-LGT offers a practical tool for off-target toxicity prediction, structure-based lead optimisation, and polypharmacology research, with potential applications in antibiotic development, safety profiling, and rational compound redesign. Full article

Coalbed methane, an abundant clean energy resource in China, is gaining significant attention. Electric submersible progressive cavity pumps, ideal for downhole extraction with high solids content, are vital in coalbed methane operations. Current fault diagnosis research for these pumps mainly relies on machine learning algorithms to identify fault features, but complex working conditions and imbalanced sample distributions challenge these models’ ability to perceive multi-scale and multi-dimensional features. To enhance the model’s perception of deep abnormal data in complex multi-case industrial datasets, this study proposes a deep learning model based on a multi-scale extraction and residual module convolutional neural network. Innovatively, a cross-attention module using global autocorrelation and local cross-correlation is introduced to constrain the multi-scale feature extraction process, making the model better suited to specific and differentiated data environments. Post feature extraction, the model employs Borderline-SMOTE to augment minority class samples and uses Tomek Links for noise removal. These enhancements improve the comprehensive perception of fault types with significant differences in period, amplitude, and dimension, as well as the learning capability for rare faults. Based on field-collected fault data and using enhanced and cleaned features for classifier training, tests on a real industrial dataset show the proposed model achieves an F1 Measure of 90.7%—an improvement of 13.38% over the unimproved model and 9.15–31.64% over other common fault diagnosis models. Experimental results confirm the method’s effectiveness in adapting to extremely imbalanced sample distributions and complex, variable field data characteristics. Full article

Magnetic resonance imaging (MRI) super-resolution (SR) enables high-resolution reconstruction from low-resolution acquisitions, reducing scan time and easing hardware demands. However, most deep learning-based SR models are large and computationally heavy, limiting deployment in clinical workstations, real-time pipelines, and resource-restricted platforms such as low-field and portable MRI. We introduce CHARMS, a lightweight convolutional–Transformer hybrid with attention regularization optimized for MRI SR. CHARMS employs a Reverse Residual Attention Fusion backbone for hierarchical local feature extraction, Pixel–Channel and Enhanced Spatial Attention for fine-grained feature calibration, and a Multi-Depthwise Dilated Transformer Attention block for efficient long-range dependency modeling. Novel attention regularization suppresses redundant activations, stabilizes training, and enhances generalization across contrasts and field strengths. Across IXI, Human Connectome Project Young Adult, and paired 3T/7T datasets, CHARMS (~1.9M parameters; ~30 GFLOPs for 256 × 256) surpasses leading lightweight and hybrid baselines (EDSR, PAN, W2AMSN-S, and FMEN) by 0.1–0.6 dB PSNR and up to 1% SSIM at ×2/×4 upscaling, while reducing inference time ~40%. Cross-field fine-tuning yields 7T-like reconstructions from 3T inputs with ~6 dB PSNR and 0.12 SSIM gains over native 3T. With near-real-time performance (~11 ms/slice, ~1.6–1.9 s per 3D volume on RTX 4090), CHARMS offers a compelling fidelity–efficiency balance for clinical workflows, accelerated protocols, and portable MRI. Full article

To address the scarcity of sub-meter remote sensing samples and structural inconsistencies such as edge blur and contour distortion in super-resolution reconstruction, this paper proposes SRCT, a super-resolution method tailored for sub-meter remote sensing imagery. The method consists of two parts: external structure guidance and internal structure optimization. External structure guidance is jointly realized by the structure encoder (SE) and structure guidance module (SGM): the SE extracts key structural features from high-resolution images, and the SGM injects these structural features into the super-resolution network layer by layer, achieving structural transfer from external priors to the reconstruction network. Internal structure optimization is handled by the backbone network SGCT, which introduces a dual-branch residual dense group (DBRDG): one branch uses window-based multi-head self-attention to model global geometric structures, and the other branch uses lightweight convolutions to model local texture features, enabling the network to adaptively balance structure and texture reconstruction internally. Experimental results show that SRCT clearly outperforms existing methods on structure-related metrics, with DISTS reduced by 8.7% and LPIPS reduced by 7.2%, and significantly improves reconstruction quality in structure-sensitive regions such as building contours and road continuity, providing a new technical route for sub-meter remote sensing image super-resolution reconstruction. Full article