Search Results (9,090)

Underground cables are critical infrastructure for electrical power and communication transmission, and their reliable operation is of paramount importance to urban public safety. Although Distributed Acoustic Sensing (DAS) enables wide-range, continuous, and real-time monitoring, traditional DAS signal processing methods suffer from poor intrusion discrimination and weak anti-interference capability. To address these limitations, we propose a dual-branch network based on multi-domain feature fusion, integrating a Multilayer Perceptron, a Long Short-Term Memory network (LSTM), and an attention mechanism. Vibration signals corresponding to four representative high-risk intrusion events were acquired through controlled field experiments, and a standardized, category-balanced dataset was constructed accordingly. Time-domain, frequency-domain and joint time-frequency features were extracted and mapped through a time-frequency weighting transformation to form one branch of the network, while the parallel branch employed an LSTM to capture long-range temporal dependencies. A multi-head attention mechanism enables deep adaptive fusion of two types of modal information and overcomes the limitations of conventional simple feature concatenation. Comparative experiments against KNN, 1D-CNN and LSTM baselines demonstrate that the proposed model achieves a test accuracy of 98.89%, outperforming all reference methods. Ablation studies further validate the necessity and effectiveness of each constituent module within the proposed architecture. The results indicate that this approach provides reliable support for DAS-based online monitoring of power cables against external damage. Full article

To address the challenges of integrating multi-source heterogeneous data, fragmented fault knowledge, and the limited capability of traditional rule engines in recognizing edge cases for ultra-high voltage (UHV) bushing fault diagnosis, this paper proposes a fault identification and decision-making method based on knowledge graph (KG) rule reasoning and inductive graph convolutional network (Inductive GCN). First, a triple-matching strategy is employed to perform entity extraction and relation mining from fault cases, constructing a fault knowledge graph that transforms unstructured fault case texts into a structured knowledge graph. Second, a rule engine based on a multi-source feature rule set is designed, utilizing the entropy weight method and the RETE algorithm to achieve interpretable symbolic reasoning. On this basis, a double-layer inductive graph convolutional network is introduced to learn implicit fault patterns by aggregating topological information from neighboring nodes, and a confidence-driven dynamic weighted fusion strategy is adopted to achieve complementary advantages between the two models. Finally, a large language model is introduced to generate operation and maintenance decision recommendations. Experimental results demonstrate that the proposed method achieves an identification accuracy of 98.1% on a test set of 159 samples, which is 10.7 percentage points higher than that of a single rule engine and 6.9 percentage points higher than that of a single inductive graph convolution network. The standard deviation of accuracy across different test batches is only 0.0029. These results demonstrate the effectiveness and stability of the proposed method, providing a practical technical solution for UHV bushing fault identification. Full article

Ensuring long-term agricultural sustainability depends heavily on preserving soil health, a process fundamentally governed by humic substances (HSs) and their vital physicochemical and biological functions. However, because intensive farming rapidly degrades natural HSs reserves, external replenishment has become essential, driving the expansion of the humic preparations market. This article constitutes a comprehensive review of the entire technological chain of humic preparations: from the identification of raw materials, through advanced extraction techniques, up to agrochemical mechanisms in the soil–plant system. Both traditional fossil deposits (leonardite, brown coal, peat) and renewable waste sources fitting into the concept of the circular economy were discussed. Classical alkaline extraction was confronted with green methods such as ultrasound-assisted (UAE), microwave-assisted (MAE) or high voltage electrical discharge (HVED) extraction, which allow for shortening the operation time and reducing the consumption of reagents. Strategies of integrating HSs with mineral fertilizers (coating, liquid formulas, organo-mineral products) and their direct impact on improving nutrient use efficiency (NUE), mitigating plant abiotic stress, agricultural performance, and environmental impact were described in detail. Research perspectives were also presented, including, among others, economic aspects of scaling up humic technologies and an assessment of the development potential of innovative nanofertilizers functionalized with HSs. Full article

Developing a multimodal tactile display is a key area of interest for haptic scientists, with researchers continuously exploring new methods to achieve this goal. This study introduces a tactile–olfactory display capable of providing touch, temperature, and odor feedback. The display utilizes airflow to deliver feedback to the user, incorporating two air sources and thermoelectric components (Peltier elements). Unlike traditional technologies, it employs convection-based temperature stimulation, where air passing over the thermoelectric modules cools or warms, resulting in temperature modulation. The system was tested under steady airflow conditions (DC airflow) and frequency-modulated airflow conditions (AC airflow). First, a finite element simulation was conducted in Ansys to gain insights into the system parameters. This was followed by experimental evaluations to extract its characteristics and assess its performance. The results indicate that not only the type of airflow but also its rate and frequency play significant roles in rendering surface parameters. Additionally, this airflow-based tactile display has potential applications in both contact and noncontact haptic technologies. Full article

Dynamic link prediction, which aims to infer future edges from historical network structures, is a fundamental task in dynamic network analysis. Traditional models fail to capture high-order information, while existing methods neglect the distinct temporal evolution patterns between low-order and high-order structures, thereby limiting prediction accuracy. To address these issues, we propose DC-GHCN, a dynamic link prediction model based on a dual-channel architecture that integrates Graph Convolutional Network (GCN) and Hypergraph Convolutional Network (HGCN). Firstly, we extract closed motifs from dynamic network snapshots to construct an initial hypergraph, then refine it via nested motif pruning and node weight compensation strategies. Secondly, we design a dual-channel architecture: the GCN channel learns low-order structural features, while the HGCN channel learns high-order structural features. Furthermore, two independent Gated Recurrent Units (GRUs) separately model the temporal evolution of the two channels. Finally, the model employs a gating mechanism to adaptively fuse the dual-channel node representations for link prediction. Experiments on five real-world dynamic network datasets demonstrate that DC-GHCN outperforms baseline models, validating the effectiveness of the proposed model in dynamic link prediction. Full article

Portfolio optimization remains a challenging problem due to the dynamic, nonlinear, and uncertain characteristics of financial markets. Traditional portfolio construction approaches, including mean variance optimization and conventional Black–Litterman models, often suffer from inaccurate estimation of expected returns and unstable allocation caused by parameter uncertainty. These limitations become more significant under structural breaks, regime transitions, volatility clustering, and extreme market events. This study proposes a Transformer-GAN enhanced Black–Litterman framework (TG-BL) that integrates temporal representation learning, uncertainty-aware scenario generation, and Bayesian portfolio optimization. The proposed framework consists of three complementary components. First, a Transformer-based encoder is employed to extract long- range temporal dependencies and latent market representations from historical financial sequences. Second, a conditional Generative Adversarial Network (GAN) is introduced to generate diverse future return scenarios conditioned on Transformer- derived market representations, enabling probabilistic modeling of future uncertainty rather than deterministic prediction. Third, the generated return distributions are incorporated into the Black–Litterman framework through dynamically calibrated views and confidence estimation. Unlike conventional approaches that directly replace equilibrium returns with machine-generated predictions, the proposed method preserves the Bayesian structure of Black–Litterman by adjusting the influence of model- generated views according to predictive uncertainty. This mechanism allows AI- based forecasts to complement rather than dominate market equilibrium information. Extensive experiments are conducted using historical financial data under multiple market conditions. The evaluation framework includes portfolio performance comparison, GAN-generated scenario validation, robustness analysis under volatility and liquidity stress, and component- wise ablation experiments. The results demonstrate that the proposed TG-BL framework improves risk-adjusted portfolio performance while maintaining robustness against market uncertainty. The findings indicate that the integration of temporal feature extraction, uncertainty modeling, and Bayesian portfolio allocation provides an effective decision-support framework for quantitative investment management. Full article

Background: Image splicing forgery detection is one of the most challenging problems in the field of image forensics as it involves identifying and localizing suspicious regions that are created by integrating contents from one or more different sources. The accurate detection and classification of splicing forgery still remains a difficult task because of the existence of overlapping image regions, which makes it complex to differentiate authentic and tampered images. This overlap causes a lack of feature representation, making it difficult to precisely detect the tampering in images. Also, the decision-making process of the model is often a black box, which makes it challenging to interpret and understand the rationale behind its decisions. Methods: To address these challenges, a Convolutional Block Attention Module (CBAM)–U-Net with Cross-Connected Filters–Convolutional Neural Network (CCF-CNN) is proposed to achieve precise detection and localization of spliced regions. The CBAM enhances spatial and channel-wise attention, enabling accurate localization of forged regions. The dual-phase CCF-CNN is incorporated with cross-connected filters to differentiate between the authentic and tampered regions by extracting global and local features. Additionally, a surrogate heatmap mechanism is introduced using intermediate decoder features to generate patch-level visual explanations, enabling precise localization of the spliced regions, thereby improving the model’s transparency in decision-making. Results: The proposed CCF-CNN obtains a high accuracy of 99.84% on the CASIA 2.0 dataset and an accuracy of 95.63% on the MISD. Conclusions: Compared to traditional CNNs such as VGG, ResNet and attention-based interpretability algorithms, the proposed model obtains higher performance in terms of detection and interpretability. Full article

Background: Male infertility, largely driven by declining sperm quality, is a growing global concern. Radix Bupleuri Chinensis (Chaihu, CH)—a key component of traditional formulas such as Chaihu Shengjing Tang and Chaihu Shugan San—has been clinically used to enhance sperm quality. However, the specific role and mechanism of CH as a single entity in ameliorating spermatogenic dysfunction remain unclear. This study aimed to determine whether CH extract ameliorates spermatogenic dysfunction and whether GnRH/GnRHR signaling and taurine biosynthesis are involved. Methods: Male Kunming (KM) mice and GC-1 spermatogonial cells were used to establish experimental models. Histopathological and cellular changes were examined via hematoxylin and eosin (H&E) staining and immunofluorescence. Enzyme and hormone levels were quantified, while gene and protein expression were analyzed using quantitative real-time polymerase chain reaction, Western blotting, and high-content imaging. Results: CH extract mitigated DOX-induced spermatogenic dysfunction by activating the GnRHR signaling pathway, enhancing taurine biosynthesis, and reducing oxidative stress. In vitro, saikosaponin D alleviated DOX-induced injury in GC-1 cells and was associated with changes in GnRHR-related signaling and taurine-biosynthesis-related proteins, supporting its potential role as a candidate bioactive constituent of CH. Conclusions: CH extract alleviated DOX-induced spermatogenic impairment in mice. The in vivo cetrorelix experiments support the involvement of the GnRHR signaling pathway, whereas the concurrent changes in taurine, CDO1, and FMO1 are consistent with enhanced taurine biosynthesis. These findings provide experimental support for further investigation of CH as a potential intervention for chemotherapy-related reproductive injury. Full article

Background/Objectives: Cardiac surgery involves unique complexities that differ from those of general ICU populations. Traditional scoring systems often underperform due to the significant class imbalance between survival and mortality. This study utilized the MIMIC-IV database, integrating machine learning (ML) and feature engineering to develop an in-hospital mortality prediction model specifically for open-heart surgery patients. Methods: We included 6941 cases (mortality: 76, 1.095%). Sixty-eight variables from the first ICU day were extracted. Following data preprocessing and imputation, four ML models—logistic regression, random forest (RF), XGBoost, and multilayer perceptron (MLP)—were constructed using stratified 10-fold cross-validation. SMOTE was applied to address class imbalance. A streamlined 17-variable model was developed and compared against the Sequential Organ Failure Assessment (SOFA) and the Oxford Acute Severity of Illness Score (OASIS). Results: Among the 68-variable models, RF achieved the highest area under the receiver operating characteristic curve (AUROC) of 0.915 (95% CI, 0.855–0.966). For the 17-variable models, MLP performed best (AUROC: 0.920; 95% CI, 0.865–0.964), significantly outperforming SOFA (0.688) and OASIS (0.690). Regarding the area under the precision-recall curve (AUCPR), the 17-variable MLP also yielded the highest score (0.203; 95% CI, 0.060–0.389) compared with SOFA (0.161) and OASIS (0.046). SHapley Additive exPlanations (SHAP) analysis identified bicarbonate levels, mechanical ventilation, and mean pulmonary arterial pressure as the top predictors, consistent with clinical expectations. Conclusions: The streamlined MLP model significantly outperforms traditional scoring systems and may serve as a useful tool for early postoperative risk stratification after open-heart surgery. Full article

Background/Objectives: Verbena officinalis has long been used in traditional medicine for treating skin disorders, yet its potential in advanced wound-dressing systems remains insufficiently explored. This study aimed to characterize V. officinalis extracts obtained using different solvents, evaluate their antimicrobial, antibiofilm, cytotoxic, and wound-healing activities, and develop electrospun polycaprolactone–polyethylene glycol (PCL–PEG) nanofibers as a delivery platform for the most active extract. Methods: Extracts were chemically characterized by LC–MS and evaluated against clinical bacterial, yeast, and dermatophyte isolates associated with skin infections. Antibiofilm activity was assessed against Staphylococcus lugdunensis. The most active hydroalcoholic extract (EW7.5) was incorporated into PCL–PEG nanofibers (1–10%, w/w). Metabolite release was monitored by semi-quantitative LC–MS analysis. Cytotoxicity and wound-healing activity were evaluated using HaCaT keratinocytes, while in vivo efficacy was assessed in a full-thickness excisional wound model in Dark Agouti rats (five animals per group in two independent experiments). Results: EW7.5 exhibited the strongest antimicrobial activity, with MIC values of 0.06 mg/mL against all tested bacterial and Candida strains and 0.06–0.5 mg/mL against dermatophytes. It inhibited S. lugdunensis biofilm formation by 79.43% at MIC/4 and reduced pre-formed biofilm biomass by 64.98% at 2 × MBC. EW7.5 and EW7.5-loaded nanofibers showed no cytotoxicity up to 400 μg/mL. In the scratch assay, the free extract achieved 42.34 ± 1.58% wound closure, while nanofibers containing 10% EW7.5 achieved 25.87 ± 2.52%, compared to 7.73 ± 0.07% for the control. In vivo, EW7.5-loaded membranes significantly accelerated wound closure and the 10% formulation significantly improved extracellular matrix deposition and vascular remodeling compared to the control membrane. Conclusions: EW7.5-loaded PCL–PEG nanofibers combine antimicrobial, antibiofilm, biocompatible, and wound-healing properties, supporting their potential as multifunctional wound dressings. Nevertheless, further studies with mechanistic investigations are required to fully establish their therapeutic applicability. Full article

Rolling bearings, as a key component of rotating machinery, require precise fault diagnosis to ensure the safe and reliable operation of industrial systems. Nevertheless, the performance of traditional narrowband filtering demodulation (NFD) methods is constrained by inherent limitations of spectral segmentation frameworks and insufficient discriminative capability of feature indicators (FIs). To address these limitations, this paper proposes a new NFD method based on an adaptive multi-level spectra segmentation strategy. Firstly, using power spectral density (PSD) as the analysis basis, an iterative framework is constructed to obtain multi-level spectral trend lines (STLs), which achieves multi-perspective characterization of spectral features. Secondly, the local minimum points of the STLs are used as the segmentation boundaries to extract the demodulation frequency band. Subsequently, a robust blind feature indicator, synergistic characterization criterion (SCC), is proposed, which can simultaneously fully evaluate periodicity and impulsiveness, guiding the selection of the optimal demodulation frequency band (ODFB). Finally, based on the enhanced demodulation spectrum, power exponent transformation is introduced to construct a generalized spectral family, and the adaptive determination of the optimal transformation parameter is guided by frequency-domain signal-to-noise ratio (FDSNR), thereby obtaining the generalized enhanced demodulation spectrum (GEDS). Validation experiments on laboratory and public datasets demonstrate that the proposed method outperforms Fast Kurtogram, Autogram, and CFFsgram, with average improvements of 63.86% and 89.06% in mean-peak ratio (MPR) and fault feature coefficient (FFC), respectively, and provides a new perspective for NFD and expands its application potential in bearing fault diagnosis and condition monitoring. Full article

Identifying core features is a crucial step in design patent infringement assessments (DPIAs), as the identification results serve as a vital basis for subsequent qualitative and quantitative assessments. Traditional DPIAs typically rely on the experience and knowledge of experts and the judge’s discretion, resulting in significant uncertainty and subjectivity. This can easily lead to inconsistent judgments in similar cases, hindering technological innovation and potentially triggering social conflicts. To address this, this paper proposes an intelligent identification framework for core features of patents that integrates multi-angle attention recognition and novelty calculation. First, design patents are downloaded, and views are extracted to construct a database. Simultaneously, the Canny operator is used to extract the product’s contour features. Second, a CBAM-ResNet50 model is constructed, trained and optimized using transfer learning to achieve quantitative identification of salient features. Subsequently, the VGG16 model combined with cosine similarity is used to calculate product feature similarity, and the results from both methods are combined to comprehensively determine the core features. Experiments are conducted using a showerhead as an example, with 400 patents randomly selected as the development set. The results show that the proposed CBAM-ResNet50 model significantly outperforms other comparative models in terms of Intersection over Union (IOU) and Dice Similarity Coefficient (DSC), and the VGG16 model combined with the cosine similarity algorithm achieves the highest discriminative power for product front view contour. Combining the two methods yields a validation set of core features, which are then evaluated by an expert panel. The expert endorsement rate (EEP) on the final test set is 86.5% (173/200; the Wilson confidence interval is 81.1% to 90.6%). This research not only provides a quantifiable and interpretable decision-support tool for DPIA but also offers feasible technical support for intelligent intellectual property examination and even product innovation design. Full article

Background: Over recent decades, Latin America has undergone significant population aging, accompanied by persistent inequalities that adversely affect healthcare access, particularly among Indigenous populations. This scoping review aimed to map and synthesize the available evidence on healthcare access for older Indigenous women in the region, identifying relevant dimensions, barriers, facilitators, and existing knowledge gaps. Methods: A systematic search was performed in scientific databases and gray literature up to 19 June 2026, including studies published between 2020 and 2026 in Spanish, English, or Portuguese. The PCC framework (Population, Concept, Context) was applied. Following a multi-reviewer selection process involving seven reviewers and data extraction using a standardized matrix, the findings were narratively synthesized in accordance with the PRISMA-ScR guidelines. Results: Eleven sources of evidence were included, representing six Latin American countries and including one regional review; qualitative approaches were the most common among the primary studies. Eight dimensions of accessibility were identified: geographic, economic, cultural, organizational, linguistic, functional, administrative, and social. Principal barriers included geographic isolation, poverty, a lack of cultural and linguistic relevance, bureaucratic hurdles, structural racism, and a scarcity of disaggregated data. Facilitators encompassed family and community networks, traditional medicine, local leadership, social programs, and technological/intercultural adaptations such as telemedicine. Compared with the Levesque framework, approachability, appropriateness, and formal system-navigation mechanisms were not clearly represented in the Latin American evidence, while engagement was insufficiently characterized. Conclusions: The findings indicate that structural and cultural barriers continue to persist. Full article

Substantial dye wastewater from the textile industry poses serious ecological threats. Spent mushroom substrate (SMS) offers a cost-effective and eco-friendly decolorization option, yet traditional methods are hindered by prolonged processing times. This study introduces a column decolorization (CD) method using Auricularia cornea SMS, demonstrating markedly higher efficiency and practicality. The CD method achieved over 50% decolorization for 10 of 12 tested dyes, outperforming conventional approaches for six dyes. Analyses via SEM, FTIR, and isotherm measurements indicated that the high efficiency stems from the SMS microstructure and functional groups, with mechanisms including electrostatic attraction, hydrogen bonding, and π-π conjugation, following Langmuir monolayer adsorption. The process requires no specific conditions, avoids extraction steps, and reduces treatment time to 4% of traditional methods. HPLC analysis further revealed partial biodegradation of Malachite Green, reducing its cytotoxicity. This work provides the first systematic comparison of SMS decolorization methods and a mechanistic analysis, supporting SMS application in dye wastewater treatment. Full article

By integrating cultural genes with extensible semantic concepts, this study explores the application forms and design methodologies of the material manifestations and underlying essence of Lu embroidery’s intangible cultural heritage within contemporary social contexts, thereby promoting outstanding national culture by selecting Lu embroidery, a representative ICH of the Qi–Lu region, and employing the dual-drill model to conduct genetic extraction and classification of its elemental characteristics. It provides a foundation for constructing a scalable element set. Using the Kegel semantic analysis method, we extract the fundamental model of Lu embroidery’s cultural genes and construct a set of Kegel-based visual design elements representing explicit cultural genes. Through graphical semantic analysis, we further develop a set of Kegel-based imagery elements corresponding to implicit genes, thereby facilitating the expression and design innovation of these cultural genes. Taking lamp design as an example, this study reconstructs and visualizes both explicit and implicit cultural elements to validate the feasibility and rationality of the research methodology. The semantic design-based translation that integrates the tangible manifestations and value essence of Shandong embroidery culture represents an innovative approach to digital preservation of this traditional craft, while also serving as a model reference for the inheritance and development of intangible cultural heritage in other regions. Full article