Search Results (20,002)

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

Background: Hepatocellular carcinoma (HCC) is characterized by marked intratumoral heterogeneity and poor clinical outcomes. Dysregulated ribosome biogenesis has emerged as a fundamental hallmark of tumor initiation and progression; however, the specific molecular drivers linking this machinery to HCC pathogenesis remain largely undefined. Methods: By integrating multi-omics data from the TCGA and ICGC cohorts, FBLL1 was identified as a key prognostic candidate gene. Its cellular and spatial distribution was analyzed using single-cell RNA sequencing and spatial transcriptomics. Its biological functions in vitro and in vivo were validated through functional experiments, including lentivirus-mediated ectopic expression and siRNA-mediated gene knockdown. Finally, its molecular mechanism was elucidated through transcriptomic analysis and Western blotting. Results: FBLL1 was significantly upregulated in HCC and correlated with poor patient survival. Spatial and single-cell analyses showed that FBLL1 expression was preferentially enriched in malignant hepatocytes within the tumor region. Functionally, knockdown FBLL1 could inhibit the proliferation and clonogenic capacity of HCC cells, while overexpression FBLL1 in non-tumorigenic hepatocytes could promote the tumorigenic phenotype in xenograft models. Transcriptomic analysis indicated that FBLL1 overexpression was associated with the synergistic upregulation of c-Myc and multiple EGFR ligands, as well as decreased expression of hepatocyte functional markers. Consistently, modulation of FBLL1 expression affected the activity of the EGFR–MAPK signaling pathway. Conclusions: Our study identifies FBLL1 as a previously unrecognized regulator associated with malignant state transition in HCC. Rather than acting as a direct regulator of core signaling components, FBLL1 is associated with ligand-dependent activation of the EGFR–MAPK pathway in conjunction with c-Myc upregulation. These findings indicate that FBLL1 represents a promising therapeutic target for disrupting oncogenic signaling programs in liver cancer. Full article

Traditional fossil-based adhesives, hindered by issues such as formaldehyde emission, dependence on fossil resources, and poor biodegradability, struggle to meet the global demand for low-carbon green development. Biomass-based adhesives have thus emerged as a core alternative. Among them, chitin/chitosan derived from biomass waste such as shrimp and crab shells demonstrates significant potential in the adhesive field due to its renewability, controllable structure, biocompatibility, and inherent antibacterial properties. However, mainstream biomass adhesives like soy protein and starch adhesives suffer from poor water resistance and insufficient wet adhesion strength. Pure chitin/chitosan-based adhesive systems also exhibit low wet strength retention. Furthermore, the overall development faces challenges including high extraction costs, insufficient performance synergy, poor industrial compatibility, and a lack of standardized systems. This review follows the framework of “resource–extraction–modification–performance–application–challenges” to systematically summarize relevant research progress. It clarifies the molecular structure and intrinsic advantages of chitin/chitosan, outlines extraction processes such as acid/alkali and enzymatic methods, and characterization techniques including FT-IR and XRD. The review focuses on analyzing modification strategies such as composite modification, chemical modification, biomineralization, and biomimetic design, and verifies the application potential of these adhesives in wood processing, biomedicine, paper-based packaging, and other fields. Research indicates that chitin/chitosan-based adhesives provide an effective pathway for the green transformation of the adhesive industry. Future efforts should concentrate on developing green extraction processes, designing multifunctional integrated systems, and achieving full resource utilization of biomass. Additionally, establishing comprehensive standardized systems and promoting the translation of laboratory research into industrial applications are crucial to driving the industry’s green transition. Full article

Reactive transport in porous media exists ubiquitously in natural and industrial systems—reformation of geological energy repository, carbon dioxide (CO${}_2$) sequestration, CO${}_2$ storage via mineralization, and soil remediation are just some examples where geo-/bio-chemical reactions play a key role. Reactive transport models are expected to provide assessments of (1) the effective property variation and (2) the reaction capability. However, the synergy among flow, solute transport, and reaction undermines the predictability of the existing model. In recent decades, the Micro-Continuum Approach (MCA) has demonstrated advantages for modeling pore-scale reactive transport and high accuracy compared with experiments. In this study, we present an MCA-based numerical framework that simulates dissolution/erosion or precipitation in digital rocks. The framework imports two- or three-dimensional digital rock samples, conducts reactive transport simulations, and evaluates dynamic changes in porosity, surface area, permeability tensor, tortuosity, mass change, and reaction rate. The results show that samples with similar effective properties, e.g., porosity or permeability, may exhibit different reaction abilities, suggesting that the pore-scale geometry has a strong impact on reactive transport. Additionally, the numerical framework demonstrates the advantage of conducting multiple reaction studies on the same sample, in contrast to reality, where there is often only one physical experiment. This advantage enables the identification of the optimal condition, quantified by the dimensionless P$\acute{\mathrm{e}}$clet number and Damk$\ddot{\mathrm{o}}$hler number, to reach the maximum reaction. We believe that the newly developed framework serves as a toolbox for evaluating reactivity capacity and predicting effective properties of digital samples. Full article

Non invasive prenatal testing, NIPT, is widely used for fetal aneuploidy screening, but its clinical utility depends on gestational timing and maternal characteristics. Low fetal fraction can lead to unreportable tests and increased false negative risk, while GC-content-related sequencing bias may contribute to both false positive and false negative findings. We propose a Bayesian decision-theoretic optimization framework to recommend personalized NIPT timing across maternal body mass index (BMI) strata, explicitly incorporating test credibility and detection errors. We performed a retrospective analysis of de-identified NIPT records from a hospital in Guangdong Province, China, covering 1 January 2023 to 18 February 2024, including 1082 male fetus tests. Y chromosome concentration was used as a proxy for test reportability, with a 4 percent reporting threshold. Detection state proportions were empirically summarized from clinical reference information, with false positives at 10.35 percent and false negatives at 2.77 percent. A logistic regression model quantified the probability of obtaining a reportable result as a function of gestational week, maternal age, height, and weight, and the estimated probabilities were used to parameterize the Bayesian risk model. The optimized BMI-stratified schedule produced six BMI groups with recommended testing weeks ranging from 11 to 16, and the overall expected risk converged to 0.531. These results indicate a nonlinear BMI–timing relationship and suggest that a single universal testing week is suboptimal. The proposed framework provides quantitative decision support for BMI-stratified NIPT scheduling in clinical practice. Full article

The automation of precise discipline inspection consultation requires question-answering (QA) systems that are both semantically nuanced and factually grounded. To address the limitations of keyword-based retrieval and the hallucination tendencies of generative language models in high-stakes discipline inspection domains, we propose a two-stage Retrieval-Augmented Generation (RAG) framework designed for Chinese discipline inspection text. Our approach synergizes token-level late interaction and cross-encoder reranking to achieve high-precision evidence retrieval. First, we employ ColBERTv2 to perform efficient, fine-grained semantic matching between queries and lengthy discipline inspection documents. Subsequently, we refine the initial candidate set using a computationally focused cross-encoder, which performs deep pairwise relevance scoring on a shortlist of passages. This retrieved evidence strictly conditions the answer generation process of a large language model (DeepSeek-chat). Through rigorous evaluation on a curated corpus of real Chinese discipline inspection documents and expert-annotated queries, we demonstrate that our pipeline significantly outperforms strong baselines—including BM25, single-stage dense retrieval (BGE), and a simplified ColBERT variant—in both retrieval metrics (Recall@k, Precision@k) and answer faithfulness. Our work provides a robust, reproducible blueprint for building reliable, evidence-based discipline inspection AI systems, highlighting the critical role of hierarchical retrieval in mitigating hallucinations for domain-specific QA. Full article

Intrinsic motivation provides a principled mechanism for driving exploration in reinforcement learning when external rewards are sparse or delayed. A central challenge, however, lies in defining meaningful novelty signals in high-dimensional and combinatorial state spaces, where observation-level density estimation and prediction-error heuristics often become unreliable. In this work, we propose an information-theoretic framework for intrinsically motivated reinforcement learning grounded in the Information Bottleneck principle. Our approach learns compact latent state representations by explicitly balancing the compression of observations and the preservation of predictive information about future state transitions. Within this bottlenecked latent space, intrinsic rewards are defined through information-theoretic quantities that characterize the novelty of state–action transitions in terms of mutual information, rather than raw observation dissimilarity. To enable scalable estimation in continuous and high-dimensional settings, we employ neural mutual information estimators that avoid explicit density modeling and contrastive objectives based on the construction of positive–negative pairs. We evaluate the proposed method on two representative combinatorial routing problems, the Travelling Salesman Problem and the Split Delivery Vehicle Routing Problem, formulated as Markov decision processes with sparse terminal rewards. These problems serve as controlled testbeds for studying exploration and representation learning under long-horizon decision making. Experimental results demonstrate that the proposed information bottleneck-driven intrinsic motivation improves exploration efficiency, training stability, and solution quality compared to standard reinforcement learning baselines. Full article

Background: Chronic diabetic wounds, particularly diabetic foot ulcers (DFUs), are prone to recurrent infection and delayed healing, resulting in substantial morbidity, mortality, and economic burden. Multifunctional wound dressings that combine antibacterial, antioxidant, conductive, and self-healing properties may help to address the complex microenvironment of chronic diabetic wounds. Methods: In this study, ε-poly-L-lysine and amino-terminated polyethylene glycol were grafted onto carboxylated single-walled carbon nanotubes (SWCNTs) via amide coupling to obtain ε-PL-CNT-PEG. Aminated chondroitin sulfate (CS-ADH) and a catechol–metal coordination complex of protocatechualdehyde and Fe³⁺ (PA@Fe) were then used to construct a dynamic covalently cross-linked hydrogel network through Schiff-base chemistry. The obtained hydrogels (Gel0–3, Gel4) were characterized for photothermal performance, rheological behavior, microstructure, swelling/degradation, adhesiveness, antioxidant capacity, electrical conductivity, cytocompatibility, hemocompatibility, and antibacterial activity in the presence and absence of near-infrared (NIR, 808 nm) irradiation. Results: ε-PL-CNT-PEG showed good aqueous dispersibility, NIR-induced photothermal conversion, and improved cytocompatibility after surface modification. Incorporation of ε-PL-CNT-PEG into the PA@Fe/CS-ADH network yielded conductive hydrogels with porous microstructures and storage modulus (G′) higher than loss modulus (G′′) over the tested frequency range, indicating stable gel-like behavior. The hydrogels exhibited self-healing under alternating strain and macroscopic rejoining after cutting. Swelling and degradation studies demonstrated pH-dependent degradation, with faster degradation in mildly acidic conditions (pH 5.0), mimicking infected chronic diabetic wounds. The hydrogels adhered to diverse substrates and tolerated joint movements. Gel4 showed notable DPPH• and H₂O₂ scavenging (≈65% and ≈60%, respectively, within several hours). The electrical conductivity was 0.19 ± 0.0X mS/cm for Gel0–3 and 0.21 ± 0.0Y mS/cm for Gel4 (mean ± SD, n = 3), falling within the range reported for human skin. In vitro, NIH3T3 cells maintained >90% viability in the presence of hydrogel extracts, and hemolysis ratios remained below 5%. Hydrogels containing ε-PL-CNT-PEG displayed enhanced antibacterial effects against Escherichia coli and Staphylococcus aureus, and NIR irradiation further reduced bacterial survival, with some formulations achieving near-complete inhibition under low-power (0.2–0.3 W/cm²) 808 nm irradiation. Conclusions: A dynamic, conductive hydrogel based on PA@Fe, CS-ADH, and ε-PL-CNT-PEG was successfully developed. The hydrogel combines photothermal antibacterial activity, antioxidant capacity, electrical conductivity, self-healing behavior, adhesiveness, cytocompatibility, and hemocompatibility. These properties suggest potential for application as a wound dressing for chronic diabetic wounds, including diabetic foot ulcers, although further in vivo studies are required to validate therapeutic efficacy. Full article

Rapid urbanisation in coastal megacities intensifies coupled climate and water-related challenges, including heat stress, ventilation deficits, and increasing sensitivity to hydro-climatic extremes. Urban morphology plays a critical role in regulating these climate–water interactions by shaping airflow, surface heat exchange, and the spatial organisation of green–blue infrastructures. This study develops a Local Climate Zone (LCZ)-conditioned generative modelling framework based on a Conditional Pix2Pix Generative Adversarial Network, using paired LCZ classification maps and urban morphology data derived from six representative cities in the Guangdong–Hong Kong–Macao Greater Bay Area: Guangzhou, Shenzhen, Hong Kong, Macao, Zhuhai, and Dongguan. By integrating remote sensing–derived LCZ classifications with urban morphology data, the proposed framework learns spatial patterns associated with key morphology-related predictors, including building density and compactness, height-related structural intensity, open-space distribution, and the continuity of green–blue and ventilation corridors. The model demonstrates robust performance (SSIM = 0.74, R² = 0.81, PSNR = 15.3 dB) and strong cross-city transferability, accurately reproducing density transitions, ventilation corridors, and green–blue spatial structures relevant to coastal climate and water adaptation. The results highlight the potential of LCZ-informed generative modelling as a scalable decision-support tool for climate–water adaptive urban planning, enabling rapid exploration of morphology configurations that support heat mitigation, ventilation enhancement, and resilient coastal transformation. Full article

High-temperature proton exchange membranes (HT-PEMs) are critical components of high-temperature fuel cells, facilitating proton transport and acting as a barrier to fuel and electrons; however, their performance is hampered by persistent issues of phosphoric acid leaching and oxidative degradation. Herein, a novel HT-PEM with abundant hydrogen bond network is constructed by incorporating nanoscale polyhedral oligomeric silsequioxane functionalized with eight pendent sulfhydryl groups (POSS-SH) into poly(4,4′-diphenylether-5,5′-bibenzimidazole) (OPBI) matrix. POSS, a cage-like nanostructured hybrid molecule, features a well-defined silica core and highly designable surface organic groups, offering unique potential for enhancing membrane performance at the molecular level. Through controlled reactions between sulfhydryl groups and allyl glycidyl ether (AGE), two functional POSS crosslinkers—octa-epoxide POSS (OE-POSS) and mixed sulfhydryl-epoxy POSS (POSS-S-E)—were synthesized. These were subsequently used to fabricate crosslinked OPBI membranes (OPBI-OE-POSS and OPBI-POSS-S-E) via epoxy–amine coupling. The OPBI-POSS-S-E membranes demonstrated exceptional oxidative stability, which is attributed to the free radical scavenging ability of the retained sulfhydryl groups on the nano-sized POSS framework. After soaking in Fenton’s reagent at 80 °C for 108 h, the OPBI-POSS-S-E-20% membrane retained 79.4% of its initial weight, significantly surpassing both the OPBI-OE-POSS-20% and pristine OPBI membranes. The PA-doped OPBI-POSS-S-E-20% membrane achieved a proton conductivity of 50.8 mS cm⁻¹ at 160 °C, and the corresponding membrane electrode assembly delivered a peak power density of 724 mW cm⁻², highlighting the key role of POSS as a nano-modifier in advancing HT-PEM performance. Full article

To address the high-temperature and high-cost challenges of the conventional dry oxidation process in boron diffusion for n-type tunnel oxide passivated contact solar cells, this study proposes a dry–wet–dry mixed oxidation drive-in process for fabricating p-type emitters in TOPCon solar cells. Through systematic investigation of oxidation temperature, O₂/H₂O flow ratio, and oxidation time effects on emitter performance, it is found that mixed oxidation at 1000 °C achieves comparable sheet resistance and doping profiles to dry oxidation at 1050 °C. For our newly developed mixed oxidation process, in which the oxidation temperature is 1000 °C, oxidation time is 80 min with O₂/H₂O flow ratio of 20:1, the same photoelectric conversion efficiency has been achieved. Comparing the data, the mixed oxidation process forms a dry/wet/dry three-layer SiO₂ structure, reducing the oxidation temperature by 50 °C while achieving an average efficiency of 26.02%, comparable to high-temperature dry oxidation. This process not only reduces the thermal budget of quartz tubes and extends equipment service life but also provides a feasible solution for the low-temperature manufacturing of high-efficiency TOPCon solar cells, showing significant industrial application prospects. Full article

Background/Objectives: The rhizome of Gastrodia elata Blume (Tianma) is a functional food with medicinal value in China, used to improve the health of the central nervous system and reported to exhibit anti-cellular senescent activity. P-hydroxybenzaldehyde (P-HBA) is a key aromatic compound isolated from Tianma; however, its potential to mitigate cellular senescence remains unclear. Methods: We employed ultra-performance liquid chromatography-mass spectrometry to identify the chemical characterization of Tianma extract. Cell viability assay, senescence-associated-β-galactosidase (SA-β-Gal) assay, and immunofluorescence staining and autophagy analysis were used to evaluate the anti-senescent activity of P-HBA and other Tianma components. Results: Our findings demonstrate that Tianma methanol extract (TME) and P-HBA significantly reduce cellular senescent inducer hydroxyurea (HU)-induced DNA damage, SA-β-Gal activity increase, and autophagic dysfunction in human SH-SY5Y cells. Notably, an autophagy inhibitor, chloroquine, can reduce anti-cellular senescent activity of P-HBA. Conclusions: These results suggest that P-HBA exhibits the effect of reducing cellular senescent phenotypes, and its effect is achieved by enhancing autophagy. Full article

The traditional Chinese medicinal herb Gynura segetum is increasingly recognized for its hepatotoxic potential, primarily attributed to its pyrrolizidine alkaloid (PA) content. PAs are a leading cause of herb-induced liver injury (HILI) in China and are strongly linked to hepatic sinusoidal obstruction syndrome (HSOS). This review systematically summarizes the pathogenesis, diagnostic advancements, and therapeutic strategies for PA-induced HSOS. Molecular mechanisms of PA metabolism are detailed, encompassing cytochrome P450-mediated bioactivation and the subsequent formation of pyrrole–protein adducts, which trigger sinusoidal endothelial cell injury and hepatocyte apoptosis. Advances in diagnostic criteria, including the Nanjing Criteria and the Roussel Uclaf Causality Assessment Method (RUCAM)-integrated Drum Tower Severity Scoring System, are discussed. Furthermore, emerging biomarkers, such as circulating microRNAs and pyrrole–protein adducts, are examined. Imaging modalities, such as contrast-enhanced computed tomography (CT) and gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) magnetic resonance imaging (MRI), have evolved from descriptive tools into quantitative and prognostic instruments. Therapeutic approaches have evolved from supportive care to precision interventions, including anticoagulation, transjugular intrahepatic portosystemic shunt (TIPS), and autophagy-modulating agents. A comprehensive literature review, utilizing databases such as PubMed and Web of Science, was conducted to summarize progress since the introduction of the “Nanjing Guidelines”. Ultimately, this review underscores the critical need for integrated diagnostic and therapeutic frameworks, alongside enhanced public awareness and regulatory oversight, to effectively mitigate PA-related liver injury. Full article

Traditional polysaccharide extraction suffers from low efficiency and high energy consumption, while deep eutectic solvents (DESs) are promising sustainable solvents. This study used DES ChCl-LA (1:2) with ultrasonic assistance to extract polysaccharides from red alga A.taxiformis. Optimized via single-factor experiments and response surface methodology (350 W, 1:30 g/mL, 75 °C), the yield reached 11.28% ± 0.50% (1.5 times higher than that obtained by water extraction). Structural characterization revealed that the DES extract was an acidic polysaccharide, mainly composed of galactose (89.2%), glucose (4.9%), xylose (4.9%), and glucuronic acid (1.0%), with a weight-average molecular weight of 99.88 kDa. Density functional theory and molecular dynamics simulations showed that ChCl-LA enhanced galactose solubility via stronger hydrogen bonding (−25.33 vs. −5.06 kcal/mol for water). Notably, the immunological activity of the DES-extracted polysaccharide was significantly compromised compared to the water-extracted counterpart (p < 0.05). At a concentration of 0.25 mg/mL, the water-extracted polysaccharide-treated group exhibited a 33.98% higher neutral red phagocytosis rate in macrophages, a nitric oxide (NO) secretion level of 34.14 μmol/L (94.98% higher) compared with the DES-extracted polysaccharide group, as well as significantly higher secretion levels of tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6). The observed disparity in bioactivity is likely due to the distinct chemical profiles resulting from the two extraction methods, including the significantly reduced molecular weight and potential alterations of sulfation degree, monosaccharide composition, and protein content in the DES-extracted polysaccharide. This mechanistic perspective is supported by the relevant literature on the structure–activity relationships of polysaccharides. This study demonstrates the potential of ChCl-LA and elucidates the complex effects of extraction methods on polysaccharide’s structure and function, thereby informing the high-value utilization of A. taxiformis in functional foods. Full article

This study introduces a mesoscale modeling methodology for polyurethane-solidified ballast beds (PSBBs) that eliminates reliance on X-ray computed tomography (XCT) and addresses constraints in specimen size, capital cost, and post-processing complexity. The approach couples the Discrete Element Method (DEM) with the Finite Element Method (FEM). A high-fidelity discrete-element geometry is reconstructed from three-dimensional laser scans of ballast particles. The virtual-ray casting algorithm is then employed to identify the spatial distribution of ballast and polyurethane and map this information onto the finite-element mesh, enabling heterogeneous material reconstruction at the mesoscale. The accuracy of the model and mesh convergence are validated through comparisons with laboratory uniaxial compression tests, determining the optimal mesh size to be 0.4 times the minimum particle size (0.4 D_min). Based on this, a parametric study on the effect of sleeper width on ballast bed mechanical responses is conducted, revealing that when the sleeper width is no less than 0.73 times the ballast bed width (0.73 W_b) an optimal balance between stress diffusion and displacement control is achieved. This method demonstrates excellent cross-material applicability and can be extended to mesoscale modeling and performance evaluation of other multiphase particle–binder composite systems. Full article