Search Results (4,355)

While direct current (DC) ice-melting is currently adopted for some transmission lines, its application to 10 kV distribution transformers—often located in remote and rugged terrain—presents significant operational challenges. Disconnecting these transformers prior to ice-melting is a complex procedure that incurs substantial labor, material, and financial costs. Leaving transformers connected risks DC current flowing into idle windings, potentially causing damage. Furthermore, existing mobile DC ice-melting power supplies are bulky and impose stringent transportation requirements, rendering them unsuitable for use on mountain roads. To overcome these limitations, this paper proposes a compact, lightweight variable-frequency ice-melting device. The operating principle and output characteristics of the variable-frequency method are investigated in detail. Using Simulink, system modeling and simulation analyses are performed to obtain the voltage and current output characteristics, along with harmonic spectra. Simulation results demonstrate that the proposed device achieves significant miniaturization compared with conventional solutions: within the typical parameter range of conventional devices, the volume can be reduced by 44–58% and the weight by 43–52%. In addition, the selected LC filter parameters (L = 10.39 mH, C = 86.62 μF) represent an optimized compromise solution that effectively suppresses input harmonics while maintaining the output current total harmonic distortion (THD) within an acceptable limit of 3.6%. Experimental results further validate the feasibility of the variable-frequency ice-melting current. Based on a matrix converter topology, the proposed device enables flexible adjustment of the output melting voltage and frequency, exhibits excellent low-frequency performance and dynamic response, and maintains low output harmonic content—fully meeting the application requirements for variable-frequency ice-melting. The key novelty lies in a compact matrix-converter-based de-icing device with systematic low-frequency performance analysis, offering superior portability and adaptability over traditional DC solutions. Full article

This study presents a systematic topological progression and multi-objective optimization of a Permanent Magnet-assisted Non-overlapping Winding Line-Start Synchronous Reluctance Motor (PMaNWLS-SynRM) for industrial applications. To explicitly highlight the core contribution, the research establishes a rigorous comparative framework evaluating the transition from a conventional 4-pole/36-slot distributed winding (DW) to a 2 × 12-slot non-overlapping winding (NW) architecture. Baseline results demonstrate that the NW configuration shortens end-turns, successfully reducing total electromagnetic losses from 417 W to 349 W and improving steady-state efficiency from 93.7% to 95.1%. To overcome the inherent starting limitations of pure synchronous reluctance machines, an aluminum squirrel-cage is integrated to enable robust direct-on-line (DOL) synchronization, while NdFeB permanent magnets are embedded within the rotor flux barriers to mitigate asynchronous spatial harmonics and elevate torque density. Finite element analysis (FEA) confirms this magnetic assistance raises the average synchronous torque to 65.8 Nm while suppressing absolute torque ripple to 1.37 Nm. Finally, an evolutionary genetic algorithm is deployed across 440 iterative configurations to resolve geometric multi-physics conflicts. The finalized optimized design achieves a 13.2 kW output power at 1800 rpm, maximizing average torque to 70.12 Nm and strictly dampening absolute torque ripple to an industry-acceptable 1.04 Nm. Operating with an aggregated total loss of 1382 W, the optimized PMaNWLS-SynRM yields a 90.5% operational efficiency, definitively validating its suitability as an ultra-premium IE4/IE5 alternative to conventional induction motors. Full article

During a survey of myxozoan diversity in fishes from Hunan Province, two new Myxidium species were discovered infecting the gallbladder of Sarcocheilichthys kiangsiensis Nichols, 1930 and Sarcocheilichthys parvus Nichols, 1930, in Dongting Lake, China. In both cases, myxospores were observed freely floating in the biles, with no typical plasmodia detected. Morphologically, both of them can be differentiated from previously described congeners by a combination of features, including myxospore dimensions, polar capsule shape, number of polar tubule coils and shell valve striations. BLASTn research indicated that neither species matched any available species in GenBank. The highest sequence similarity for Myxidium kiangsiensis n. sp. was 98.54% with M. asiaticum Chen et al., 2020 (PQ776264), and that for Myxidium parvusis n. sp. was 93.06% with Zschokkella guelaguetza Alama-Bermejo et al., 2023 (OQ888223). This study represents the first record of Myxidiidae infection in Sarcocheilichthys hosts. Phylogenetic analysis based on the obtained SSU rDNA sequences placed the two species in separate subclades interspersed with other Myxidium and Zschokkella species. This topology further corroborates the polyphyletic nature of these two genera. Full article

Social Network Analysis (SNA) has been widely applied to risk identification research. However, two key constraints, namely rating bias and the subjectivity of threshold selection, undermine the reliability and reproducibility of analytical results. To address this di-lemma, this study aims to construct a standardized and robust analytical framework for SNA-based risk identification. The core research objectives are as follows: elucidate the differential impact mechanism of threshold variation on the macro-topological structure and micro-level node ranking of risk networks, examine the cross-threshold robustness of core risk node rankings, and delimit the effective threshold range for stable risk identification. Accordingly, to fulfill the above objectives, this study proposes a multi-threshold robustness inspection method based on individual rating patterns, and conducts systematic empirical analysis with industrial projects in the post-support period of reservoir resettlement as research cases. The results indicate that threshold variation exerts marked systematic effects on the macro-topological structure of risk networks, whereas the relative rankings of core risk nodes remain robust. The effective threshold range for risk identification in such projects is α ∈ [0.1,0.3]. This study provides a repeatable quality control framework for SNA-based risk identification, with favorable cross-domain transferability. Full article

The support functions of crisp sets have been widely used in the topic of nonlinear analysis. This observation inspires to introduce the support functions of fuzzy sets. Two different support functions of fuzzy sets were introduced in the literature by using different domains of functions. This paper will focus on the simple type of support functions that are defined on a simple domain. Three applications using the simple type of support functions are studied in this paper. The first one is to generate fuzzy sets from the real-valued functions that satisfy some regular conditions such that the simple type of support function of a generated fuzzy set is identical with the pre-determined real-valued function. The second one is to study the embedding theorems of some interesting families of fuzzy sets. In this case, the continuities of simple types of support functions should be studied under some suitable topologies. In order to make the embedded Banach spaces as small as possible, the concept of weak* topology should be involved in the study. The third one is to solve the fuzzy optimization problems using the embedding theorems. Full article

Gas concentration series in coal mining faces are jointly affected by multiple coupled factors, including geological conditions, mining disturbances, ventilation organization, and gas drainage intensity, and therefore exhibit pronounced nonstationarity, strong fluctuations, spatiotemporal correlations across multiple monitoring points, and occasional abrupt spikes. To address these challenges, this study proposes a gas concentration prediction and early-warning method that integrates CEEMDAN–SST with GraphPINN-TimesFM (Graph Physics-Informed Neural Network–Time Series Foundation Model). First, based on multi-source monitoring data such as wind speed, gas concentrations at multiple monitoring points, and equipment operating status, anomaly removal, operating-condition segmentation, and change-point detection are performed to construct stable operating-state labels. Feature selection is then conducted by combining optimal time-lag correlation, Shapley value contribution, and dynamic time warping. Second, WGAN-GP is employed to augment samples from minority operating conditions, while CEEMDAN–SST is used to decompose and reconstruct the target series so as to reduce the interference of nonstationary noise and enhance sequence predictability. On this basis, TimesFM is adopted as the backbone for long-sequence forecasting to capture long-term dependency features in gas concentration evolution. Furthermore, GraphPINN is introduced to embed the topological associations among monitoring points, airflow transmission delays, and convection–diffusion mechanisms into the training process, thereby enabling collaborative modeling that integrates data-driven learning with physical constraints. Finally, the predictive performance, early-warning capability, and interpretability of the proposed model are systematically evaluated through regression forecasting, warning discrimination, and Shapley-based interpretability analysis. The results demonstrate that the proposed method can effectively improve the accuracy, robustness, and physical consistency of gas concentration prediction under complex operating conditions, thereby providing a new technical pathway for gas over-limit early warning and safety regulation in coal mining faces. Full article

Subgraph isomorphism counting is a fundamental yet computationally challenging task in graph analysis, with broad applications in bioinformatics and social network mining. With the tightening of data privacy regulations and the emergence of data silos, traditional centralized Graph Neural Network (GNN) approaches face significant deployment hurdles. Existing federated subgraph counting methods are primarily designed for database federation scenarios, focusing on exact queries and the privacy and security concerns of databases. However, this rigid focus on exactness and heavy cryptographic security severely limits their scalability and generalizability to complex, arbitrary query patterns. To bridge this gap, we propose a general Federated Edge-Centric Framework for Subgraph Isomorphism Counting (FedCount), shifting the paradigm from exact querying on federated databases to neural approximate counting under federated architectures. Rather than relying on heavy cryptographic techniques, we exclusively leverage the inherent structural isolation of federated learning as a lightweight empirical privacy measure. While this framework does not theoretically defend against advanced gradient-based inference attacks, it successfully prevents the direct leakage of raw graph topology and node features, achieving high-precision approximate counting without the prohibitive cryptographic overheads. Specifically, we introduce two key technical innovations to enhance local counting capability: (1) we integrate a provable edge encoding scheme into the interaction-based GNN architecture, explicitly modeling edge-to-edge interactions to break the expressiveness bottleneck of standard message passing; (2) we design a Residual Edge-Centric Readout mechanism that mitigates the gradient vanishing problem, enabling the effective training of deeper networks to capture high-order topological dependencies. Extensive experiments on multiple benchmark datasets demonstrate that our framework significantly outperforms existing distributed enumeration baselines in terms of generalization and efficiency, approaching the performance of centralized state-of-the-art models. Full article

Mixed-signal simulation is indispensable for verifying modern integrated circuits that tightly couple analog and digital subsystems, yet the field lacks a unified framework for systematically comparing its diverse methodologies. This paper addresses that gap by proposing a novel three-axis taxonomy that classifies simulation methods along abstraction level, solver methodology, and analysis type, together with a comparative evaluation framework based on five quantitative metrics: accuracy, throughput, capacity, convergence reliability, and scalability. Applying this framework, we systematically compare thirteen classical method categories—spanning SPICE, FastSPICE, RF/periodic steady-state, behavioral modeling, co-simulation, and model order reduction—and eight AI/ML approaches including Gaussian process surrogates, graph neural networks, physics-informed neural networks, Bayesian optimization, and reinforcement learning. Our analysis reveals a clear maturity stratification: classical methods remain the only signoff-accurate approaches, Bayesian optimization represents the most industrially validated AI contribution with integration across all three major EDA platforms, while Neural ODE solvers and LLM-based design tools remain at the research stage. We identify a persistent academic-to-industry gap driven by foundry model complexity, limited benchmark diversity, and topology-specific overfitting. The proposed taxonomy and comparative framework provide practitioners with structured guidance for simulation method selection and highlight specific research directions needed to bridge the gap between AI promise and industrial deployment. Full article

The aviation industry is increasingly prioritizing sustainability, with significant focus on the development of Hybrid-Electric Aircraft (HEA). By integrating electric motors with conventional combustion engines, HEA systems offer substantial environmental benefits and operational efficiency improvements. However, the successful implementation of HEA technologies is contingent upon advancements in power converter systems. This paper addresses the critical need for sustainable aviation solutions by examining the challenges and opportunities associated with High-Efficiency Aviation Power (HEAP) technology. Specifically, the study investigates the role of power converters in Hybrid-Electric Aircraft Propulsion systems, with a particular emphasis on addressing key concerns such as weight reduction, compact design, and system reliability. A comparative analysis of three converter topologies is conducted: two established configurations serve as baseline references, while a third topology, a modular, fault-tolerant DC-DC converter, is proposed for the first time in the context of hybrid-electric aircraft. Its novelty lies in the system-level use of redundancy to offer an inherent architectural advantage against cosmic-ray-induced failures a critical aviation reliability challenge that existing converter topologies do not address through hardware redundancy. This qualitative reliability advantage is presented as an architectural feature, pending quantitative validation through future hardware testing and mean-time-between-failures (MTBF) analysis. This exploration is essential for identifying the most suitable configuration for HEA integration, with the goal of overcoming challenges related to lightweight design, high efficiency, and reliability. The findings contribute to the advancement of more sustainable and efficient aviation solutions by demonstrating the potential of the proposed converter architecture. Full article

Persistent Homology (PH) is a method of Topological Data Analysis that characterizes the topological structure of a space. Unfortunately, the computation of PH for high-dimensional and big data is not possible due to the exponential growth of the constructed complex. Fortunately, sparsification techniques can substantially reduce the size of the complex. This paper examines a sparsification technique ($\beta$-Sparsification) that produces a complex reduction capability that is scalable to a user-specified value $\beta$. At $\beta =0$ this scaling generates complexes that can have the same 1-Skeleton as the Vietoris–Rips complex; $\beta =1$ produces a Delaunay complex, and other values of $\beta$ produce a range of (unnamed) complexes. Experiments with $\beta$-Sparsification reveal that the topology of the sparsified simplicial complex is preserved for $0\le \beta \le 1$; for $\beta >1$, the complex begins to lose (potentially insignificant) topological features. Full article

High-penetration renewable energy significantly increases uncertainty, dynamic network coupling, and the need for secure and coordinated smart-grid dispatch. To address the limitations of conventional forecasting-based and static graph-based methods, this paper proposes a unified dispatch framework that integrates topology-informed dynamic graph learning, privacy-aware multi-agent symbiotic reinforcement learning, and structural causal intervention analysis. The dispatch problem is formulated as a constrained partially observable stochastic game, in which multiple agents coordinate generation adjustment, reserve allocation, and congestion-aware corrective actions under engineering constraints. A physics-informed dynamic graph convolutional module captures both fixed physical topology and stress-dependent operational couplings, while a KL-regularized multi-agent reinforcement learning scheme improves cooperative task allocation under renewable fluctuations. Federated optimization with Rényi differential privacy is introduced to protect sensitive local operational information during training. In addition, a structural causal module provides intervention-based interpretation of how wind variation, load escalation, and line stress affect dispatch cost, congestion risk, and renewable curtailment. Experiments on a public-trace-driven benchmark based on a modified IEEE 30-bus system show that the proposed method achieves the best overall performance among the compared baselines, reducing dispatch-cost RMSE to 3.82, locational-price MAE to 2.95, renewable curtailment to 4.8%, and the constraint-violation rate to 0.30%. Overall, the framework shows favorable performance on the test benchmark, provides post hoc intervention-based interpretation of dispatch outcomes, and is evaluated under a reproducible benchmark construction and assessment protocol. Full article

Mill relining manipulator is essential maintenance equipment used to replace liners in a grinding mill. However, its excessive structural weight significantly constrains maneuverability and operational efficiency. To address this problem, this paper proposed a lightweight design framework for the manipulator’s upper arm, integrating improved multiple operating conditions topology optimization with size optimization. Firstly, a finite element model of the manipulator was established in ANSYS Workbench 2022R2. The loads under the corresponding operating conditions were extracted and applied to the finite element model of the upper arm to perform multi-condition finite element simulations. Secondly, a mathematical model for multi-condition topology optimization was developed using the variable density method combined with the Analytic Hierarchy Process (AHP), and the weight coefficients for each operating condition were determined. Finally, a combined response surface methodology (RSM) and genetic algorithm (GA) approach was employed to optimize the structural parameters of the upper arm. A response surface model with maximum equivalent stress and maximum deformation as the response variables was constructed, and the Pareto optimal set was obtained using the non-dominated sorting genetic algorithm (NSGA-II) to determine the optimal structural design. Quasi-static load tests were conducted on a scaled prototype to verify the reliability of the numerical optimization results. The results demonstrate that the optimized upper arm satisfies the strength and stiffness requirements while achieving a 12% mass reduction (2463 kg), confirming the effectiveness and engineering applicability of the proposed lightweight design methodology. Full article

Background: Accurate identification of cancer driver genes is essential for understanding tumorigenesis and developing targeted therapies. Although graph neural networks (GNNs) have advanced multi-omics integration, existing methods often simply concatenate omics features and underutilize the topological information of biological networks. Methods: We propose M-GNN, a multi-modal GNN framework for cancer driver gene prediction. It employs separate Graph Convolutional Network (GCN) encoders to process four types of omics data (mutation, expression, methylation, copy number variation (CNV)), each represented as a 16-dimensional vector. We incorporate knowledge distillation by using soft labels from a pre-trained teacher model to enhance feature representation. An attention mechanism adaptively fuses the encoded omics features, and a dual-path classifier combining a GCN and a Multilayer Perceptron (MLP) preserves both intrinsic gene properties and network topology. Results: Experiments on three public protein–protein interaction (PPI) networks show that M-GNN consistently achieves the highest or second-highest AUPRC compared to five state-of-the-art methods. Ablation studies confirm the contribution of each module, and biological interpretability analysis—including analysis of GO enrichment and drug sensitivity—validates the reliability of the predicted genes. Conclusions: M-GNN provides a robust and interpretable computational tool for systematic cancer driver gene identification, effectively integrating multi-omics and network data. Full article

Background: Hepatitis B virus (HBV) infection continues to be a major public health concern, especially in sub-Saharan Africa, where widespread epidemics and restricted availability of long-term antiviral therapies result in higher mortality and morbidity rates. Drug repurposing represents a strategic approach to accelerate the discovery of effective therapies by leveraging agents with demonstrated antiviral and immunomodulatory activity. Product Nkabinde (PN) is a patented African polyherbal formulation initially developed for the treatment of HIV. Recent experimental studies demonstrate PN’s potent anti-HIV activity and significant immunomodulatory effects in human immune cells, implicating host-directed mechanisms relevant to chronic viral infections. This study combines an integrative application of network pharmacology and molecular docking to evaluate the repurposing potential of PN as a multi-target agent in HBV. Method: Bioactive components of PN were screened, and compound-associated targets were intersected with HBV-associated genes (proteins) to construct a protein–protein interaction (PPI) network. Topological analysis identified 10 hub targets (STAT1, STAT3, SRC, HCK, EGFR, SYK, PIK3CA, PIK3CB, PIK3R1, and PTPN11). Gene Ontology and KEGG pathway enrichment were performed with an FDR cut-off < 0.05. Significantly enriched pathways included JAK–STAT signaling, chemokine signaling, EGFR-TKI resistance, PI3K complex signaling, and viral infection pathways, particularly those related to Kaposi sarcoma virus and HSV-1, indicating immunoregulatory and antiviral roles. Molecular docking was performed using AutoDock Vina 1.1.2 to evaluate binding affinity and interaction mode of key PN phytochemicals against the hub proteins, and results were compared to their respective co-crystallized ligands. Results: Molecular docking indicated that major phytochemicals from PN exhibited significant binding affinities across all 10 hub host targets, typically outperforming or closely matching their respective co-crystallized ligands. The strongest contacts were observed for β-sitosterol–PIK3CB (−14.2 kcal/mol) and oleanolic acid–SYK (−14.0 kcal/mol), which were significantly stronger than the co-crystallized ligands (−7.9 and −8.3 kcal/mol, respectively), indicating robust stabilization within catalytic and regulatory pockets. Procyanidin B2 toward HCK (−10.5 vs. −7.9 kcal/mol) and PIK3CA (−9.5 vs. −7.3 kcal/mol), quercetin toward PIK3R1 (−10.6 vs. −8.2 kcal/mol) and PTPN11 (−9.2 vs. −7.5 kcal/mol), rutin toward SRC (−10.5 vs. 7.8 kcal/mol), and diosgenin toward EGFR (−9.4 vs. 8.4 kcal/mol). Procyanidin B2 maintained robust multi-hydrogen bonding networks, demonstrating significant binding, despite STAT1 and STAT3 docking showing identical affinities to co-crystals. Conserved hydrogen bonds, π–cation interactions, and significant hydrophobic packing at ATP-binding clefts and regulatory domains supported these interaction patterns, indicating competitive suppression of host signaling nodes taken over by HBV. Conclusions: Together, these results demonstrate that the components of PN possess strong multitarget binding capabilities across the PI3K/AKT, JAK–STAT, SRC-family kinase, EGFR, and SYK pathways, supporting their potential repurposing as host-directed HBV therapeutics with the ability to impede immune evasion, viral persistence, and HBV-associated oncogenic progression. Full article

Neural language models have achieved remarkable progress in semantic representation learning. However, cross-domain representation learning still suffers from prominent semantic noise propagation issues. Existing methods still face challenges in cross-domain semantic modeling, including limited robustness across different semantic granularities, difficulty in separating transferable semantics from task-irrelevant semantic interference, and insufficient adaptability to specialized scenarios. These issues may reduce feature discriminability in fine-grained semantic tasks and complex application settings. To address these problems, we propose the Diffusion-Coded Attention Network (DCANet), a novel cross-domain representation learning architecture with three synergistic core modules: a multi-granular parallel diffusion masking mechanism for cross-scale context fusion via stochastic path activation, an implicit semantic encoder that distills domain-invariant patterns into adaptive bias codes via shared latent manifolds, and a self-correcting attention topology realizing dynamic semantic purification via closed-loop interactions between local features and global bias states. Extensive evaluations are conducted on nine well-recognized benchmark datasets to verify DCANet’s effectiveness and reliability. Experimental results show that DCANet attains state-of-the-art results on the majority of the benchmark datasets, with significant accuracy improvements on text classification and sentiment analysis tasks. Full article