To address node heterogeneity, complex functional dependences, and the limited ability of traditional metrics to capture system-level failure impacts in combat networks, this paper proposes a node-importance evaluation and critical-node identification method that couples failure propagation modeling with flow-blockage theory. We first construct a directed heterogeneous network with five functional node types and explicitly define their resource interfaces and dependency paths. An improved threshold-based propagation mechanism and a composite influence function integrating propagation probability, neighbor overlap, and the KHC topological index are then introduced, and a propagation-efficiency coupled identification algorithm is developed using the max-flow/min-cut principle to quantify traffic degradation under failures. Simulations across multiple failure scenarios and network topologies show that the proposed method significantly outperforms conventional centrality measures in identifying system-level high-loss nodes, yielding more actionable, task-chain–focused results with strong adaptability and robustness. These findings provide theoretical and algorithmic support for combat-network vulnerability assessment, resilient command-system design, and suppression-path planning.

Emerging applications, such as AI, AR/VR, and large-scale data processing, have significantly increased the demand for distributed data center resources. Consequently, the inter-datacenter wide area networks(WANs) face a surge in large-scale, long-distance network traffic. As the number of data centers increases, network congestion exhibits localized characteristics. Centralized link-grained schedules lack scalability, fairness and prolong the control cycle. To address this issue, we propose a Hop-by-hop Multi-Topology Traffic Engineering method(MTTE) on overlay network to provide on-time transmission services for Inter-DC WANs. The method divides the next hop set of hop-by-hop multipath routing into multiple topologies, which supports cost-based dynamic path switching at intermediate nodes. When traffic volume exceeds the adjustment of topologies, feedback is sent hop-by-hop to the upstream nodes, ultimately triggering rate control at the edge. Then, local bottlenecks are modeled as the Local Bandwidth Maximization(LBM) problem, enabling the adjustment of topology ratios, feedback, and transmission rates. Simulations show that MTTE can effectively optimize the link resources by adaptively adjusting the topology ratio. Compared to state-of-the-art heuristic-based centralized methods, MTTE improves the on-time ratio and fairness by increasing link utilization by approximately 30%.

Topology-Adaptive ground fault location method for distribution networks

Osteosarcoma (OS) is the most common primary malignant bone tumor in adolescents, and outcomes for metastatic disease have remained poor, highlighting the need for molecular biomarkers. We integrated three Gene Expression Omnibus (GEO) mRNA expression datasets (GSE12865, GSE14359, and GSE246405) to identify differentially expressed genes (DEGs) between OS and non-malignant bone-related controls. Overlapping DEGs were used to build a protein-protein interaction network, and hub genes were prioritized using multiple network topology algorithms. Prognostic associations were evaluated using the R2 Genomics Platform. Putative upstream miRNAs targeting the top candidate were obtained from prediction databases and intersected with dysregulated circulating miRNAs from GSE65071 (localized OS plasma vs. healthy controls). Functional enrichment analyses (Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and cancer hallmarks) were performed to contextualize the candidate signature. We identified 107 overlapping DEGs and prioritized eight hub genes. CD81 was significantly associated with overall survival (Bonferroni-adjusted p = 0.043) and showed reduced expression in OS tissues and cell line models. hsa-miR-582-5p was nominated as a candidate miRNA predicted to target CD81 and was upregulated in OS plasma. Enrichment results linked the signature to angiogenesis, extracellular matrix remodeling, focal adhesion, and metastasis-associated signatures. These findings support CD81 as a candidate prognostic biomarker and nominate a putative hsa-miR-582-5p-CD81 relationship for future validation.

Conventional approaches for designing and integrating charging stations into the grid are time-consuming and computationally expensive. For the purpose of power quality enhancement of EVCS, more focus has been paid on charging station design infrastructure, hence neglecting the need for the technical design of grid topology. Therefore, this paper focuses on the design and development of multiple distribution grid topologies for topology-aware characterization of power quality in grid-tied solar-powered EV charging stations. The control and energy management strategy is implemented solely to enable consistent grid-PV-EV interaction. The models have been successfully developed and tested for four modes of operations, PV to EV, PV to Grid, V2G and G2V, in MATLAB/Simulink 2022b. From the results, it is clear that the grid voltage THD during V2G remains at 0.01%, 0.08% and 0.01% and the grid-connected current THD remains at 0.19%, 1.88% and 0.19% for three different grid topologies, GT1, GT2 and GT3, respectively, while, during G2V, the voltage THD are valued at 0.02%, 0.05% and 0.03% and the grid-connected current THD at 0.45%, 1.28% and 0.75% for grid topologies GT1, GT2 and GT3 respectively. The results demonstrate that grid topology-aware analysis is required for consistent harmonic characterization of PV-integrated EV charging stations under V2G, G2V and PV-assisted operating modes.

Model inaccuracies arising from operational and environmental uncertainties often compromise the performance of controllers for power electronic systems. This paper develops an end-to-end adaptive control framework to address this challenge by combining physical principles with expert knowledge within a unified Physics-Informed Neural Network architecture. To this end, expert control policies acquired via imitation learning are formulated as differentiable constraints within the loss function. By co-optimizing these expert-knowledge constraints with fundamental physical laws and safety-guided stability regularization, the neural network is trained to simultaneously identify real-time system dynamics and directly map them to an optimal control action. The resulting controller generates adaptive control signals to effectively counteract system uncertainties. Its effectiveness is comprehensively validated across multiple topologies, including DC/DC converters and a DC/AC inverter. Under parameter variations and external disturbances, the proposed method achieves faster transient response and robust stability, exhibiting improved performance over both well-tuned linear controllers and advanced data-driven under adverse conditions.

Distribution network data asset protection considering multiple topology and multi-dimensional knowledge graph

This letter presents an ultra-broadband amplifier architecture that extends the bandwidth of conventional distributed amplifiers beyond their fundamental limitations. The proposed approach combines a baseband (BB) distributed amplifier with a radio frequency (RF) band amplifier, forming a “BB-RF amplifier” topology. To ensure smooth transition between the two bands, band joint adjusters (BJAs) based on transmission lines are introduced to mitigate phase mismatch at the band joint frequency. The amplifier was fabricated in 250 nm InP double heterojunction bipolar transistor (DHBT) technology with a maximum oscillation frequency (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f_{\max }$</tex-math> </inline-formula>) of 480GHz. Measurements show a gain of 11 dB at 1 GHz and a bandwidth of 270 GHz, ranging from 1 to 271 GHz. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f_{\max }$</tex-math> </inline-formula>-normalized bandwidth is 0.56, marking the highest value ever reported.

Free‐riding behavior poses a critical challenge to achieving collective immunity in vaccination campaigns, particularly when individuals make decisions based on short‐term self‐interest. This study investigates how punitive interventions can mitigate free‐riding and enhance vaccination uptake under adaptive decision‐making. We develop an integrated evolutionary epidemic framework that combines evolutionary game theory with Q‐learning, where individuals update their vaccination strategies by weighing vaccination cost, infection risk, vaccine effectiveness, and penalties for noncompliance. The model incorporates spatial interactions on structured populations, allowing local learning feedback and epidemic spreading to coevolve dynamically. Analytical results and numerical simulations reveal that punitive measures reshape the reinforcement learning reward structure, progressively discouraging free‐riding and promoting cooperative vaccination behavior over repeated seasons. Vaccination effectiveness is a critical determinant: punishment alone is insufficient under low efficacy, whereas moderate‐to‐high efficacy regimes enable punitive incentives to align individual learning with collective welfare. Spatial clustering emerges as vaccinated individuals form stable blocks that impede transmission, reducing epidemic size and preventing resurgence. Extending the analysis across multiple network topologies demonstrates that while punitive intervention is broadly effective, its system‐level impact is strongly modulated by network structure through local reinforcement, hub‐driven dynamics, and information propagation. These findings highlight the importance of integrating adaptive learning dynamics, network heterogeneity, and behaviorally informed incentives into epidemic modeling, offering practical guidance for designing robust and resilient vaccination policies that mitigate free‐riding and strengthen population‐level immunity.

Impedance matching represents a fundamental challenge in radio frequency circuit design, where mismatches between source and load impedances cause signal reflections that degrade system performance and efficiency. This research presents a computer-aided design methodology for synthesising optimal impedance matching networks across multiple topology classes including L-networks, pi-networks, T-networks, and transmission line configurations [1]. The developed software implements analytical synthesis algorithms derived from classical network theory combined with numerical optimisation routines that refine component values for maximum bandwidth and minimum return loss. Testing encompassed 150 design scenarios spanning frequencies from 100 MHz to 6 GHz with diverse source and load impedance combinations representative of practical RF applications [2]. The proposed CAD approach achieved median return loss of -33 dB for hybrid topologies, exceeding the -28 dB performance of simple L-networks and substantially surpassing the typical -20 dB specification for RF systems. Bandwidth comparisons demonstrated 149% improvement over manual design methods, with the CAD-optimised networks achieving 112 MHz average bandwidth compared to 45 MHz for manually designed equivalents [3]. Design time averaged 2.3 minutes per network including full electromagnetic simulation and parasitic extraction, representing dramatic acceleration compared to traditional iterative bench-top methods requiring hours of adjustment [4]. The software exports standard netlist formats compatible with commercial simulation tools alongside component bill-of-materials for procurement automation. Validation against fabricated prototypes confirmed agreement within 1.2 dB for return loss and 8% for bandwidth, establishing the methodology as suitable for production design workflows. The research demonstrates that systematic CAD approaches can substantially outperform traditional manual design whilst reducing engineering effort and accelerating time-to-market for RF products [5].

Healthcare IoT network loss their potential of reliability transmission, continuous monitoring, and intervention system due to critical network conditions along with its traffic characteristics. Thereby, it can directly affect the performance of the remote healthcare applications includes healthcare life quality check, patient safety, and clinical effectiveness in terms of QoS and robustness performance. In this paper presents a novel neuro-fuzzy multi-topology adaptive routing (NF-MTAR) method for enhancing reliable transmission of the healthcare IoT network. It integrates the neuro-fuzzy intelligence with multi-topology virtual partitioning enables dynamic optimization of the network resources based on network condition and its traffic characteristic. NF-MTAR method incorporates two unique innovations such as (i) neuro-fuzzy search engine identifies an optimum path to reach specific root node selected by incorporating five key parameters includes traffic flow intensity, resource utilization, residual energy, link quality, and node connectivity. (ii) Virtual Software Defined Networking (V-SDN) provides multi-topology virtual partitioning (elliptical, linear, and random) within the network, carry data transmission over multiple topologies for different traffic critical simultaneously. COOJA simulator is used to create three-layer 6LoWPAN architecture which capable of allowing dynamic network configuration and improve centralized policy management. Evaluation metrics are confirmed that the reasonable improvement is achieved such as high throughput (94.3%), reduce end to end delay (18.4ms), improve energy efficiency (31.2%), network lifetime (42.8%), and reliability (99.8%) by the proposed NF-MTAR method as compared to other state-of-art-methods. Thus, it provides potential improvement for next-generation medical monitoring and intervention system.

This paper presents a data-driven probabilistic framework for analysing power system faults using Monte Carlo simulations. The study evaluates the operational reliability of multiple high-voltage switchgear topologies—including single-busbar systems, double-busbar systems, and ring-type configurations—by modelling the stochastic behaviour of disconnectors, circuit breakers, busbars, and withdrawable switching elements with bypass arrangements. Realistic unavailability parameters derived from statistical reliability data are used to generate fault intervals for each device, enabling the simulation of millions of operational scenarios and capturing both full and partial outage events. The proposed methodology quantifies outage probabilities, identifies critical components, and reveals how devices count, switching logic, and system redundancy influence overall resilience. Results show significant reliability differences between topologies and highlight the importance of optimized substation design for fault tolerance. The developed probabilistic framework provides a transparent and computationally efficient tool to support planning, modernization, and predictive maintenance strategies in transmission and distribution networks. Findings contribute to improved fault diagnosis, enhanced grid stability, and increased reliability in both conventional and renewable-integrated power systems.

Topology has emerged as a fundamental property of many systems yet mostly limited to low dimensions. Here, we reveal the hidden topology in entangled states carrying orbital angular momentum (OAM), in arbitrary dimensions. For two-dimensional systems, we demonstrate multiple skyrmion topologies and their equivalence to ’t Hooft-Polyakov magnetic monopoles, experimentally connecting them to the Higgs field. In higher dimensions, we use non-Abelian gauge fields of SU(d) Yang-Mills theory to predict a rich tapestry of topological maps and their invariants, which we confirm experimentally for dimensionality up to seven, showing an underlying topology of 48 dimensions and a topological spectrum spanning over 17000 invariants. In addition to inducing robustness to perturbation, the topological spectrum enables probing them, by observing their emergent signatures in its non-topological spaces. The only degree of freedom we use to construct the topology is OAM, breaking away from the optical paradigm of polarisation-based spin-textured fields and forgoing the need for quantum state engineering. Our theoretical framework can be extrapolated to any dimension and degree of freedom, opening a distinct path for finding topologies in light.

Heterobifunctional proteolysis-targeting chimeras (PROTACs) have emerged as a powerful strategy to degrade disease-relevant proteins, enabling targeting of previously “undruggable” proteins. Current degrader molecules primarily target cytosolic substrates, yet nearly one-third of the proteome resides in or transits the endoplasmic reticulum (ER), including receptors, secreted factors, and biosynthetic enzymes with high therapeutic relevance. Whether ER-localized proteins can be broadly targeted for induced degradation remains an open question. To address this gap, we employed a panel of fluorescent reporter cell lines and used the dTAG chemical-genetic system to recruit cytosolic E3 ligases. While lumenal substrates segregated from the cytosol were resistant to degradation, recruitment of cytosolic ligases effectively degraded ER membrane proteins across multiple topologies and with post-translational modifications. CRISPR genetic screens revealed that the induced degradation required the expected cullin RING ligase complexes but surprisingly bypassed ER-associated degradation (ERAD) machinery, with the exception of the AAA ATPase VCP. Mechanistic studies demonstrated that substrate ubiquitination was essential for VCP binding, and cleavage of ubiquitin chains released VCP, suggesting a model in which VCP directly extracts substrates independent of a dislocation apparatus. Extending this strategy to an endogenous substrate, we synthesized an HMGCR ERAD-TAC by linking atorvastatin to a cereblon E3 ligase recruiter and found that HMGCR degradation was likewise VCP-dependent. Together, these findings demonstrate that ER membrane proteins are generally susceptible to induced degradation via cytosolic ligase recruitment, uncovering a VCP-centered mechanism that operates independently of membrane-embedded ERAD machinery. This work establishes foundational principles for extending targeted protein degradation to the early secretory pathway.

This work presents a comparative study on the mechanical homogenisation of Triply Periodic Minimal Surface (TPMS) lattice structures in the linear elastic regime, which have attracted significant interest for their unique ability to combine lightweight design with tailored properties. The study investigates the effective mechanical behaviour of Representative Unit Cells (RUCs) generated using the open-source Python tool Microgen. Two homogenisation strategies are considered: (i) Finite Element (FE)-based homogenisation carried out in Abaqus, and (ii) the Mechanics of Structure Genome (MSG), a unified theory for multi-scale constitutive modelling, implemented in an in-house software tool. The comparison encompasses multiple TPMS topologies, including well-studied cases used for validation, namely gyroid and diamond, as well as less-explored ones, such as PMY and F-Rhombic Dodecahedron, to provide new insights. RUCs are analysed across relative densities ranging from 10 to 50%. Equivalent linear elastic properties (Young's moduli, shear moduli, and Poisson's ratios) are derived and compared to assess the consistency, accuracy, and computational efficiency of the two approaches. The results show that both methods yield effective properties with less than 1% difference between them, and less than 5% deviation from experimental data reported in the literature for the effective Young's modulus. Furthermore, the anisotropy of each TPMS topology across the range of relative densities is examined through the directional distribution of Young's moduli. The outcomes are expected to clarify the strengths and limitations of FE versus MSG in capturing the effective behaviour of architected cellular solids, thus supporting the selection of homogenisation strategies for the design of lattice-based lightweight structures.

Although experimental evidence indicates that mitochondrial collapse is a common effect of both Chagas disease and post-ischemic heart failure and that cardiac anatomy and function are partially restored by stem cell therapy, the responsible molecular mechanisms are still under debate. Gene expression data from our publicly accessible transcriptomic dataset obtained by profiling the left ventricle myocardia of mouse models of Chagas disease and post-ischemic heart failure were re-analyzed from the perspective of the Genomic Fabric Paradigm. In addition to the regulation of the gene expression levels, we determined the changes in the strength of the homeostatic control of transcript abundance and the remodeling of the gene networks responsible for the mitochondrial respiration. The analysis revealed that most of the mitochondrial genes assigned to the five complexes of the respiratory chain were significantly downregulated by both Chagas disease and ischemia but exhibited outstanding recovery of the normal expression levels following direct injection of bone-marrow-derived stem cells. However, instead of regaining the original expression control and gene networking, the treatment induced novel mitochondrial arrangements, suggesting that multiple transcriptomic topologies might be compatible with any given physiological or pathological state. This study confirmed several established mechanisms and identified novel gene expression signals, especially Cox4i2, Cox6b1, Cox7b, Ndufb11, and Tmem186, that warrant further investigations. Their broad rescue with cell therapy underscores mitochondria as a convergent, tractable target for cardiac repair.

In this paper, a synthesis method is proposed for designing filtering antennas. Based on the multiple radiation port topology, the prediction of antenna radiation characteristics is achieved by combining the filter synthesis theory with the pattern multiplication theory for array. Firstly, the conventional coupling matrix is expanded according to the number of radiation ports, and the transmission coefficients of each radiation port are calculated from the expanded coupling matrix. Subsequently, the radiation characteristics of the antenna can be calculated based on the transmission coefficient and the gain of each radiation port. The proposed method can be used to design filtering antennas with different topologies and can be used to analyze the impedance response of different antennas. For verification, antennas with the same radiation structure are designed according to different coupling matrices to obtain different radiation characteristic.

Crossfire attacks disrupt network services by targeting critical links of server groups, causing traffic congestion and server failures that prevent legitimate users from accessing services. To counter this threat, this study proposes a novel topology spoofing defense mechanism based on a sequence-based Graph Neural Network–Moving Target Defense (ENRNN-MTD). During the reconnaissance phase, the method employs a GNN to generate multiple random and diverse virtual topologies, which are mapped to various external hosts. This obscures the real internal network structure and complicates the attacker’s ability to accurately identify it. In the attack phase, an IP random-hopping mechanism using a chaotic sequence is introduced to conceal node information and increase the cost of launching attacks, thereby enhancing the protection of critical services. Experimental results demonstrate that, compared to existing defense mechanisms, the proposed approach exhibits significant advantages in terms of deception topology randomness, defensive effectiveness, and system load management.

Wireless Sensor Networks (WSNs) have emerged as a foundational technology for monitoring and data collection in diverse domains such as environmental sensing, smart agriculture, and industrial automation. Precise node localization plays a vital role in WSNs, enabling effective data interpretation, reliable routing, and spatial context awareness. The challenge intensifies in range-free settings, where a lack of direct distance data demands efficient indirect estimation methods, particularly in large-scale, energy-constrained deployments. This work proposes a hybrid localization framework that integrates the distance vector-hop (DV-Hop) range-free localization algorithm with the Fire Hawk Optimizer (FHO), a nature-inspired metaheuristic method inspired by the predatory behavior of fire hawks. The proposed FHODV-Hop method enhances location estimation accuracy while maintaining low computational overhead by inserting the FHO into the third stage of the DV-Hop algorithm. Extensive simulations are conducted on multiple topologies, including random, circular, square-grid, and S-shaped, under various network parameters such as node densities, anchor rates, population sizes, and communication ranges. The results show that the proposed FHODV-Hop model achieves competitive performance in Average Localization Error (ALE), localization ratio, convergence behavior, computational, and runtime efficiency. Specifically, FHODV-Hop reduces the ALE by up to 35% in random deployments, 25% in circular networks, and nearly 45% in structured square-grid layouts compared to the classical DV-Hop. Even under highly irregular S-shaped conditions, the algorithm achieves around 20% improvement. Furthermore, convergence speed is accelerated by approximately 25%, and computational time is reduced by nearly 18%, demonstrating its scalability and practical applicability. Therefore, these results demonstrate that the proposed model offers a promising balance between accuracy and practicality for real-world WSN deployments.

This study aims to design a high-efficiency Pulsed Electric Field (PEF) device for bacterial inactivation in food at a laboratory scale. Bacterial inactivation is closely affected by the consistency, intensity, and duration of the applied electric field; thus, although multiple circuit topologies can be employed in PEF devices, not all provide compactness and efficiency. For this study, an iterative design approach was used to evaluate solid-state cascade PEF topologies by comparing transformer-based (2 A and 5 A) and ignition coil-based (mini-cylinder, cylinder, and canister) configurations. The results show that PEF devices employing a 2 A transformer current and a mini-cylindrical ignition coil offer superior reliability and compactness, making them suitable for treating liquid, semi-solid, and solid samples. Experimental validation of the PEF effect on bacterial membrane damage, using the Vibrio parahaemolyticus strain, demonstrated that an electric field intensity of 10.5 kV/cm caused significant cell damage. Extended treatment durations led to progressively higher bacterial mortality (p &lt; 0.05), as confirmed by flow cytometry and Scanning Electron Microscope (SEM) observations of cell morphology. Therefore, this study successfully developed a PEF device that potentially replaces the traditional Pulse Forming Network (PFN) with a mini-cylindrical ignition coil, thereby improving replicability and accessibility for laboratory-scale applications.