Network Energy Research Articles

Research on proactive defense and dynamic repair of complex networks considering cascading effects

Cascading effects can result in the nonlinear propagation of failures in complex networks, ultimately leading to network collapse. Research on the fault propagation principles, defense strategies, and repair strategies can help mitigate the effects of cascading failures. Especially, proactive defense and dynamic repair are flexible and effective methods to ensure network security. Most studies on the cascade of complex networks are based on the unprocessed initial information of the network. However, marginal nodes are a type of node that cloaks the initial information of the network. In this study, we rank the importance of nodes according to the intensity of network energy confusion after the removal of this node, clarify the meaning of marginal nodes and proposed two methods to screen marginal nodes. The results indicated that the proactive removal of marginal nodes can effectively reduce the effect of cascading failures without causing any negative disturbance to the energy flow of the network. In addition, network repair according to the proposed strategy can minimize the cascade effect in the repair process and improve repair efficiency.

Tailoring Network Topology in Mechanically Robust Hydrogels for 3D Printing and Injection.

Tissue engineering and regenerative medicine are confronted with a persistent challenge: the urgent demand for robust, load-bearing, and biocompatible scaffolds that can effectively endure substantial deformation. Given that inadequate mechanical performance is typically rooted in structural deficiencies─specifically, the absence of energy dissipation mechanisms and network uniformity─a crucial step toward solving this problem is generating synthetic approaches that enable exquisite control over network architecture. This work systematically explores structure-property relationships in poly(ethylene glycol)-based hydrogels constructed utilizing thiol-yne chemistry. We systematically vary polymer concentration, constituent molar mass, and cross-linking protocols to understand the impact of architecture on hydrogel mechanical properties. The network architecture was resolved within the molecular model of Rubinstein-Panyukov to obtain the densities of chemical cross-links and entanglements. We employed both nucleophilic and radical pathways, uncovering notable differences in mechanical response, which highlight a remarkable degree of versatility achievable by tuning readily accessible parameters. Our approach yielded hydrogels with good cell viability and remarkably robust tensile and compression profiles. Finally, the hydrogels are shown to be amenable to advanced processing techniques by demonstrating injection- and extrusion-based 3D printing. Tuning the mechanism and network regularity during the cell-compatible formation of hydrogels is an emerging strategy to control the properties and processability of hydrogel biomaterials by making simple and rational design choices.

Dynamic compensation of active and reactive power in distribution systems through PV-STATCOM and metaheuristic optimization

This article presents a heuristic methodology to address the operation problem of PV-STATCOMs, focusing on the dynamic compensation of active and reactive power to minimize daily energy losses and costs associated with purchasing energy in distribution networks. The methodology is developed using a master-slave type optimization approach. In the master stage, potential solutions are generated using an optimization algorithm based on the salp swarm. In contrast, the slave stage evaluates these solutions using the power flow method by successive approximations. The methodology was evaluated in two test systems, with 33 and 69 nodes, varying the power factor of each PV-STATCOM. The results demonstrate a significant reduction in both energy losses in the network and the cost of purchasing energy at the substation. Furthermore, the efficiency of the salp swarm algorithm for effective resolution of the problem is confirmed. This approach contributes to optimizing the operation of PV-STATCOMs and highlights the proposed algorithm's applicability and effectiveness in practical distribution network environments.

Shenling Baizhu powder alleviates non-alcoholic fatty liver disease by modulating autophagy and energy metabolism in high-fat diet-induced rats

BackgroundNonalcoholic fatty liver disease (NAFLD) has emerged as a burgeoning health problem worldwide, but no specific drug has been approved for its treatment. Shenling Baizhu powder (SL) is extensively used to treat NAFLD in Chinese clinical practice. However, the therapeutic components and pharmacological mechanisms of SL against NAFLD have not been thoroughly investigated. PurposeThis study aims to investigate the pharmacological impact and molecular mechanism of SL on NAFLD. MethodsFirst, we established an animal model of NAFLD by high-fat diet (HFD) feeding, and evaluated the therapeutic efficacy of SL on NAFLD by physiological, biochemical, pathological, and body composition analysis. Next, the effect of SL on autophagic flow in NAFLD rats was evaluated by ultrastructure, immunofluorescence staining, and western blotting. Moreover, an integrated strategy of targeted energy metabolomics and network pharmacology was performed to characterize autophagy-related genes and explore the synergistic effects of SL active compounds. UPLC-MS/MS, molecular docking combined with in vivo and in vitro experiments were conducted to verify the key compounds and genes. Finally, a network was established among SL-herb-compound-genes-energy metabolites-NAFLD, which explains the complicated regulating mechanism of SL on NAFLD. ResultsWe discovered that SL decreased hepatic lipid accumulation, hepatic steatosis, and insulin resistance, and improved systemic metabolic disorders and pathological abnormalities. Subsequently, an integrated strategy of targeted energy metabolomics and network pharmacology identified quercetin, ellagic acid, kaempferol, formononetin, stigmasterol, isorhamnetin and luteolin as key compounds; catalase (CAT), AKT serine/threonine kinase 1 (AKT), nitric oxide synthase 3 (eNOS), NAD(P)H quinone dehydrogenase 1 (NQO1), heme oxygenase 1 (HO-1) and hypoxia-inducible factor 1 subunit alpha (HIF-1α) were identified as key genes; while nicotinamide adenine dinucleotide phosphate (NADP) and succinate emerged as key energy metabolites. Mechanistically, we revealed that SL may exert its anti-NAFLD effect by inducing autophagy activation and forming a comprehensive regulatory network involving key compounds, key genes, and key energy metabolites, ultimately alleviating oxidative stress, endoplasmic reticulum stress, and mitochondrial dysfunction. ConclusionOur study demonstrated the therapeutic effect of SL in NAFLD models, and establishes a basis for the development of potential products from SL plant materials for the treatment of NAFLD.

Clean energy pipeline energy storage system and its economy

The economic problem of a clean energy heating system under a peak and valley electricity pricing system is investigated, and a pipe network energy storage system is correspondingly proposed to solve this problem. The system makes use of large-capacity primary network pipe network water storage to store heat during the valley electricity hours when the electricity price is lower, and releases the stored heat to supply heat during the peak electricity hours when the electricity price is higher, with a view to reducing the operating time of the heating system during the peak electricity hours when the electricity price is higher. The principle of the system and the efficient regulation scheme are given, and a mathematical model of primary pipe network energy storage and release is established, and the reliability of the model is verified with a heating project in Qingdao as an example, and the results show that the maximum temperature deviation of the return temperature of the primary pipe network obtained by the calculation of the energy storage and release model is 0.23°C, and the maximum temperature hysteresis error is in 15%, which meets the requirements of engineering practice. Based on the energy storage model, the simulation of the actual heating economy of the project using the pipe network energy storage system was carried out, and the results showed that compared with the original scheme based on the maximum heat load adjustment of the proportion of heating units and boilers, the electricity consumption of the project using the pipe network heat storage scheme was increased by 18.8% in the whole heating season, the total operating costs were reduced by 14.5%, with an annual saving of 800,600 yuan in operating costs, and the payback period for the investment was relatively short, at 2.1 years, which is of good economy.

Design and implementation of duty cycle-based futuristic clustering technique in WSN

In recent times, wireless sensor networks (WSNs) and their applications have exhibited a remarkable surge. These networks strive to devise and implement strategies that optimize network energy utilization, thereby extending their operational lifespan. An energy efficient network can be achieved using renewable source of energy and by controlling the duty cycle of nodes. The pivotal role of duty cycle in curtailing energy consumption in WSNs cannot be overstated. In this work, we introduce a novel duty cycle based futuristic clustering technique (DCBFCT) employing a nearest neighbor approach. This technique selectively induces sleep and awake modes in nodes, effectively minimizing the network’s overall energy consumption and, consequently, prolonging its lifespan. It calculates optimal node duty cycle values based on distance. Results demonstrate a substantial reduction in energy consumption, exhibiting an improved network lifetime. Empirical results presented in this study not only affirm the effectiveness of DCBFCT but also contribute valuable insights toward the development of sustainable and resilient WSNs in the era of burgeoning sensor network applications. The experimentation is conducted using the MATLAB/Simulink tool, considering diverse cases. The scalability and versatility of DCBFCT make it suitable for deployment in real-world applications, ranging from environmental monitoring to industrial automation.

Multilayer analysis of energy networks

Modern interconnected and vulnerable energy infrastructures highlight the critical need for resilience and robustness to maintain the functioning of modern societies amidst growing environmental and operational challenges. This study introduces both multilayer and multiplex network analysis of Belgium’s electricity and gas systems, addressing the complex interdependence inherent in such infrastructure. The resilience is evaluated through random and targeted disruptions, while the robustness is assessed by employing the Molloy-Reed criteria for structural integrity and setting minimum operational thresholds for continuity. Multiplex layer similarities are assessed and subnetworks are identified. The coverage evolution metric quantifies the network’s adaptability under various disruption scenarios. Belgium’s energy infrastructure is used as a case study to uncover critical nodes from different centrality perspectives whose stability is essential for overall network resilience and identify vulnerabilities to disruptions, whether random or targeted. Our insights into the structural robustness, cluster cohesion, and Navigability of energy networks pave the way for more informed decisions in energy infrastructure planning and complex systems management, offering valuable lessons that can be applied globally.

An enumerative analysis of major power blackouts in Asian region in the last two decades

Power System represents an interconnected network, where generation, transmission and distribution of electrical energy takes place. They are complex systems constituting various components like synchronous generators, motors, transformers, circuit breakers, transmission lines etc. A power system is required to operate in a reliable manner, thus providing continuous power supply of good quality. This requires the power system to be stable. Blackouts can occur due to equipment failure, human errors, natural disasters, overloading / imbalance, cyber-attacks, lack of redundancy etc. When they act in a combined manner/ if one of those issues lead to the other issue, widespread and long - lasting blackouts happen. Severities of the blackouts depend on geographical extent, duration of its existence, financial repercussions, and initial services being broken, public safety. This paper discusses the major blackouts that have occurred in the Asian region, in the last two decades, along with their causes and consequences.

“Clustering Protocol Based on Territorial Energy for Random Deployment of Wireless Sensor Networks"

Wireless Sensor Networks (WSNs) have become increasingly essential for monitoring and gathering data in diverse environments. The efficient utilization of energy resources is crucial for prolonging the network lifetime in WSNs. This paper presents a comparative analysis of three prominent clustering protocols: Low Energy Adaptive Clustering Hierarchy (LEACH), Residual Energy Aware Clustering (REAC), and Territorial Energy-Driven Clustering Protocol (TEDC). TEDC introduces a novel approach where cluster heads are elected based on territorial dominance and energy levels, aiming to enhance energy efficiency and prolong network lifetime. The performance of LEACH, REAC, and TEDC is evaluated under various scenarios, including node density, network size, and communication range. Key performance metrics such as energy consumption, network lifetime, and packet delivery ratio are analyzed to assess the effectiveness of each protocol. The results provide valuable insights into the comparative strengths and weaknesses of these protocols, aiding network designers and researchers in selecting the most suitable protocol for specific WSN deployments. Additionally, the paper discusses potential optimizations and future research directions to further enhance the performance of clustering protocols in WSNs.    

A new evolutionary forest model via incremental tree selection for short-term global solar irradiance forecasting under six various climatic zones

The increasing integration of solar sources into the energy mix presents significant challenges, particularly in short-term energy management. Accurate solar irradiance forecasts can greatly assist solar power plant operators and energy network managers in making informed decisions about energy production and consumption. This paper aims to develop a new accurate forecasting model for short-term global solar irradiance based on an innovative evolutionary forest approach. Our model, baptized EFITS, performs incremental tree selection through appropriate evolutionary operators maintaining a good tradeoff between accuracy and diversity, generating progressively near-optimal decision trees to construct the final evolutionary forest forecaster. This new evolution process also automatically selects near-optimal input parameters, enhancing the overall model accuracy and generalization ability. Six climatically diverse locations in Morocco and three types of inputs (endogenous, exogenous, and hybrid) are used to assess the performance of the proposed. The results demonstrate that our proposed model exhibits excellent performance across all studied sites and horizons. Among all input types, hybrid inputs delivered the best forecasting accuracy across all studied sites and horizons. Notably, the continental climate site (Bni Mellal) achieved the highest accuracy, with nRMSE ranging from 4.94% to 7.54% and nMBE from 0.71% to −0.46% for 1 to 6 h forecasts. Conversely, Ifrane city, characterized by a humid temperate climate, showed the lowest accuracy, with nRMSE ranging from 10.34% to 18.94% and nMBE from 1.21% to −1.54%. Finally, a detailed comparison with benchmarking models (random forest, bagging, gradient boosting, single decision tree, bidirectional long short-term memory network, and scaled persistence models), revealed that our model consistently outperforms them across all tested scenarios, locations, and forecasting horizons.

Intelligent Network-Connected New Energy Vehicle Technology and Application Under the Dual-Carbon Strategy

In responding to the “dual carbon” strategy, intelligent networked new energy vehicle technology plays a crucial role. This type of vehicle combines the advantages of new energy technology and intelligent network technology, effectively reduces carbon emissions in the transportation sector, improves energy utilization efficiency, and contributes to the green transportation system through intelligent transportation management and collaborative work between vehicles, making significant contributions. This article aims to explore the development of intelligent network-connected new energy vehicle technology and applications under the dual-carbon strategy and lay the foundation for the future development direction of the automotive industry.

Bidding strategies for multi-microgrid markets taking into account risk indicators

A large proportion of new energy generation is integrated into the power grid, making it difficult for the power grid system to maintain reliable, stable, and efficient operation. To address these challenges, this article proposes a multiple microgrid hierarchical optimization structure based on energy routers as the core equipment for energy regulation within microgrids. Considering the uncertainty of renewable energy generation within microgrids, a two-layer energy management bidding strategy based on risk indicators is further proposed. In the process of trading, with the goal of maximizing a comprehensive economy, the energy trading model of the distribution network center and energy routers is divided into two sub-objectives for solving. In the first stage, based on the interests and energy supply and demand relationships of each microgrid, a risk assessment model considering wind and solar uncertainty is established to determine the risk preferences and expected returns of each microgrid. In the second stage, the original problem is decomposed into two sub-problems: the minimum cost sub-problem and the maximum transaction volume sub-problem. An asymmetric bargaining mechanism is adopted to determine the production and sales payment of the microgrid containing energy routers based on the contribution values of energy routers in each microgrid. Finally, the rationality and effectiveness of energy routers as an intelligent decision-maker in energy optimization are verified in a three-node system.

Optimal pricing of integrated community energy system for building prosumers with P2P multi-energy trading

Buildings are typically integrated with multiple distributed energy resources (DERs), enabling them to act as building prosumers engaged in both energy production and consumption. Peer-to-peer (P2P) energy trading among building prosumers is crucial to improve their benefits. However, further exploration is required to balance the benefits between building prosumers and the system operator (e.g., the integrated community energy system (ICES) operator) since they are different entities. In this context, this paper proposes a comprehensive network charge and energy sale pricing scheme for the ICES operator on heterogeneous building prosumers with P2P multi-energy trading. The interaction between the ICES operator and building prosumers is modelled as a bi-level optimization problem that belongs to the hierarchical structure, while considering the heterogeneity of thermal insulation performance of buildings. At the upper level, the ICES operator optimizes the electricity/heat network charge prices and electricity/heat sale prices to maximize its revenue. At the lower level, building prosumers with a parallel structure optimize the schedules including P2P multi-energy trading and buildings' heating loads to minimize their costs. Furthermore, to address the bi-level optimization problem with parallel and hierarchical coupling structures, an accelerated asynchronous distributed algorithm based on alternating direction method of multipliers (ADMM) is developed, integrating a warm start strategy and a dual update accelerated iteration strategy for further improving computational efficiency. Finally, case studies demonstrate that the proposed scheme can effectively benefit both the ICES operator and building prosumers in P2P multi-energy trading at the same time. Meanwhile, the feasibility and effectiveness of the proposed algorithm are validated.

Advanced Machine Learning-Driven Security and Anomaly Identification in Inverter-Based Cyber-Physical Microgrids

In a landscape dominated by the digitalization of energy networks, safeguarding cyber-physical microgrids and against breaches anomalies emerges as a critical imperative. This study pioneers inventive methodologies for Intelligent Detection of Intrusions and Anomalies in Inverter-centric Cyber-Physical Microgrids, leveraging state-of-the-art machine learning paradigms. Using the combined power of LSTM and Convolutional Neural Networks, our suggested approach finds breaches and aberrations in microgrid structures with remarkable accuracy and efficiency. Additionally, we integrate the effectiveness of Gradient Boosting Machines to enhance the overall detection capabilities. Experimental findings underscore the efficacy of our machine learning-driven approach, with CNN achieving a precision of 95%, recall of 92%, and F1-score of 93% in intrusion detection, while LSTM attains a precision of 93%, recall of 91%, and F1-score of 92% in anomaly identification. Furthermore, GBM contributes to achieving an overall efficiency of 96%. Our approach not only bolsters microgrid resilience but also lays the foundation for a secure energy future. In an increasingly interconnected world, the integration of these sophisticated techniques not only bolsters microgrid resilience but also lays the foundation for a secure energy future. By embracing innovation at the convergence of machine learning and energy systems, we stride toward fortified and dependable cyber-physical microgrids.

An Enhanced Mechanism for Fault Tolerance in Agricultural Wireless Sensor Networks

Fault tolerance is a critical aspect for any wireless sensor network (WSN), which can be defined in plain terms as the quality of being dependable or performing consistently well. In other words, it may be described as the effectiveness of fault tolerance in the event of crucial component failures in the network. As a WSN is composed of sensors with constrained energy resources, network disconnections and faults may occur because of a power failure or exhaustion of the battery. When such a network is used for precision agriculture, which needs periodic and timely readings from the agricultural field, necessary measures are needed to handle the effects of such faults in the network. As climate change is affecting many parts of the globe, WSN-based precision agriculture could provide timely and early warnings to the farmers about unpredictable weather events and they could take the necessary measures to save their crops or to lessen the potential damage. Considering this as a critical application area, in this paper, we propose a fault-tolerant scheme for WSNs deployed for precision agriculture. Along with the description of our mechanism, we provide a theoretical operational model, simulation, analysis, and a formal verification using the UPPAAL model checker.

Hybrid Secure Cluster-Based Routing Algorithm for Enhanced Security and Efficiency in Mobile Ad Hoc Networks

ABSTRACT This research addresses critical gaps in Mobile Ad hoc Networks (MANETs) by proposing a hybrid secure cluster-based routing algorithm, focusing on enhancing network security, robustness, and reliability through multipath routing. Methodologically, the approach integrates Convolutional Neural Networks (CNN) for optimal path routing and Emperor Penguin Optimization (EPO) for clustering, introducing a novel combination for efficient cluster head selection. A novel contribution lies in the development of a prediction technique utilizing a trust assessment algorithm to calculate direct trust ratings at each node, incorporating fuzzy values between zero and one. Trust values are further influenced by node performance, adding a dynamic dimension to the trust evaluation process. Key novelties include the emphasis on energy efficiency, network longevity, remaining energy, security level, bandwidth, and packet delivery ratio as evaluation criteria. The proposed CNN-EPO model demonstrates superior results compared to traditional routing protocols, achieving a remarkable 95% energy efficiency, a heightened security level of 99%, and a throughput reaching up to 8 Mbps. Additionally, the Packet Delivery Ratio (PDR) attains close to 99% and routing overhead remains below 0.5, ensuring efficiency in challenging network scenarios with 50 adversaries. In summary, this research contributes a comprehensive solution to MANET challenges, introducing a novel hybrid routing algorithm, incorporating advanced methodologies for path optimization and clustering. These outcomes highlight how important the suggested strategy is to improve the existing state of the art in MANETs.

Spatio-temporal patterns and cascading risks of embodied energy flows in China

The increasingly complex interrelation between energy use and trade is challenging the secure management of energy. However, limited knowledge of the cascading risks of embodied energy in trade is understood. An embodied energy network of China's interprovincial trade was established based on China's multiregional input-output tables for 2012 and 2017. The pivotal nodes, main flows, and flow trajectories within the embodied energy network were identified using the complex network model. Subsequently, the cascading effects within the embodied energy network were simulated using a cascading failure model. The results indicate that a substantial portion of embodied energy flows is concentrated within a few provinces and sectors. Within the provincial-sectoral network, approximately 50 % of the edges carry 90 % of the embodied energy flows. Sectors exhibiting robust influence, significant intermediary capacity, and pronounced centrality assume pivotal positions within the network. Due to cascading effects, a supply crisis in one crucial province or sector can spread quickly, affecting the entire energy network. This study contributes to ensuring regional energy supply security and offers fresh insights into mitigating cascading risks in the energy system within and beyond China.

Crossover Boosted Grey Wolf Optimizer‐based framework for leader election and resource allocation in Intrusion Detection Systems for MANETs

AbstractMobile Ad hoc Networks (MANETs) is a self‐organizing networks without having a fixed infrastructure for making them susceptible to security threats. Intrusion Detection Systems (IDS) promotes security in MANETs by identifying malicious activities. Leader election is a fundamental aspect of IDS deployment, impacting resource allocation and system efficiency. This article presents a novel approach, the Crossover Boosted Grey Wolf Optimizer (CBGWO), for leader election and resource allocation in MANET‐based IDS. The proposed CBGWO algorithm integrates the Grey Wolf Optimizer (GWO) with innovative crossover operators that have an ability to enhance the capabilities of exploration and exploitation in the optimization process. The leader election problem is solved through applying multi‐objective optimization by considering energy consumption, reputation, and communication overhead. Objective functions are defined to maximize energy efficiency while maintaining a balanced distribution of leadership roles. Extensive simulations are conducted, varying network densities and the percentage of selfish nodes. Results demonstrate the effectiveness of the CBGWO‐based model in balancing energy consumption, prolonging network lifespan, and enhancing intrusion detection by comparing different state‐of‐the‐art models such as PCA‐FELM, CTAA‐MPSO, FLS‐RE, LEACH, DCAIDS, WOA‐GA, and VOELA. The proposed model achieved an energy consumption of 4.31 J, network lifetime of 560.482 ms, and average intrusion detection latency of 0.12 s, respectively. The proposed model outperforms than existing random and connectivity‐based leader election methods that is evaluated by taking main consideration of energy efficiency and network survivability. This research contributes to the field by introducing a robust algorithm for leader election in MANET‐based IDS, addressing challenges posed by network dynamics and resource constraints. The CBGWO‐based approach showcases its potential to achieve effective leader election and efficient resource allocation, thereby enhancing the security and sustainability of MANETs.

Reconfiguration of distribution network-based wind energy resource allocation considering time-varying load using hybrid optimization method

This study proposed a method for optimizing a radial distribution network by integrating wind turbine allocation, considering fluctuating load demands, through the use of a hybrid Grey Wolf Optimizer-Genetic Algorithm (HGWOGA). This approach aims to decrease the network’s energy loss costs. By incorporating genetic algorithm techniques, the method enhances the Grey Wolf Optimizer’s efficiency, speeding up convergence and avoiding local optima. The strategy determines the network’s open lines and the placement and capacity of wind turbines, adhering to radiality and operational constraints. It categorizes load levels into residential, commercial, and industrial, providing a comprehensive analysis of energy losses and their cost implications under various scenarios, including constant and dynamic loads. The study suggests that managing time-varying demand offers a more accurate depiction of network challenges, enabling effective reconfiguration throughout different demand phases. Moreover, HGWOGA demonstrates its ability to find the global optimum efficiently, even with reduced population sizes—a feat not achievable with the Grey Wolf Optimizer alone. Comparative analyses reveal HGWOGA’s effectiveness in curbing network energy loss costs better than previous methodologies. By simultaneously applying network reconfiguration and wind turbine allocation, as opposed to merely reconfiguring the network, this approach notably reduces power loss, diminishes the cost of losses, and enhances the voltage profile. This synergistic strategy leverages the dynamic allocation of wind turbines within the network, optimizing energy flow and distribution efficiency, thereby offering a substantial improvement over conventional network reconfiguration methods.

VENet: Variational energy network for gland segmentation of pathological images and early gastric cancer diagnosis of whole slide images

Background and objective: Gland segmentation of pathological images is an essential but challenging step for adenocarcinoma diagnosis. Although deep learning methods have recently made tremendous progress in gland segmentation, they have not given satisfactory boundary and region segmentation results of adjacent glands. These glands usually have a large difference in glandular appearance, and the statistical distribution between the training and test sets in deep learning is inconsistent. These problems make networks not generalize well in the test dataset, bringing difficulties to gland segmentation and early cancer diagnosis.Methods: To address these problems, we propose a Variational Energy Network named VENet with a traditional variational energy Lv loss for gland segmentation of pathological images and early gastric cancer detection in whole slide images (WSIs). It effectively integrates the variational mathematical model and the data-adaptability of deep learning methods to balance boundary and region segmentation. Furthermore, it can effectively segment and classify glands in large-size WSIs with reliable nucleus width and nucleus-to-cytoplasm ratio features.Results: The VENet was evaluated on the 2015 MICCAI Gland Segmentation challenge (GlaS) dataset, the Colorectal Adenocarcinoma Glands (CRAG) dataset, and the self-collected Nanfang Hospital dataset. Compared with state-of-the-art methods, our method achieved excellent performance for GlaS Test A (object dice 0.9562, object F1 0.9271, object Hausdorff distance 73.13), GlaS Test B (object dice 94.95, object F1 95.60, object Hausdorff distance 59.63), and CRAG (object dice 95.08, object F1 92.94, object Hausdorff distance 28.01). For the Nanfang Hospital dataset, our method achieved a kappa of 0.78, an accuracy of 0.9, a sensitivity of 0.98, and a specificity of 0.80 on the classification task of test 69 WSIs.Conclusions: The experimental results show that the proposed model accurately predicts boundaries and outperforms state-of-the-art methods. It can be applied to the early diagnosis of gastric cancer by detecting regions of high-grade gastric intraepithelial neoplasia in WSI, which can assist pathologists in analyzing large WSI and making accurate diagnostic decisions.