Efficient Network Research Articles

Optimal Co-Planning of Multi-Port Soft Open Points and Energy Storage Systems for Improving Hosting Capacity and Operation Efficiency in Distribution Networks

Optimal Co-Planning of Multi-Port Soft Open Points and Energy Storage Systems for Improving Hosting Capacity and Operation Efficiency in Distribution Networks

A novel game approach to integrating traffic assignment and signal control for enhanced efficiency and environmental performance in mixed networks

A novel game approach to integrating traffic assignment and signal control for enhanced efficiency and environmental performance in mixed networks

Enhancing micro-scale SiOx anode durability: Electro-mechanical strengthening of binder networks via anchoring carbon nanotubes with carboxymethyl cellulose

With the increasing prevalence of lithium-ion batteries (LIBs) applications, the demand for high-capacity next-generation materials has also increased. SiOx is currently considered a promising anode material due to its exceptionally high capacity for LIBs. However, the significant volumetric changes of SiOx during cycling and its initial Coulombic efficiency (ICE) complicate its use, whether alone or in combination with graphite materials. In this study, a three-dimensional conductive binder network with high electronic conductivity and robust elasticity for graphite/SiOx blended anodes was proposed by chemically anchoring carbon nanotubes and carboxymethyl cellulose binders using tannic acid as a chemical cross-linker. In addition, a dehydrogenation-based prelithiation strategy employing lithium hydride was utilized to enhance the ICE of SiOx. The combination of these two strategies increased the CE of SiOx from 74% to 87% and effectively mitigated its volume expansion in the graphite/SiOx blended electrode, resulting in an efficient electron-conductive binder network. This led to a remarkable capacity retention of 94% after 30 cycles, even under challenging conditions, with a high capacity of 550 mA h g−1 and a current density of 4 mA cm−2. Furthermore, to validate the feasibility of utilizing prelithiated SiOx anode materials and the conductive binder network in LIBs, a full cell incorporating these materials and a single-crystalline Ni-rich cathode was used. This cell demonstrated a ∼27.3% increase in discharge capacity of the first cycle (∼185.7 mA h g−1) and exhibited a cycling stability of 300 cycles. Thus, this study reports a simple, feasible, and insightful method for designing high-performance LIB electrodes.

Efficient Physics-Informed Neural Network for Ultrashort Pulse Dynamics in Optical Fibers

Efficient Physics-Informed Neural Network for Ultrashort Pulse Dynamics in Optical Fibers

FGA-YOLO: A one-stage and high-precision detector designed for fine-grained aircraft recognition

Aircraft recognition in remote sensing images (RSIs) plays a pivotal role in various applications, such as disaster relief, airport monitoring. However, various aircraft types exhibit subtle inter-class variations, and the same model can display diverse visual characteristics under varying imaging conditions, presenting a significant challenge for the fine-grained aircraft recognition. This paper proposes an efficient detection model called FGA-YOLO. Firstly, we introduce a triple feature fusion module based on feature selection (STFF) to aggregate feature maps from three different levels, thereby constructing an efficient feature pyramid network that enables the network to capture salient information hidden across different levels of feature maps. Furthermore, we design a global multi-scale gather-and-distribute module (GMGD) to inject global information into the network, enhancing the extraction of the objects’ key discriminative features. Finally, we develop EMA Slide Loss by employing exponential moving average (EMA) method to mitigate the imbalance between easy and hard samples in aircraft recognition. Extensive experiments on two aircraft recognition datasets (i.e., MAR20 and FAIR1M) demonstrate that our proposed method can significantly improve the accuracy of fine-grained aircraft recognition. Moreover, experiments on embedded devices demonstrate that the proposed FGA-YOLO is also friendly for lightweight deployment.

A Novel Nickel and Cobalt-Layered Double Hydroxide@Nickel Borate with Three-Dimensional Flower-like Structure as Electrode Material for High-performance Hybrid Supercapacitor

It is crucial for enhancing electrochemical properties of existing supercapacitors to design and develope electrode materials with unique structures. Herein, a novel design of nickel and cobalt-layered double hydroxide@nickel borate (NiCo-LDH@Ni(BO2)2) with three-dimensional flower-like structure as the positive by a two-step method for asymmetric supercapacitor is reported. The synthesis strategy of the unique structure formed by stacking two-dimensional lamels provides efficient mesoporous networks, ion diffusion channels for quicker electron transfer, and enhances more active sites for nickel and cobalt-layered double hydroxide (NiCo-LDH) and nickel borate (Ni(BO2)2). The specific electric capacity of optimized NiCo-LDH@Ni(BO2)2 composite achieves 1373 F·g−1 at 0.5 A·g−1 in a three-electrode system. The self-assembled hybrid supercapacitors (HSC) NiCo-LDH@Ni(BO2)2//AC owns the an maximum energy density of 46.5 Wh·kg-1 at the power density 850 W·kg-1. Meanwhile, it displays significant cycle stability with an impressive capacitance retention rate of 84.9% over 5000 charging/discharging cycles. In the end, two serial-connected HSC devices can light up (Light Emitting Diodes) LEDs with difference color (red, yellow and green). Hence, the present research can match the demand of high-property electrode materials for energy storing equipment in the future.

2A,6A-F-Hepta-O-tert-butyldimethylsilyl α-cyclodextrin - A carbohydrate undecaol with all OH groups visible in NMR indicative of a partially disrupted hydrogen bond network.

During a synthesis of the well-known and useful building block 6A-F-hexa-O-tert-butyldimethylsilyl α-cyclodextrin (2) by silylation of α-cyclodextrin (1) we isolated as a byproduct the oversilylated 2A,6A-F-hepta-O-tert-butyldimethylsilyl α-cyclodextrin (3) where one 2-OH group has also been silylated. This unsymmetrical new compound has a remarkable 1H NMR spectrum in CDCl3 where all 11 alcohol groups are visible. We have analyzed the spectrum of 3 using 1D and 2D 800MHz NMR and are able to assign all the 11 alcohol protons. The remarkable chemical shifts of these protons are interpreted as being due to a partial disruption of an otherwise efficient hydrogen bond network.

An Efficient Large Kernel Convolution Network Designed for Neural Processing Unit

An Efficient Large Kernel Convolution Network Designed for Neural Processing Unit

Functional MRI and cognition in multiple sclerosis-Where are we now?

Multiple sclerosis-related cognitive impairment (MSrCI) affects most patients with multiple sclerosis (MS), significantly contributing to disability and socioeconomic challenges. MSrCI manifests across all disease stages, mainly impacting working memory, information processing, and attention. To date, the underlying mechanisms of MSrCI remain unclear, with its pathogenesis considered multifactorial. While conventional MRI findings correlate with MSrCI, there is no consensus on reliable imaging metrics to detect or diagnose cognitive impairment (CI). Functional MRI (fMRI) has provided unique insights into the brain's neuroplasticity mechanisms, revealing evidence of compensatory mechanisms in response to tissue damage, both beneficial and maladaptive. This review summarizes the current literature on the application of resting-state fMRI (rs-fMRI) and task-based fMRI (tb-fMRI) in understanding neuroplasticity and its relationship with cognitive changes in people with MS (pwMS). Searches of databases, including PubMed/Medline, Embase, Scopus, and the Web of Science, were conducted for the most recent fMRI cognitive studies in pwMS. Key findings ifrom rs-fMRI studies reveal disruptions in brain connectivity and hub integration, leading to CI due to decreased network efficiency. tb-fMRI studies highlight abnormal brain activation patterns in pwMS, with evidence of increased fMRI activity in earlier disease stages as a beneficial compensatory response, followed by reduced activation correlating with increased lesion burden and cognitive decline as the disease progresses. This suggests a gradual exhaustion of compensatory mechanisms over time. These findings support fMRI not only as a diagnostic tool for MSrCI but also as a potential imaging biomarker to improve our understanding of disease progression.

Evaluation of fracture network efficiency to CO2 storage with a DFN approach

Evaluation of fracture network efficiency to CO2 storage with a DFN approach

Optimizing JavaScript application performance: A comprehensive guide

Modern JavaScript applications have evolved significantly, presenting both opportunities and challenges in web development. This comprehensive article explores various optimization strategies across JavaScript loading, code-level improvements, network efficiency, CSS optimization, data management, and performance monitoring. The article examines how modern frameworks impact application complexity and discusses practical approaches to maintain optimal performance while delivering feature-rich experiences. Through an article of client-side and server-side optimization techniques, caching strategies, and monitoring systems, this research provides insights into creating high-performance web applications that meet contemporary user expectations while managing technical complexity.

Supply chain management: Balancing efficiency and environmental responsibility

Supply chain management (SCM) is a cornerstone of global commerce, driving efficiency and cost-effectiveness in production and distribution networks. However, the rising urgency of environmental sustainability has placed traditional SCM practices under scrutiny, as their reliance on linear models often leads to resource depletion, carbon emissions, and waste generation. Balancing efficiency with environmental responsibility requires a paradigm shift toward sustainable supply chain practices that integrate economic, social, and ecological goals. This approach emphasizes optimizing resource utilization, minimizing waste, and reducing the environmental footprint without compromising operational efficiency. This article explores the multifaceted challenges and opportunities of aligning supply chain efficiency with sustainability. It examines key practices such as green procurement, waste reduction, and the adoption of circular economy principles that promote recycling, reuse, and resource regeneration. The role of technology, including artificial intelligence, blockchain, and IoT, in enhancing supply chain transparency and efficiency is also discussed. Additionally, the article highlights the importance of collaboration among stakeholders—suppliers, manufacturers, policymakers, and consumers—in fostering sustainable supply chains. Global examples and case studies from industries such as manufacturing, retail, and agriculture illustrate the feasibility and benefits of sustainable SCM. Metrics for assessing success, barriers to scaling sustainable practices, and future directions for innovation and policy are examined. By integrating efficiency with environmental responsibility, sustainable supply chain management emerges as a vital strategy for achieving long-term economic resilience and environmental stewardship.

Efficient deep neural network training via decreasing precision with layer capacity

Low-precision training has emerged as a practical approach, saving the cost of time, memory, and energy during deep neural networks (DNNs) training. Typically, the use of lower precision introduces quantization errors that need to be minimized to maintain model performance, often neglecting to consider the potential benefits of reducing training precision. This paper rethinks low-precision training, highlighting the potential benefits of lowering precision: (1) low precision can serve as a form of regularization in DNN training by constraining excessive variance in the model; (2) layer-wise low precision can be seen as an alternative dimension of sparsity, orthogonal to pruning, contributing to improved generalization in DNNs. Based on these analyses, we propose a simple yet powerful technique–DPC (Decreasing Precision with layer Capacity), which directly assigns different bit-widths to model layers, without the need for an exhaustive analysis of the training process or any delicate low-precision criteria. Thorough extensive experiments on five datasets and fourteen models across various applications consistently demonstrate the effectiveness of the proposed DPC technique in saving computational cost (−16.21%–−44.37%) while achieving comparable or even superior accuracy (up to +0.68%, +0.21% on average). Furthermore, we offer feature embedding visualizations and conduct further analysis with experiments to investigate the underlying mechanisms behind DPC’s effectiveness, enhancing our understanding of low-precision training. Our source code will be released upon paper acceptance.

Leveraging SDN for scalable and sustainable fat tree networks: a multi-objective performance and energy efficiency evaluation of an 8-pod fat tree data center

Modern data centers increasingly rely on Software-Defined Networking (SDN) to address challenges related to scalability, performance, and efficient resource management. This research investigates the scalability and performance optimization of fat-tree topology within SDN environments, focusing on the impact of a sleep mode technique on network efficiency and energy consumption. Using the Mininet emulator, an 8-pod fat-tree network is simulated and compared against traditional routing methods like Equal-Cost Multi-Path (ECMP) and Dijkstra’s algorithm. The findings show that the sleep mode technique improves bandwidth utilization and throughput by reducing energy consumption during low-traffic periods without significantly affecting data flow. In contrast, Dijkstra’s algorithm exhibited reduced throughput due to inefficient path management, while ECMP did not fully optimize load balancing or energy efficiency. The sleep mode approach efficiently redistributes traffic across active switches, preventing congestion and outperforming both Dijkstra and ECMP in terms of average load. The results demonstrate that implementing sleep mode in fat-tree SDN networks enhances both network performance and energy efficiency, offering a practical solution for large-scale data center operations. These findings provide valuable insights for optimizing SDN-based traffic management in modern network infrastructures.

Aberrant Structural-Functional Coupling of Large-scale Brain Networks in Older Women with Subthreshold Depression.

Subthreshold depression (SD) is common in the older population, more so in females than males, and can lead to serious physical and mental ill-health. However, the underlying neurobiology remains unclear. This study used multimodal magnetic resonance imaging (MRI) to investigate the topological organization and coupling of the structural and functional brain networks in older women with SD. We constructed the structural network from diffusion MRI and the functional network from resting-state functional MRI in 50 older women with SD and 52 demographically-matched older women healthy controls (HC). We used graph theory analysis to examine the topological properties of functional and structural networks, and structural-functional connectivity (SC-FC) coupling, and their potential relationship to depressive symptoms. Globally, compared with older women HC, the older women with SD showed lower local efficiency in the structural network but not the functional network. Locally, older women with SD showed altered convergent nodal metrics in the default mode, salience, and sensorimotor network regions in both structural and functional networks. Moreover, SC-FC coupling reduced in older women with SD compared to older women HC. These network metric alterations were correlated with depressive symptoms. Older women with SD showed alterations in both structural and functional networks, and in their coupling, which throw light on the role of large-scale brain networks in older female SD.

Manufacturing Feature Recognition With a Sparse Voxel-Based Convolutional Neural Network

Abstract Automated manufacturing feature recognition is a crucial link between computer-aided design and manufacturing, facilitating process selection and other downstream tasks in computer-aided process planning. While various methods such as graph-based, rule-based, and neural networks have been proposed for automatic feature recognition, they suffer from poor scalability or computational inefficiency. Recently, voxel-based convolutional neural networks have shown promise in solving these challenges but incur a tradeoff between computational cost and feature resolution. This paper investigates a computationally efficient sparse voxel-based convolutional neural network for manufacturing feature recognition, specifically, an octree-based sparse voxel convolutional neural network. This model is trained on a large-scale manufacturing feature dataset, and its performance is compared to a voxel-based feature recognition model (FeatureNet). The results indicate that the octree-based model yields higher feature recognition accuracy (99.5% on the test dataset) with 44% lower graphics processing unit (GPU) memory consumption than a voxel-based model of comparable resolution. In addition, increasing the resolution of the octree-based model enables recognition of finer manufacturing features. These results indicate that a sparse voxel-based convolutional neural network is a computationally efficient deep learning model for manufacturing feature recognition to enable process planning automation. Moreover, the sparse voxel-based neural network demonstrated comparable performance to a boundary representation-based feature recognition neural network, achieving similar accuracy in single-feature recognition without having access to the exact 3D shape descriptors.

Low-power artificial neuron networks with enhanced synaptic functionality using dual transistor and dual memristor.

Artificial neurons with bio-inspired firing patterns have the potential to significantly improve the performance of neural network computing. The most significant component of an artificial neuron circuit is a large amount of energy consumption. Recent literature has proposed memristors as a promising option for synaptic implementation. In contrast, implementing memristive circuitry through neuron hardware presents significant challenges and is a relevant research topic. This paper describes an efficient circuit-level mixed CMOS memristor artificial neuron network with a memristor synapse model. From this perspective, the paper describes the design of artificial neurons in standard CMOS technology with low power utilization. The neuron circuit response is a modified version of the Morris-Lecar theoretical model. The suggested circuit employs memristor-based artificial neurons with Dual Transistor and Dual Memristor (DTDM) synapse circuit. The proposed neuron network produces a high spiking frequency and low power consumption. According to our research, a memristor-based Morris Lecar (ML) neuron with a DTDM synapse circuit consumes 12.55 pW of power, the spiking frequency is 22.72 kHz, and 2.13 fJ of energy per spike. The simulations were carried out using the Spectre tool with 45 nm CMOS technology.

Ethernet Passive Mutual Authentication Scheme on Quantum Networks

In the context of increasing demand for secure and efficient communication networks, addressing the issue of mutual authentication in ethernet passive optical networks (EPONs) has become both valuable and practically significant. This paper proposes a solution based on ideal lattices. The proposed scheme leverages the security of the ring learning with errors (RLWE) problem to establish a robust public-key cryptosystem. By involving ONUs, OLTs, and an SDN controller in the authentication process, it enables mutual authentication through a series of message exchanges facilitated by the SDN controller. Utilizing approximate smooth projection hash functions for secure key exchange and verification, the scheme ensures robust security performance against various attacks, including man-in-the-middle, impersonation, replay, and known key secrecy attacks. Simulation results demonstrate that the proposed solution introduces minimal delay and maintains a high registration success rate compared to traditional authentication methods. Additionally, this paper explores the convergence of quantum network protocols with EPONs, highlighting their potential to achieve unprecedented levels of communication security. Integrating quantum technology with EPON networks, due to the unique security properties of quantum, can also better prevent man-in-the-middle attacks. Secure interception detection techniques based on fundamental quantum properties provide a fundamental security direction for future communication systems, aligning with the growing interest in quantum-resistant cryptographic protocols.

Shipping Logistics Network Optimization with Stochastic Demands for Construction Waste Recycling: A Case Study in Shanghai, China

In this paper, we introduce a shipping logistics network optimization method for construction waste recycling. In our case, construction waste is transported by a relay mode integrating land transportation, shipping transportation, and land transportation. Under the influence of urban economic life, the quantity (demand) of construction waste is uncertain and stochastic. Considering the randomness of construction waste generation, a two-stage stochastic integer programming model for the design of a shipping logistics network for construction waste recycling is proposed, and an accurate algorithm based on Benders decomposition is presented. Based on an actual case in Shanghai, numerical experiments are carried out to evaluate the efficacy of the proposed model and algorithm. Based on an actual case study in Shanghai, numerical experiments demonstrate that the proposed model can help to reduce transportation costs of construction waste. Sensitivity analysis highlights that factors like the penalty for untransported waste and capacity constraints play a crucial role in network optimization decisions. The findings provide valuable theoretical support for developing more efficient and sustainable logistics networks for construction waste recycling.

New Energy Access Distribution Network Line Loss Management Based on Deep Learning and Fuzzy Algorithm

This paper introduces a novel method for managing line losses in energy access distribution networks, utilizing deep learning and fuzzy algorithms. As new energy sources become more integrated into distribution networks, traditional line loss management techniques struggle to address the complexities and uncertainties that arise. To tackle this issue, a comprehensive model for line loss management is developed, leveraging the predictive strengths of deep learning alongside the fuzzy algorithm’s capability to handle uncertainty. The deep learning model forecasts the operational conditions of the power grid following the integration of new energy. At the same time, the fuzzy algorithm assesses and adjusts potential line losses, facilitating effective management of distribution network line losses. The model is trained and validated using a comprehensive dataset from the IEEE 39-Bus System, with performance metrics like Accuracy, F1-score, and recall rate evaluated against traditional models like CNN and GAT. The results show that the FGAN model achieves an accuracy of 96.32%, an F1-score of 95.17%, and a recall rate of 97.05%, significantly outperforming baseline models. Experimental findings indicate that this proposed method substantially reduces line losses and improves the efficiency and reliability of the distribution network. This research offers a fresh perspective on line loss management for intelligent distribution networks in the context of new energy integration, demonstrating considerable practical value.