Scheme For Networks Research Articles

A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks

Recently, the application of fog-computing technology to vehicular ad hoc networks (VANETs) has rapidly advanced. Despite these advancements, challenges remain in ensuring efficient communication and security. Specifically, there are issues such as the high communication and computation load of authentications and insecure communication over public channels between fog nodes and vehicles. To address these problems, a lightweight and secure authenticated key agreement protocol for confidential communication is proposed. However, we found that the protocol does not offer perfect forward secrecy and is vulnerable to several attacks, such as privileged insider, ephemeral secret leakage, and stolen smart card attacks. Furthermore, their protocol excessively uses elliptic curve cryptography (ECC), resulting in delays in VANET environments where authentication occurs frequently. Therefore, this paper proposes a novel authentication protocol that outperforms other related protocols regarding security and performance. The proposed protocol reduced the usage frequency of ECC primarily using hash and exclusive OR operations. We analyzed the proposed protocol using informal and formal methods, including the real-or-random (RoR) model, Burrows–Abadi–Nikoogadam (BAN) logic, and automated validation of internet security protocols and applications (AVISPA) simulation to show that the proposed protocol is correct and secure against various attacks. Moreover, We compared the computational cost, communication cost, and security features of the proposed protocol with other related protocols and show that the proposed methods have better performance and security than other schemes. As a result, the proposed scheme is more secure and efficient for fog-based VANETs.

Intelligent planning and scheduling strategies for low-carbon distribution networks under high proportions of renewable energy integration

Abstract This article proposes an intelligent planning and scheduling strategy for low-carbon distribution networks under the condition of high-penetration renewable energy. Initially, based on the deep neural network and particle swarm optimization algorithm models, it forecasts the power loads as well as photovoltaic and wind generation capacities. Through the multitime scale optimal scheduling theory, it dynamically adjusts the operational state of the distribution network, striving to minimize carbon emissions and enhance energy utilization efficiency. This holds significant guiding implications for the realization of intelligent planning in distribution networks.

Hydrophobic PU fabric with synergistic conductive networks for boosted high sensitivity, wide linear-range wearable strain sensor

Strain sensing fabrics are able to sense the deformation of the outside world, bringing more accurate and real-time monitoring and feedback to users. However, due to the lack of clear sensing mechanism for high sensitivity and high linearity carbon matrix composites, the preparation of high performance strain sensing fabric weaving is still a major challenge. Here, an elastic polyurethane (PU)-based conductive fabric (GCPU) with high sensitivity, high linearity and good hydrophobicity is prepared by a novel synergistic conductive network strategy. The GCPU fabric consists of graphene sheets (GS)/carbon nanotubes (CNTs) elastic conductive layer and a PU elastic substrate. GS and CNTs can be constructed into a synergistic conductive network, and the fabric is endowed with high conductivity (1.193 S m-1). Simulated equivalent circuits show that GS in the conductive network will break violently under applied strain, making the GCPU fabric extremely sensitive (gauge factor 102). CNTs are spatially distributed in GS lamellae, avoiding the phenomenon that the constructed synergistic conductive network is violently fractured under the applied strain, which leads to the decrease of linearity (0.996). Styrene-ethylene-butylene-styrene (SEBS) was used as a dispersant and binder to uniformly disperse and closely bond GS and CNTs into PU fabrics. In addition, the hydrophobicity of SEBS makes the GCPU fabric resistant to water environment (The contact angle is 123°). Due to the good mechanical stability of GCPU fabric, GCPU fabric has a wide strain range (0%-50%) and high cycle stability (over 1000 cycles). In practice, GCPU fabric can accurately simulate and detect the size and deformation motion of human body. Therefore, the successful construction of elastic fabrics with synergistic conductive networks provides a feasible path for the design and manufacture of wearable intelligent fabrics.

Reducing maladaptive behavior in neuropsychiatric disorders using network modification

Neuropsychiatric disorders are caused by many factors and produce a wide range of symptomatic maladaptive behaviors in patients. Despite this great variance in causes and resulting behavior, we believe the maladaptive behaviors that characterize neuropsychiatric disorders are most proximally determined by networks of neurons and that this forms a common conceptual link between these disorders. Operating from this premise, it follows that treating neuropsychiatric disorders to reduce maladaptive behavior can be accomplished by modifying the patient’s network of neurons. In this proof-of-concept computational psychiatry study, we tested this approach in a simple model organism that is controlled by a neural network and that exhibits aversion-resistant alcohol drinking – a key maladaptive behavior associated with alcohol use disorder. We demonstrated that it was possible to predict personalized network modifications that substantially reduced maladaptive behavior without inducing side effects. Furthermore, we found that it was possible to predict effective treatments with limited knowledge of the model and that information about neural activity during certain types of trials was more helpful in predicting treatment than information about model parameters. We hypothesize that this is a general feature of developing effective treatment strategies for networks of neurons. This computational study lays the groundwork for future studies utilizing more biologically realistic network models in conjunction with in vivo data.

Hybrid Optimization-Based Sequential Placement of DES in Unbalanced Active Distribution Networks Considering Multi-Scenario Operation

The increasing penetration of distributed generation (DG) brings about great economic and environmental benefits, while also negatively affecting the operation of distribution networks due to its high intermittency. Although distributed energy storage (DES) can effectively deal with the problems caused by massive DG penetrations by decoupling the generation and consumption of electricity, the placement of DES significantly determines the effectiveness of its capabilities. Unfortunately, existing DES placement studies are commonly based on a balanced network model, whereas practical distribution networks are unbalanced. In addition, existing DES placement studies are mostly based on an extreme scenario and rarely consider the operational complexity resulting from the uncertainties of DGs and loads. To address the aforementioned challenges, this paper proposes a hierarchical and sequential DES placement strategy in distribution networks by considering multi-scenario operations. Specifically, the proposed hierarchical framework for DES placement includes three sequential layers: outer, inter, and inner. In the outer layer, a multi-scenario comprehensive loss sensitivity index (MSCLSI) is first introduced to search for the most effective DES placement location. Subsequently, the sizing and scheduling of DES for the selected location are conducted through coordinated optimization across the inter and inner layers, which can be solved using a hybrid method combining particle swarm optimization and second-order cone programming (PSO-SOCP). Finally, a series of detailed simulations are carried out over the IEEE-33 test system and the experimental results demonstrate that the proposed scheme can provide significant effectiveness and superiority compared to the state-of-the-art schemes.

Systematic identification of the chemical components of leaves and roots of Didymocarpus heucherifolius Hand.-Mazz. based on UHPLC Q-Exactive Orbitrap MS technology coupled with molecular networking strategy

Didymocarpus heucherifolius Hand.-Mazz. is plant of the genus Didymocarpus wall. (Gesneriaceae). Species in this genus have long been used in folk medicine to treat various illnesses, including wounds, kidney stones, inflammations, asthma, influenza, etc. However, there are few studies on chemical components. Crude plant extracts are a complex mixture of several biologically active specialized metabolites. Therefore, a rapid and accurate identification and quantification are critical in phytochemical analysis. An efficient approach based on molecular networking strategy coupled with ultra high-performance liquid chromatography coupled with Q-Exactive Orbitrap tandem mass spectrometry (UHPLC Q-Exactive Orbitrap MS) was used to analyze the chemical components systematically. The chemical was identified using retention time, accurate molecular weight, parent peaks, fragment peaks, fragmentation characteristics, comparative analysis with the literature, reference standards, and standard databases, including mzVault. A total of 108 compounds (32 phenylpropanoids, 14 phenolic acid, 6 fatty acids, 14 organic acid, 9 flavonoid, 15 amino acids, and 28 other components) were identified in the leaves and roots of D. heucherifolius in which 84 and 84 compounds were found in the leaves and roots, respectively. The results showed that 68 components were different in plant roots and leaves, and these compounds were mainly represented in organic acids. Moreover, 50 identical chemical compounds were identified in plant roots and leaves, and these compounds were mainly phenylpropanoid compounds. The UHPLC Q-Exactive Orbitrap MS method was used for the first time to accurately, quickly, and systematically identify the compounds of the roots and leaves of Didymocarpus heucherifolius Hand.-Mazz., thereby providing a theoretical basis for its quality control, in-depth research, and further development.

Differentiated Relational Strategies in Major Supplier Networks: A Blessing or a Curse in Collectivist Cultures?

ABSTRACTExtant literature highlights the positive performance outcomes of a differentiated supplier management strategy. However, this research presents counterintuitive findings in major supplier networks in a collectivist culture like China. In such contexts, differentiated supplier management strategies may violate the equality principle expected by major suppliers, hindering their collective value creation process and ultimately undermining the buyers' financial performance. Using a combination of survey data, secondary archival data from Chinese manufacturing firms and their major supplier networks, and qualitative triangulation, this research reveals that a buyer's differentiated relational strategy (DRS) in major supplier networks negatively affects its financial performance. The negative effects of a DRS intensify under demand uncertainty but lessen under technological uncertainty. This research reveals a contextualized perspective on differentiated supplier management strategies and enriches the literature on the effects of collectivist cultures in supply chain management.

Three-dimensional semi-supervised lumbar vertebrae region of interest segmentation based on MAE pre-training

Background: The annotation of the regions of interest (ROI) of lumbar vertebrae by radiologists for bone density assessment is a tedious and time-intensive task. However, deep learning (DL) methods for image segmentation has the potential to substitute manual annotations which can significantly improve the efficiency of clinical diagnostics. Objective: The paper proposes a semi-supervised three-dimensional (3D) segmentation method for the ROI of lumbar vertebrae by integrating the tube masking masked autoencoder (MAE) pre-training. Methods: The paper proposes a method that modifies the masking strategy of the original MAE pre-training network. And the pre-training network is only trained by images without segmentation labels, when the training is finished, the weights will be saved for segmentation tasks. In downstream tasks, a semi-supervised approach utilizing pseudo-label generation is employed for training. This method leverages a small amount of labeled data to achieve the segmentation of ROI of the lumbar vertebrae. Results: The experimental results demonstrate that under the condition of limited annotated data, the proposed network improves the dice coefficient by 5–7% and reduces the hausdorff distance by 0.2∼0.6 mm compared to using the UNetr network alone for segmentation. When compared to the conventional MAE, the tube masking MAE presented in this paper assists effectively in segmentation, resulting in a 2% increase in the dice coefficient and a 0.24 mm reduction in the hausdorff distance. Conclusion: Automatic segmentation of the ROI of the lumbar vertebrae helps to shorten the time for doctors to annotate vertebrae during clinical bone density examinations. The paper employs the tube masking MAE pre-trained model to effectively extract contextual information of the 3D lumbar vertebrae, combining it with a semi-supervised network leveraging pseudo-label generation for fine-tuning, which leads to effective 3D segmentation of the lumbar vertebrae.

Networks of climate obstruction: Discourses of denial and delay in US fossil energy, plastic, and agrichemical industries

The use of fossil-derived hydrocarbons in fossil energy, plastic production, and agriculture makes these three sectors mutually reinforcing and reliant on sustained fossil fuel extraction. In this paper, we examine the ways the fossil fuel energy, plastics, and agrichemicals industries interact on social media using Twitter (renamed X as of 2023) data analysis, and we explore the implications of these interactions for policy. Content analysis of the text of tweets from the two largest US corporations and a major trade association for each sector (three discrete social media accounts for each sector) reveals coordinated messaging and identifies synergistic themes among these three sectors. Network analysis shows substantial engagement among the three sectors and identifies common external entities frequently mentioned in each sector. To understand the discursive strategies of the twitter networks of these three petrochemical derivative and fuel sectors, we propose the discourses of climate obstruction framework, adapted from and expanding on Lamb et al.’s (2020) discourses of climate delay framework. Our framework integrates both discourses of delay and discourses of denial because an integration of both were found in our analysis suggesting coordinated efforts to obstruct climate action. Our analysis suggests that discourses to deny and delay climate policy are aligned and coordinated across the three sectors to reinforce existing infrastructure and inhibit change. Exceptions in this alignment emerge for a few distinct sector-specific goals, including contrasting messages about biofuel. Despite some disparate views and different policy priorities among these three sectors, similar efforts to reinforce existing extractive petrochemical hegemony and undermine climate policy are clearly evident in each sector. These findings suggest that more research is needed to understand collaborative efforts among fossil energy, plastic, and agrichemical producers to influence climate and energy policy.

New acorane-sesequiterpenes and anti-retinoblastoma constituents from the marine algicolous fungus Trichoderma harzianum NTU2180 guided by molecular networking strategy

BackgroundTrichoderma species, known as biocontrol agents against plant diseases, contain diverse compounds, especially terpenoids, with various bioactivities. To facilitate the exploration of bioactive secondary metabolites of Trichoderma harzianum NTU2180, the OSMAC approach MS/MS molecular networking was applied in the current study.ResultsThe feature-based molecular networking (FBMN) analysis showed that T. harzianum NTU2180 fermented on germinated brown rice (GBR) produced more terpenoids. Here, two new acorane-sesequiterpenes, trichospirols A (1) and B (2), and 12 known compounds (3 − 14) were isolated from the EtOAc layer of T. harzianum NTU2180 fermentation on GBR. Structures of these compounds were determined through NMR, UV, IR, and MS analyses. The absolute configuration of trichospirols A (1) was also elucidated by x-ray with Cu K-α radiation. Among them, six compounds (1, 2, 3, 4, 5, and 11) were annotated as terpenoids by the NPClassifier on FBMN. 5-Hydroxy-3-hydroxmethyl-2-methyl-7-methoxychromone (7) and ergosterol peroxide (11) showed significant anti-angiogenic activity in ex vivo experiments with respective 0.57 ± 0.12- and 0.20 ± 0.12-fold changes. In addition, compound 11 displayed cytotoxicity against Y79 retinoblastoma cells with IC50 value of 35.3 ± 6.9 µM.ConclusionsThe current study utilizes FBMN concept with OSMAC approach to accelerate the exploration of potential metabolites of the fungus Trichoderma harzianum NTU2180. Through a series of FBMN-guided isolation and purification, two new acorane-sesequiterpenes and 12 known compounds were obtained. The ex vivo and in vitro experiments were evaluated to assess anticancer isolates. It is worth noting that compound 11 was identified as a dual inhibitor targeting both angiogenesis and proliferation of retinoblastomas. Altogether, the results revealed the novel potential of T. harzianum for developing natural therapeutics against retinoblastomas.

Deep Learning and Numerical Analysis for Bladder Outflow Obstruction and Detrusor Underactivity Diagnosis in Men: A Novel Urodynamic Evaluation Scheme.

To automatically identify and diagnose bladder outflow obstruction (BOO) and detrusor underactivity (DUA) in male patients with lower urinary tract symptoms through urodynamics exam. We performed a retrospective review of 1949 male patients who underwent a urodynamic study at two institutions. Deep Convolutional Neural Networks scheme combined with a short-time Fourier transform algorithm was trained to perform an accurate diagnosis of BOO and DUA, utilizing five-channel urodynamic data (consisting of uroflowmetry, urine volume, intravesical pressure, abdominal pressure, and detrusor pressure). We used fivefold cross-validation, constructing training and internal test sets from 1725 patients from Renmin Hospital of Wuhan University (RHWU) at a 4:1 ratio, and used an independent external validation set consisting of 224 patients from The Central Hospital of Wuhan (TCHO) to build and evaluate the DI model. We further conducted subgroup analyses to provide a more detailed description of the AI model's interpretability regarding urodynamics. The AUC scores of BOO and DUA, which were measured through the STFT-based deep learning method, were 0.945 ± 0.020 and 0.929 ± 0.039 in RHWU and 0.881 and 0.850 in TCHO, respectively. The diagnostic efficiency of other subgroup analyses and indicators was also effective. In this study, the proposed deep neural network combined with the short-time Fourier transform method is robust and feasible for interpreting the results of urodynamics in men and has the potential for application to assist clinicians in real clinical settings.

Research on Fault Section Location in an Active Distribution Network Based on Improved Subtraction-Average-Based Optimizer

The high penetration of distributed generation (DG) in the distribution system poses a challenge to the protection techniques and strategies of active distribution networks, making it difficult to adapt traditional methods to the fault diagnosis of the new power system. A method based on the improved subtractive optimiser algorithm for fault diagnosis is proposed to address this situation. Firstly, a fault localization model applicable to DG grid connection is constructed, which can effectively deal with the impact of the dynamic switching of DGs on the system and make up for the shortcomings of the traditional single-power network model; secondly, to solve the model, the original algorithm is improved using multi-strategy fusion, and the improved subtraction-average-based optimizer (ISABO) is obtained. Through the test of classical functions, its excellent solving performance and decoupling ability are verified; finally, the ISABO algorithm is applied to the 33-node test system to make it operate in various complex fault conditions. The results show that the ISABO algorithm is feasible in solving the fault location problem and can adapt to the connect/disconnect state of the interconnection switch and the dynamic casting and cutting of multiple DGs. Compared with the original SABO algorithm, its positioning accuracy can always be maintained at 100%, and the positioning speed is increased by 46.68%, symmetrically improving positioning speed, positioning accuracy, and fault tolerance.

Mining of antioxidant sesquiterpene lactones from the aerial parts of Saussurea involucrata with feature-based molecular network strategy.

Sesquiterpene lactones (SLs) are a class of natural products with diverse structural scaffoldings and biological activities, making them intriguing objects in the fields of pharmaceutical industry, drug development, and pharmacology. Herein, fifteen SLs, including eleven undescribed SLs compounds sauruintones A-K (1-8 and 13-15), were isolated and identified from the aerial parts of Saussurea involucrata. Their structures were characterized by using mass spectrometry, spectroscopic methods, computational calculations, and single crystal X-ray diffraction. The Feature-Based Molecular Network (FBMN) strategy was utilized for the annotation of SLs with their MS/MS fragmentation patterns. Most of the SLs exerted their protective effects against oxidative stress by enhancing the levels of antioxidant enzyme (CAT) and reducing the malondialdehyde (MDA) levels. Among them, compound 12 exhibited potent reactive oxygen species (ROS) inhibitory activities and enhanced the activities of CAT, superoxide dismutase (SOD), and glutathione peroxidase (GSH), and decreased the content of MDA at concentrations of 10-25 μM. Further RT-qPCR assays and western blot analysis revealed it reduced the level of ROS by activating the nuclear factor erythroid 2-related factor 2 (Nrf-2) signaling pathway.

A Multi-Radio Channel Hopping Rendezvous Scheme in Cognitive Radio Networks for Internet of Things

A Multi-Radio Channel Hopping Rendezvous Scheme in Cognitive Radio Networks for Internet of Things

Location identifier based addressing scheme for molecular communication networks

Location identifier based addressing scheme for molecular communication networks

Impact analysis of flexible interconnection devices in distribution networks and a setting calculation method for overcurrent protection scheme

AbstractThe integration of flexible interconnection devices (FIDs) into distribution networks introduces complexities in power flow management under both normal and transient/fault conditions, thereby decreasing the selectivity, sensitivity, and reliability of common current protection schemes. This article examines the impact of FIDs on existing current protection arrangements and proposes a novel method for recalculating the overcurrent protection setting. The AC output characteristics of the FID for a short period after AC fault occurrence are first investigated. It is followed by an analysis of FID's impact on the three‐stage current protection scheme in flexible interconnected distribution networks, conducted through theoretical calculations in MATLAB and simulations in PSCAD/EMTDC. A quantitative assessment of the mal‐operation and non‐operation of current protection caused by FIDs of varying capacities, power flow directions, and locations, with a focus on the typical 10 kV 10MVA FID, is also performed. Based on the above findings, a calculation and modification method for Stage III overcurrent protection setting is presented to address mal‐operation issues in FID‐based distribution networks, with its feasibility validated through electromagnetic transient simulations.

An Efficient and Multi-Dimensional Privacy-Preserving Platoon Communication Scheme in Vehicular Networks

An Efficient and Multi-Dimensional Privacy-Preserving Platoon Communication Scheme in Vehicular Networks

Exploring continual learning strategies in artificial neural networks through graph-based analysis of connectivity: Insights from a brain-inspired perspective.

Artificial Neural Networks (ANNs) aim at mimicking information processing in biological networks. In cognitive neuroscience, graph modeling is a powerful framework widely used to study brain structural and functional connectivity. Yet, the extension of graph modeling to ANNs has been poorly explored especially in terms of functional connectivity (i.e. the contextual change of the activity's units in networks). In the perspective of designing more robust and interpretable ANNs, we study how a brain-inspired graph-based approach can be extended and used to investigate ANN properties and behaviors. We focus our study on different continual learning strategies inspired by the biological mechanisms and modeled with ANNs. We show that graph modeling offers a simple and elegant framework to deeply investigate ANNs, compare their performances, and explore deleterious behaviors such as catastrophic forgetting.

Multi-Stage Voltage Control Optimization Strategy for Distribution Networks Considering Active-Reactive Co-Regulation of Electric Vehicles

Multi-Stage Voltage Control Optimization Strategy for Distribution Networks Considering Active-Reactive Co-Regulation of Electric Vehicles

Photovoltaic consumption strategy of distribution network considering carbon emission and user experience

Photovoltaic consumption strategy of distribution network considering carbon emission and user experience