Network System Research Articles

Analysis of problems and countermeasures in computer network engineering security

This paper analyzes the existing security issues in computer network engineering and proposes a series of countermeasures. To address the problems of hacker attacks and information leakage, it is recommended to strengthen network monitoring and the application of encryption technology. For the issues of malware and virus dissemination, it is advised to regularly update antivirus software and enhance employee security awareness training. Regarding network data leakage and privacy breaches, the use of secure transmission protocols and multi-factor authentication technology is advocated. For network security management and maintenance issues, establishing a sound security management system and emergency response plans is recommended to ensure the stable operation of network systems. Through the implementation of the above measures, the security level of computer network engineering can be effectively improved, ensuring the normal operation of network systems and the security of information.

Early Predictor for the Onset of Critical Transitions in Networked Dynamical Systems

Numerous natural and human-made systems exhibit critical transitions whereby slow changes in environmental conditions spark abrupt shifts to a qualitatively distinct state. These shifts very often entail severe consequences; therefore, it is imperative to devise robust and informative approaches for anticipating the onset of critical transitions. Real-world complex systems can comprise hundreds or thousands of interacting entities, and implementing prevention or management strategies for critical transitions requires knowledge of the exact condition in which they will manifest. However, most research so far has focused on low-dimensional systems and small networks containing fewer than ten nodes or has not provided an estimate of the location where the transition will occur. We address these weaknesses by developing a deep-learning framework which can predict the specific location where critical transitions happen in networked systems with size up to hundreds of nodes. These predictions do not rely on the network topology, the edge weights, or the knowledge of system dynamics. We validate the effectiveness of our machine-learning-based framework by considering a diverse selection of systems representing both smooth (second-order) and explosive (first-order) transitions: the synchronization transition in coupled Kuramoto oscillators; the sharp decline in the resource biomass present in an ecosystem; and the abrupt collapse of a Wilson-Cowan neuronal system. We show that our method provides accurate predictions for the onset of critical transitions well in advance of their occurrences, is robust to noise and transient data, and relies only on observations of a small fraction of nodes. Finally, we demonstrate the applicability of our approach to real-world systems by considering empirical vegetated ecosystems in Africa. Published by the American Physical Society 2024

The rhythmic mind: brain functions of percussionists in improvisation

IntroductionPercussionists stand out for their expertise in rhythm, with the network for musical rhythm (NMR) serving a vital neurological function in their improvisation, which is deeply rooted in comprehensive musical knowledge. Our research examines the central representations of various improvisation tactics used by percussionists and investigates the interactions between the NMR and other relevant neural networks.MethodsTwenty-five percussionists participated in functional magnetic resonance imaging (fMRI) sessions, which included two cognitive strategies of improvisation. Structural improvisation (SIMP) emphasized rhythmic patterns, while free improvisation (FIMP) focused on musical spontaneity. Sight-reading scenario served as the reference condition. Paired t-tests were utilized for comparative analyses.ResultsThe findings revealed a dynamic interplay characterized by increased activity in the executive control network and NMR, along with decreased activity in the default mode network during SIMP. During FIMP, heightened activity was observed in the executive control network, NMR, limbic, and memory systems. In both SIMP vs. sight-reading and FIMP vs. sight-reading comparisons, the visual network’s activity decreased, a trend also observed in the comparative analysis of FIMP vs. SIMP.DiscussionIn SIMP, percussionists leverage external rhythmic signals, resulting in heightened NMR and ECN activity and reduced DMN activity. In contrast, FIMP is characterized by a rise in activity within the NMR, ECN, limbic system, memory system, and reward system, underscoring the vital roles of motivation and memory in the rapid production of spontaneous musical ideas within set frameworks. The diminished activity in the visual network during FIMP compared to SIMP suggests less reliance on visual stimuli in FIMP. These findings suggest that various improvisational tactics may engage different neural pathways.

Low-carbon oriented planning of shared photovoltaics and energy storage systems in distribution networks via carbon emission flow tracing

With the targets of carbon peaking and carbon neutrality, carbon emission reduction measures have become significant issues in new-type power systems. Distribution networks (DNs), which multiple power sources and flexible loads access to, play an essential role in exploiting the potential of reducing carbon emissions on the demand side. To achieve a global carbon emission reduction considering the carbon quota of each customer, shared photovoltaics (PVs) and energy storage systems (ESSs) are allocated with a centralized calculation and optimization conducted by DNs. To solve two key points in demand-side planning of shared PVs and ESSs in distribution networks, i.e., the accuracy of carbon emission flow (CEF) calculation and carbon quota-oriented optimization planning, this paper proposes a low-carbon oriented planning method for shared PVs and ESSs via CEF tracing. Firstly, a novel CEF calculation method is proposed based on the forward current sweep and backward voltage sweep algorithm, which considers the tracing of reactive power and power losses. Next, carbon emission index and economic index of allocating shared PVs and ESSs are defined. Subsequently, a bi-level optimization model is presented, considering the excess carbon emissions of each load based on carbon accounting and carbon quotas. Finally, the superiority of the proposed method is verified through a modified IEEE 33-node distribution network, and the effects of various investment constraints and carbon quotas are discussed.

Optical Glaucoma Detection using Deep Learning And streamlit

The Project “Glaucoma is a disease that relates to the vision of the human eye”. This disease is considered as the irreversible disease that results in the vision deterioration. Much deep learning (DL) models have been developed for the proper detection of glaucoma so far. So this paper presents architecture for the proper glaucoma detection based on the deep learning by making use of the convolutional neural network (CNN). The differentiation between the patterns formed for glaucoma and non-glaucoma can find out with the use of the CNN. The CNN provides a hierarchical structure of the images for differentiation. Proposed work can be evaluated with a total of six layers. Here the dropout mechanism is also used for achieving the adequate performance in the glaucoma detection. The datasets used for the experiments are the SCES and ORIGA. Glaucoma is a group of related eye disorders that cause damage to the optic nerve that carries information from the eye to the brain which can get worse over time and lead to blindness. It is very important that glaucoma is detected as early as possible for proper treatment. In this paper, we have proposed a Convolutional Neural Network (CNN) system for early detection of Glaucoma. Initially, eye images are augmented to generate data for Deep learning. The eye images are then pre- processed to remove noise using Gaussian Blur technique and make the image suitable for further processing. The system is trained using the pre-processed images and when new input images are given to the system it classifies them as normal eye or glaucoma eye based on the features extracted during training..

Application of triple-branch artificial neural network system for catalytic pellets combustion

On the international level, it is common to act on reducing emissions from energy systems. However, in addition to industrial emissions, low-stack emissions also make a significant contribution. A good step in reducing its environmental impact, is to move to biofuels, including biomass. This paper examines the impact of placing a catalytic system in a retort boiler to minimize emissions of greenhouse gases, dust and other pollutants when burning pellets. The effect of platinum, and oxides of selected metals placed on the deflector as a solid catalyst was studied. Based on the experimental data, a branched artificial neural network was constructed and trained. The routing of three parallel topologies made it possible to achieve high accuracy while keeping the input data relatively simple. The system showed an average error of 3.54% against arbitrary test data. On the basis of experimental data as well as predictions returned by the artificial neural network, recommendations were shown for the catalysts used and their amounts. Depending on the biomass from which the pellet was produced, the experiment suggested the use of titanium or copper oxides. In the case of the neural network, it was able to select a better system, based on platinum, improving emission reductions by up to more than 19%, depending on the type of pellet used.

The Evolution of Forest Landscape Connectivity and Ecological Network Construction: A Case Study of Zhejiang’s Ecological Corridors

As main components of terrestrial ecosystems, forests play irreplaceable roles in maintaining ecological balance and protecting the basic environment for human survival and development. In this study, the dynamic changes in the forest landscape connectivity in Zhejiang province in 2000, 2010, and 2020 were detected by identifying ecological sources and evaluating connectivity indexes based on morphological spatial analysis (MSPA) and a minimum cumulative resistance (MCR) model. The results are as follows: (1) The forest area of Zhejiang increased by 64.88% from 2000 to 2020, indicating that the overall habitat quality has improved and that ecological risks have decreased, which are attributed to Zhejiang’s adherence to comprehensive environmental management. (2) Regions with low connectivity are distributed mainly in the north, where human activities are intensive. The overall pattern of high connectivity in the middle of the region and low connectivity elsewhere demonstrates the uneven distribution of forest landscape connectivity across the province. (3) The extracted ecological corridors have a mesh-like structure that is dense in the middle and slack in the north. Important corridors have disappeared over time, indicating potential issues in maintaining connectivity for species migration. (4) These results can provide optimization strategies for ecological infrastructure planning in Zhejiang province and offer a theoretical reference for the optimization of the ecological network system.

Disentangling high-order effects in the transfer entropy

Transfer entropy (TE), the primary method for determining directed information flow within a network system, can exhibit bias—either in deficiency or excess—during both pairwise and conditioned calculations, owing to high-order dependencies among the dynamic processes under consideration and the remaining processes in the system used for conditioning. Here, we propose a novel approach. Instead of conditioning TE on all network processes except the driver and the target, as in its fully conditioned version, or not conditioning at all, as in the pairwise approach, our method searches for both the multiplets of variables that maximize information flow and those that minimize it. This provides a decomposition of TE into unique, redundant, and synergistic atoms. Our approach enables the quantification of the relative importance of high-order effects compared to pure two-body effects in information transfer between two processes, while also highlighting the processes that contribute to building these high-order effects alongside the driver. We demonstrate the application of our approach in climatology by analyzing data from El Niño and the Southern Oscillation. Published by the American Physical Society 2024

Disentangling high-order effects in the transfer entropy

Transfer entropy (TE), the primary method for determining directed information flow within a network system, can exhibit bias—either in deficiency or excess—during both pairwise and conditioned calculations, owing to high-order dependencies among the dynamic processes under consideration and the remaining processes in the system used for conditioning. Here, we propose a novel approach. Instead of conditioning TE on all network processes except the driver and the target, as in its fully conditioned version, or not conditioning at all, as in the pairwise approach, our method searches for both the multiplets of variables that maximize information flow and those that minimize it. This provides a decomposition of TE into unique, redundant, and synergistic atoms. Our approach enables the quantification of the relative importance of high-order effects compared to pure two-body effects in information transfer between two processes, while also highlighting the processes that contribute to building these high-order effects alongside the driver. We demonstrate the application of our approach in climatology by analyzing data from El Niño and the Southern Oscillation. Published by the American Physical Society 2024

Adaptive Radio Access Technology Selection Algorithm for Heterogeneous Wireless Networks

In Heterogeneous Wireless Networks (HWNs), Radio Access Technologies (RAT) can only consider the situation of one particular Radio Resource Management (RRM) which is unsuitable for managing multiple RATs. This study deployed an adaptive RAT selection scheme model to allocate users to the best RAT with the use of the cost function variable. The adopted model uses different input criteria like signal strength, network loads, service type and QoS requirement for the best access network selections. The adaptive RAT selection algorithm was executed in different service mixes (voice and data service) to access model suitability for users in Global System for Mobile Communications with Enhanced Data Rates for Global Evolution Radio Access Network (GERAN) and Universal Mobile Telecommunications System Radio Access Network (UTRAN). The proposed algorithm resulted in the call blocking probability reduction by 0.03 for GERAN and 0.14 for UTRAN as validated with the existing algorithm based on load balancing, service-based and priority-based. The drop implied an increased probability of ensuring session stability and high quality of the active service, leading to a high load distribution.

Event‐triggered fault detector design in networked fuzzy control systems under denial‐of‐service attacks

AbstractThis article addresses the problem of designing an event‐triggered fault detector within a networked nonlinear system described by a Takagi–Sugeno (T–S) fuzzy model, accounting for time‐varying delays and denial‐of‐service (DoS) attacks characterized by a binary Markov chain. Our focus is on constructing an event‐triggered residual system, which aims to detect malfunctions in the original fuzzy system while optimizing communication resource allocation. Specifically, it is tailored to combat DoS attacks in the event generator‐to‐fault detector channel and mitigate network‐induced time‐varying delays. Utilizing the Lyapunov–Krasovskii function approach, we formulate linear matrix inequalities to establish the necessary conditions ensuring the asymptotic mean‐square stability of the residual system, meeting a predefined performance criterion. Finally, the simulation results are presented to validate the effectiveness of the proposed algorithms.

Novel Dynamic Defense Strategies in Networked Control Systems under Stochastic Jamming Attacks

In contemporary networked control systems (NCSs), ensuring robust and adaptive security measures against dynamic threats like jamming attacks is crucial. These attacks can disrupt the control signals, leading to degraded performance or even catastrophic failures. This paper introduces a novel approach to enhance NCS security by applying stochastic game theory to model and resolve interactions between a defender and a jammer. We develop a two-player zero-sum game where the defender employs mixed strategies to minimize the expected cost of maintaining system stability and control effectiveness in the face of potential jamming. Our model discretizes the state space and employs backward induction to dynamically update the value functions associated with various system states, reflecting the ongoing adjustment of strategies in response to the adversary’s actions. Utilizing linear programming in MATLAB, we optimize the defender’s mixed strategies to systematically mitigate the impact of jamming. The results from extensive simulations demonstrate the efficacy of our proposed strategies in attack scenarios, indicating a substantial enhancement in the resilience and performance of NCSs against jamming attacks. Specifically, the proposed method improved network state stability by 75%, reducing the fluctuation range by over 50% compared with systems without defense mechanisms. This study not only advances the theoretical framework for security in NCSs but also provides practical insights for the design of resilient control systems under uncertainty.

Novel Dynamic Defense Strategies in Networked Control Systems under Stochastic Jamming Attacks

In contemporary networked control systems (NCSs), ensuring robust and adaptive security measures against dynamic threats like jamming attacks is crucial. These attacks can disrupt the control signals, leading to degraded performance or even catastrophic failures. This paper introduces a novel approach to enhance NCS security by applying stochastic game theory to model and resolve interactions between a defender and a jammer. We develop a two-player zero-sum game where the defender employs mixed strategies to minimize the expected cost of maintaining system stability and control effectiveness in the face of potential jamming. Our model discretizes the state space and employs backward induction to dynamically update the value functions associated with various system states, reflecting the ongoing adjustment of strategies in response to the adversary’s actions. Utilizing linear programming in MATLAB, we optimize the defender’s mixed strategies to systematically mitigate the impact of jamming. The results from extensive simulations demonstrate the efficacy of our proposed strategies in attack scenarios, indicating a substantial enhancement in the resilience and performance of NCSs against jamming attacks. Specifically, the proposed method improved network state stability by 75%, reducing the fluctuation range by over 50% compared with systems without defense mechanisms. This study not only advances the theoretical framework for security in NCSs but also provides practical insights for the design of resilient control systems under uncertainty.

Altered Inflammatory State and Mitochondrial Function Identified by Transcriptomics in Paediatric Congenital Heart Patients Prior to Surgical Repair.

Congenital heart disease (CHD) remains the most common birth defect, with surgical intervention required in complex cases. Right ventricle (RV) function is known to be a major predictor of sustained cardiac health in these patients; thus, by elucidating the divergent profiles between CHD and the control through tissue analysis, this study aims to identify new avenues of investigation into the mechanisms surrounding reduced RV function. Transcriptomic profiling, in-silico deconvolution and functional network analysis were conducted on RV biopsies, identifying an increase in the mitochondrial dysfunction genes RPPH1 and RMPR (padj = 4.67 × 10-132, 2.23 × 10-107), the cytotoxic T-cell markers CD8a, LAGE3 and CD49a (p = 0.0006, p < 0.0001, and p = 0.0118) and proinflammatory caspase-1 (p = 0.0055) in CHD. Gene-set enrichment identified mitochondrial dysfunctional pathways, predominately changes within oxidative phosphorylation processes. The negative regulation of mitochondrial functions and metabolism was identified in the network analysis, with dysregulation of the mitochondrial complex formation. A histological analysis confirmed an increase in cellular bodies in the CHD RV tissue and positive staining for both CD45 and CD8, which was absent in the control. The deconvolution of bulk RNAseq data suggests a reduction in CD4+ T cells (p = 0.0067) and an increase in CD8+ T cells (p = 0.0223). The network analysis identified positive regulation of the immune system and cytokine signalling clusters in the inflammation functional network, as there were lymphocyte activation and leukocyte differentiation. Utilising RV tissue from paediatric patients undergoing CHD cardiac surgery, this study identifies dysfunctional mitochondrial pathways and an increase in inflammatory T-cell presence prior to reparative surgery.

Altered Inflammatory State and Mitochondrial Function Identified by Transcriptomics in Paediatric Congenital Heart Patients Prior to Surgical Repair

Congenital heart disease (CHD) remains the most common birth defect, with surgical intervention required in complex cases. Right ventricle (RV) function is known to be a major predictor of sustained cardiac health in these patients; thus, by elucidating the divergent profiles between CHD and the control through tissue analysis, this study aims to identify new avenues of investigation into the mechanisms surrounding reduced RV function. Transcriptomic profiling, in-silico deconvolution and functional network analysis were conducted on RV biopsies, identifying an increase in the mitochondrial dysfunction genes RPPH1 and RMPR (padj = 4.67 × 10−132, 2.23 × 10−107), the cytotoxic T-cell markers CD8a, LAGE3 and CD49a (p = 0.0006, p &lt; 0.0001, and p = 0.0118) and proinflammatory caspase-1 (p = 0.0055) in CHD. Gene-set enrichment identified mitochondrial dysfunctional pathways, predominately changes within oxidative phosphorylation processes. The negative regulation of mitochondrial functions and metabolism was identified in the network analysis, with dysregulation of the mitochondrial complex formation. A histological analysis confirmed an increase in cellular bodies in the CHD RV tissue and positive staining for both CD45 and CD8, which was absent in the control. The deconvolution of bulk RNAseq data suggests a reduction in CD4+ T cells (p = 0.0067) and an increase in CD8+ T cells (p = 0.0223). The network analysis identified positive regulation of the immune system and cytokine signalling clusters in the inflammation functional network, as there were lymphocyte activation and leukocyte differentiation. Utilising RV tissue from paediatric patients undergoing CHD cardiac surgery, this study identifies dysfunctional mitochondrial pathways and an increase in inflammatory T-cell presence prior to reparative surgery.

Synergy of Target-Induced Magnetic Network and Single-Drop Chromogenic System for Ultrasensitive "All-in-Tube" Detection of miRNA in Whole Blood.

The development of liquid biopsy methods for the accurate and reliable detection of miRNAs in whole blood is critical for the early diagnosis and monitoring of diseases. However, accurate quantification of miRNA expression levels remains challenging due to the complex matrix and low abundance of miRNAs in blood samples. Herein, we report a contactless signal output strategy with low background interference that ensures "zero-contact" between the reaction system and the colorimetry system. The designed target-induced magnetic ZnS/ZIF-90/ZnS network can serve as a unique signal amplifier and transducer. It releases hydrogen sulfide (H2S) gas in an acidic solution which can be concentrated in a droplet of only a few microliters in volume, etching the silver layer of Au@Ag nanostars (NSTs) in the droplet. This will lead to changes in the localized surface plasmon resonance signals of the NSTs. Finally, quantitative detection of let-7a is realized by measuring the offset value of the UV-vis absorption peak. Therefore, by virtue of the synergistic action of quadruple signal amplification methods, including catalytic hairpin assembly, ZnS/ZIF-90/ZnS, magnetic separation, and microextraction, the "All-in-Tube" ultrasensitive detection of low-abundance let-7a in whole blood is achieved with a detection limit as low as the aM level. In addition, the "zero-contact" signal output mode effectively solves the problem of complex matrix interference, demonstrating the great potential of this method for miRNA quantification in complex samples, such as whole blood.

Adoption of network and plan-do-check-action in the international classification of disease 10 coding.

with the widespread application of computer network systems in the medical field, the plan-do-check-action (PDCA) and the international classification of diseases tenth edition (ICD-10) coding system have also achieved favorable results in clinical medical record management. However, research on their combined application is relatively lacking. Objective: it was to explore the impact of network systems and PDCA management mode on ICD-10 encoding. Material and Method: a retrospective collection of 768 discharged medical records from the Medical Record Management Department of Meishan People's Hospital was conducted. They were divided into a control group (n = 232) and an observation group (n = 536) based on whether the PDCA management mode was implemented. The two sets of coding accuracy, time spent, case completion rate, satisfaction, and other indicators were compared. To study the adoption of network and PDCA in the ICD-10. A retrospective collection of 768 discharged medical records from the Medical Record Management Department of Meishan People's Hospital was conducted. They were divided into a control group (n = 232) and an observation group (n = 536) based on whether the PDCA management mode was implemented. The two sets of coding accuracy, time spent, case completion rate, satisfaction, and other indicators were compared. In the 3, 6, 12, 18, and 24 months of PDCA cycle management mode, the coding accuracy and medical record completion rate were higher, and the coding time was lower in the observation group as against the controls (P < 0.05). The satisfaction of coders (80.22% vs 53.45%) and patients (84.89% vs 51.72%) in the observation group was markedly higher as against the controls (P < 0.05). The combination of computer networks and PDCA can improve the accuracy, efficiency, completion rate, and satisfaction of ICD-10 coding.

Min–max consensus of multi-agent systems in random networks

This paper focuses on the study of the min–max consensus problem for multi-agent systems in random networks. A min–max consensus algorithm is proposed, which incorporates the weighted average of the agent’s state, the maximum value of its neighbors’ states, and the minimum value of its neighbors’ states. The interaction network is composed of a directed random network where edges exist with probability and are independent of each other. Then, the convergence of the min–max consensus algorithm is investigated utilizing max-plus algebra, min-plus algebra, and stochastic analysis theory. Sufficient and necessary conditions are given to ensure that the multi-agent systems achieve min–max consensus almost surely and in mean-square, respectively. Finally, some numerical simulations are conducted to confirm the effectiveness of the proposed consensus algorithm.

IQ-channel multiplexed DP-4ASK signal using power loading for downlink coherent PON system with access-span length difference

This study explores the integration of dual-polarized (DP) four-level amplitude shift keying (4ASK) signals within an intensity and quadrature division multiplexing framework, mainly focusing on coherent passive optical network (PON) systems. The primary objective is to address the challenges associated with distance disparities in downstream transmissions, which are prevalent in coherent PON architectures. We introduce a novel approach that significantly reduces the power consumption of digital-to-analog converters by implementing power loading strategies tailored explicitly for IQ channel division multiplexing (IQDM)-DP-4ASK signals. Our simulations confirm that ASK standard frequency offset compensation is adaptable to these signals, providing a robust method for managing frequency offsets in a dynamic network environment. Moreover, we propose and empirically validate an energy-efficient adaptive downlink solution capable of supporting 128 Gb/s per optical network unit (ONU), utilizing IQ-imbalanced DP-4ASK signals. This solution is particularly advantageous for systems where the transmission conditions include a 10 km variance in access span lengths between ONUs. Results from our experimental setup demonstrate that reducing the digital-to-analog converter output current for the Q channel by 137 mA is feasible without compromising signal integrity or system performance. Additionally, the research verifies the effectiveness of the automatic bias controller on the transmitter side, ensuring stable operation across varying conditions. Equally, the receiver-side digital signal processing effectively handles polarization separation, illustrating the system's capability to maintain high data integrity and reliability under diverse operational scenarios. These findings highlight the technical feasibility and operational benefits of using IQDM-DP-4ASK signals in coherent PON systems and underscore the potential for significant power savings and enhanced system efficiency in optical networks. The study paves the way for future research into scalable, energy-efficient solutions suitable for high-capacity optical access networks striving for increased sustainability and reduced operational costs.

Two-step data clustering for improved intrusion detection system using CICIoT2023 dataset

The issue of network security is an important and delicate issue when it comes to the privacy of organizations and individuals, especially when important and sensitive information is transmitted across these networks. The importance of intrusion detection systems, which is a very important component of protecting information and reducing the damage resulting from attacks and penetrations of network systems, has increased due to the adoption of the most recent regulations on advanced web services, whether government services, banking services, e-mail, or e-marketing. The goal of this paper is to construct an intrusion detection system using deep learning algorithms based on a new dataset named the CICIoT2023. The proposed intrusion detection model addresses challenges associated with intrusion detection datasets in terms of high dimensionality by adopting new methods to reduce their size and improve efficiency. A new clustering technique for intrusion detection datasets based on a new method combination between an optimization algorithm and static tools was proposed. The proposed model was evaluated to determine its efficiency using several evaluation measures. The results show that in comparison to earlier research conducted on the same datasets, the suggested model performs better in attack detection. As a result, the proposed model offers a high level of network security trust.