Network System Research Articles

Real-Time Diagnostics on a QKD Link via QBER Time-Series Analysis

The integration of QKD systems in metro optical networks raises challenges that cannot be fully resolved with current technological means. In this work, we devised a methodology for identifying different types of impairments for a QKD link embedded in a communication network. Identification occurs in real time using a supervised machine learning model designed for this purpose. The model takes only QBER and SKR time-series data as the input, making its applicability not restricted to any specific QKD protocol or system. The output of the model specifies the working conditions for the QKD link, which is information that can be valuable for users and key management systems.

Predicting the risk of chronic kidney disease based on uric acid concentration in stones using biosensors integrated with a deep learning-based ANN system

Elevated levels of uric acid (UA) in the body may not only lead to the formation of stones but also increase the risk of developing chronic kidney disease (CKD). This study presents a biosensor for detecting UA concentration in stones and a deep learning-based artificial neural network (ANN) system for analyzing CKD risk. The biosensor is a screen-printed electrode (SPE) chip, whose surface was modified using oxygen plasma, enabling the detection of UA concentration via cyclic voltammetry. Experimental results show a good linear relationship between UA concentration and anodic peak current within the range of 0.15–5 mM. The surface modification method for this biosensor is simple and cost-effective. The ANN system took age and creatinine values as inputs, utilizing the Chronic_Kidney_Disease dataset and supplementary data from literatures for training. After detecting the UA concentration in stones using the biosensor, the result was converted into serum uric acid concentration, allowing the estimation of creatinine level, which was then used by the ANN to assess the risk of developing CKD. This system can assist urologists in determining whether patients should seek consultation with nephrologists for early diagnosis and treatment.

Integrated metaheuristic approaches for estimation of fracture porosity derived from fullbore formation micro-imager logs: Reaping the benefits of stand-alone and ensemble machine learning models

Fracture porosity is one of the most effective parameters for reservoir productivity and recovery efficiency. This study aims to predict and improve the accuracy of fracture porosity estimation through the application of advanced machine learning (ML) algorithms. A novel approach was introduced for the first time to estimate fracture porosity by reaping the benefits of petrophysical and fullbore formation micro-imager (FMI) data based on employing various stand-alone, ensemble, optimisation and multi-variable linear regression (MVLR) algorithms. This study proposes a ground-breaking two-step committee machine (CM) model. Petrophysical data containing compressional sonic-log travel time, deep resistivity, neutron porosity and bulk density (inputs), along with FMI-derived fracture porosity values (outputs), were employed. Nine stand-alone ML algorithms, including back-propagation neural network, Takagi and Sugeno fuzzy system, adaptive neuro-fuzzy inference system, decision tree, radial basis function, extreme gradient boosting, least-squares boosting, least squares support vector regression and k-nearest neighbours, were trained for initial estimation. To improve the efficacy of stand-alone algorithms, their outputs were combined in CM structures using optimisation algorithms. This integration was applied through five optimisation algorithms, including genetic algorithm, ant colony, particle swarm, covariance matrix adaptation evolution strategy (CMA-ES) and Coyote optimisation algorithm. Considering the lowest error, the CM with CMA-ES showed superior performance. Subsequently, MVLR was employed to improve the CMs further. Employing MVLR to combine the CMs yielded a 57.85% decline in mean squared error and a 4.502% improvement in the correlation coefficient compared to the stand-alone algorithms. The results of the benchmark analysis validated the efficacy of this approach.

Attack-Dependent Adaptive Event-Triggered Security Fuzzy Control for Nonlinear Networked Cascade Control Systems Under Deception Attacks

This article investigates the issue of H∞ security output feedback control for a nonlinear networked cascade control system with deception attacks. First, to further reduce the amount of communication data, reasonably schedule network resources, and alleviate the impact of multi-channel deception attacks, an attack-dependent adaptive event-triggered mechanism is introduced into the primary network channel, and its adaptive triggered threshold can be adjusted according to the random attack probability. Secondly, the output dynamic quantization of the secondary network channel is considered. Then, a novel security cascade output feedback controller design framework based on the Takagi–Sugeno (T-S) fuzzy networked cascade control system under deception attacks is established. In addition, by introducing the Lyapunov–Krasovskii stability theory, the design conditions of the controller are given. Finally, the effectiveness and superiority of the proposed design strategies are verified by two simulation examples of power plant boiler–turbine system and power plant boiler power generation control system.

Approximation-based adaptive two-bit-triggered bipartite tracking control for nonlinear networked MASs subject to periodic disturbances

Purpose This paper aims to investigate the problem of adaptive bipartite tracking control in nonlinear networked multi-agent systems (MASs) under the influence of periodic disturbances. It considers both cooperative and competitive relationships among agents within the MASs. Design/methodology/approach In response to the inherent limitations of practical systems regarding transmission resources, this paper introduces a novel approach. It addresses both control signal transmission and triggering conditions, presenting a two-bit-triggered control method aimed at conserving system transmission resources. Additionally, a command filter is incorporated to address the problem of complexity explosion. Furthermore, to model the uncertain nonlinear dynamics affected by time-varying periodic disturbances, this paper combines Fourier series expansion and radial basis function neural networks. Finally, the effectiveness of the proposed methodology is demonstrated through simulation results. Findings Based on neural networks and command filter control method, an adaptive two-bit-triggered bipartite control strategy for nonlinear networked MASs is proposed. Originality/value The proposed control strategy effectively addresses the challenges of limited transmission resources, nonlinear dynamics and periodic disturbances in networked MASs. It guarantees the boundedness of all signals within the closed-loop system while also ensuring effective bipartite tracking performance.

A novel optimization framework for natural gas transportation pipeline networks based on deep reinforcement learning

Natural gas is an emerging and reliable energy source in transition to a low-carbon economy. The natural gas transportation pipeline network systems are crucial when transporting natural gas from the production endpoints to processing or consuming endpoints. Optimizing the operational efficiency of compressor stations within pipeline networks is an effective way to reduce energy consumption and carbon emissions during transportation. This paper proposes an optimization framework for natural gas transportation pipeline networks based on deep reinforcement learning (DRL). The mathematical simulation model is derived from mass balance, hydrodynamics principles of gas flow, and compressor characteristics. The optimization control problem in steady state is formulated into a one-step Markov decision process (MDP) and solved by DRL. The decision variables are selected as the discharge ratio of each compressor. By the comprehensive comparison with dynamic programming (DP) and genetic algorithm (GA) in three typical element topologies (a linear topology with gun-barrel structure, a linear topology with branch structure, and a tree topology), the proposed method can obtain 4.60% lower power consumption than GA, and the time consumption is reduced by 97.5% compared with DP. The proposed framework could be further utilized for future large-scale network optimization practices.

PEMETAAN PERMASALAHAN BANJIR DAN GENANGAN PADA DRAINASE PERKOTAAN BERBASIS GIS DI KOTA JAYAPURA

The improvement in quality and normalization of drainage throughout the Jayapura City area has already beenincluded in the flood control system plan, starting from enhancing and optimizing the primary, secondary, andtertiary drainage network systems. However, suboptimal drainage conditions are still found under existingconditions, indicating that the drainage network system in Jayapura City has not yet been well optimized. Thisresearch aims to determine the strategies for managing and maintaining the drainage system in the city ofJayapura. The analysis used in this research is qualitative descriptive analysis and spatial analysis. The resultsobtained for creating flood-prone area analysis as information included in the Jayapura City Spatial Plan (RTRW),providing socialization to the community, conducting outreach to the community, and implementing spatialplanning for flood prevention.

Initial experience with a 5G cellular network and robotic arm-based remote echocardiographic system in an all-comers setting

Abstract Background Echocardiography is the test-of-choice for the initial evaluation of many cardiac diseases and requires expertise that is often limited in rural area. Robotic arm-assisted remote echocardigram has been attampted for teleconsultation but was limited to heart failure patients(ref), primarily due to the network delay in telecommunication and the subsequent incoordination in robotic arm control. Purpose We aim to assess the feasibility and diagnostic accuracy of a 5G cellular network and robotic arm-based remote echocardiographic system in an all-comers setting. Methods 51 pts were enrolled from the outpatient cardiology clinic of our institute. All pts underwent standard comprehensive echocardiography on a 5G cellular network and robotic arm-based remote echocardiographic system (the robotic part was deployed at our hospital and the control part was located at another campus 20 kilometers away) (Figure 1)and a conventional echocardiographic platform (at our hospital) successively. The order in which patients were examined on the remote and conventional instruments was randomly determined. There was no interval between the two examinations, and examinations of the same patient were performed by experienced but different cardiologists, who were blinded to each other’s diagnosis. The doctor who used the remote system was also randomly allocated and had not been previously specifically trained. The exams were real-time and diagnoses were made right after the exams. Results Of the 51 pts, the image quality was sufficient for diagnosis in 50 patients (48% female, 40±18 years). 1 pt was excluded because some key views could not be obtained using the remote system (98% of exams successful). 17 pts had positive findings, including 10 with a primary diagnosis of valvulopathy (1 Barlow's syndrome, 1 bicuspid aortic valve and 8 less-than-moderate regurgitation), 2 cardiac surgery follow-ups (1 case of aortic valve replacement and septal myectomy, and 1 case of mitral valve replacement and tricuspid annuloplasty), and 2 hypertrophic cardiomyopathy (including 1 case of obstruction at papillary muscle level), 2 with abnormal left ventricular wall motion(including 1 case of apical mural thrombus) (Figure 2), and 1 with congenital heart disease (secumdum atrial septal defect). The main diagnosis was consistent in 98% of cases (papillary muscle level obstruction was missed in one case), using conventional echocardiographic findings as the standard. Time for image acquision using remote echocardiography was significantly longer than conventional one (24 minutes 36 seconds ± 4 minutes 52 seconds vs. 16 minutes 15 seconds ± 1 minute 54 seconds, p<0.0001, paired t-test). Conclusions Comprehensive echocardiographic exam with a 5G cellular network and robotic arm-based remote system is feasible with relatively good diagnostic accuracy.Figure 1Figure 2

3D Digital Modeling Technology for Thermal Power Plant Boilers Based on Digital Twins

With the rapid advancement of Digital Twins (DTs) technologies, they have emerged as viable solutions for enhancing the control and optimization of thermal power combustion processes. This paper presents a pioneering approach that leverages DTs to revolutionize the combustion process control system within thermal power generating units. Focused on bridging the physical and digital realms, our research establishes DTs of the combustion process control system (CPCS), situated within the Networked Control System Laboratory (NCSLab). We propose a novel five-dimensional (5D) DTs model encompassing the physical, data, service, connection, and virtual models, thereby facilitating a comprehensive fusion of physical and digital information. By analyzing the demands and technical intricacies of the combustion process control system, we design and implement a virtual 3D model, enriching the visualization and analytical capabilities of the DT environment. Furthermore, through controlled experiments on the combustion process and rigorous comparison with offline simulations, we validate the efficacy and feasibility of our approach. Importantly, our research opens avenues for future exploration, particularly in harnessing industrial real-time data to drive the virtual 3D model. Through the integration of Artificial Intelligence (AI) techniques such as Deep Learning, we elevate the capabilities of our DT framework. This enhanced predictive analytics, dynamic optimization, human–machine interaction, and virtual model realism. This research contributes to the advancement of DT technology in the context of thermal power plants and holds promise for driving innovation and efficiency across diverse industrial sectors.

A framework for measuring the training efficiency of a neural architecture

Measuring Efficiency in neural network system development is an open research problem. This paper presents an experimental framework to measure the training efficiency of a neural architecture. To demonstrate our approach, we analyze the training efficiency of Convolutional Neural Networks and Bayesian equivalents on the MNIST and CIFAR-10 tasks. Our results show that training efficiency decays as training progresses and varies across different stopping criteria for a given neural model and learning task. We also find a non-linear relationship between training stopping criteria, training Efficiency, model size, and training Efficiency. Furthermore, we illustrate the potential confounding effects of overtraining on measuring the training efficiency of a neural architecture. Regarding relative training efficiency across different architectures, our results indicate that CNNs are more efficient than BCNNs on both datasets. More generally, as a learning task becomes more complex, the relative difference in training efficiency between different architectures becomes more pronounced.

Capture and catalytic conversion of CO2 from marine and offshore applications – A review

Abstract This contribution examines recent advances in modeling fluid dynamics and capture/conversion of CO2 from marine and offshore applications in packed-bed columns/trickle-bed reactors subjected to static inclination, externally-induced rolling and heaving motions. Breaking the axial symmetry of the two-phase flow once the packed bed system is tilted results in a significant decrease in process performance, the extent of which is determined by the magnitude of the tilt and the column/reactor diameter. Only the conversion of CO2 from marine engine emissions on-board large cargo ships via catalytic cycloaddition of CO2 to styrene oxide in multiphase large-diameter trickle-bed reactors increases slightly with increasing reactor inclination. Under the externally induced column/reactor oscillations, CO2 capture/conversion performance shifts toward the steady-state solution of the vertical column/reactor as the asymmetry between the two inclined positions decreases. Oscillating (between two inclined symmetrical positions) and heaving packed-bed columns/trickle-bed reactors generate an oscillating performance around the steady-state solution of the vertical static position, which is driven by the amplitude and period of the angular and heaving motions via the continuous evolution of the intensity of the reverse secondary flow and by the magnitude of the reactor/column diameter. The catalytic conversion of CO2 captured from marine emissions via an integrated process coupling reverse water-gas shift reaction and methanol synthesis in a fixed-bed reactors network system shows a remarkable enhancement with the addition of H2O adsorbent to the reaction systems in the sorption-enhanced process periods.

Fault-tolerant control of nonlinear networked control systems based on a dynamic event-triggered mechanism under dual cyber attacks

In this paper, the fault-tolerant control problem of actuator failures for nonlinear networked control systems under deception attacks and denial-of-service attacks is investigated based on a dynamic event-triggered mechanism. Firstly, denial-of-service attacks and deception attacks are modeled as two independent variables that conform to the Bernoulli probability distribution, and the fault-tolerant control problem of nonlinear networked control systems is considered under the possible actuator failures. Then, an appropriate Lyapunov-Krasovskii functional is constructed, and sufficient conditions for ensuring asymptotic stability of the closed-loop systems are obtained using Jensen inequality. Meanwhile, the designed fault-tolerant controller can handle possible actuator failures. Finally, the effectiveness of the proposed method is proved through an example.

Secure Data Aggregation and Transmission System for Wireless Body Area Networks Using Twofish Symmetric Key Generation

Nowadays, Wireless Body Area Networks (WBANs) are mostly used in the healthcare industry. They represent a portable, inexpensive network that exhibition adaptability. The data developed using WBAN devices is vulnerable to transmission-related internal and external attacks; nevertheless, this vulnerability arises due to resource restrictions; by employing data aggregation technologies to conduct statistical analyses of medical data while protecting patient privacy, medical professionals can enhance the precision of diagnoses and assist medical insurance firms in selecting optimal plans for their clients. Maintaining the confidentiality and integrity of sensitive health information becomes more stimulating at the stages of aggregation and transmission due to security issues. This study proposes a novel method, Twofish Symmetric Key Generation (TFSKG), combined into a Secure Data Aggregation (SDA) and transmission system intended for WBANs. The Twofish technique is animatedly employed to make the secure symmetric keys chosen for its robust encryption capabilities. These keys are used to encrypt and decrypt aggregated health data through transmission. The proposed TFSKG-SDA method implements effective algorithms for aggregating data to safeguard end-to-end privacy and preserve data accuracy while reducing bandwidth consumption. Thus, for improved performance, an innovative genetic algorithm for data security is presented in this study. This paper introduces TFSKG-SDA, a system that, by employing rigorous simulation testing, enhances security protocols, resistance against recognized threats, and data transmission efficacy in the context of resource-constrained WBANs. We assess the encryption strength, computational cost, and communication efficiency of the TFSKG- SDA method to prove its significance to real-world healthcare applications.

Optimal Energy Management Systems and Voltage Stabilization of Renewable Energy Networks

This paper addresses the challenge of integrating multiple energy sources into a single-domain microgrid, commonly found in urban buildings, while also providing a platform for energy management. A Lyapunov stability analysis of a simple boost converter was used as a basis for designing the dual control loop of the grid. The versatility of the developed control structure allows for the incorporation of an arbitrary number of sources hence achieving scalability. Next, the energy in the microgrid was separated into exogenous energy and actuator energy. This yielded a description of the system that quantified the condition of stability independent of the decision made by a would-be energy management system. This, in turns, liberates the process of designing an optimized energy management system from stability concerns. The acquired theoretical findings were then translated to a simulation model, where multiple components of the grid were simulated under a typical scenario of operation. Once the simulation phase was concluded, a prototype of the designed grid was constructed to emulate the theoretical results. The prototype exhibited promising performance, matching the simulation predictions to a reasonable degree.

Estimation of missing streamflow data using various artificial intelligence methods in peninsular Malaysia

ABSTRACT Missing streamflow data is a common issue in Peninsular Malaysia, as the technologies used in hydrological studies often fail to collect data accurately. Additionally, conventional methods are still widely used in the region, which are less accurate compared to artificial intelligence (AI) methods in estimating missing streamflow data. Therefore, this study aims to estimate the missing streamflow data from 11 stations in Peninsular Malaysia by using different AI methods and determine the most appropriate method. Four homogeneity tests were applied to check the quality of data, and the results of the tests indicated that the streamflow data in most stations were homogenous. Two AI methods were applied in this study, which were artificial neural network and artificial neuro-fuzzy inference systems (ANFIS). The proposed AI methods were compared with five different conventional methods. All streamflow missing data, constituting 30% of data from each year were estimated on a daily time scale, and evaluated using root mean square error, mean absolute error and correlation coefficient values. The results indicated that ANFIS was the best due to its learning abilities and the fuzzy inference systems, which enable it to handle complicated input–output patterns and provide highly accurate estimation results.

Artificial Intelligence in Cybersecurity : Advancing Threat Modeling and Vulnerability Assessment

This article examines the transformative role of Artificial Intelligence (AI) in revolutionizing cybersecurity practices, with a particular focus on threat modeling and vulnerability assessment. As cyber threats grow increasingly sophisticated, traditional security measures often fall short in providing comprehensive protection. We explore how AI and machine learning algorithms enhance the accuracy and efficiency of threat modeling by analyzing vast datasets to identify patterns and anomalies indicative of potential security risks. The article also investigates AI's contribution to vulnerability assessment, highlighting its capacity for continuous monitoring and real-time analysis of diverse data sources, including network traffic, system logs, and threat intelligence feeds. By simulating various attack scenarios and prioritizing security vulnerabilities, AI-driven tools enable organizations to adopt a more proactive and dynamic approach to cybersecurity. While acknowledging the challenges and ethical considerations associated with AI implementation in this domain, this article underscores the significant potential of AI in fortifying cyber defenses and safeguarding digital assets in an ever-evolving threat landscape.

The role of light rail systems in urban regionalisation processes and in defining the bioregional vision of the Pontine plain in Italy

ABSTRACT Urban regionalisation or new regionalism is a land transformation process that involves the emergence of new sprawl settlements in the European Union and North America. These settlements are often composed of residential or micro-productive units that cause ecological fragmentation, biodiversity loss, and spatial reorganisation. This process challenges conventional spatial analytical and planning frameworks and calls for a new approach. This paper argues that bioregional units which account for complex human-environment interactions and the sustainability of natural resources and ecosystems can provide a new analytical and planning framework. Bioregions represent the main living environments for people, and mobility is a key driver shaping their lifestyle choices. This paper explores the role of light rail systems (LRSs) in enhancing the sustainability and well-being of bioregions, using the Pontine bioregion in Italy as a case study. It reviews the bioregional theory and applies the bioregional framework to the Pontine plain presenting the main features of the Pontine bioregion, including its internal and external urban relationships. Building on these analyses, the paper then discusses the proposal of a new railway network system that integrates LRSs as strategic elements for efficient mobility, urban regeneration, social cohesion, and environmental sustainability of the bioregional territory.

Analysis and Optimization of Super Duplex Stainless Steel Deposition in Wire Arc Additive Manufacturing Using Machine Learning Techniques

<div>This article presents experimental investigations and machine learning-based analysis on depositions of super duplex stainless steel (SDSS ER2594) material in wire arc additive manufacturing (WAAM) considering the process parameters namely voltage, wire feed rate, torch travel speed, and gas flow rate. Deposition efficiency and surface height values of the accumulated material were measured to build machine learning models using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The developed ANN model could predict the deposition efficiency and surface height with mean absolute deviations (MADs) of 8.9% and 16.1%, respectively. The MAD for prediction of the two responses for ANFIS model was found to be 6.1% and 14.9% as compared to the experimental data. Multi-objective optimization was also performed to obtain optimal solutions to achieve desired deposition results. Mechanical properties and microstructures of the deposited materials with optimal processing parameters were found comparable to that of the base materials.</div>

The dysfunction of complement and coagulation in diseases: the implications for the therapeutic interventions.

The complement system, comprising over 30 proteins, is integral to the immune system, and the coagulation system is critical for vascular homeostasis. The activation of the complement and coagulation systems involves an organized proteolytic cascade, and the overactivation of these systems is a central pathogenic mechanism in several diseases. This review describes the role of complement and coagulation system activation in critical illness, particularly sepsis. The complexities of sepsis reveal significant knowledge gaps that can be compared to a profound abyss, highlighting the urgent need for further investigation and exploration. It is well recognized that the inflammatory network, coagulation, and complement systems are integral mechanisms through which multiple factors contribute to increased susceptibility to infection and may result in a disordered immune response during septic events in patients. Given the overlapping pathogenic mechanisms in sepsis, immunomodulatory therapies currently under development may be particularly beneficial for patients with sepsis who have concurrent infections. Herein, we present recent findings regarding the molecular relationships between the coagulation and complement pathways in the advancement of sepsis, and propose potential intervention targets related to the crosstalk between coagulation and complement, aiming to provide more valuable treatment of sepsis.

Design and implementation of anti-mapping security access technology based on illegal scanning

Abstract In the current field of information security, illegal network scanning activities are prevalent, and such behaviors are usually aimed at detecting security vulnerabilities in network systems and preparing for future attack activities. This study proposes a secure access system based on anti-mapping technology, which aims to effectively block illegal scanning behaviors while ensuring that the normal access of legitimate users is not affected. The system integrates advanced behavioral analysis algorithms that utilize machine learning techniques for deep learning and pattern recognition of network traffic, and is able to accurately distinguish between normal user activities and malicious scanning attempts. At the core of the system is a set of dynamic adaptive identification mechanisms that update the detection algorithms in real time to adapt to emerging scanning techniques and attack strategies by continuously learning from changes in network traffic. In addition, the system employs role-based access control (RBAC) policies to enhance the protection of sensitive resources. The Secure Access Gateway is deployed at the boundary of the network to monitor and filter all ingress traffic, effectively intercepting unauthorized scanning activities by comprehensively evaluating the source, behavior and frequency of traffic. Experimental results show that the proposed two-layer network structure performs well in detecting common threats such as port scanning, DDoS attacks, and SQL injections, with an accuracy rate of over 95%. Especially for complex and covert APT (advanced persistent threat) attacks, the system can significantly reduce the false alarm rate and effectively improve the detection speed. However, when dealing with some highly customized malware, the system's recognition ability still needs to be improved, which indicates that future research needs to focus more on enhancing the ability to learn and adapt to unknown threats.