Network System Research Articles

Integration of photovoltaic systems in Algeria's building electrical network: technical and financial analysis

Integration of photovoltaic systems in Algeria's building electrical network: technical and financial analysis

Enhancing Building Energy Efficiency: Leaf Transpiration Inspired Construction of Lignin-Based Wood Plastic Composites for Building Energy Conservation

Incorporating phase change materials (PCMs) into buildings represents a strategic method for reducing reliance on air conditioning systems and decreasing energy usage consequently. Inspired by leaf veins providing channels for transpiration, lignin porous carbon (LPC) was synthesized by chemical activation, showing a vein pore structure. The capillary network system of the LPC provided more channel to adsorb PCM through surface tension and capillary force, achieving an impressive loading capacity of LPC for polyethylene glycol (PEG) (75%), which demonstrated exceptional heat storage capability (133.21 J/g). The lignin-derived wood-plastic composite (L-WPC) was further prepared. With the gradual increase of LPC/PEG, the latent heat of L-WPC increased gradually. The ΔHm, ΔHc and energy storage efficiency of L-WPC-50 (incorporated 50 wt% LPC/PEG-75) reached 39.99 J/g, 42.65 J/g and 90.26%, respectively. The vein pore structure of LPC also provided a thermal conductivity link for L-WPC. L-WPC-50 demonstrated an increased thermal conductivity (0.66 W/(m·K)) and a reduced thermal diffusion coefficient (0.24 mm2/s), optimizing the effectiveness of the phase transition. Additionally, L-WPC-50 displayed satisfactory mechanical properties and dimensional stability, with tensile strength, bending modulus and water absorption thickness swelling rate measuring 14.40 MPa, 1043.72 MPa, and 1.61% respectively. It introduced a feasible approach for utilizing biomass in enhancing building energy efficiency, marking a significant stride towards reducing building energy consumption and advocating sustainable energy practices.

An optimized cooperative data transmission scheme in relay‐enabled cellular communication networks

SummaryCooperative transmission is of great significance in data transmission and resource schedule in mobile cellular communication networks. To attain the desired network transmission performance objectives over the whole cellular network system, we propose an appropriate cooperative data transmission scheme to optimize the data delivery efficiency in terms of the transmission time consumption for each and every transmission group. The proposed transmission optimization scheme includes the tasks of determining the transmission group, allocating the channel rate resource, specifying the cooperative transmission mechanism, and performing resource allocation and schedule as well. Firstly, we propose a utility function based rate allocation scheme to optimize the channel rate resource utilization for all those transmission groups over the cellular communication network. Based on it, we further present a cooperative data transmission scheme to deal with the data delivery and reception for all those subgroups with high efficiency over the whole network system. Particularly, we propose a resource efficiency function‐based coordination scheduling (EFCS) scheme to conduct the cooperative data transmission and resource schedule as well for each transmission group within the network. The EFCS scheme facilitates to determine the subgroups for schedule over each scheduling cycle, which can produce the best data transmission efficiency across both the direct and cooperative transmission hop. Both the theoretical analysis and the simulation evaluation results verify the superior performance of our proposed cooperative data transmission scheme.

Hardware Implementation of a Resilient Energy Management System for Networked Microgrids

A networked microgrid is composed of multiple nearby microgrids linked together to gain additional flexibility for resilient operations. Networked microgrids collaborate to prevent power shortages in microgrid clusters by sharing critical renewable and energy storage resources. However, controlling the local resources of each microgrid, including the energy storage systems’ charging and discharging, maintaining the DC bus voltage, and even overseeing the power shared by multiple microgrids, is challenging. Therefore, a microgrid control technique and distributed energy management are used cooperatively in this study to handle the shared power between a system of networked microgrids incorporating photovoltaics and battery energy storage systems. Numerical simulation results from a networked microgrid system verify the accuracy and soundness of the suggested distributed energy management under several operating conditions, including renewable uncertainties and sequential load variations in different zones. The applicability of the suggested technique is confirmed by hardware implementation, and several operational scenarios further evaluate the proposed system on a practical two-microgrid system located in the Florida International University (FIU) testbed.

Review on development prospect of operation scheduling strategies for flexible interconnected distribution network in renewable energy-penetrated power system

As the renewable energy integrates in the power system, the interactions between source, load, and storage equipment in the distribution network become frequent. The original distribution network could not meet the growing demand of the power grid. The use of controlled power electronic devices for flexible interconnection transformation of the distribution network could help improve the controllability, reliability, and safety of the power system and promote the consumption of distributed power sources. It is an important means of evolving towards future intelligent distribution networks. Typical devices for flexible interconnection are discussed of the operating characteristics. In addition, the core technologies of flexible interconnected distribution network system are introduced from the view of point of dispatching. Finally, to achieve a wider application of flexible interconnected distribution networks, the development trend is prospected.

Entities Involved in the Policy of Ensuring the Security of Network and Information Systems in the Light of the NIS 2 Directive (Part 1)

Entities Involved in the Policy of Ensuring the Security of Network and Information Systems in the Light of the NIS 2 Directive (Part 1)

Evaluation and influencing factors of green transformation in three major urban agglomerations in China

Since the 21st century, the world has increasingly focused on the issue of sustainable development, and the green transformation issues have become a new hot topic worldwide. Green and low-carbon transformation has become an international consensus. Urban agglomerations are important connections between urban development and regional coordination, as well as important spatial carriers for economic activities. They are not only the main source of carbon emissions, but also the main battlefield for energy conservation and emission reduction. As an important field for carbon reduction, the green transformation of cities is crucial for achieving the “dual carbon” goals. This article focuses on 48 cities in the three most mature and influential urban agglomerations in China from 2011 to 2019, namely, the Beijing-Tianjin-Hebei urban agglomeration, the Yangtze River Delta urban agglomeration, and the Pearl River Delta urban agglomeration. The three-stage DEA model and Malmquist index model are used to measure the green transformation efficiency of the three urban agglomerations from both dynamic and static perspectives, and a Tobit regression model is constructed to explore the influencing factors of green transformation efficiency in urban agglomerations. Research has found that: 1) From a static perspective, the overall efficiency of green transformation in the three major urban agglomerations is at a high level, but from a temporal perspective, it shows a downward trend. The Pearl River Delta urban agglomeration is known for its green development, with the highest average efficiency of green transformation, followed by the Yangtze River Delta urban agglomeration. The Beijing-Tianjin-Hebei urban agglomeration has the lowest level of green transformation; 2) From a dynamic perspective, technological progress is the main driving factor for improving the efficiency of green transformation in the three major urban agglomerations. Therefore, the government should pay special attention to the progressiveness of technology when formulating relevant policies to promote urban green transformation; 3) From the perspective of spatiotemporal differences, there are significant differences in the spatiotemporal characteristics of green transformation among the three major urban agglomerations, and there are significant differences in green transformation strategies among different urban agglomerations. Eliminating environmental factors and random interference is necessary for accurately measuring the efficiency of green transformation in urban agglomerations; 4) From the perspective of influencing factors, factors such as industrial structure upgrading, green innovation level, and environmental regulation intensity jointly affect the efficiency of green transformation in urban agglomerations. Based on this, we should pay attention to the differences between urban agglomerations and implement policies tailored to local conditions; Strengthen the network system of urban agglomerations and avoid conflicts between cities; Encourage green technology innovation, accelerate industrial structure upgrading, and so on. This article focuses on the issue of green transformation in urban agglomerations, and conducts research from three perspectives: efficiency measurement, influencing factors, and implementation mechanisms. A relatively systematic theoretical framework for green transformation in urban agglomerations is formed, and an evaluation index system for green transformation efficiency in urban agglomerations is constructed and optimized. The composition mechanism of the five dimensional evaluation system for transformation efficiency is analyzed, and the bottleneck and breakthrough direction of the three major urban agglomerations in China in the development process are grasped. It has a good demonstration effect on the green transformation of other urban agglomerations.

Anomaly-based Intrusion Detection System in Industrial IoT-Healthcare Environment Network

The Internet of Things (IoT) technology facilitates automation, monitoring, and control of tangible objects and surroundings by enabling connected devices to interact and exchange data over the Internet. Developments in edge computing, blockchain, and artificial intelligence (AI) are incorporated into IoT technologies for more reliable operations. Inadequate authorization, authentication, and encryption protocols could render IoT networks insecure and open the door to illegal access and data breaches which can have terrible consequences, most notably in the healthcare industry. In this regard, to identify malicious and incursion traffic, machine learning (ML) is crucial to Internet of Things (IoT) cybersecurity. The paper proposes a framework to detect intrusion or malicious traffic in IoT-enabled different medical equipment such as medical sensors, and controllers for real-time data collection, creating communication channels and data monitoring and analysis over locally available network nodes. IoT-Flock has been utilized for both normal and malicious traffic generation in a wide dataset found by the sensors connected to IoT integrated healthcare network. The feature selection-based proposed framework has been evaluated by three distinct machine learning classifiers, KNN, RF, and DT where corresponding accuracy, sensitivity, precision, and F1-score have been measured for performance analysis. With an accuracy of 99.74%, the KNN technique performed better than the other tactics used by RF and DT regarding intrusion detection in IoT networks. The suggested framework will be helpful in developing or analyzing security solutions in IoT-integrated network systems.

Symmetric Collaborative Fault-Tolerant Control of Multi-Intelligence under Long-Range Transmission in Air–Ground Integrated Wireless High-Mobility Self-Organizing Networks

With the continuous development and progress of wireless self-organizing network communication technology, how to carry out long-distance cooperative control of multiple intelligences under the framework of an air–ground integrated wireless high-mobility self-organizing network has become a hot and difficult topic that needs to be solved urgently. This paper takes the air–ground integrated wireless high-mobility self-organizing network system as the basic framework and focuses on solving the long-distance cooperative fault-tolerant control of multi-intelligent bodies and the topological stability of a wireless mobile self-organizing network. To solve the above problems, a direct neural network with a robust adaptive fault-tolerant controller is designed in this paper. By constructing a symmetric population neural network model and combining it with the Lyapunov stabilization criterion, the system feedback matrix K has the ability of autonomous adaptive learning, and symmetrically distorts, rotates, or scales the training data to instantly adjust the system’s fault-tolerant corrections and adaptive adjusting factors to resist the unknown disturbances and faults, to achieve the goals of multi-intelligent body stable control and the stable operation of a wireless high-mobility self-organizing network topology. Simulation results show that with the feedback adjustment of the multi-system under the designed controller, the multi-system as a whole has good fault-tolerant performance and autonomous learning approximation performance, and the tracking error asymptotically converges to zero. The experimental results show that the multi-flight subsystems fly stably, the air–ground integrated wireless high-mobility self-organizing network topology has good stability performance, and the maximum relative improvement of the topology stability performance is 50%.

An energy efficient grid-based clustering algorithm using type-3 fuzzy system in wireless sensor networks

An energy efficient grid-based clustering algorithm using type-3 fuzzy system in wireless sensor networks

Graphical pinch analysis-based method for heat exchanger networks retrofit of a residuum hydrogenation process

Sustainable energy systems are crucial for reducing carbon emissions because renewable energy sources leave a footprint. The petrochemical industry often suffers from inefficient heat exchange network (HEN) systems, leading to substantial energy wastage. In the current work, a real case study of the residue hydrogenation process was analyzed to identify potential energy savings. A new method combining Pinch Analysis and Thot–Tcold diagram analysis methods was proposed. This graphical analysis method plots the cold-flow temperature of each heat exchanger unit on the x-axis and the hot-flow temperature on the y-axis. By applying the Thot–Tcold diagram to a practical case of residue hydrogenation in Zhejiang, the existing process energy state was evaluated, and HEN was retrofitted to achieve energy savings and carbon emission reduction. Following optimization, the energy recovery amounted to 202.71 GJ/h with an energy recovery rate of 14.3 %. The proposed method saves approximately 4.058 × 105 GJ/y compared to current operations, resulting in an annual cost saving of approximately $ 2.76 M/y, with an investment payback period of less than 0.36 y. This study offers a solution to the energy challenges of industrial residue hydrogenation by enhancing the economic and environmental sustainability of existing process flows.

Computable phenotype for diagnostic error: developing the data schema for application of symptom-disease pair analysis of diagnostic error (SPADE).

Diagnostic errors are the leading cause of preventable harm in clinical practice. Implementable tools to quantify and target this problem are needed. To address this gap, we aimed to generalize the Symptom-Disease Pair Analysis of Diagnostic Error (SPADE) framework by developing its computable phenotype and then demonstrated how that schema could be applied in multiple clinical contexts. We created an information model for the SPADE processes, then mapped data fields from electronic health records (EHR) and claims data in use to that model to create the SPADE information model (intention) and the SPADE computable phenotype (extension). Later we validated the computable phenotype and tested it in four case studies in three different health systems to demonstrate its utility. We mapped and tested the SPADE computable phenotype in three different sites using four different case studies. We showed that data fields to compute an SPADE base measure are fully available in the EHR Data Warehouse for extraction and can operationalize the SPADE framework from provider and/or insurer perspective, and they could be implemented on numerous health systems for future work in monitor misdiagnosis-related harms. Data for the SPADE base measure is readily available in EHR and administrative claims. The method of data extraction is potentially universally applicable, and the data extracted is conveniently available within a network system. Further study is needed to validate the computable phenotype across different settings with different data infrastructures.

Functional alterations of the brain default mode network and somatosensory system in trigeminal neuralgia

Mapping the localization of the functional brain regions in trigeminal neuralgia (TN) patients is still lacking. The study aimed to explore the functional brain alterations and influencing factors in TN patients using functional brain imaging techniques. All participants underwent functional brain imaging to collect resting-state brain activity. The significant differences in regional homogeneity (ReHo) and amplitude of low frequency (ALFF) between the TN and control groups were calculated. After familywise error (FWE) correction, the differential brain regions in ReHo values between the two groups were mainly located in bilateral middle frontal gyrus, bilateral inferior cerebellum, right superior orbital frontal gyrus, right postcentral gyrus, left inferior temporal gyrus, left middle temporal gyrus, and left gyrus rectus. The differential brain regions in ALFF values between the two groups were mainly located in the left triangular inferior frontal gyrus, left supplementary motor area, right supramarginal gyrus, and right middle frontal gyrus. With the functional impairment of the central pain area, the active areas controlling memory and emotion also change during the progression of TN. There may be different central mechanisms in TN patients of different sexes, affected sides, and degrees of nerve damage. The exact central mechanisms remain to be elucidated.

Intellectual Property Law's Confiscatory Losses: Its Application in Dynamic Network Environments

This paper discusses confiscatory losses in the dynamic network enforcement of intellectual property (IP). IP law protects copyrights, patents, and trademarks. Instead, IP enforcement may consequence in "confiscatory damages." This occurs when the defendant's property is apprehended pursuant to a court order. For both individuals and companies, the internet has made sharing and disseminating innovative creations simpler. IP infringement is also increased by this. In these circumstances, IP law enforcement has become more crucial. Penalisations and sanctions for IP infringement in these conditions may be severe and upshot in confiscatory losses. So as to apply confiscatory losses in dynamic network conditions, this paper suggests reviewing IP law enforcement strategies. Hence, IP infringement penalisations and compensation should be proportionate to the harm caused and avoid confiscatory losses. Moreover, IP infringement can also be minimized by education, information, and alternative dispute resolution. IP law enforcement in dynamic network systems must strike a compromise between protecting IP rights and sidestepping confiscatory damages. 

A enhanced voltage control method for fair active power curtailment of rooftop PV systems in LV distribution networks

Solutions based on active power curtailment (APC) of photovoltaic inverters (PVIs) have been effectively used to alleviate overvoltages in low-voltage distribution networks (LVDNs) due to real power injection supplied by rooftop photovoltaic (PV) systems. However, volt-watt control methods presently available can lead to unfair APC among the PVIs, penalizing financially customers located far away from the distribution transformer of the LVDN. This paper proposes an innovative real-time voltage control method to address the unfair APC issue in LVDNs with rooftop PV systems. The proposed method is based on a centralized control architecture to calculate two fair curtailment factors for all PVIs of the LVDN. Additionally, to explore the extended communication infrastructure brought by centralized control architecure, this paper proposes a novel simplified method to compute the voltage sensitivity matrix (VSM) for applications on unbalanced three-phase LVDNs. Perfomance tests were conducted on a set of LVDNs, evaluating the effectiveness and fairness of APC quantitatively using Jain’s Fairness Index (JFI) as a metric. The results confirmed the effectiveness of the proposed control method for mitigating the overvoltage issue, ensuring fairness by presenting average JFI values close to one (the maximum allowed value). Furthermore, in comparison to conventional VWC strategies, penalties arising from the local dependence of PVIs on APC were eliminated for both curtailment factors. Finally, an accuracy test was performed on the proposed VSM model, based on voltage estimation through the linearized model of LVDN at a specific operating point. The findings demonstrated outstanding accuracy, with an error of less than 0.2%, compared to voltage estimation via power flow.

Optimal switching for Networked Control Systems with information multiplexing

In this article, we examine a Networked Control System (NCS) in which the plant and the controller communicate over a network subject to a certain communication constraint. The plant is described by discrete-time nonlinear dynamics subject to bounded disturbances. Due to an overloaded communication network, we assume that the control signal and the information from the plant (the measured output signal) cannot be transmitted simultaneously and are subject to a multiplexing constraint. The goal is to design a switching strategy that allows us to sequentially communicate given these constraints while optimizing a quadratic cost over a finite horizon. Consequently, we proceed by emulation and assume that a controller that satisfies performance requirements is already provided. The resulting optimization problem is observed to be an integer programming problem that is generally NP-complete, i.e., the complexity is exponential in the time horizon considered. To overcome this issue, we provide a different perspective on this problem than what has been presented by the community before. Our main contribution is to reformulate the problem with all its constraints to a form that renders it amenable to apply the discrete-time Pontryagin Maximum Principle to get the necessary conditions for the optimality of the control action sequence. These necessary conditions are then solved numerically by a multiple-shooting method. To validate the approach, we present some illustrative numerical experiments on an inverted pendulum. Different setups are considered and numerically analyzed: usage of a predictor when the output is not transmitted and usage of the previous value of the output when the new value is not transmitted, with or without the choice of non-transmission.

Enhanced adsorption of Bismark Brown R dye by chitosan conjugated magnetic pectin loaded filter mud: A comprehensive study on modeling and mechanisms

Herein, a polymer-based bioadsorbent was prepared by cross-linking chitosan to filter mud and magnetic pectin (Ch-mPC@FM) for the removal of Bismark Brown R dye (BB-R) from wastewater. Morphological characterization analysis indicated that Ch-mPC@FM had a higher surface area and better pore structure than its components. The Artificial Neuron Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were employed to evaluate the simulation and prediction of the adsorption process based on input variables like temperature, pH, dosage, initial BB-R dye concentration, and contact time. ANFIS and ANN demonstrated significant modeling and predictive accuracy, with R2 > 0.93 and R2 > 0.96, and root mean square error < 0.023 and <0.020, respectively. The Langmuir isotherm and the pseudo-second-order kinetic models provided the best fits to the equilibrium and kinetic data. The thermodynamic assessment showed spontaneous and endothermic adsorption with average entropy and enthalpy changes of 119.32 kJ mol−1 K and 403.47 kJ mol−1, respectively. The study of BB-R dye adsorption on Ch-mPC@FM revealed multiple mechanisms, including electrostatic, complexation, pore filling, cation-π interaction, hydrogen bonding, and π-π interactions. The approximate production cost of US$ 5.809 Kg−1 and excellent adsorption capability render Ch-mPC@FM an inexpensive, pragmatic, and ecologically safe bioadsorbent for BB-R dye removal from wastewater.

Model-Free RBF Neural Network Intelligent-PID Control Applying Adaptive Robust Term for Quadrotor System

This paper proposes a quadrotor system control scheme using an intelligent–proportional–integral–differential control (I-PID)-based controller augmented with a radial basis neural network (RBF neural network) and the proposed adaptive robust term. The I-PID controller, similar to the widely utilized PID controller in quadrotor systems, demonstrates notable robustness. To enhance this robustness further, the time-delay estimation error was compensated with an RBF neural network. Additionally, an adaptive robust term was proposed to address the shortcomings of the neural network system, thereby constructing a more robust controller. This supplementary control input integrated an adaptation term to address significant signal changes and was amalgamated with a reverse saturation filter to remove unnecessary control input during a steady state. The adaptive law of the proposed controller was designed based on Lyapunov stability to satisfy control system stability. To verify the control system, simulations were conducted on a quadrotor system maneuvering along a spiral path in a disturbed environment. The simulation results demonstrate that the proposed controller achieves high tracking performance across all six axes. Therefore, the controller proposed in this paper can be configured similarly to the previous PID controller and shows satisfactory performance.

Resource management for computational offload in MEC networks with energy harvesting and relay assistance

In mobile edge computing (MEC) networks, the integrated application of energy harvesting (EH) and relay cooperation is a promising technology, which ensures the real-time performance of the system and computing power supply. Relay and EH cooperation also expand communication range and extend the battery life of energy-constrained devices. However, in wireless powered and relay-assisted MEC networks, it is challenging to jointly dispatch energy, and computing resources to coordinate heterogeneous performance requirements. To fill this gap, this paper examines the fundamental compromise between utility energy efficiency (UEE) and stable queue length in wireless powered and relay-assisted MEC network systems, and the amount of data transferred for each unit of energy is called UEE. The data queue and energy queue models are introduced and the constraints of stability of two queues in long term are included in the formulated problem. To tackle the fractional and nonconvex optimization problems, the Dinkelsbach method is utilized. Since optimization problems change from time to time and the long-term averaging queue stability is considered, the Lyapunov optimization theory is introduced to convert the non-convex problems into the solvable one. An online resource offloading allocation algorithm is proposed to determine the solution. In addition, the algorithm implements the control parameter V to achieve the compromise between UEE and stable queue length, while the impact of various parameters are also revealed on system performance.

Study on geothermal energy self-recycling extraction and ScCO2 storage in the horizontal well annuli with fracture network system

With the extensive exploitation of fossil fuels, a substantial number of production and injection wells have been or are on the verge of being abandoned. However, many of these wells present viable opportunities for geothermal energy extraction, as they are situated in geothermal reservoirs with favourable temperatures. In alignment with carbon neutrality policies, supercritical CO2 (ScCO2) has emerged as the preferred working fluid for geothermal extraction, facilitating subsequent storage. This study has developed three transient models to characterize the heat and mass transfer processes of ScCO2 circulating within a horizontal wellbore and the reservoir's fracture network. Finite element method and Comsol Multiphysics 6.1 software are employed to solve these models. Results show that ScCO2 efficiently flows from the annuli into the hydraulic fracture network and then circulates back through the annuli completing a self-recycling heat extraction process. The findings reveal several key insights: (a) Lower injection mass flow rates and flow rates lead to more efficient extraction of thermal energy within horizontal wellbores. (b) Low-temperature ScCO2 enhance thermal energy recovery. (c) The larger the coverage area of the fracture network, the more effective the heat extraction. (d) The efficiency of heat extraction and ScCO2 storage in the horizontal well-fracture network self-circulation system is 3 to 5 times and 6 to 7 times higher than that of the horizontal well self-recycling and injection-production systems. (e) Periodic thermal extraction proves to be more efficient than continuous methods. (f) The cumulative heat production of the vertical dual well injection-production system is the highest, reaching 2.45 × 1011 kJ. But it's still lower than the self-circulating system. The heat extraction effect of the diagonal dual well injection-production system is the lowest, which is 1.83 × 1011 kJ. (g) Three developed transient models facilitate efficient heat extraction and ScCO2 storage reservoir screening and evaluation. In conclusion, the self-recycling system has achieved efficient heat extraction and carbon dioxide storage, providing a theoretical basis for future energy conversion and management.