Network Optimization Research Articles

Gansu, Qinghai and Ningxia regions of the new western land and sea corridor Freight network optimization

The new western land and sea corridor makes use of a variety of transport modes to reach major ASEAN countries such as Singapore to the south, connects Southeast Asia and North America to the east, and connects Chongqing, Lanzhou and Xinjiang to the north with the China-European Union (CEU) liner train, which is a composite opening-up corridor in the western region for realising the regional linkage and international co-operation with ASEAN and other countries, and organically connecting with the "One Belt, One Road". In order to explore the regional freight network of Gan-Qing-Ning region of the new western land and sea corridor, we establish the model of node socio-economic attractiveness, topological charisma and comprehensive utility maximization, and analyze the index factors by using entropy weighting, Delphi and comprehensive evaluation methods through the Matlab Genetic Algorithm Toolbox to study the influence of node socio-economic attractiveness, topological charisma, node freight volume, logistics and transportation costs and construction costs on the utility of nodes of the new western land and sea corridor. The influence of the regional node utility of Gansu, Qinghai and Ningxia in the corridor. Analyze the problems and factors affecting the selection of nodes in the existing freight transport corridors in the three provinces. Completing the site selection of Gan-Qing-Ning regional node of the new western land and sea corridor and combining the import and export cargo volume of the three provinces to propose the construction of the South-Middle East three-lane freight corridor, and completing the optimization plan of the freight network. The results show that: scientific optimization and improvement of Gan-Qing-Ning regional freight network can promote the construction of freight network in the northwest region of the new western land and sea corridor, to meet the demand for freight transportation in the corridor, and to promote the development of the corridor industry and economic growth. Thus, it provides reference for the development of Gansu, Qinghai and Ningxia region and freight network optimization.

AI Telco Research: Advancements in Telecommunications Scientific Discovery

This article explores the transformative impact of Artificial Intelligence (AI) on telecommunications research and development. It examines how the integration of AI technologies is revolutionizing various aspects of the industry, including network optimization, spectrum management, and scientific discovery. The article highlights the synergy between human expertise and AI capabilities in enhancing research processes, improving data analysis, and driving innovation. Key areas of focus include AI-powered data analysis, network behavior simulation, signal processing enhancements, and the evolving nature of human-AI collaboration in telco research. The article also addresses challenges such as data privacy and algorithmic bias, while emphasizing the potential of AI to unlock new possibilities in telecommunications, from self-optimizing networks to quantum communication systems.

Neural Network Optimization Using Hybrid Adaptive Mutation Particle Swarm Optimization and Levenberg-Marquardt in Cases of Cardiovascular Disease

Abstract. Cardiovascular disease is a condition generally characterized by the narrowing or blockage of blood vessels, which can lead to heart attacks, chest pain, or strokes. It is the leading cause of death worldwide, accounting for approximately 31% or 17.9 million deaths each year globally. Deaths caused by cardiovascular disease are projected to continue increasing until 2030, with the number of patients reaching 23.3 million. As cases of death due to cardiovascular disease become more prevalent, early detection is crucial to reduce mortality rates. Purpose: Many previous researchers have conducted studies on predicting cardiovascular disease using neural network methods. This study extends these methods by incorporating feature selection and optimization with Hybrid AMPSO-LMA. The research is designed to explore the implementation and predictive outcomes of Hybrid AMPSO-LMA in optimizing MLP for cases of cardiovascular disease. Methods/Study design/approach: The first step in conducting this research is to download the Heart Disease Dataset from Kaggle.com. The dataset is processed through preprocessing by removing duplicates and transforming the data. Then, data mining processes are carried out using the MLP algorithm optimized with Hybrid AMPSO-LMA to obtain results and conclusions. This system is designed using the Python programming language and utilizes Flask for website access in HTML. Result/Findings: The research results demonstrate that the method employed by the author successfully improves the accuracy of predicting cardiovascular disease. Predicting cardiovascular disease using the MLP algorithm yields an accuracy of 86.1%, and after optimization with Hybrid AMPSO-LMA, the accuracy increases to 86.88%. Novelty/Originality/Value: This effort will contribute to the development of a more reliable and effective cardiovascular disease prediction system, with the goal of early identification of individuals exhibiting symptoms of cardiovascular disease.

Multi-Objective Optimization and Reconstruction of Distribution Networks with Distributed Power Sources Based on an Improved BPSO Algorithm

The continuous integration of distributed power into the distribution network has increased the complexity of the distribution network and created challenges in distribution-network reconfiguration. In order to make the distribution network operate in the optimal mode, this paper establishes a multi-objective reconfiguration-optimization model that takes into account active network loss, voltage offset, number of switching actions and distributed power output. For a distribution network with a distributed power supply, it is easy for the traditional binary particle swarm optimization algorithm to fall into a local optimum. In order to improve the convergence speed of the algorithm and avoid premature convergence, this paper adopts an improved binary particle swarm optimization algorithm to solve the problem. The IEEE33 node system is used as an example for simulation verification. The experimental results show that the algorithm improves the convergence speed and global search ability, effectively reduces the system network loss, and greatly improves the voltage level of each node. It improves the stability and economy of distribution-network operation and can effectively solve the problem of multi-objective reconfiguration.

Energy consumption optimization in green cognitive radio networks based on collaborative spectrum sensing

In the realm of green communications, the focus is on achieving high spectrum efficiency and low energy consumption. This paper addresses the crucial goal of reducing energy usage in green cognitive radio networks (CRNs) during communication between secondary users (SUs) and primary users (PUs). This paper proposed an energy consumption optimization model (ECOM) for green CRN utilizing collaborative spectrum sensing thereby minimizing the environmental impact and prolonging the operational lifetime of devices. The collaborative spectrum sensing proved its role in optimizing the energy consumption in the green CRN. An energy-efficient scheduling algorithm is implemented in ECOM, in which the SUs can be scheduled to perform their sensing process in a time-division manner to reduce energy consumption. Applied collaborative spectrum sensing serves as a valuable resource for researchers, network operators, and policymakers seeking to balance the increasing demand for wireless communication services with the imperative of sustainability. The simulation results and mathematical proof emphasize that ECOM demonstrates reduced energy usage and increased average effective throughput when compared to other recent models.

Development and optimization of an Artificial Neural Network (ANN) model for predicting the cadmium fixation efficiency of biochar in soil

This study addresses the issue of soil cadmium pollution and the common problem of data missing during the training of artificial neural network models. A substantial amount of characteristic data regarding the immobilization of cadmium in soil through biochar was collected from the literature. Subsequently, data was organized and analyzed using correlation matrix analysis and feature importance analysis. The results revealed that the feature importance of biochar (52%) was relatively significant. A novel approach is introduced, involving a non-deterministic optimization technique based on linear interpolation to supplement the missing data within the dataset. The completed dataset effectively preserves the distributional characteristics of the original data, thereby enhancing the training performance of the neural network model. Through three phases, namely data incompleteness, data supplementation, and genetic algorithm optimization, the neural network model was progressively refined. The outcome of this iterative process is a high-performance artificial neural network model capable of predicting the efficiency of biochar in immobilizing cadmium in soil. The significance of these findings lies in their potential to advance the application of machine learning in environmental science, particularly in addressing complex pollution scenarios.

Perception management as an approach to optimizing the image of the social network “VKontakte”

INTRODUCTION. The application of the concept of perception management for image optimization of the social network “VKontakte” is investigated. The scientific evolution of the application of theoretical aspects of this approach from the initial, military paradigm to the field of communications both inside and outside social networks for the purpose of promoting the services they offer in the commercial environment is considered. The goal is to study and analyze the application of the concept of perception management for image optimization of the social network “VKontakte”.MATERIALS AND METHODS. The existing modern theoretical aspects and practical approaches used to influence the perception of the online community brand by existing and potential participants are analyzed. Such research methods as content analysis and statistical data analysis are used. Using examples of publicly available materials, the advertising campaign of the social network “VKontakte” is analyzed and a comparative analysis is conducted with the best international practices in the paradigm of perception management.RESULTS AND DISCUSSION. The article proposes tools and techniques that can be used to manage the perception of the image of a social network in the context of modern international trends and challenges.CONCLUSION. The study shows that social network brand perception management is an effective approach to optimizing the image of social networks, and an advertising campaign for the “VKontakte” social network, conducted taking into account the basic principles of this approach, can contribute to the successful promotion of this social network in the modern information environment.

Sparse regularized graph pooling network optimal sensor placement method for diesel engine vibration fault perception system

Vibration sensor network optimization increases monitoring effectiveness and reduces sensor quantity and transmission burden. However, the traditional model-driven methods depend on precise finite element models, challenging for complex machinery. This paper presents a data-driven approach using the Sparse Regularized Graph Pooling Network (SRGPN), which conceptualizes sensor networks as graphs and uses graph pooling to identify optimal sensor combinations. A sparsity regularization term related to the number of sensors is included in the loss function, aiming for the minimal yet effective sensor combination. Additionally, a monitoring capability metric suited for diesel engines with multi-source impulse signals is proposed, reflecting the sensors’ monitoring capacity. Validated through simulations and tests on a diesel engine test bench, the results show that SRGPN optimally places sensors near excitation sources, balancing sensor count and monitoring needs. This approach shows potential for optimizing sensor placements in condition monitoring.

Optimization of Waste Power Battery Recycling Process from the Perspective of Supply Chain

With the rapid popularization of electric vehicles, the recycling of used power batteries has become an increasingly prominent issue. Recycling of used power batteries not only involves environmental protection, but also relates to the efficient utilization of resources. The current recycling system faces problems such as low recycling efficiency, high cost and resource waste. From the perspective of supply chain management, this paper analyzes the current situation and problems in the recycling process of used power batteries, and puts forward a series of optimization strategies, including recycling network optimization, logistics management improvement and inventory control strategies. This paper aims to provide theoretical support and practical guidance for the optimization of waste power battery recycling supply chain, and promote the sustainable development of recycling industry.

Design and practice of integrating artificial intelligence into the teaching of "Synthetic Biology" under the background of discipline crossing

In recent years, artificial intelligence has been employed to empower synthetic biology, demonstrating great potential in the simulation and prediction of protein structures as well as the design and optimization of regulatory elements and metabolic networks. Integrating artificial intelligence into the teaching of Synthetic Biology is in line with the development trends of synthetic biology and can promote the cultivation of interdisciplinary high-level talents and collaborative innovation. This paper expounds the idea of integrating artificial intelligence into the teaching of Synthetic Biology from establishing interdisciplinary course contents and teaching methods, simultaneously considering the fundamentals and application of artificial intelligence in synthetic biology, cultivating independent learning and innovative practice abilities, and enhancing the ethics education related to artificial intelligence. Furthermore, a system integrating artificial intelligence with the teaching contents of Synthetic Biology is designed, which focuses on supplementing fundamentals of artificial intelligence and incorporating artificial intelligence into the classroom and experimental teaching contents of Synthetic Biology. Moreover, with the course of Synthetic Biology in Jiangnan University as an example, this paper presents the pathway of integrating artificial intelligence into the teaching of this course under the background of discipline crossing. Finally, the teaching effects are expected.

Study on configuration design and microstructure optimization of quasi-continuous network structured (Ti3AlC2-Al3Ti)/2024Al

Configuration design and microstructure regulation are paramount strategies to surmount strength and ductility trade-offs in Aluminum Matrix Composites (AMCs). In this study, both methods were harnessed in fabricating, strengthening, and toughening quasi-continuous network structured (Ti3AlC2-Al3Ti)/2024Al composites: configuration design was applied through hot rolling, whereas microstructure regulation was facilitated via heat treatment. Hot rolling modified the network configuration and optimized the strain distribution by improving the matrix connectivity and the directional load-bearing capacity of the network structure. Subsequent heat treatment weakened grain orientation, gave full play to dynamic recrystallization and refined grains, and enhanced the dispersion of precipitates. Superior strength-ductility compatibility of 405 MPa (ultimate tensile strength), 242 MPa (tensile yield strength), and 12 % (elongation) was achieved, positioning it among the most competitive AMCs to date. Finite element analysis was employed for the in-situ monitoring of strain behaviors, elucidating the strengthening and toughening mechanisms.

Correlations between an Urban Three-Dimensional Pedestrian Network and Service Industry Layouts Based on Graph Convolutional Neural Networks: A Case Study of Xinjiekou, Nanjing

Urban high-density development has led to the emergence of complex three-dimensional pedestrian networks. As a crucial component of city centers, these networks significantly influence the spatial distribution of service industries. Understanding the correlation between pedestrian networks and service industry layouts is vital for effective planning and development. This study proposes a technical framework for analyzing the relationship between three-dimensional pedestrian networks and service industry layouts. Using the Xinjiekou central area in Nanjing as a case study, we constructed a three-dimensional pedestrian network model using the sDNA method. Focusing on catering formats, we introduced a method to study the spatial distribution characteristics of service industries in three-dimensional spaces and employed a graph convolutional network model to systematically analyze the correlation between pedestrian network closeness and betweenness with catering formats. The results indicate that pedestrian network closeness is significantly positively correlated with the number and average spending of catering formats, while betweenness shows almost no correlation. High-closeness areas, due to their traffic convenience and walkability, are more conducive to the concentration of catering formats and higher spending levels. Our findings provide valuable insights for catering format location decisions and the optimization of three-dimensional pedestrian networks, contributing to sustainable urban development.

Strategic Water Management in the Great Lakes: Integrating Network Models and Optimization

Strategic Water Management in the Great Lakes: Integrating Network Models and Optimization

Operation optimization of large-scale natural gas pipeline networks based on intelligent algorithm

With the expansion of natural gas pipelines into larger networks, there is a growing concern regarding energy consumption in the operations of pipeline systems. However, the intricate network structure and hydraulic properties pose challenges for operation optimization. In this paper, a Mixed Integer Nonlinear Programming model is proposed to optimize the steady-state operation of natural gas pipeline network, aiming to minimize the energy consumption of compressor units. The model incorporates constraints related to flow direction, flow balance, pressure drop in pipeline sections, and pressure increase at compressor stations. To solve the complex nonlinear model efficiently, a stochastic optimization algorithm that integrates particle swarm optimization and high-fidelity simulation is proposed. The case study is conducted on a large-scale natural gas pipeline network consisting of forty-three pipeline sections and four compressor stations. The optimal operation scheme is calculated, and the outlet pressures of each compressor are determined. The results demonstrate that the stochastic optimization algorithm proposed in this paper can reduce energy consumption by 21.23 % at most and 19.77 % on average during pipeline operation, which can provide guidance for the operation management of large-scale natural gas pipeline network.

Optimization of the power–transportation coupled power distribution network based on stochastic user equilibrium

With the increasing penetration of electric vehicles (EVs) in road traffic, the spatial and temporal stochasticity of the travel pattern and charging demand of EVs as a mode of transportation and an electrical load have generated different degrees of congestion impacts on both the power grid and the transportation network. Based on this, this paper proposes a power–transportation-coupled distribution network optimization strategy based on stochastic user traffic equilibrium theory. First, a stochastic user equilibrium-mixed traffic flow allocation model that expresses user non-completely rational path selection behavior is established, and the traffic flow equilibrium solution is obtained using an improved method of successive algorithm and mapped to charging loads. Second, a distribution network power flow optimization model under the coupled power–transportation architecture is established to optimize the operation state of the distribution network by combining distributed resources such as energy storage, demand response loads, wind power, photovoltaic, and gas turbines.

Activated biochar production from young coconut waste (Cocos nucifera) as bioadsorbent: a pathway through Artificial Neural Network (ANN) optimization.

This pioneering work explores the immense potential of young coconut waste, a continuously marginalized residue of the food and beverage industry, to serve as an indispensable feedstock in the production of biochar. Through an examination of the key carbonization factors that include time, temperature, and concentrations of the activating agent, KOH, the outcomes offer relevant insights that could be leveraged to maximize biochar production for tailored applications. This study stands out for its innovative use of Artificial Neural Network (ANN) approaches for predictive modeling. Fifty datasets, supplemented with secondary data obtained from the literature and experiments, were utilized for the purposes of training, testing, and validating the neural network model. Here, the datasets were processed utilizing the Deep Neural Network (DNN) framework, which was designed and implemented with the minimal loss function framework feasible. The architectural configuration comprises the following; an input layer, four hidden layers (128-neuron dense layer, batch normalization, and 64-neuron dense layer, batch normalization), a dropout layer, and an output layer. With an R2 of 0.8238 for biochar yield and 0.7324 for iodine number, the trained DNN model showed a relatively high degree of accuracy in making predictions.

Coupling artificial neural network and sperm swarm optimization for soil temperature prediction at multiple depths.

Soil temperature (ST) stands as a pivotal parameter in the realm of water resources and irrigation. It serves as a guide for farmers, enabling them to determine optimal planting and fertilization timings. In the backdrop of regions like Iran, where water resources are scarce, a proficient and economical prediction model for ST, particularly at lower depths, becomes imperative. While recent models have demonstrated adeptness in predicting ST, in general, their error decreases with increasing depth, so that they had the lowest error at a depth of 100 cm. Addressing this gap, our study pioneers a novel hybrid model that excels in accurate daily ST prediction as it delves deeper. The models deployed encompass the multilayer perceptron (MLP) and an enhanced version, MLP coupled with the Sperm Swarm Optimization Algorithm (MLP-SSO). These models prognosticate daily ST across varying depths (5-100cm), leveraging meteorological parameters such as air temperature, relative humidity, wind speed, sunshine hours, and precipitation. These parameters are anchored to the Ahvaz and Sabzevar synoptic stations in Iran, spanned over the period from 1997 to 2022. Evaluation of our research outcomes unveils that the root mean square error (RMSE) witnesses its most substantial reduction at a depth of 100cm. For instance, at the Ahvaz station, the MLP-SSO model diminishes the RMSE value from 1.25 to 1.12°C, in contrast to the MLP model. Similarly, at the Sabzevar station, the RMSE value drops from 1.78 to 1.49°C using the coupled MLP-SSO model. These results robustly highlight the considerable enhancement brought about by the utilization of the MLP-SSO model, clearly surpassing the performance of the standalone MLP model. This emphasizes the potential and promise of the MLP-SSO model for future investigations, offering insights that can significantly advance the domain of soil temperature prediction and its implications for agricultural decision-making.

Striving for Sustainability in Healthcare Management: Waste Handling and Disposal Network Optimization

Striving for Sustainability in Healthcare Management: Waste Handling and Disposal Network Optimization

An adaptive multi-objective optimization method for structure-connection-performance design of commercial vehicle cab

A variable screening-approximate model-adaptive multi-optimization algorithm integrated method is proposed to solve the high-dimensional nonlinear problems for lightweight optimization of cabs. Firstly, a full-parametric body-in-white model of the cab is established using implicit parameterization technology, and the simulation analysis of the bending and torsional stiffness, natural modal characteristics, and passive safety are established. Then, considering the structure and connection process parameters, the design variables are screened based on a gray relational analysis-TOPSIS (GRA-TOPSIS). Finally, integrated multi-objective optimization of cab structure-connection-performance is carried out by an adaptive RBF neural network optimization method, and the optimal solution is determined by the GRA-TOPSIS method. Compared with the initial model, the mass of cab is reduced by 3.28% after optimization design. It provides guidance for the lightweight design of body.

Collaborative Optimization for Active Distribution Network with Soft Open Point and Energy Storage

Background: The increasing use of high-penetration distributed clean energy has led to voltage fluctuations in the distribution network that surpass safety limits and pose challenges to economic and low-carbon operation. Traditional voltage regulation devices are unable to meet the real-time high-precision voltage control needs when encountering frequent fluctuations due to physical limitations. The Soft Open Point (SOP) can provide continuous reactive power to achieve rapid voltage regulation. Objective: We propose a collaborative optimization approach that fully considers the regulatory capabilities of SOP and energy storage to eliminate voltage violations and reduce network losses. Methods: This study introduces an active distribution network reactive power voltage optimization approach that incorporates intelligent SOP and energy storage, combining conventional devices (such as on-load tap changers and capacitor banks) with contemporary devices (such as SOP, distributed generators, and energy storage) to establish synergy. A coordinated optimization model was developed, considering various operational constraints to minimize network losses and regulate the voltage of distribution network nodes. By using linearization and quadratic relaxation, the original non-convex mixed-integer non-linear optimization model was transformed into a Mixed-Integer Second-Order Cone Programming (MISOCP) model to meet the requirements of rapid voltage regulation. Results: A case study was conducted on the IEEE 33-node test system, showing that the proposed approach effectively reduces network losses and eliminates voltage violations. Conclusion: The feasibility and effectiveness of the proposed methodology have been validated, confirming its ability to ensure the safe and economic operation of active distribution networks.