A Matheuristic Iterative Approach for Profit-Oriented Line Planning Applied to the Chinese High-Speed Railway Network by Di Liu, Javier Durán Micco, Gongyuan Lu, Qiyuan Peng, Jia Ning, Pieter Vansteenwegen

“…Extensive computational experiments are executed to show the effectiveness of the proposed approach to deal with the real-world railway network line planning problem. Through extensive computational experiments on the small example network and real-world-based instances, the results show that the proposed model can improve the profits by 22.4% on average comparing to their initial solutions. …”
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Solution for Solving Dynamic Partial Differential Equation Model of Natural Gas Pipeline Network Based on Discretization and Linearization by Peiyao ZHAO, Zhengshuo LI

“…The average error in the simulation of a simple pipeline network is 0.279 4%, and the time is 37.285 seconds. …”
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A Dynamic Variance-Based Triggering Scheme for Distributed Cooperative State Estimation over Wireless Sensor Networks by Hongbo Zhu, Jiabao Ding

“…In this paper, the problem of performing cooperative state estimation for a discrete linear stochastic dynamical system over wireless sensor networks with a limitation on the sampling and communication rate is considered, where distributed sensors cooperatively sense a linear dynamical process and transmit observations each other via a common wireless channel. …”
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Federated learning-based user access strategy and energy consumption optimization in cell-free massive MIMO network by Yuanyuan YAO, Yiqiu LIU, Sai HUANG, Chunyu PAN, Xuehua LI, Xin YUAN

“…To solve the problem that how users choose access points in cell-free massive multiple-input multiple-output (CF-mMIMO) network, a prioritized access strategy for poorer users based on channel coefficient ranking was proposed.First, users were evaluated and ranked for their channel quality and stability after channel sensing, and suitable access points were selected in sequence according to the order of the channel state information.Second, considering issues such as users' energy consumption and data security, a federal learning framework was used to enhance user's data privacy and security.Meanwhile, an alternating optimization variables algorithm based on energy consumption optimization was proposed to optimize the multi-dimensional variables, for the purpose of minimizing the total energy consumption of the system.Simulation results show that compared with the traditional user-centric in massive MIMO, the proposed access strategy can improve the average uplink reachable rate of users by 20%, and the uplink rate of users with poor channels can be double improved; in terms of energy consumption optimization, the total energy consumption can be reduced by much more than 50% after optimization.…”
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Rolling bearing fault identification with acoustic emission signal based on variable-pooling multiscale convolutional neural networks by Yue Zhang, Yang Yu, Zheng Yang, Qiang Liu

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Case Study in Network Security System Using Random Port Knocking Method on The Principles of Availability, Confidentiality and Integrity by Tati Ernawati, Idham Kholid, Dahlan, Dini Rohmayani

“…Preventing unidentified individuals from misusing their access to information is a major concern when it comes to data security. Network administrators are charged with working harder to be able to secure the computer network they manage. …”
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An Efficient Framework for Large Scale Multimedia Content Distribution in P2P Network: I2NC by M. Anandaraj, P. Ganeshkumar, K. P. Vijayakumar, K. Selvaraj

“…The randomization introduced by network coding makes all packets equally important and resolves the problem of locating the rarest block. …”
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Application of 4.9 GHz Timeslot Flipping Technology Based on Triple-frequency Networking in Xiwan Coal Mine by MA Xiaolong, LEI Jian, WANG Xiaoqi

“…After the implementation of the triple-frequency networking scheme, the proportion 50 Mbit/s upstream edge rate in the whole coal mine business area is more than 99%, and the average time delay is less than 20 ms. …”
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Application of 4.9 GHz Timeslot Flipping Technology Based on Triple-frequency Networking in Xiwan Coal Mine by MA Xiaolong, LEI Jian, WANG Xiaoqi

“…After the implementation of the triple-frequency networking scheme, the proportion 50 Mbit/s upstream edge rate in the whole coal mine business area is more than 99%, and the average time delay is less than 20 ms. …”
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Influence Maximization in Social Networks Using Discrete Manta-Ray Foraging Optimization Algorithm and Combination of Centrality Criteria by Zaynab Azizpour, Saeid Taghavi Afshord, Bagher Zarei, Mohammad Ali Jabraeil Jamali, Shahin Akbarpour

“…Influence Maximization (IM) is a fundamental problem in social network analysis that seeks to identify a small set of highly influential nodes that can maximize the spread of information. …”
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Hydraulic System Fault Diagnosis Based on Dual-Channel Convolutional Neural Network and Improved D-S Evidence Theory by Jiyang Qi, Yue Qi, Shenghua Luo, Jiahao Zhang

“…To address the problem that the traditional convolutional neural network (CNN) cannot fully extract the fault feature information in a single channel and the accuracy of hydraulic system fault diagnosis based on a single signal is not high, a fault diagnosis method combining dual-channel CNN and improved D-S evidence theory is proposed. …”
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An integrated stacked convolutional neural network and the levy flight-based grasshopper optimization algorithm for predicting heart disease by Syed Muhammad Salman Bukhari, Muhammad Hamza Zafar, Syed Kumayl Raza Moosavi, Majad Mansoor, Filippo Sanfilippo

“…This study proposes a novel hybrid model integrating a Stacked Convolutional Neural Network (SCNN) with the Levy Flight-based Grasshopper Optimization Algorithm (LFGOA) to address this challenge. …”
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CAML-PSPNet: A Medical Image Segmentation Network Based on Coordinate Attention and a Mixed Loss Function by Yuxia Li, Peng Li, Hailing Wang, Xiaomei Gong, Zhijun Fang

“…Compared with Deeplabv3, HrNet, U-Net and PSPNet networks, the average intersection rates of CAML-PSPNet are increased by 2.84%, 3.1%, 5.4% and 3.08% on lung cancer data, 7.54%, 3.1%, 5.91% and 8.78% on Kvasir-SEG data, and 1.97%, 0.71%, 3.83% and 0.78% on ISIC 2017 data, respectively. …”
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Fault diagnosis of micro gas turbine using a hybrid scheme of thermodynamic analysis and artificial neural network by S.S. Talebi, A. Madadi, A.M. Tousi

“…A set of transformed variables is defined to reduce the dimension of the problem. An artificial neural network is used to recognize the pattern of noisy transformed variables and find the faulty component. …”
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SPARQ: Efficient Entanglement Distribution and Routing in Space–Air–Ground Quantum Networks by Mohamed Shaban, Muhammad Ismail, Walid Saad

“…To solve the entanglement routing problem, a deep reinforcement learning (RL) framework is proposed and trained using deep Q-network (DQN) on multiple graphs of SPARQ to account for the network dynamics. …”
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Effects of Biological Adhesion on the Hydrodynamic Characteristics of Different Panel Net Materials: A BP Neural Network Approach by Yongli Liu, Wei Liu, Lei Wang, Minghua Min, Lei Li, Liang Wang, Shuo Ma

“…Based on backpropagation (BP) neural network training, the relationship between biological characteristics (average adhesion thickness and density) and the drag force of three kinds of net materials was determined. …”
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AoI-enabled multi-node cooperative sensing based on integration of sensing, communication, and computing in vehicular networks by Yiqing ZHOU, Haoyue ZHANG, Yanli QI, Qing CAI, Ling LIU, Jiangzhou WANG

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Optimization Strategies for Urban Waterlogging Warning in Complex Environments: Based on Particle Swarm Optimization and Deep Neural Networks by Xiande Hu, Fenfei Gu

“…The study’s findings indicate that the PSO + BP model’s average accuracy in 10 early warning tests is as high as 97.95%, with a response time of only 0.022 ms; the average accuracy and response time of the BP model are 89.06% and 0.255 ms, respectively; the Bayesian network model (BN model) is 82.78% and 0.275 ms. …”
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Short-Term Wind Speed Forecasting Using Decomposition-Based Neural Networks Combining Abnormal Detection Method by Xuejun Chen, Jing Zhao, Wenchao Hu, Yufeng Yang

“…In particular, short-term wind speed forecasting, an essential support for the regulatory actions and short-term load dispatching planning during the operation of wind farms, is currently regarded as one of the most difficult problems to be solved. This paper contributes to short-term wind speed forecasting by developing two three-stage hybrid approaches; both are combinations of the five-three-Hanning (53H) weighted average smoothing method, ensemble empirical mode decomposition (EEMD) algorithm, and nonlinear autoregressive (NAR) neural networks. …”
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Neural Network Models for Ionospheric Electron Density Prediction at a Fixed Altitude Using Neural Architecture Search by Yang Pan, Mingwu Jin, Shun‐Rong Zhang, Simon Wing, Yue Deng

“…In this work, we propose to use neural architecture search (NAS), an automatic machine learning method, to mitigate this problem. NAS aims to find the optimal network structure through the alternate optimization of the hyperparameters and the corresponding network parameters within a pre‐defined hyperparameter search space. …”
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