Random Undersampled Digital Elevation Model Super-Resolution Based on Terrain Feature-Aware Deep Learning Network by Ziqiang Huo, Meng Xi, Jingyi He, Zhengjian Li, Jiabao Wen

“…In order to solve these problems, we conducted in-depth research on the terrain feature patterns of DEMs and proposed a DEM super-resolution reconstruction network that is terrain feature-aware, named D-ResDCN. …”
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A Self-Adaptive Particle Swarm Optimization Based Multiple Source Localization Algorithm in Binary Sensor Networks by Long Cheng, Yan Wang, Shuai Li

“…With the development of wireless communication and sensor techniques, source localization based on sensor network is getting more attention. However, fewer works investigate the multiple source localization for binary sensor network. …”
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Reconstruction of the object inhomogeneity parameters from near-field measurements in microwave tomography problem using neural networks by A.V. Medvedev, M.Yu. Medvedik

“…An experiment was conducted demonstrating the features of restoring object parameters using neural networks. …”

Study on the Shortest Reliable Path of Stochastic Time-Dependent Transportation Networks considering Waiting Time at Signalized Intersections by Yanming Sun, Jie Li, Shixian Liu

“…Theoretical research is conducted on finding the shortest path with stochastic and time-dependent characteristics of link travel time in urban road networks. …”
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Traffic signal optimization control method based on attention mechanism updated weights double deep Q network by Huizhen Zhang, Zhenwei Fang, Youqing Chen, Haotian Dai, Qi Jiang, Xinyan Zeng

“…The AMUW–DDQN method enhances the perceptual ability of the network by introducing the attention mechanism of Squeeze And Excitation Networks (SENet) to make the neural network pay attention to important state components automatically, and based on the idea that accurate representation of potentially optimal action values is better than the balanced representation of all the action values, it is considered that underestimated actions have a certain probability of being the optimal action and the loss function is weighted to optimize the action values. …”
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Air quality prediction using stacked bi- long short-term memory and convolutional neural network in India by S Karkuzhali, Thendral Puyalnithi, R Nirmalan

“…Furthermore, this study conducts a detailed temporal analysis, unraveling diurnal, seasonal, and long-term trends in air quality fluctuations. …”
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Crosstalk between the gut microbiota and brain network topology in poststroke aphasia patients: perspectives from neuroimaging findings by Yun Cao, Jiaqin Huang, Danli Zhang, Jianguang Ji, Xiaojing Lei, Zhongjian Tan, Jingling Chang

“…Objectives: The aim of this study was to investigate the relationships among the gut microbiota, neuroendocrine-immune network, brain network properties, and language function in patients with PSA. …”
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Research on the application of a model combining improved optimization algorithms and neural networks in trajectory tracking of robotic arms by Yanhui Lai, Zuobing Chen, Ya Mao

“…To further enhance optimization effectiveness, we integrated the new algorithm MMGO with Radial Basis Function (RBF) neural networks, resulting in the development of two optimization algorithm models: RBF_MMGO and RBF_MGO. …”
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A Data Augmented Method for Plant Disease Leaf Image Recognition based on Enhanced GAN Model Network by MingYuan Xin, Ling Weay Ang, Sellappan Palaniappan

“…An enhanced GAN model network based on the Wasserstein GAN loss function has been developed to address the limited training images of plant disease leaves. …”
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From NeurODEs to AutoencODEs: A mean-field control framework for width-varying neural networks by Cristina Cipriani, Massimo Fornasier, Alessandro Scagliotti

“…While the global results obtained for high Tikhonov regularisation may not hold globally, we show that many of them can be recovered in regions where the loss function is locally convex. Inspired by our theoretical findings, we develop a training method tailored to this specific type of Autoencoders with residual connections, and we validate our approach through numerical experiments conducted on various examples.…”
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Understanding epistatic networks in the B1 β-lactamases through coevolutionary statistical modeling and deep mutational scanning by J. Z. Chen, M. Bisardi, D. Lee, S. Cotogno, F. Zamponi, M. Weigt, N. Tokuriki

“…Abstract Throughout evolution, protein families undergo substantial sequence divergence while preserving structure and function. Although most mutations are deleterious, evolution can explore sequence space via epistatic networks of intramolecular interactions that alleviate the harmful mutations. …”
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Optimal planning of integrated nuclear-hybrid renewable energy systems for electrical distribution networks based on artificial intelligence by Samira M. Nassar, A. A. Saleh, Ayman A. Eisa, E. M. Abdallah, Ibrahim A. Nassar

“…The proposed modeling and simulations are conducted using MATLAB software, and the optimization methodology is applied to three types of HES on two standard radial distribution networks; the IEEE 33-bus and IEEE 69-bus systems. …”
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Multi-Level Spectral Attention Network for Hyperspectral BRDF Reconstruction from Multi-Angle Multi-Spectral Images by Liyao Song, Haiwei Li

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Edge-Enhanced GCIFFNet: A Multiclass Semantic Segmentation Network Based on Edge Enhancement and Multiscale Attention Mechanism by Long Chen, Zhiyuan Qu, Yao Zhang, Jingyang Liu, Ruwen Wang, Dezheng Zhang

“…Moreover, we enhance the network's ability to extract edge information during training by sharing parameters between the backbone and employing a specialized loss function. …”
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Virtual Cluster Partitioning Method of Active Distribution Networks Using Quantum Particle Swarm Optimization and Sector Search by Wei Liu, Yifan Tong, Xinran Xing, Xi Chen, Bo Hu, Qian Sun, Yufei Wang, Huaxin Li, Huidong Guo

“…Building upon the traditional particle swarm optimization (PSO) algorithm, this study introduces the wave function and Schrödinger equation to enhance algorithm performance. …”
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Identification of a novel chemotherapy benefit index for patients with advanced ovarian cancer based on Bayesian network analysis. by Shuxiao Ma, Lu Zhou, Yi Liu, Hui Jie, Min Yi, Chenglin Guo, Jiandong Mei, Chuan Li, Lei Zhu, Senyi Deng

“…<h4>Methods</h4>Based on The Cancer Genome Atlas (TCGA) transcriptome data, we conducted correlation and Bayesian network analyses to identify key genes strongly associated with chemotherapy prognosis. …”
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Manual acupuncture enhanced therapeutic efficacy in vascular dementia rat model: systematic review and network meta-analysis by Yuanyu Song, Yinghua Chen, Junfeng Li, Wei Sun, Fangfang Jin

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Comparisons of efficacy and safety of immunotherapies for Alzheimer’s disease treatment: A network meta-analysis of randomised controlled trials by Ching-Hui Su, Ying-Tzu Chang, Huan-Shu Tseng, Chan-Yen Kuo, Jin-Hua Chen, Po-Yu Chien, Yao-Jen Chang, Chin-Chuan Hung

“…Donanemab and lecanemab ranked among the most effective treatments for improving cognitive function (Clinical Dementia Rating Scale – Sum of Boxes P-scores: 0.88 and 0.77) and daily activities (Alzheimer’s Disease Cooperative Study – Activities of Daily Living P-scores: 0.85 and 0.90), based on network meta-analysis findings. …”
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Joint P- and S-Wave VSP Traveltime Tomography via Well Log-Guided Physics-Informed Neural Networks by Tengyu Wang, Dingding Deng, Duoming Zheng, Zhen Zhang, Wenjun Luo, Yang Liu

“…The W-PINNPStomo framework incorporates two neural networks: a travel time network dedicated to predicting P- and S-wave traveltimes, and a velocity network tasked with estimating the corresponding wave velocities. …”
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Interactions between Sugarcane Leaf Return and Fertilizer Reduction in Soil Bacterial Network in Southern China Red Soil by Yufeng Liu, Dan Liang, Jincheng Xing, Ziyan Xue, Zhenhua Zhang

“…According to the results of the soil bacterial community compositions and diversity, the soil bacterial network was changed during maize growth. SLR exerted a stronger effect on soil bacterial function than FR. …”
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