Pore Structure and Its Controlling Factors of Cambrian Highly Over-Mature Marine Shales in the Upper Yangtze Block, SW China by Dadong Liu, Mingyang Xu, Hui Chen, Yi Chen, Xia Feng, Zhenxue Jiang, Qingqing Fan, Li Liu, Wei Du

“…The results demonstrate that the shales exhibit high TOC contents (average 4.78%) and high thermal maturity (average Ro 3.64%). …”
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Toward 6G: Deep GRU and RNN Empowered MVDR and LCMV Adaptive Beamformers for IRS-Aided Wireless Environments by D. L. Sharini, Ravilla Dilli, M. Kanthi, G. D. Goutham Simha

“…The architecture seamlessly integrates constrained optimization techniques—specifically Minimum Variance Distortionless Response (MVDR) and Linearly Constrained Minimum Variance (LCMV)—with advanced deep recurrent learning models, including Gated Recurrent Units (GRU) and Recurrent Neural Networks (RNN). …”
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Wearable peripheral nerve stimulator reduces essential tremor symptoms through targeted brain modulation by Cuong P. Luu, Jordan Ranum, Youngwon Youn, Jennifer L. Perrault, Bryan M. Krause, Matthew I. Banks, Laura Buyan-Dent, Kip A. Ludwig, Wendell B. Lake, Aaron J. Suminski

“…Of note, TAPS-related modulation of LFPs and spiking activity was greatest near the optimal placement location for the DBS lead in treating ET (R2 = 0.122, p = 0.006). …”
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State-of-Health Estimation for Lithium-Ion Batteries via Incremental Energy Analysis and Hybrid Deep Learning Model by Yan Zhang, Anxiang Wang, Chaolong Zhang, Peng He, Kui Shao, Kaixin Cheng, Yujie Zhou

“…This paper proposes a novel SOH estimation method for lithium-ion batteries, utilizing incremental energy features and a hybrid deep learning model that combines Convolutional Neural Network (CNN), Kolmogorov–Arnold Network (KAN), and Bidirectional Long Short-Term Memory (BiLSTM) (CNN-KAN-BiLSTM). …”
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Minimizing Age of Information in Slotted ALOHA With Short-Packet Communications by Yoora Kim

“…We aim to determine the average AoI and the average peak AoI (PAoI) as key performance metrics and to optimize both the transmission probability of a device in the SA protocol and the blocklength of a packet in short-packet communications. …”
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A Differential Coupled-Line-Based Active Microwave Sensor System for Retrieving Real Permittivity of Binary Aqueous Solution by Lina Shang, Guang Chen, Wen-Jing Wu, Wen-Sheng Zhao

“…To eliminate the need for a vector network analyzer (VNA) and reduce costs, active microwave circuits are incorporated. …”
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Empowering Sustainability: The Crucial Role of IoT-Enabled Distributed Learning Systems in Reducing Carbon Footprints by Anjana M S, Aryadevi Remanidevi Devidas, Maneesha Vinodini Ramesh

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Vehicle speed measurement method using monocular cameras by Hao Lian, Meian Li, Ting Li, Yongan Zhang, Yanyu Shi, Yikun Fan, Wenqian Yang, Huilin Jiang, Peng Zhou, Haibo Wu

“…Finally, the algorithm is combined with You Only Look Once version 7 (YOLOv7) and Deep Simple Online and Realtime Tracking (DeepSORT) algorithms, integrating multiple model structures to optimize the network, achieving precise multi-target speed measurement. …”
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Transplantation and Pregnancy by Pinar Erkan Uc, Sermet Sagol

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Impact of PM_2.5 Pollution on Solar Photovoltaic Power Generation in Hebei Province, China by Ankun Hu, Zexia Duan, Yichi Zhang, Zifan Huang, Tianbo Ji, Xuanhua Yin

“…The optimal stacking configuration achieved superior performance (MAE = 0.479 MW, indicating an average prediction error of 479 kilowatts; R² = 0.967, reflecting that 96.7% of the variance in power output is explained by the model), demonstrating robust predictive capability under diverse atmospheric conditions. …”
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FLDQN: Cooperative Multi-Agent Federated Reinforcement Learning for Solving Travel Time Minimization Problems in Dynamic Environments Using SUMO Simulation by Abdul Wahab Mamond, Majid Kundroo, Seong-eun Yoo, Seonghoon Kim, Taehong Kim

“…Addressing these issues requires innovative approaches for optimizing road network utilization. While Deep Reinforcement Learning (DRL)-based methods have shown remarkable effectiveness in dynamic scenarios like traffic management, their primary focus has been on single-agent setups, limiting their applicability to real-world multi-agent systems. …”
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Enhancing prosthetic hand control: A synergistic multi-channel electroencephalogram by Pooya Chanu Maibam, Dingyi Pei, Parthan Olikkal, Ramana Kumar Vinjamuri, Nayan M. Kakoty

“…These features were employed to train a Bayesian optimizer-based support vector machine (SVM). The optimized SVM classifier could achieve an average testing accuracy of 94.39 $ \pm $ .84% using synergistic features. …”
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Resource allocation for coexistence of eMBB and bursty URLLC based on queueing with preemption by Wei Guo, Kai Liang, Yuewen Song, Xiaoli Chu, Gan Zheng, Kai-Kit Wong

“…Based on this analysis, we formulate a mixed-integer nonlinear programming problem to minimize the average delay of URLLC packets while satisfying the average delay and throughput requirements of eMBB by jointly optimizing the eMBB subcarrier allocation, the URLLC subcarrier scheduling and the computing resource allocation. …”
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ARIMA Markov Model and Its Application of China’s Total Energy Consumption by Chingfei Luo, Chenzi Liu, Chen Huang, Meilan Qiu, Dewang Li

“…We propose an auto regressive integrated moving average Markov model (ARIMAMKM) for predicting annual energy consumption in China and enhancing the accuracy of energy consumption forecasts. …”
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Efficient high-resolution microscopic ghost imaging via sequenced speckle illumination and deep learning from a single noisy image by Sukyoon Oh, Sukyoon Oh, Tong Tian, Tong Tian, Zhe Sun, Christian Spielmann, Christian Spielmann

“…To enhance image quality, we incorporated the Deep Neural Network-based Noise2Void (N2V) model, which effectively denoises ghost images without requiring a reference image or a large dataset. …”
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A marine ship detection method for super-resolution SAR images based on hierarchical multi-scale Mask R-CNN by Jiancong Fan, Miaoxin Guo, Lei Zhang, Jianjun Liu, Jianjun Liu, Yang Li, Yang Li

“…Firstly, a TaylorGAN super-resolution network is designed, and the TaylorShift attention mechanism is introduced to enhance the generator’s ability to restore the edge and texture details of the ship. …”
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An insightful analysis of CNN-based dietary medicine recognition by Mohammad Didarul Alam, Tanjir Ahmed Niloy, Aurnob Sarker Aurgho, Mahady Hasan, Md. Tarek Habib

“…The hybrid model uses an average ensemble approach. Nevertheless, our unwavering commitment to excellence continues to drive us to explore further refinements and optimizations to augment the resilience and precision of our seed classification models.…”
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TBM shield mud cake prediction model based on machine learning by Qi Zhang, Peng Xu, Jing Zhang, Zhao Yang, Yu Li, Xintong Kong, Xiao Yuan

“…IntroductionDuring tunnel boring machine (TBM) shield tunneling in clayey strata, the excavated soil consolidates on the cutter head or cutting tools, forming mud cakes that significantly impact the efficiency of shield tunneling.MethodsTo predict mud cakes during shield tunneling, four distinct supervised machine learning models, including logistic regression, support vector machine, random forest, and BP neural network were employed. The optimal predictive model for mud cake formation was determined by assessing the precision, recall, and F1 scores of the models. …”
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Vehicle detection in drone aerial views based on lightweight OSD-YOLOv10 by Yang Zhang, Xiaobing Chen, Su Sun, Hongfeng You, Yuanyuan Wang, Jianchu Lin, Jiacheng Wang

“…Finally, we introduce the DySample dynamic upsampling module to enhance feature fusion in the neck network from a point sampling perspective. Extensive experiments on the VisDrone-DET2019 and UAVDT datasets demonstrate that OSD-YOLOv10 achieves a 40.7% reduction in parameter count and a 3.6% decrease in floating-point operations, while improving accuracy and mean average precision by 1.3% and 1.6%, respectively. …”
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Improved Field Obstacle Detection Algorithm Based on YOLOv8 by Xinying Zhou, Wenming Chen, Xinhua Wei

“…Secondly, a BiFPN (Bi-directional Feature Pyramid Network) architecture took the place of the original PANet to enhance the fusion of features across multiple scales, thereby increasing the model’s capacity to distinguish between the background and obstacles. …”
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