Research progress,key scientific issues,and prospects of segmented fracturing and pressure relief gas drainage technology for coal seam roof horizontal wells by Ke YANG, Zhainan ZHANG, Xinzhu HUA, Wenjie LIU, Xin LYU, Xiaolou CHI, Changcheng WANG, Caiqing LI

“…The segmented fracturing and pressure relief gas drainage technology in the horizontal well of the coal seam roof is a key means to guide the efficient gas extraction in deep, high gas, soft and low permeability coal seams.Based on this, a full lifecycle development concept for the segmented fracturing, pressure relief,and gas drainage engineering of the coal seam roof horizontal well is proposed, including three stages: early scientific planning, mid-term engineering construction, and later safety management.This paper summarizes the research progress on the full life cycle development of segmented fracturing and pressure relief gas drainage in coal seam roof horizontal wells, constructs an overall research framework for key scientific issues related to segmented fracturing and pressure relief gas drainage in coal seam roof horizontal wells, and looks forward to its future development direction.The results show that the expansion of segmented fracturing cracks in the horizontal well of the coal seam roof is extremely complex due to geological factors, construction parameters, and physical properties.It is urgent to comprehensively evaluate the primary and secondary relationships under the influence of multiple factors in crack expansion, and reveal the mechanism of coal seam roof fracturing crack expansion under the influence of multiple factors.Exploring the critical relationship between the coupling of stress, water, temperature, and coal factors on the promotion and inhibition of coalbed methane adsorption, desorption, and migration, and establishing an optimal model for coalbed methane adsorption and desorption under multiple critical indicators, is the key to achieving efficient coalbed methane extraction. …”
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Large-Scale Demand Driven Design of a Customized Bus Network: A Methodological Framework and Beijing Case Study by Jihui Ma, Yang Yang, Wei Guan, Fei Wang, Tao Liu, Wenyuan Tu, Cuiying Song

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Design of a reliable and coordinated protection strategy for zonal shipboard power systems by Mehdi Mohammadzamani, Iman Sadeghkhani, Majid Moazzami

“…However, the protection of such systems is a challenging task due to their complex topology and the potential for multiple sources of fault. …”
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Combining metabolomic and transcriptomic approaches to assess and improve crop quality traits by Delphine M. Pott, Sara Durán-Soria, Sonia Osorio, José G. Vallarino

“…In addition, they allow to establish correlation between genotype and metabolite composition, and to clarify the genetic architecture of complex biochemical pathways, such as the accumulation of secondary metabolites in plants, many of them being highly valuable for the human diet. …”
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Finite element analysis and structural optimization design of multifunctional robotic arm for garbage truck by Hainan Li, Yang Li

“…IntroductionWith the increasing demand for garbage disposal, complex operating environments and frequent use require garbage truck manipulators to have better performance.MethodsThis study designs a multifunctional robotic arm for garbage trucks, which has three basic degrees of freedom, is driven by hydraulic cylinders, and has a claw type end effector. …”
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Defect identification of fan blade based on adaptive parameter region growth algorithm by Wang Yifan, Wang Xueyao, Yang Dongmei, Ru Xinqin, Zhang Yuxin

“…Abstract China’s wind power generation is rich in resources and mature technology, but has the problems of harsh power generation environment, high operation and maintenance costs due to complex operating conditions, and serious consequences of failures. …”
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Advanced AI approaches for the modeling and optimization of microgrid energy systems by Mohammed Amine Hoummadi, Badre Bossoufi, Mohammed Karim, Ahmed Althobaiti, Thamer A. H. Alghamdi, Mohammed Alenezi

“…GA, from natural selection, is constantly seeking the best configuration. …”
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Optimized Navigation for SLAM Using Marker-Assisted Region Scanning, Path Finding, and Mapping Completion Control by Luigi Maciel Ribeiro, Nadia Nedjah, Paulo Victor Rodrigues de Carvalho

“…Specifically, it achieves a 64.39% reduction in poses compared to ACO and 71.07% compared to zigzag navigation, highlighting its efficiency in complex environments.…”
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Forecasting Tunnel-Induced Ground Settlement: A Hybrid Deep Learning Approach and Traditional Statistical Techniques With Sensor Data by Syed Mujtaba Hussaine, Linlong Mu, Yimin Lu, Syed Sajid Hussain

“…The proposed DL models, Convolutional Long Short-Term Memory (Conv-LSTM2D) and Convolutional Gated Recurrent Unit (Conv-GRU2D), extend traditional LSTM and GRU architectures with 2D convolutional mechanisms to capture complex spatiotemporal dependencies. Additionally, the statistical Autoregressive Integrated Moving Average (ARIMA)/Seasonal ARIMA (SARIMA) models were enhanced through seasonality removal, automated model selection using the auto_arima algorithm, and parameter fine-tuning via grid search to improve their predictive accuracy. …”
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FORMALIZATION OF THE PROBLEM OF TRANSPORT LOGISTICS OPTIMIZATION NETWORKS AT THE STAGE OF REENGINEERING by Vladimir Beskorovainyi, Lyudmyla Kolesnyk, Dmytro Yevstrat

“…The practical use of the proposed complex of mathematical models will reduce the time and capacity complexity of project decision-making support technologies, and due to the use of the proposed options selection procedures, increase their quality based on a number of functional and cost indicators. …”
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A Comparative Analysis of Machine Learning Models for Predicting EFL Student Language Performance in Smart Learning Environments by Banchakarn Sameephet, Wirapong Chansanam, Mahboubeh Rakhshandehroo, Chawin Srisawat, Kittichai Nilubol, Arnon Jannok, Bhirawit Satthamnuwong, Kornwipa Poonpon

“…The analysis results indicated that ensemble models, particularly XGBoost and Stacking Ensemble, performed the best in predicting students’ English language performance. These models can efficiently capture complex relationships in educational data. …”
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Network access and spectrum allocation in next-generation multi-heterogeneous networks by Xiaoqing Dong, Lianglun Cheng, Gengzhong Zheng, Tao Wang

“…Based on this, two kinds of technical paths to solve the complex network selection and spectrum allocation problem are applied in this article. …”
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Review on development and utilization of offshore renewable energy: An analysis based on a five-dimensional framework by DU Wenjie, CAI Guotian, Qi Xiaoling, WANG Peng, ZHANG Jixiang

“…However, it also faces challenges such as complex environmental conditions, low reliability, and spatial competition. …”
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A Systematic Review of the Advances and New Insights into Copy Number Variations in Plant Genomes by Saimire Silaiyiman, Jiaxuan Liu, Jiaxin Wu, Lejun Ouyang, Zheng Cao, Chao Shen

“…The advancement in sequencing technologies has made the detection and analysis of CNVs more efficient, not only revealing their crucial roles in plant disease resistance, adaptability, and growth development, but also demonstrating broad application potential in crop improvement, particularly in selective breeding and genomic selection. …”
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High‐dimensional Bayesian optimization for metamaterial design by Zhichao Tian, Yang Yang, Sui Zhou, Tian Zhou, Ke Deng, Chunlin Ji, Yejun He, Jun S. Liu

“…First, variational autoencoders (VAEs) are employed for efficient dimensionality reduction, mapping complex, high‐dimensional metamaterial microstructures into a compact latent space. …”
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EDHD-FG-XGBoost: Entropy-driven high-dimensional feature gain XGBoost model for short-term wind power prediction and application by Xing Liu, Xingyu Mou, Ying Zhang, Hui Chen, Xin Wang, Xihao Sun, Chengcheng Wang, Ruixiang Shong, Yunfeng Li

“…Wind power prediction is critical for the efficient utilization of wind energy and the stable operation of power grids. …”
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Multi-feature stock price prediction by LSTM networks based on VMD and TMFG by Zhixin Zhang, Qingyang Liu, Yanrong Hu, Hongjiu Liu

“…This paper proposes a novel stock price forecasting model—the Variational Mode Decomposition—Triangulated Maximally Filtered Graph—Long Short-Term Memory (VMD–TMFG–LSTM) combined model—aimed at improving prediction accuracy, stability, and computational efficiency. The proposed model first employs Variational Mode Decomposition (VMD) to decompose the stock price time series into multiple smooth intrinsic mode functions (IMFs), reducing data complexity and mitigating noise interference. …”
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Task Allocation and Path Planning for Collaborative Autonomous Underwater Vehicles Operating through an Underwater Acoustic Network by Yueyue Deng, Pierre-Philippe J. Beaujean, Edgar An, Edward Carlson

“…Dynamic and unstructured multiple cooperative autonomous underwater vehicle (AUV) missions are highly complex operations, and task allocation and path planning are made significantly more challenging under realistic underwater acoustic communication constraints. …”
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Maize Seed Variety Classification Based on Hyperspectral Imaging and a CNN-LSTM Learning Framework by Shuxiang Fan, Quancheng Liu, Didi Ma, Yanqiu Zhu, Liyuan Zhang, Aichen Wang, Qingzhen Zhu

“…This method successfully minimized data dimensionality, reduced variable collinearity, and boosted the model’s stability and computational efficiency. A CNN-LSTM model, built on the selected wavelengths, achieved an accuracy of 95.27% in maize variety classification, outperforming traditional chemometric models like partial least squares discriminant analysis, support vector machines, and extreme learning machines. …”
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A machine learning framework for predicting healthcare utilization and risk factors by Yead Rahman, Prerna Dua

“…A rigorous preprocessing pipeline ensured data integrity, while exploratory data analysis (EDA) guided feature selection, ultimately retaining 20 key variables to capture complex interactions. …”
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