Online Intelligent Monitoring System and Key Technologies for Dam Operation Safety by Shuangping Li, Bin Zhang, Guangqin Tong, Yonghua Li, Zuqiang Liu, Bo Shi, Jun Geng, Dingming Liu, Huawei Wang, Qingsong Ai, Jianxin Ding, Zheng Gan

“…Leveraging our proprietary innovations, including a GIS + BIM digital base, smart algorithm matrix, and BIM-based finite element computing system, we successfully developed the Three Gorges Dam intelligent monitoring platform, delivering five core value propositions: (1) Achieve real-time and historical aggregation of comprehensive data with dam safety management as the core, fully encompassing various types of environmental monitoring data. (2) Utilizing “GIS + BIM” as the technical foundation, construct a digital twin geometric model of the hub monitoring physical world, enabling intuitive and precise representation of engineering status. (3) Implement online rapid structural calculation, analysis, and early warning based on “BIM + Finite Element” technology, providing timely and reliable support for safety decision-making. (4) Establish a monitoring data analysis model through machine learning intelligent algorithms, deeply mining data value to enable intelligent prediction of potential safety hazards. (5) Promote digital transformation of manual inspection workflows using “IOT + Micro-INS” technology, enhancing inspection efficiency and accuracy. …”
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Fast optimization of the emission reduction pathways of major air pollutants in China: From the perspective of different decision preferences by Yihao Wang, Xuying Wang, Yixuan Zheng, Zeyuan Liu, Yu Zhang, Jiani Kang, Wenbo Xue, Jing Zhang, Yu Lei

“…To address the concern of optimization problem of China’s PM2.5 control and the limitation of computational efficiencies for traditional air quality models, we developed an integrated analysis framework to efficiently establish the identification and cost-benefit assessment of PM2.5 control pathways in China by constructing a rapid PM2.5 exposure response method based on the high-order decoupled direct method (HDDM) and coupling the sequential least square algorithm (SLSQP) and health impact assessment model. …”
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Bioinformatics analysis and experimental studies reveal KPNA2 as a novel biomarker of hepatocellular carcinoma progression and telomere maintenance by Ke Ding, Lei Liu, Wang Yong, Beicheng Sun, Wenjie Zhang

“…A novel prognostic signature was then constructed using the LASSO Cox regression algorithm. …”
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Predicting bearing capacity of gently inclined bauxite pillar based on numerical simulation and machine learning by Deyu WANG, Defu ZHU, Biaobiao YU, Chen WANG

“…Additionally, two optimization algorithms, Genetic Programming (GP) and Improved Quantum Particle Swarm Algorithm (IQPSO), were used to enhance model performance and establish a non-linear mapping relationship between the influencing factors and the strength of the gently inclined pillars. …”
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HyQ2: A Hybrid Quantum Neural Network for NextG Vulnerability Detection by Yifeng Peng, Xinyi Li, Zhiding Liang, Ying Wang

“…In addition, the noise resilience and robustness of the HyQ2 algorithm were substantiated through experiments on the IBM quantum machine with only a 0.2% decrease compared to the simulation results.…”
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Two-stage denoising method for complex underground tunnel scene three-dimensional point clouds by Zhuli REN, Ruifu YUAN, Liguan WANG, Haokun DENG, Wen WANG, Jinlong ZHANG

“…Through the two-stage optimization algorithm, effective repair of surface holes on the tunnel is achieved. …”
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On the academic ideology of “Sorting the gangue is sorting the images” by Hongwei MA, Ye ZHANG, Peng WANG, Xiangang CAO, Zhen NIE, Xiaorong WEI, Wenjian ZHOU, Mingzhen ZHANG

“…A coal gangue sorting cube is constructed, which can improve the accuracy of coal gangue sorting; An image-based coal gangue matching tracking and path planning method is proposed for dynamic coal gangue tracking, which can improve the accuracy and reliability of sorting; Aiming at the problem of intelligent collaborative sorting of multiple sorters, it is proposed to construct a comprehensive benefit function of sorters based on the coal gangue image information database to achieve optimal allocation of multiple tasks for multiple sorters, and to integrate reinforcement learning methods to achieve intelligent collaborative control of multiple sorters and optimal configuration of the number of sorters. …”
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Multi‐sequence MRI‐based clinical‐radiomics models for the preoperative prediction of microsatellite instability‐high status in endometrial cancer by Zhuang Li, Yi Su, Yongbin Cui, Yong Yin, Zhenjiang Li

“…Finally, the clinical‐radiomics models integrating clinical risk factors and radiomic features were constructed. …”
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ICP-Based Mapping and Localization System for AGV with 2D LiDAR by Felype de L. Silva, Eisenhawer de M. Fernandes, Péricles R. Barros, Levi da C. Pimentel, Felipe C. Pimenta, Antonio G. B. de Lima, João M. P. Q. Delgado

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Can Fire Season Type Serve as a Critical Factor in Fire Regime Classification System in China? by Huijuan Li, Sumei Zhang, Xugang Lian, Yuan Zhang, Fengfeng Zhao

“…Among the five zones, FST consistently ranks within the top five contributors, with contribution rates of 0.39, 0.31, 0.44, 0.27, and 0.55, respectively, confirming that the inclusion of FST is a reasonable and necessary choice when constructing FR zones. By integrating multi-source remote sensing data, this study has established a novel FR classification system that encompasses fire frequency, intensity, and particularly FST. …”
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SLC3A2 as a key anoikis−related gene for prognosis and tumor microenvironment remodeling in melanoma by Xiaojin Liu, Jiaheng Xie, Yingying Xiao

“…We aimed to identify anoikis-related prognostic markers using integrated machine learning and single-cell analysis. …”
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Enhancing one-year mortality prediction in STEMI patients post-PCI: an interpretable machine learning model with risk stratification by Wenqiang Li, Wenqiang Li, Dongdong Yan, Dongdong Yan, Dongdong Yan, Dongdong Yan, Wei Hu, Wei Hu, Wei Hu, Wei Hu, Xiaoling Su, Zheng Zhang, Zheng Zhang, Zheng Zhang, Zheng Zhang

“…By integrating risk stratification, the model’s performance improved, achieving an AUROC of 0.97 (95% CI: 0.96–0.99) and an AUPRC of 0.74 (95% CI: 0.60–0.84).ConclusionThis study highlights the feasibility of constructing accurate and interpretable ML models using a minimal set of predictors, supplemented by risk stratification, to improve long-term outcome prediction in STEMI patients.…”
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Multi-omics derivation of a core gene signature for predicting therapeutic response and characterizing immune dysregulation in inflammatory bowel disease by Mingming Wang, Liping Liang, Zibo Tang, Jimin Han, Lele Wu, Le Liu, Le Liu, Ye Chen, Ye Chen

“…Current precision medicine approaches lack robust molecular tools integrating transcriptomic signatures with immune dynamics for personalized treatment guidance.MethodsWe performed multi-omics analyses of GEO datasets using machine learning algorithms (LASSO/Random Forest) to derive a four-gene signature. …”
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Improved YOLO-Goose-Based Method for Individual Identification of Lion-Head Geese and Egg Matching: Methods and Experimental Study by Hengyuan Zhang, Zhenlong Wu, Tiemin Zhang, Canhuan Lu, Zhaohui Zhang, Jianzhou Ye, Jikang Yang, Degui Yang, Cheng Fang

“…To address this, this study proposes YOLO-Goose, an improved YOLOv8s-based method, which designs five high-contrast neck rings (DoubleBar, Circle, Dot, Fence, Cylindrical) as individual identifiers. The method constructs a lightweight model with a small-object detection layer, integrates the GhostNet backbone to reduce parameter count by 67.2%, and employs the GIoU loss function to optimize neck ring localization accuracy. …”
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Precise prediction of choke oil rate in critical flow condition via surface data by Qing Wang, Muntadher Abed Hussein, Bhavesh Kanabar, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Mohammad Mahtab Alam, Hojjat Abbasi

“…The k-fold cross-validation technique is utilized in every algorithm to mitigate the overfitting problem during the training of models. …”
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Multi-omics exploration of chaperone-mediated immune-proteostasis crosstalk in vascular dementia and identification of diagnostic biomarkers by Wentong Li, Yiyi Zhang, Chuanhong Li, Mingyang Jiang, Dong Wang, Luomeng Chao, Luomeng Chao, Luomeng Chao, Yuxia Yang

“…Protein-protein interaction (PPI) networks were constructed using the STRING database. Biomarker validation was performed through cross-validation using LASSO, SVM-RFE, and Random Forest algorithms. …”
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Physics-Informed Neural Networks: A Review of Methodological Evolution, Theoretical Foundations, and Interdisciplinary Frontiers Toward Next-Generation Scientific Computing by Zhiyuan Ren, Shijie Zhou, Dong Liu, Qihe Liu

“…The contributions include threefold: First, identifying the co-evolutionary path of algorithmic architectures from adaptive optimization (neural tangent kernel-guided weighting achieving 230% convergence acceleration in Navier-Stokes solutions) to hybrid numerical-deep learning integration (5× speedup via domain decomposition) and second, constructing bidirectional theory-application mappings where convergence analysis (operator approximation theory) and generalization guarantees (Bayesian-physical hybrid frameworks) directly inform engineering implementations, as validated by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>72</mn><mo>%</mo></mrow></semantics></math></inline-formula> cost reduction compared to FEM in high-dimensional spaces (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo><</mo><mn>0.01</mn><mo>,</mo><mi>n</mi><mo>=</mo><mn>15</mn></mrow></semantics></math></inline-formula> benchmarks). …”
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Identification of biomarkers for knee osteoarthritis through clinical data and machine learning models by Wei Chen, Haotian Zheng, Binglin Ye, Tiefeng Guo, Yude Xu, Zhibin Fu, Xing Ji, Xiping Chai, Shenghua Li, Qiang Deng

“…Based on these rankings, predictive models were constructed using Logistic Regression (LR), Random Forest (RF), eXtreme Gradient Boosting (xGBoost), Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT) algorithms. …”
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PREACT-digital: study protocol for a longitudinal, observational multicentre study on digital phenotypes of non-response to cognitive behavioural therapy for internalising disorder... by Christine Knaevelsrud, Sebastian Burchert, Till Langhammer, Leona Hammelrath, Annette Brose, Manuel Heinrich, Pavle Zagorscak

“…Recent advances in wearable technology and smartphone integration enable new, ecologically valid approaches to capture dynamic processes in real time. …”
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Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis by Yi Xu, Ying Mao, Xiaoting Hua, Yan Jiang, Yi Zou, Zhichao Wang, Zubi Liu, Hongrui Zhang, Lingling Lu, Yunsong Yu

“…Methods We used 18 binary matrices, each consisting of genotypes and antimicrobial susceptibility testing phenotypes from a specific MTB-antimicrobial dataset. By constructing training and test datasets on all SNPs, intersected SNPs, and randomly generated SNPs, we developed a Machine learning (ML) framework using twelve different algorithms. …”
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