Construction of application platform for operational data of natural gas pipeline network by Hongtao DIAO, Weichao YU, Kaihong WANG, Huai SU, Yi ZHANG, Zhaoming YANG

“…Furthermore, deep learning algorithms and feature enhancement technologies were introduced to support the prediction of key gas volume parameters in the natural gas pipeline network. …”
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Applicability Analysis of Log Calculation Method for Organic Matter Maturity of Changning Shale Gas Reservoir in Southern Sichuan Basin by QIU Dingguo, YAN Jianping, GUO Wei, LIAO Maojie, QIU Xiaoxue, HUANG Yi

“…The color combination of Rt-AC logging curve is “blue + dark red” mode.③CatBoost algorithm selected GR, AC, CNL and Rt curves as input variables, and the correlation coefficients between the calculated RO values and the measured RO values of maturity wells Ⅰ and Ⅱ were all more than 0.95, which achieved a good prediction effect of organic matter maturity. …”
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Prevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic review by Paolo Giaccone, Federico D’Antoni, Fabrizio Russo, Luca Ambrosio, Giuseppe Francesco Papalia, Onorato d’Angelis, Gianluca Vadalà, Albert Comelli, Luca Vollero, Mario Merone, Rocco Papalia, Vincenzo Denaro

“…Several different machine learning and deep learning algorithms were employed, and their predictive ability on clinical, demographic, psychosocial, and imaging data was assessed. …”
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Tissue Is the Issue: A Systematic Review of Methods for the Determination of Infarct Volume in Acute Ischaemic Stroke by Fatimah Al Ahmed, Patrick Kennelly, Darragh Herlihy, Jorin Bejleri, David J. Williams, John J. Thornton, Shona Pfeiffer

“…However, differences in operating algorithms and lack of standardisation of image acquisition parameters, quality, and format may impact performance and reproducibility. …”
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Vibration Signal Analysis for Intelligent Rotating Machinery Diagnosis and Prognosis: A Comprehensive Systematic Literature Review by Ikram Bagri, Karim Tahiry, Aziz Hraiba, Achraf Touil, Ahmed Mousrij

“…In the context of fault detection, support vector machines (SVMs), convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, k-nearest neighbors (KNN), and random forests have been identified as the five most frequently employed algorithms. Meanwhile, transformer-based models are emerging as a promising venue for the prediction of RUL values, along with data transformation. …”
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AI-powered visual E-monitoring system for cattle health and wealth by Aung Si Thu Moe, Pyke Tin, Masaru Aikawa, Ikuo Kobayashi, Thi Thi Zin

“…Through integrated deep learning algorithms, the platform performs key health-related tasks, including ear-tag, body-based, and face-based cattle identification, body condition scoring (BCS), lameness detection, feeding time estimation, and real-time localization. …”
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Transcriptomic insights into the mechanism of action of telomere-related biomarkers in rheumatoid arthritis by Lijuan Feng, Kaiyong Bai, Limeng He, Hao Wang, Wei Zhang

“…Additionally, a nomogram model was employed to predict the diagnostic ability of biomarkers for RA. …”
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Cetacean feeding modelling using machine learning: A case study of the Central-Eastern Mediterranean Sea by Carla Cherubini, Giulia Cipriano, Leonardo Saccotelli, Giovanni Dimauro, Giovanni Coppini, Roberto Carlucci, Carmelo Fanizza, Rosalia Maglietta

“…Behavioural data from April 2016 to October 2023, coupled with 20 environmental variables from Copernicus Marine Service and EMODnet-bathymetry datasets, were used to build Cetacean Feeding Models (CFMs) for the target species using Random Forest and RUSBoost algorithms. Multiple subsets of environmental predictors—physiographic, physical, inorganic, and bio-chemical—were employed to develop and evaluate ML models tailored to feeding prediction. …”
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Machine learning identification of key genes in cardioembolic stroke and atherosclerosis: their association with pan-cancer and immune cells by Tianxiang Zhang, Chunhui Yuan, Mo Chen, Jinjiang Liu, Wei Shao, Ning Cheng

“…To validate the prediction results, blood samples were collected from healthy controls and patients with CS and AS for quantitative real-time PCR. …”
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Development of a PPP1R14B-associated immune prognostic model for hepatocellular carcinoma by Ligang Zhao, Xiaoling Jin, Qiushi Yu, Yigang He, Facai Yang, Weihu Ma, Zhengqing Lei, Jiahua Zhou

“…The study constructed a PPP1R14B-linked immune prediction model, demonstrating acceptable prognostic capability for HCC patients. …”
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Machine learning-enhanced discovery of tsRNA-mRNA regulatory networks: identifying novel diagnostic biomarkers and therapeutic targets in breast cancer by Zhongling Ma, Rui Wang, Ming Yuan, Bo Wang, Li Li, Tianfu Zhao, Xinhan Zhao

“…Random forest algorithm was employed to develop a diagnostic model. …”
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From smoking cessation to physical activity: Can ontology-based methods for automated evidence synthesis generalise across behaviour change domains? [version 2; peer review: 2 appr... by Candice Moore, Emily Hayes, Oscar Castro, Ella Howes, Alison J Wright, Emma Norris, Susan Michie, Robert West

“…The Human Behaviour-Change Project (HBCP) aims to improve evidence synthesis in behavioural science by compiling intervention reports and annotating them with an ontology to train information extraction and prediction algorithms. The HBCP used smoking cessation as the first ‘proof of concept’ domain but intends to extend its methodology to other behaviours. …”
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Combined influence of crushed brick powder and recycled concrete aggregate on the mechanical, durability and microstructural properties of eco-concrete: An experimental and machine... by Md. Habibur Rahman Sobuz, Mahmudur Hossain Khan, Md. Rakibul Islam, Md. Kawsarul Islam Kabbo, Abdullah Alzlfawi, M Jameel, Md. Munir Hayet Khan

“…Additionally, the study evaluates machine learning algorithms such as extreme gradient boosting (XG Boost), random forest (RF), and bagging model (BAG) for predicting the mechanical strength of concrete specimens. …”
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Novel multi-omics analysis revealing metabolic heterogeneity of breast cancer cell and subsequent development of associated prognostic signature by Peng Zhang, Cuicui Li, Fen Li, Jiezhong Wu, Kunpeng Hu, He Huang

“…Drug sensitivity prediction was performed via the OncoPredict tool. Functional assays investigated the oncogenic role of PDCD1 in breast cancer cell lines. …”
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Reliability evaluation and multi-objective optimization of combustion chamber’s key components of marine engine by Lei Hu, Wentong Wang, Xu Wang, Jianguo Yang, Yonghua Yu, Chunyang Mei

“…Constrained multi-objective optimization of reliability is conducted through contrastive analysis of different optimization algorithms. The research shows that the multi-objective particle swarm optimization algorithm achieves the best performance, the maximum temperatures of the piston, cylinder head, and liner decrease by 3.90 %, 5.66 %, and 6.52 %, the maximum thermo-mechanical coupling stresses reduced by 9.41 %, 7.83 %, and 4.97 % respectively, and creep-fatigue life enhancements reach 3.84 % and 12.67 % for the piston and cylinder head. …”
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Passive Microwave Imagers, Their Applications, and Benefits: A Review by Nazak Rouzegari, Mohammad Bolboli Zadeh, Claudia Jimenez Arellano, Vesta Afzali Gorooh, Phu Nguyen, Huan Meng, Ralph R. Ferraro, Satya Kalluri, Soroosh Sorooshian, Kuolin Hsu

“…This review examines the relevance, applications, and benefits of PMWI data, focusing on their practical use and benefits to society rather than the specific techniques or algorithms involved in data processing. Specifically, it assesses the impact of PMWI data on Tropical Cyclone (TC) intensity and structure, global precipitation and extreme events, flood prediction, the effectiveness of tropical storm and hurricane watches, fire severity and carbon emissions, weather forecasting, and drought mitigation. …”
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Monitoring Canopy Height in the Hainan Tropical Rainforest Using Machine Learning and Multi-Modal Data Fusion by Qingping Ling, Yingtan Chen, Zhongke Feng, Huiqing Pei, Cai Wang, Zhaode Yin, Zixuan Qiu

“…The results showed that RH80 was the optimal choice for the prediction model regarding percentile selection, and the RF algorithm exhibited the optimal performance in terms of accuracy and stability, with R² values of 0.71 and 0.60 for the training and testing sets, respectively, and a relative root mean square error of 21.36%. …”
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Integrating Single‐Cell Transcriptomics and Machine Learning to Define an ac4C Gene Signature in Lung Adenocarcinoma by Yuan Wang, Wei Su, Guangyao Zhou, Yijie Wang, Chunnuan Wu, Pengpeng Zhang, Lianmin Zhang

“…Ten machine learning algorithms were applied to develop and validate an ac4C‐related gene signature (ARGSig) for prognosis prediction across multiple independent cohorts. …”
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Exploring high entropy alloys: A review on thermodynamic design and computational modeling strategies for advanced materials applications by Peter Ifeolu Odetola, Bukola Joseph Babalola, Ayodeji Ebenezer Afolabi, Ufoma Silas Anamu, Emmanuel Olorundaisi, Mutombo Christian Umba, Thabang Phahlane, Olusoji Oluremi Ayodele, Peter Apata Olubambi

“…By dissecting the foundational ''four core effects'' intrinsic to HEAs—high entropy, sluggish diffusion, severe lattice distortion, and cocktail effect—we illuminate the path towards predictable and tailored material properties. Central to the present discourse is the application of valence electron concentration (VEC) and cutting-edge strategies, including the CALculation of PHAse Diagrams (CALPHAD) method, first-principles approach, and machine-learning algorithms, which collectively empower the prediction and understanding of HEA behavior. …”
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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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