A Machine Learning Approach for Predicting Maternal Health Risks in Lower-Middle-Income Countries Using Sparse Data and Vital Signs by Avnish Malde, Vishnunarayan Girishan Prabhu, Dishant Banga, Michael Hsieh, Chaithanya Renduchintala, Ronald Pirrallo

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Deformation Influencing Factor Analysis for Shield Tunnelling under Micro-shallow Gas Strata by YANG Xin, HONG Yi, WANG Lizhong

“…Based on triaxial undrained unloading test of saturated soil and gas-bearing soil, as well as the numerical simulation results, the excavation risks of shield construction in micro-shallow gas strata are analyzed, and risk control factors are proposed. …”
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Machine learning-based identification of exosome-related biomarkers and drugs prediction in nasopharyngeal carcinoma by Zhengyu Wei, Guoli Wang, Yanghao Hu, Chongchang Zhou, Yuna Zhang, Yi Shen, Yaowen Wang

“…The least absolute shrinkage and selection operator regression, support vector machine, and random forest approaches were utilized to develop NPC diagnostic model. …”
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Experimental investigation of shaft misalignment effects on bearing reliability through vibration signal analysis using machine learning and deep learning by Fransiskus Tatas Dwi Atmaji, Jamasri, Hari Agung Yuniarto, I Made Miasa

“…Six classification models—five machine learning algorithms (Multilayer Perceptron, Random Forest, Decision Tree, K-Nearest Neighbors, and Adaptive Boosting) and one deep learning model (Long Short-Term Memory, LSTM)—were evaluated for classifying four levels of misalignment severity. …”
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Classical and machine learning tools for identifying yellow-seeded Brassica napus by fusion of hyperspectral features by Fan Liu, Fang Wang, Zaiqi Zhang, Liang Cao, Jinran Wu, You-Gan Wang

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Land‐Air Exchanges of Various Gaseous Nitrogen Species in an Urban Wetland Based on In Situ Flux Observations by Qian Yu, Zimeng Zhang, Piaopiao Ke, Jiale Chang, Han Li, Wenjia Wang, Lei Duan, Ronghua Kang, Lei Zhang, Yu Zhao

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High-isolation dual-band MIMO antenna for next-generation 5G wireless networks at 28/38 GHz with machine learning-based gain prediction by Md Ashraful Haque, Redwan A. Ananta, Md. Sharif Ahammed, Jamal Hossain Nirob, Narinderjit Singh Sawaran Singh, Liton Chandra Paul, Reem Ibrahim Alkanhel, Ahmed A. Abd El-Latif, May Almousa, Abdelhamied A. Ateya

“…Among the five different regression machine learning models considered, it was discovered that the Random Forest Regression (RFR) model performed the best in accuracy and achieved the lowest error when predicting gain. …”
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Machine Learning Based Flexible Transmission Time Interval Scheduling for eMBB and uRLLC Coexistence Scenario by Jingxuan Zhang, Xiaodong Xu, Kangjie Zhang, Bufang Zhang, Xiaofeng Tao, Ping Zhang

“…Moreover, we design the random forest-based ensemble TTI decision algorithm (RF-ETDA) to accomplish the TTI selection for each service. …”
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Modelling and Optimisation of Hysteresis and Sensitivity of Multicomponent Flexible Sensing Materials by Kai Chen, Qiang Gao, Yijin Ouyang, Jianyong Lei, Shuge Li, Songxiying He, Guotian He

“…First, multifactor experiments were conducted to obtain experimental data for the prediction models; the prediction models for the hysteresis and sensitivity performance of sensing materials were constructed using response surface methodology (RSM), Random Forest (RF), long short-term memory (LSTM) network, and HKOA-LSTM. …”
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Rapid screening and optimization of CO2 enhanced oil recovery operations in unconventional reservoirs: A case study by Shuqin Wen, Bing Wei, Junyu You, Yujiao He, Qihang Ye, Jun Lu

“…Three different methods, namely random forest (RF), support vector regression (SVR), and artificial neural network (ANN), were used to establish proxy models using the data from a specific unconventional reservoir, and the RF model demonstrated a preferable performance. …”
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Predicting Thermal Performance of Aquifer Thermal Energy Storage Systems in Depleted Clastic Hydrocarbon Reservoirs via Machine Learning: Case Study from Hungary by Hawkar Ali Abdulhaq, János Geiger, István Vass, Tivadar M. Tóth, Tamás Medgyes, Gábor Bozsó, Balázs Kóbor, Éva Kun, János Szanyi

“…A Random Forest model trained on simulation outputs predicted thermal recovery performance with high accuracy (R² ≈ 0.87) for candidate wells beyond the original modeling domain, demonstrating computational efficiency gains exceeding 90% compared to conventional simulations. …”
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Two decades of cropland monitoring in Changsha-Zhuzhou-Xiangtan city group: trends and future predictions by Wenbo Zhang, Dazhao Fan, Song Ji, Yang Dong, Dongzi Li, Ming Li

“…Furthermore, an Inertial Development Scenario and a Cropland Priority Scenario were designed to simulate land use/land cover (LULC) changes in the CZTCG in 2025 and 2035, and, in particular, to analyze the characteristics of future spatiotemporal changes of cropland. …”
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Wolverines use spatial memory to plan efficient routes through rugged terrain by Thomas W. Glass, Jeffery P. Copeland, Lucretia E. Olson, John S. Waller, John R. Squires

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A Novel Anomaly Forecasting in Time‐Series Data: Feedback Connection between Forecasting and Detecting Algorithms with Applications to Power Systems by Hyung Tae Choi, Hae Yeon Park, Taewan Kim, Jung Hoon Kim

“…The effectiveness of the proposed algorithms is verified through some comparative simulations of an IEEE 3‐bus system with various faults. …”
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Density‐dependent responses of moose to hunting and landscape change by Mateen Hessami, Robert Serrouya, Clayton T. Lamb, Melanie Dickie, Adam T. Ford

“…Our simulations indicated that the only forest harvesting scenario where moose carrying capacity would be low enough to stabilize caribou population growth rates by 2040 was to cease forest harvesting entirely in 2020. …”
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Carbon Footprint and Techno-economic Analysis to Decarbonize the Production of Linerboard via Fuel Switching in the Lime Kiln and Boiler: Development of a Marginal Abatement Cost C... by Rodrigo Buitrago-Tello, Richard A. Venditti, Hasan Jameel, Peter W. Hart, Ashok Ghosh

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Future methane fluxes of peatlands are controlled by management practices and fluctuations in hydrological conditions due to climatic variability by V. Tyystjärvi, T. Markkanen, L. Backman, M. Raivonen, A. Leppänen, X. Li, P. Ojanen, P. Ojanen, K. Minkkinen, R. Hautala, M. Peltoniemi, J. Anttila, R. Laiho, A. Lohila, R. Mäkipää, T. Aalto

“…Both management practices and climate change are expected to influence peatland CH<span class="inline-formula">₄</span> fluxes during this century, but the magnitude and net impact of these changes is still insufficiently understood. In this study, we simulated the impacts of two forest management practices, rotational forestry and continuous cover forestry, as well as peatland restoration, on hypothetical forestry-drained peatlands across Finland using the land surface model JSBACH (Jena Scheme for Biosphere–Atmosphere Coupling in Hamburg) coupled with the soil carbon model YASSO and a peatland methane model HIMMELI (Helsinki Model of Methane Buildup and Emission for Peatlands). …”
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Development of Debris Flow Impact Force Models Based on Flume Experiments for Design Criteria of Soil Erosion Control Dam by Song Eu, Sangjun Im, Dongyeob Kim

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Modelling seasonal fluctuations and aspect characteristics of ice-cliff melt on the debris-covered Trakarding Glacier, Rolwaling Valley, Nepal Himalaya by Yota Sato, Pascal Buri, Marin Kneib, Evan S. Miles, Sojiro Sunako, Tika R. Gurung, Akiko Sakai, Francesca Pellicciotti, Koji Fujita

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Modeling sunflower yield and soil water–salt dynamics with combined fertilizers and irrigation in saline soils using APSIM and deep learning by Qingfeng Miao, Dandan Yu, Haibin Shi, Zhuangzhuang Feng, Weiying Feng, Zhen Li, José Manuel Gonçalves, Isabel Maria Duarte, Yuxin Li

“…Although the APSIM-sunflower model can be used to simulate growth and development (R 2 = 0.7–0.9; NRMSE = 0.1–0.2), its simulation of soil water dynamics is unsatisfactory (R 2 = 0.4–0.5; NRMSE = 0.3). …”
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