An efficient method for predicting the morphology of proppant packs based on a surrogate model by Tao ZHANG, Hangyu ZHOU, Yifan ZHANG, Jianchun GUO, Haoran GOU, Tang TANG

“…Through correlation analysis, the primary factors influencing these characteristic parameters were identified. Intelligent proxy models for the prediction of proppant placement patterns were established on the basis of the cascade neural network, including a time-concentration model for predicting particle volume fraction and a displacement-height model for predicting particle placement height. …”
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Adaptive machine learning framework: Predicting UHPC performance from data to modelling by Yinzhang He, Shaojie Gao, Yan Li, Yongsheng Guan, Jiupeng Zhang, Dongliang Hu

“…Ultra-High Performance Concrete (UHPC) is vital for next-generation infrastructure, necessitating complex interaction modeling beyond empirical methods. This study proposes an interpretable machine learning (ML) framework to predict the compressive strength (CS) of UHPC and analyze input variable influences. …”
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Predicting oxytocin binding dynamics in receptor genetic variants through computational modeling by Preeti Dubey, Yingye Fang, K. Lionel Tukei, Shobhan Kuila, Xinming Liu, Annika Sahota, Antonina I. Frolova, Erin L. Reinl, Manasi Malik, Sarah K. England, Princess I. Imoukhuede

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Enhancing mental well-being: An artificial intelligence model for predicting mental disorders by Jahanur Biswas, Md. Nahid Hasan, Md. Shakil Rahman Gazi, Md. Mahbubur Rahman

“…This imbalanced dataset is balanced by the Random Oversampling model. In our study, we introduced a state-of-the-art approach to predicting mental conditions such as depression, anxiety, and stress. …”
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A Nomogram Model for Predicting Recurrent Coronary Thrombosis in Kawasaki Disease Patients by Zhou X, Peng Y, Yi Q

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Factors predicting the mathematics anxiety of adolescents: a structural equation modeling approach by Suman Ahmmed, Jashodhan Saha, Maruf Ahmed Tamal, Khondaker Abdullah Al Mamun, Sajani Islam

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Establishment of a nomogram model for predicting the risk of diabetic nephropathy in diabetic patients by HOU Xin-yue, HU Song, FEI Chun-xiao, LIU Shu-hao, SHAO Li-yan, XING Ang

“…Bootstrap was used to verify the model, to plot the ROC curve, and to calculate the predictive performance of the C-index evaluation model. …”
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A New Mathematical Model for Predicting the Surface Vibration Velocity on the Step Topography by Xu Wu, Qifeng Guo, Yunpeng Zhang

“…The regression analysis results show that the fitting coefficient of determination of the new prediction model is 0.8152 in horizontal and 0.8902 in vertical, respectively, and the prediction error is less than 20%, which is much better than other formulas. …”
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Model for predicting metabolic activity in athletes based on biochemical blood test analysis by Victoria A. Zaborova, Evgenii I. Balakin, Ksenia A. Yurku, Olga E. Aprishko, Vasiliy I. Pustovoyt

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Predicting EFL Learners’ Self-Regulated Learning through Technology Acceptance Model by Yusup Supriyono, Francisca Maria Ivone, Dedi Heryadi, Lorna Beduya, Luis Luigi Eugenio A. Valencia

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Construction of a Column Chart Model for Predicting TCRP Recurrence in Gravid Women by Xuqing Chen, Jing Li, Hui Liang, Nanxiang Lei

“…Conclusion: The nomogram model constructed in this study is conducive to predicting the recurrence of women of childbearing age after TCRP, and may be helpful for preventing and treating polyp recurrence.…”
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Predicting Soft Soil Settlement with a FAGSO-BP Neural Network Model by Binhui Ma, Yarui Xiao, Tian Lan, Chao Zhang, Zengliang Wang, Zeshi Xiang, Yuqi Li, Zijing Zhao

“…The FAGSO-BP neural network forecasting model is used to predict the soft foundation settlement of Hunan Wuyi Expressway Project. …”
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Light source classification and colour change modelling for understanding and predicting pigments discolouration by Panagiotis Siozos, Letizia Monico, Aldo Romani, Costanza Miliani, Brenda Doherty, Irina Crina Anca Sandu, Hartmut Kutzke, Ingrid M T Flåte, Petros Stavroulakis, Sophia Sotiropoulou

“…This model is experimentally validated by artificial ageing tests on two sets of model samples made of historical pigments (strontium yellow and Prussian blue mixed with lead white) using three white light sources (two WLEDs and a xenon light source). …”
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Proposing a machine learning-based model for predicting nonreassuring fetal heart by Nasibeh Roozbeh, Farideh Montazeri, Mohammadsadegh Vahidi Farashah, Vahid Mehrnoush, Fatemeh Darsareh

“…Although this study found that the classification tree models performed well in predicting NFH, more research is needed to make a better conclusion on the performance of ML models in predicting NFH.…”
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Predicting DNA Reactions with a Quantum Chemistry‐Based Deep Learning Model by Likun Wang, Na Li, Mengyao Cao, Yun Zhu, Xiewei Xiong, Li Li, Tong Zhu, Hao Pei

“…Abstract In this study, a deep learning model based on quantum chemistry is introduced to enhance the accuracy and efficiency of predicting DNA reaction parameters. …”
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Predicting the potential distribution of Taxus cuspidata in northeastern China based on the ensemble model by Baoliang Chang, Chen Huang, Bingming Chen, Ziwen Wang, Xingyuan He, Wei Chen, Yanqing Huang, Yue Zhang, Shuai Yu

“…In this study, a combined model was employed to predict potentially suitable habitats for T. cuspidata based on extant data of T. cuspidata distributions in northeastern China. …”
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Hybrid TCN-transformer model for predicting sustainable food supply and ensuring resilience by Ibrahim Alrashdi, Rasha M. Abd El-Aziz, Ahmed I. Taloba, Mohammed Farsi

“…Hybrid design enables faster training, increased interpretability, and better prediction accuracy than current methods. Results from experiments have revealed that the suggested model surpasses the performance of the stand-alone TCN, ARIMA, LSTM, and GRU models in terms of accuracy of predictions, efficiency of computations, and adaptability. …”
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A correlation for predicting the abrasive water jet cutting depth for natural stones by Irfan Engin

“…The relationships between the rock properties or operating parameters and the cutting depth were evaluated using multiple linear and nonlinear regression analyses, and estimation models were developed. Some of the models included only rock properties under fixed operating conditions, and others included both rock properties and operating parameters to predict cutting depth. …”
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Does machine learning outperform logistic regression in predicting individual tree mortality? by Aitor Vázquez-Veloso, Astor Toraño Caicoya, Felipe Bravo, Peter Biber, Enno Uhl, Hans Pretzsch

“…However, innovative classification algorithms can go deep into data to find patterns that can model or even explain their relationship. We use Logistic binomial Regression as the reference algorithm for predicting individual tree mortality. …”
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Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling study by Wenting Feng, Wen Zhang, Yan Guo, Naixing Zhang, Liang Zhou, Dafeng Lin, Linlin Chen, Caiping Li, Liuwei Shi, Xiangli Yang, Peimao Li, Dianpeng Wang

“…Routine blood and biochemical indicators were extracted from the case data, and a range of machine learning algorithms including extreme gradient boosting (XGBoost) were employed to construct predictive models. The model underwent refinement to identify the most representative variables, and decision curve analysis was conducted to evaluate the net benefit of the model across various threshold levels.Primary outcome measures Model creation data set and validation data sets: ONIHL.Results The prediction model, developed using XGBoost, demonstrated exceptional performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.942, a sensitivity of 0.875 and a specificity of 0.936 on the validation data set. …”
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