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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Prognostic factors and prediction models for acute aortic dissection: a systematic review by Ling Li, Xin Sun, Jing Tan, Qianrui Li, Kang Zou, Shiyao Huang, Yan Ren, Chunrong Liu

“…Most prediction models were considered at high risk of bias. …”
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Environment ensemble models for genomic prediction in common bean (Phaseolus vulgaris L.) by Isabella Chiaravallotti, Owen Pauptit, Valerio Hoyos‐Villegas

“…For models with low prediction accuracy, the ensemble approach can increase accuracy. …”
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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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Dynamic Multiobjective Optimization Algorithm Based on Average Distance Linear Prediction Model by Zhiyong Li, Hengyong Chen, Zhaoxin Xie, Chao Chen, Ahmed Sallam

“…The simulation results show that our proposed prediction model outperforms other prediction models for DMOP-TPS.…”
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Evaluation of Total Electron Content Prediction Using Three Ionosphere‐Thermosphere Models by O. Verkhoglyadova, X. Meng, A. J. Mannucci, J.‐S. Shim, R. McGranaghan

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Nonlinear prediction model of vehicle network traffic management based on the internet of things by Zhijie Peng, Lili Yin

Subjects: “…Nonlinear prediction model…”
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Research on Stock Index Prediction Based on the Spatiotemporal Attention BiLSTM Model by Shengdong Mu, Boyu Liu, Jijian Gu, Chaolung Lien, Nedjah Nadia

“…Stock index fluctuations are characterized by high noise and their accurate prediction is extremely challenging. To address this challenge, this study proposes a spatial–temporal–bidirectional long short-term memory (STBL) model, incorporating spatiotemporal attention mechanisms. …”
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Energy Consumption Prediction Model for Electric Buses Considering Actual Quantifiable Features by Guowei Zhu, Miao Shi, Jia He

“…Meanwhile, to address the problem that the current electric bus energy consumption prediction model is not conducive to realistic application, this paper proposes an energy consumption prediction model that considers actual electric bus operation data to predict trip energy consumption. …”
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Using the TSA-LSTM two-stage model to predict cancer incidence and mortality. by Rabnawaz Khan, Wang Jie

“…As a result, the model's natural learning trend and prediction quality are enhanced. …”
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Models Development for Prediction of Blast Efficiency and Total Charge in a Typical Quarry by Kayode Augustine Idowu, Zakari Adamu

“… The prediction of blast efficiency is usually achieved by using models; this in turn, gives better and more efficient rock fragmentation. …”
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Simulation and Prediction of Spring Snow Cover in Northern Hemisphere by CMIP6 Model by Xulei WANG, Hui SUN, Hui GUO, Chula SA, Fanhao MENG, Min LUO

“…As one of the most sensitive natural elements in response to climate change， snow cover has a significant effect on the Earth's surface radiation balance and water cycle.The global snow cover area is approximately 46×106 km2 and 98% of the snow cover distributed in the Northern Hemisphere.Due to its distinctive radiative properties （high surface albedo） and thermal characteristics （low thermal conductivity）， changes in snow cover play a crucial role in the energy balance and water cycle between land and the atmosphere.In the context of global warming， the snow cover in the Northern Hemisphere has been decreasing in recent decades， especially in the spring.Therefore， the capabilities of CMIP6 （Coupled Model Intercomparison Project Phase 6） data to simulate the snow cover area were evaluated based on observational data and the future changes in snow cover were also assessed using a multi-model average in this study.By using the snow cover products from the National Oceanic and Atmospheric Administration/National Climatic Data Center （NOAA/NCDC） as reference data， the Taylor skill scoring， relative deviation， and other methods were applied to evaluate the spring snow cover （SCF） data in the Northern Hemisphere from the International Coupled Model Comparison Project Phase 6 （CMIP6） during 1982 -2014.The ensemble average of the top three models was further selected to predict the spatiotemporal variation characteristics of SCF under different emission scenarios from 2015 to 2099， providing insights into the modeling capabilities of CMIP6 and future changes in SCF.During the historical period （1982 -2014）， SCF was characterized by high coverage at high latitudes and low coverage at low latitudes， with high-altitude regions such as Tibetan Plateau and eastern Asia having higher snow coverage than those at the same latitudes.Overall， 68.37% of the regions in the Northern Hemisphere showed a decreasing trend in SCF， while 31.63% of the regions showed an increasing trend in SCF.Most CMIP6 models overestimated SCF in the Tibetan Plateau region compared to the reference data.In addition， most models simulated larger areas with a decreasing trend in SCF than those evaluated by the reference data and underestimated SCF in March， April， and May.Various models exhibited differing abilities to simulate SCF， with NorESM2-MM， CESM2， BBC-CSM2-MR， NorESM2-LM， and CESM2-WACCM demonstrating superior capabilities.The Multi-Model Ensemble Mean （MME） consistently outperformed individual models， closely aligning with observational data.There were significant differences in the ability of the CMIP6 models to simulate the spatial distribution， inter-annual variation trends， and intra-annual variations of SCF in the Northern Hemisphere.At the end of the 21st-century （2067 -2099）， SCF in the Northern Hemisphere exhibited a decreasing trend in most areas， which intensifies with increasing emission intensity.The changes in SCF were relatively consistent under different emission scenarios before 2040.SCF maintains a steady state under the SSP1-2.6 scenario， showed a slight decreasing trend under the SSP2-4.5 scenario， and showed a significant decreasing trend under the SSP5-8.5 scenario after 2040.…”
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Uncertainty-Based Model Averaging for Prediction of Corrosion Ratio of Reinforcement Embedded in Concrete by Siqing Zeng, Fulin Yang, Zengwei Guo, Ruiqi Guo, Guowen Yao

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Machine learning model for postpancreaticoduodenectomy haemorrhage prediction: an international multicentre cohort study by Zhe Zhang, Xiaowei Wang, Chengfeng Wang, Jianwei Zhang, Xueping Zhao, Minjie Shang, Qiuran Xu, Zongting Gu

“…Decision curve analysis confirmed net clinical benefit, and SHapley Additive exPlanations values highlighted HCT and operative time as top contributors. The model was deployed as an interactive application for real-time risk assessment.Conclusions This novel machine learning model for PPH prediction integrates interpretable risk stratification and demonstrates robust performance across international cohorts. …”
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Neuroscience-informed nomogram model for early prediction of cognitive impairment in Parkinson's disease by Sudharshan Putha, Swaroop Reddy Gayam, Bhavani Prasad Kasaraneni, Krishna Kanth Kondapaka, Sateesh Kumar Nallamala, Praveen Thuniki

“…Subsequently, these variables were integrated into a visualized nomogram model to facilitate early prediction of cognitive impairment (CI) risk. …”
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Maximum entropy model-based spatial sinkhole occurrence prediction in Karapınar, Turkey by Fatih Sarı, Mustafa YALÇIN

“…The Maximum Entropy (MaxEnt) model is applied for sinkhole susceptibility mapping by evaluating 17 variables affecting sinkhole occurrence in meteorological, topographic, environmental, and geological aspects. …”
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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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Validation of sleep-based actigraphy machine learning models for prediction of preterm birth by Benjamin C. Warner, Peinan Zhao, Erik D. Herzog, Antonina I. Frolova, Sarah K. England, Chenyang Lu

“…We evaluate motion actigraphy data collected from a cohort of participants undergoing pregnancy, and train several machine learning models based on aggregate features engineered from this data. …”
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Developing a nomogram for risk prediction of the low T3 syndrome by Xiao-Li Feng, Hui-Min Chen, Liu-Juan Yin, Jin Chen, Li-Liang Chen

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Analysis of influencing factors and construction of predictive model for major gastrointestinal bleeding after receiving VTE prophylactic anticoagulants in non-tumor patients by Xiaoxue Fu, Huiya Ren, Yingbo Yang, Zhixue Niu, Linjie Zhang

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