Impact of Solvents on the Crystal Morphology of CL-20/TFAZ Cocrystals: A Predictive Study by Yuanyuan Sun, Le Yu, Yichen Wang, Nian-Tzu Suen

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Comparative Evaluation of Ensemble Machine Learning Models for Methane Production from Anaerobic Digestion by Dorijan Radočaj, Mladen Jurišić

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Secondary School Students’ Perceptions of Subjects in Integrated STEM Teaching by Anna Kellinghusen, Sandra Sprenger, Catharina Zieriacks, Anna Orschulik, Katrin Vorhölter, Sandra Schulz

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Predictive model for risk of gastric cancer using genetic variants from genome‐wide association studies and high‐evidence meta‐analysis by Lixin Qiu, Xiaofei Qu, Jing He, Lei Cheng, Ruoxin Zhang, Menghong Sun, Yajun Yang, Jiucun Wang, Mengyun Wang, Xiaodong Zhu, Weijian Guo

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Weakly-consolidated sandstone fracture conductivity under combined action of compressing, proppant embedding and fines plugging: experiments, model and application by Guolong Li, Changyin Dong, Haobin Bai, Yaotu Han, Qilong Zhang

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A novel multi-task learning model based on Transformer-LSTM for wind power forecasting by Rongquan Zhang, Siqi Bu, Yuxia Zheng, Gangqiang Li, Xiupeng Wan, Qiangqiang Zeng, Min Zhou

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Design of Hybrid MPC-PID Controller Based on Fractional Order Model of AMR by Orkan Murat Celik, Furkan Nur Deniz, Murat Koseoglu

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Linear B-cell epitope prediction for SARS and COVID-19 vaccine design: Integrating balanced ensemble learning models and resampling strategies by Fatih Gurcan

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Evaluation of Prediction Models for the Capping and Breaking Force of Tablets Using Machine Learning Tools in Wet Granulation Commercial-Scale Pharmaceutical Manufacturing by Sun Ho Kim, Su Hyeon Han, Dong-Wan Seo, Myung Joo Kang

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Association between the postoperative glycemic variability and mortality after craniotomy: a retrospective cohort study and development of a mortality prediction model by Yuanshuo Ge, Guangdong Wang, Yun Huang, Yaxin Zhang

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Construction and evaluation of a nomogram prediction model for the risk of epistaxis following nasotracheal intubation: a single-center retrospective cohort study by Jie Chen, Hao Fan, Yuxing Wang, Yuhang Zhu, Ren Zhou, Hua Kang, Hong Jiang

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Development and external validation of a machine learning model for cardiac valve calcification early screening in dialysis patients: a multicenter study by Xiaoxu Wang, Yinfang Li, Zixin Cao, Yunuo Li, Jingyuan Cao, Yao Wang, Min Li, Jing Zheng, Siqi Peng, Wen Shi, Qianqian Wu, Junlan Yang, Yaping Fang, Aiqing Zhang, Xiaoliang Zhang, Bin Wang

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The Structural Relationship Model Predicting Students’ Academic Performance Based on Problem-solving Skills and Intelligence Beliefs with the Mediating Role of Academic Challenge by Mahshid Keyvanlou, Zahra Akhavi Samarein, Shirin Ahmadi

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Prediction of suitable habitat for tundra swans in the Nanji wetland Nature Reserve of Poyang Lake under different water level conditions by Tianwei Fang, Jin Jin, Chao Chen, Li Zhang, Yankuo Li, Chaoyang Li

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Status of cognitive frailty in elderly patients with chronic kidney disease and construction of a risk prediction model: a cross-sectional study by Xiaoyun Zhang, Baolin Luo, Zebing Luo, Meiwan Xu, Chujun Shi

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From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers by Heather Shaw, Pinkie Chambers, Matthew Watson, Luke Steventon, James Harmsworth King, Angelo Ercia, Noura Al Moubayed

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Enhancing Urban Parking Efficiency Through Machine Learning Model Integration by Sai Sneha Channamallu, Sharareh Kermanshachi, Jay Michael Rosenberger, Apurva Pamidimukkala

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Predicting the potential geographical distribution of mango, an important tropical economic tree species, under current and climate change based on Maxent model by Sangui Yi, Yuanhe Huang, Zongling Liu, Zhengjie Zhu, Hongxin Su, Hongxin Su

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Early-Stage State-of-Health Prediction of Lithium Batteries for Wireless Sensor Networks Using LSTM and a Single Exponential Degradation Model by Lorenzo Ciani, Cristian Garzon-Alfonso, Francesco Grasso, Gabriele Patrizi

“…Various architectures and hyperparameters were explored to optimize the models’ performance. The key finding is that training one of the models with only 50 records (equivalent to 30% of battery usage) enables accurate SOH prediction, achieving a Mean Squared Error (MSE) of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.68</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>4</mn></mrow></msup></mrow></semantics></math></inline-formula> and Root Mean Squared Error (RMSE) of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.30</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula>. …”
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Generating Anticancer Peptides Sequences Using Seq2Seq Modeling and Machine Learning Methods by Muhammad Sohail Ibrahim, Saheed Ademola Bello, Yunsang Kwak, Minseok Kim, Shujaat Khan

“…The proposed computational approach offers a streamlined and economical alternative to traditional experimental methods, expediting the discovery of new ACPs and enhancing the accuracy of anticancer peptide predictions. The relevant models, codes, and results are also available on the authors github page at (<uri>https://github.com/mhdshl/ACP-Seq2Seq</uri>).…”
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