Research and Modeling of Commercial Location Selection Based on Geographic Big Data and Mobile Signaling Data—A Case Study of the Central Urban Area of Beijing by Jin Zou, Xun Zhang, Yangxiao Cong, Zhentong Gao, Jinlian Shi

“…An immersive interactive commercial site selection system is implemented using the Unreal Engine.…”
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Machine-learning prediction models for any blood component transfusion in hospitalized dengue patients by Md. Shahid Ansari, Dinesh Jain, Sandeep Budhiraja

“…A python package SHAP (SHapley Additive exPlanations) was used to explain the output of the “XGBoost” model. …”
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Population-level gene copy number variations reveal distinct genetic properties of different Malus species by Aafreen Sakina, Can Alkan, Awais Khan

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Evaluation Model for Emergency Material Suppliers in Emergency Logistics Systems Based on Game Theory–TOPSIS Method by Lihao Lei, Songtao Yu, Yuxian Ke, Linhong Deng, Qian Kang

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Advances in research on the bioactive compounds and genetic improvement of Eucommia ulmoides by Yun Li, Xiangyang Kang

“…Additionally, integrating intensive cultivation techniques with comprehensive utilization strategies is crucial. …”
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Ecological Niche Adaptations Influence Transposable Element Dynamics in Pollinating and Non‐Pollinating Fig Wasps by Jing Liu, Yun‐Heng Miao, Hong‐Xia Hou, Da‐Wei Huang, Jin‐Hua Xiao

“…We also analyzed the effects of natural selection and population dynamics on TE accumulation. …”
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Partitioning risks of tree mortality by modes of death in managed and unmanaged northern hardwoods and mixedwoods by François Guillemette, Martin-Michel Gauthier, Rock Ouimet

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Enhancing Emergency Response in Road Accidents: A Severity Prediction Framework Using RF-RFE and Deep Learning Model by Chaimaa Chaoura, Hajar Lazar, Zahi Jarir

“…The dataset is refined using Random Forest Recursive Feature Elimination (RF-RFE) to select key features influencing accident severity. …”
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Mutable Composite Firefly Algorithm for Microarray-Based Cancer Classification by Fathima Fajila, Yuhanis Yusof

“…In addition, the local optima issue is overcome by the population reinitialisation method. …”
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Eliminating Effect of Moisture Content in Prediction of Lower Heating Value and Ash Content in Sugarcane Leaves Biomass by Kanvisit Maraphum, Kantisa Phoomwarin, Nirattisak Khongthon, Jetsada Posom

“…Sixty-two samples were scanned using an NIR spectrometer over three week-long storage periods to get different MCs with the same sample. Additionally, variable selection methods such as a genetic algorithm (GA) and moisture-related wavelength exclusion were explored. …”
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Identifying influential factors using machine learning techniques on the intention to receive a COVID-19 booster dose and vaccine fatigue among partially vaccinated individuals by Athina Bikaki, Justin M. Luningham, Erika L. Thompson, Brittany Krenek, Jamboor K. Vishwanatha, Ioannis A. Kakadiaris

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Multi scale supervised entropy weighted binary pattern for texture classification by Xiaochun Xu, Bin Li

“…A local entropy-based optimal selection mechanism (LEOSM) is designed to adaptively select representative texture scales from the Gaussian scale space, based on the uniformity properties of the proposed local entropy-weighted histogram, thereby enhancing scale robustness. …”
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Mathematical Modeling of Cyberattack Defense Mechanism Using Hybrid Transfer Learning With Snow Ablation Optimization Algorithm in Critical Infrastructures by Mohamad Khairi Ishak

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Survival outcomes of lymph node dissection in early-stage epithelial ovarian cancer: identifying suitable candidates by Huan Wang, Shuanghui Wang, Pengyu Wang, Yimin Han

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DEVELOPMENT OF HEALTH INSURANCE CLAIM PREDICTION METHOD BASED ON SUPPORT VECTOR MACHINE AND BAT ALGORITHM by Syaiful Anam, Abdi Negara Guci, Fery Widhiatmoko, Mila Kurniawaty, Komang Agus Arta Wijaya

“…Nonetheless, SVM performance is controlled by the suitable selection of SVM parameters. The SVM parameters is typically selected by trial and error, sometimes resulting in not optimal performance and taking a long time to complete. …”
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Full-Length Transcriptomes Reconstruction Reveals Intraspecific Diversity in Hairy Vetch (<i>Vicia villosa</i> Roth) and Smooth Vetch (<i>V. villosa</i> Roth var. <i>glabrescens</i... by Weiyi Kong, Bohao Geng, Wenhui Yan, Jun Xia, Wenkai Xu, Na Zhao, Zhenfei Guo

“…Furthermore, the estimation of Ka/Ks ratios showed that purifying selection was the predominant force. A putative 3-dehydroquinate dehydratase/shikimate dehydrogenase (DHD/SDH) gene underwent strong positive selection and might regulate phenotypic differences between hairy vetch and smooth vetch. …”
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Assessment of optimal seasonal selection for RSEI construction: a case study of ecological environment quality assessment in the Beijing-Tianjin-Hebei region from 2001 to 2020 by Shaodong Huang, Yujie Li, Haowen Hu, Pengcheng Xue, Jia Wang

“…This study theoretically demonstrates that summer is the optimal season for constructing RSEI, filling the research gap in previous studies regarding the rationale for selecting images from periods of vigorous vegetation growth for RSEI construction, which can provide a reference for selecting the optimum season for the ecological quality monitoring of urban in the future.…”
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Evaluation of TaMFT-3A and TaMKK3-4A alleles on wheat pre-harvest sprouting by Bo-Wen Zhang, Bo-Wen Zhang, Bai-Song Yang, Xiao-Neng Wan, Xin Ma, Kai-Di Lyu, Han Wang, Shu-Ying Yang, Hui-Hui Zhang, Shu-Nv Hao, Jian Ma, Guo-Zhong Sun

“…Resistance to PHS is governed by both genetic and environmental factors, making reliable molecular markers essential for enhancing PHS resistance through molecular marker‐assisted selection (MAS). Genes TaMFT-3A and TaMKK3-4A have been cloned in wheat and are known to regulate PHS resistance in white-grained varieties. …”
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Classification of benign and malignant solid breast lesions on the ultrasound images based on the textural features: the importance of the perifocal lesion area by А.А. Kolchev, D.V. Pasynkov, I.A. Egoshin, I.V. Kliouchkin, О.О. Pasynkova

“…The use of LASSO regression for feature selection enabled us to identify the most significant features for classification. …”
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Development of a midwifery regulatory environment index using data from the Global Midwives’ Associations map survey by Emma Virginia Clark, Marianna LaNoue, Kate Clouse, Alexandra Zuber, Jeremy Neal

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