AlphaFold 2, but not AlphaFold 3, predicts confident but unrealistic β-solenoid structures for repeat proteins by Olivia S. Pratt, Luc G. Elliott, Margaux Haon, Shahram Mesdaghi, Rebecca M. Price, Adam J. Simpkin, Daniel J. Rigden

“…Prompted by a highly confident prediction for a biologically meaningless, randomly permuted repeat sequence, we assessed AF2 performance on sequences composed of perfect repeats of random sequences of different lengths. …”
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Real-Time Plant Health Detection Using Deep Convolutional Neural Networks by Mahnoor Khalid, Muhammad Shahzad Sarfraz, Uzair Iqbal, Muhammad Umar Aftab, Gniewko Niedbała, Hafiz Tayyab Rauf

“…It captured the entire image in one shot and forecasted adjacent boxes and class certainty. A random dataset image served as the model’s input via a cell phone. …”
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Microbiota analysis of perimenopausal women experiencing recurrent vaginitis in conjunction with urinary tract infection by Yingying Bi, Yuezhu Wang, Wu Li, Yuhang Chen, Jinlong Qin, Huajun Zheng

“…A total of 147 predicted pathways were significantly different between patients and healthy controls, with the microbiota of the anus exhibiting the greatest number of functional changes, followed by the urine microbiota. A random forest model composed of 16 genera in the lower vaginal end had the highest discriminatory power (AUC 81.48%) to predict RV/UTI. …”
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Identifying high-risk population segments for underweight, overweight, and obesity among reproductive-age women in sub-Saharan Africa by Amare Abera Tareke, Amare Abera Tareke, Anissa Mohammed, Amare Muche, Yeshimebet Ali

“…We computed the pooled prevalence of different forms of malnutrition using the random effects inverse variance method. We evaluated the factors associated with different forms of malnutrition using multilevel multinomial regression. …”
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Enhancing Drought Forecast Accuracy Through Informer Model Optimization by Jieru Wei, Wensheng Tang, Pakorn Ditthakit, Jiandong Shang, Hengliang Guo, Bei Zhao, Gang Wu, Yang Guo

“…In order to validate the VMD-JAYA-Informer model, four meteorological stations in the Songliao River Basin were chosen at random. The validation results demonstrate that VMD-JAYA-Informer outperforms the Informer model in terms of prediction accuracy on the 1-month time scale (NSE values of 0.8663, 0.8765, 0.8822, and 0.8416, respectively). …”
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Classification of Periodic Variable Stars from TESS by Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu

“…We used 19 parameters including period, physical parameters, and light-curve (LC) parameters to classify periodic variable stars into 12 subtypes using the random forest method. Pulsating variable stars and eclipsing binaries are distinguished mainly by period, LC parameters, and physical parameters. …”
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Research on the Simulation Model of Dynamic Shape for Forest Fire Burned Area Based on Grid Paths from Satellite Remote Sensing Images by Xintao Ling, Gui Zhang, Ying Zheng, Huashun Xiao, Yongke Yang, Fang Zhou, Xin Wu

“…Four machine learning models, such as Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were trained using 80% effective samples from four forest fires, and 20% used to verify the above models. …”
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Assessing CNN and Semantic Segmentation Models for Coarse Resolution Satellite Image Classification in Subcontinental Scale Land Cover Mapping by Tesfaye Adugna, Wenbo Xu, Jinlong Fan, Haitao Jia, Xin Luo

“…In this study, we evaluated the performance and feasibility of three CNN models (1-D CNN, 2-D CNN, and 3-D CNN), and U-net for coarse-resolution satellite image classification and compared them to a random forest (RF) classifier. We utilized time-series, coarse resolution (1 km) composite imageries acquired by FengYun-3C visible and infrared radiometer. …”
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An Automated Compliance Framework for Critical Infrastructure Security Through Artificial Intelligence by Sardar Muhammad Ali, Abdul Razzaque, Muhammad Yousaf, Rafi Us Shan

“…We evaluate the performance of the model using 3-ML classifiers including Random Forest (RF), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). …”
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Assessing the association between tourism and the alcohol urban environment in Barcelona: a cross-sectional study by Xisca Sureda, Manuel Franco, Susanna Puigcorbé, Joan R Villalbí, Marina Bosque-Prous, Ester Teixidó-Compañó, Montserrat Bartroli, Albert Espelt

“…We explore with a valid instrument in a large city whether there is an association between high tourism pressure and a greater presence of alcohol-related elements in the urban environment.Design Observational ecological study.Setting The study was conducted in Barcelona during 2017–2018.Participants We assessed urban exposure to alcohol by performing social systematic observation using the OHCITIES Instrument in a stratified random sample of 170 census tracts within the city’s 73 neighbourhoods.Primary and secondary outcome measures For each census tract we calculated the density of alcohol premises, and of promotion in public places per 1000 residents. …”
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Effectiveness and utilization of a cognitive screening program for primary geriatric care by David P. Salmon, Anna Malkina, Melanie L. Johnson, Christina Gigliotti, Emily A. Little, Douglas Galasko

“…Retrospective medical records review was carried out for a random (stratified) half of the sample to determine how screening results were utilized. …”
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Exploration of the Mountainous Urban Rail Transit Resilience Under Extreme Rainfalls: A Case Study in Chongqing, China by Chenhui Liu, Xue Su, Zhichun Wu, Yingjun Zhang, Cuizhu Zhou, Xiangguo Wu, Yong Huang

“…When all affected nodes fail, network service efficiency decreases by 84.0% and 75.2% under landslide and flood disasters, respectively. Compared with the random recovery strategy, recovery strategies based on topological importance and service efficiency index, the optimal recovery strategy based on genetic algorithm performs much better.…”
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Risk factors for Toxoplasma gondii seropositivity in buffaloes in a Mediterranean area by Giovanna Cappelli, Antonio Bosco, Alessandra Martucciello, Gabriele Di Vuolo, Domenico Vecchio, Carlo Grassi, Maria Serrapica, Michele Napoletano, Paola Pepe

“…The random selection of farms allowed us to consider different areas of the Campania region where the highest concentration of buffalo herds exists. …”
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Association between normal saline infusion volume in the emergency department and acute kidney injury in heat stroke patients: a multicenter retrospective study by Lan Chen, Junlu Zhao, Liyun Lu, Zhumei Gong, Shuying Xu, Xiaoling Yang, Yuping Zhang, Xiuqin Feng

“…Multivariate regression models, random forest imputation, and genetic and propensity score matching were used to explore the relationship between NS infusion and outcomes.Results The mean volume of NS infusion in the emergency department (ED) was 3.02 ± 1.45 L. …”
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The Relative Contribution of Root Morphology and Arbuscular Mycorrhizal Fungal Colonization on Phosphorus Uptake in Rice/Soybean Intercropping Under Dry Cultivation by Huimin Ma, Hongcheng Zhang, Qian Gao, Shilin Li, Yuanyuan Yu, Jiaying Ma, Congcong Zheng, Meng Cui, Zhihai Wu, Hualiang Zhang

“…This was primarily attributable to a considerable enhancement in biomass and phosphorus uptake in soybean by 40.4% and 49.7%, which offset a slight decline in the rice of NS compared to PS by 26.8% and 18.0%, respectively. The results of random forest analysis indicate that the P uptake by the dominant species, soybean, was mainly contributed by root morphology, while rice was more dependent on AMF colonization in the intercropping system. …”
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Work-related temporary hearing loss and associated factors among textile industry workers in Amhara region, Ethiopia: a cross-sectional study by Giziew Abere, Eshetu Abera Worede, Atalay Tadele Yirdaw, Azanaw Asega Belay, Beletu Kinfe

“…Participants were selected via a simple random sampling technique.Setting Textile industry workers in Bahir Dar and Kombolcha.Participants A total of 413 study participants were included in the data analysis.Procedures and analysis The data were collected using a structured questionnaire. …”
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The effects of artificial sweeteners on body weight, body fat, and energy intake: A meta-analysis of meta-analyses by Fatemeh Hamedi-Kalajahi, Sanaz Asemani, Kousalya Prabahar, Neda Jourabchi-Ghadim, Alireza Ostadrahimi

“…Meta-analyses studies investigating the effect/association of AS on/with BW, BMI, and energy intake were included. Random-effects model was used for performing a meta-analysis. …”
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Advanced Machine Learning Ensembles for Improved Precipitation Forecasting: The Modified Stacking Ensemble Strategy in China by Tiantian Tang, Yifan Wu, Yujie Li, Lexi Xu, Xinyi Shi, Haitao Zhao, Guan Gui

“…We developed and compared five deterministic precipitation forecasting models, including elastic net regression (ENR), support vector regression, random forest, extreme gradient boosting, and light gradient boosting to provide forecasts with lead times ranging from 0 to 5 months at a spatial resolution of 0.5<inline-formula><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula>. …”
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Unsupervised deep learning method for single image super-resolution of the thick pinhole imaging system using deep image prior by Guoguang Li, Liang Sheng, Baojun Duan, Yang Li, Dongwei Hei, Qingzi Xing

“…Furthermore, the convergence and robustness of the proposed DIP algorithm under different random seeds and SNR conditions are also verified.…”
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Effect analysis of narrative nursing intervention on anxiety in neurological patients (叙事护理对神经内科患者焦虑情绪的干预效果) by WU Yinxia (吴印霞), ZHANG Xuefeng (张学凤), HU Yuanyuan (胡媛媛)

“…Objective To investigate the effect of narrative nursing intervention on anxiety in neurological patients. Methods A random drawing balls method was conducted to divide 80 neurological patients into two groups. …”
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