Predictive factors for long-term patency in duodenal stenting for malignant gastric outlet obstruction by David Razzaz, Stefan Linder, Alexander Waldthaler, Marcus Holmberg, Poya Ghorbani

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Diagnostic Accuracy of NICE Classification System for Optical Recognition of Predictive Morphology of Colorectal Polyps by Josipa Patrun, Lucija Okreša, Hrvoje Iveković, Nadan Rustemović

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Correction: Time series models for prediction of leptospirosis in different climate zones in Sri Lanka. by PLOS ONE Staff

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TopoQual polishes circular consensus sequencing data and accurately predicts quality scores by Minindu Weerakoon, Sangjin Lee, Emily Mitchell, Haynes Heaton

“…Conclusion TopoQual represents a significant advancement in genomics by improving the accuracy of base quality predictions for PacBio HiFi sequencing data. By correcting a substantial proportion of errors and achieving high base quality validation, TopoQual enables confident and accurate somatic variant calling. …”
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Lithium-Ion Battery State of Health Degradation Prediction Using Deep Learning Approaches by Talal Alharbi, Muhammad Umair, Abdulelah Alharbi

“…Timely prediction of the State of Health (SoH) of lithium-ion batteries is important for battery management and longevity. …”
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Predicting Flight Trajectory in Convective Weather through Boosted Spatiotemporal Deep Learning Ensemble by Xi Zhu, Ke Zhang, Zhuxi Zhang, Lifei Tan

“…Flight trajectory prediction is one of the key issues in ensuring the safety of air traffic, providing the air traffic controller with the foresight of flight conflicts so that control instructions for pilots can be preconceived. …”
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Photovoltaic-Power Prediction Model Based on Quantum Long Short-Term Memory Network by PAN Dong, YANG Xin, SHI Tiancheng, FANG Yuan, WANG Xuli, DOU Menghan

Subjects: “…quantum computer|quantum long short-term memory network|dual-stage attention|photovoltaic power prediction…”
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A Machine Learning Model for Predicting Prognosis in HCC Patients With Diabetes After TACE by Wu L, Chen L, Zhang L, Liu Y, Ouyang D, Wu W, Lei Y, Han P, Zhao H, Zheng C

“…Further, five predictive models were employed to establish prognosis models for 1-, 2-, and 3-year survival, respectively. …”
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Airport Arrival Flow Prediction considering Meteorological Factors Based on Deep-Learning Methods by Zhao Yang, Yifan Wang, Jie Li, Liming Liu, Jiyang Ma, Yi Zhong

“…The model parameters are sequentially updated based on the recently collected data and the new predicting results. It is found that the prediction accuracy is greatly improved by incorporating the meteorological features. …”
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The Predictive Role of Parathyroid Hormone for Nonalcoholic Fatty Liver Disease following Bariatric Surgery by Tannaz Jamialahmadi, Mohsen Nematy, Mohammed Abdalla, Ali Jangjoo, Ladan Goshayeshi, Matthew Kroh, Seyed Adel Moallem, Mitra Abbasifard, Thozhukat Sathyapalan, Amirhossein Sahebkar

“…Serum PTH concentration was not predictive of postsurgical liver fibrosis and steatosis at six months but could predict weight loss success rate. …”
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Artificial intelligence for the prediction of the physical and mechanical properties of a compressed earth reinforced by fibers by Bentegri Houcine, Rabehi Mohamed, Kherfane Samir, Boukansous Sarra

Subjects: “…Compressed earth block .Artificial neural networks .Fibers. Cement. Prediction.…”
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Research on Traffic Accident Severity Level Prediction Model Based on Improved Machine Learning by Jiming Tang, Yao Huang, Dingli Liu, Liuyuan Xiong, Rongwei Bu

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Fault-Tolerant Model Predictive Control for Autonomous Underwater Vehicles Considering Unknown Disturbances by Yimin Chen, Shaowen Hao, Jian Gao, Jiarun Wang, Le Li

Subjects: “…model predictive control…”
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Can the Risk Factors Predicting Surgical Treatment be Determined in Patients with Tubo-Ovarian Abscess? by Tayfun Vural, Burak Bayraktar, Suna Yildirim Karaca, Emir Gurbuz, Mehmet Ozeren, Cuneyt Eftal Taner

“… OBJECTIVES: In this study, we aimed to determine the risk factors in predicting the need for surgical treatment due to medical treatment failure in patients with tubo-ovarian abscess. …”
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Different orthology inference algorithms generate similar predicted orthogroups among Brassicaceae species by Irene T. Liao, Karen E. Sears, Lena C. Hileman, Lachezar A. Nikolov

“…Discussion Three algorithms—OrthoFinder, SonicParanoid, and Broccoli—are helpful for initial orthology predictions. Results produced using OrthNet were generally outliers but could still provide detailed information about gene colinearity. …”
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Adapting to evolving MRI data: A transfer learning approach for Alzheimer’s disease prediction by Rosanna Turrisi, Sarthak Pati, Giovanni Pioggia, Gennaro Tartarisco

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A prediction model for sleep disorders in shift workers of a chemical fiber enterprise by SHEN Lili, PAN Yahui, FENG Jiafeng

“…Objective: To construct a prediction model for sleep disorders in shift workers of a chemical fiber enterprise, so as to provide the basis for early identification and prevention of sleep disorders in shift workers. …”
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Residual Life Prediction of Metro Traction Motor Bearing Based on Convolutional Neural Network by Yanwei Xu, Weiwei Cai, Tancheng Xie, Pengfei Zhao

“…In order to solve the problem that a single type of sensor cannot fully reflect the bearing life information in the process of bearing residual life prediction of metro traction motor, a bearing residual life prediction method based on multi-information fusion and convolutional neural network is proposed. …”
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Genetic inbreeding load and its individual prediction for milk yield in French dairy sheep by Simona Antonios, Silvia T. Rodríguez-Ramilo, Andres Legarra, Jean-Michel Astruc, Luis Varona, Zulma G. Vitezica

“…Third, we included these coefficients in a mixed model as random regression covariates, to predict genetic variance and breeding values of the inbreeding load for milk yield in the same breeds. …”
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Spatial differences in predicted Phalaris arundinacea (reed canarygrass) occurrence in floodplain forest understories by John T. Delaney, M. Van Appledorn, N. R. De Jager, K. L. Bouska, J. J. Rohweder

“…The ensemble of the three models (i.e., the average prediction) was used to map and summarize potential reed canary grass habitat suitability across the landscape. …”
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