From Continent to Ocean: Investigating the Multi-Element and Precious Metal Geochemistry of the Paraná-Etendeka Large Igneous Province Using Machine Learning Tools by J. J. Lindsay, H. S. R. Hughes, C. M. Yeomans, J. C. Ø. Andersen, I. McDonald

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Digital Humanities & Large Language Models: Practice and Research in Semantic Retrieval of Ancient Documents by Haoxian WANG, Ziming ZHOU, Feifei DING, Chengfu WEI

“…Utilizing the previously constructed vector engine, the model can efficiently retrieve relevant documents and intelligently sort the search results based on specific algorithms, ensuring that users can quickly obtain the most relevant and valuable information. …”
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Development and validation of interpretable machine learning models for postoperative pneumonia prediction by Bingbing Xiang, Yiran Liu, Shulan Jiao, Wensheng Zhang, Shun Wang, Mingliang Yi

“…Through correlation screening, chi-square tests, and feature importance ranking, 47 variables were narrowed down to 5 potential predictive factors based on the main cohort of 528 patients. Nine machine learning models, including k-nearest neighbors, support vector machine, random forest, decision tree, gradient boosting machine, adaptive boosting, naive bayes, general linear model, and linear discriminant analysis, were developed and validated to predict postoperative pneumonia. …”
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Using Digital Phenotyping to Discriminate Unipolar Depression and Bipolar Disorder: Systematic Review by Rongrong Zhong, XiaoHui Wu, Jun Chen, Yiru Fang

“…For each included study, the following information was extracted: demographic characteristics, diagnostic criteria or psychiatric assessments, details of the technological tools and data types, duration of data collection, data preprocessing methods, selected variables or features, machine learning algorithms or statistical tests, validation, and main findings. …”
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Long-term Variation of Sensible Heat Flux over the Qinghai-Xizang Plateau from 1984 to 2020 by Runzhi LEI, Ye YU, Guobin ZHOU, Jianglin LI, Yanfei LIU

“…Sensible heat flux （SH） is one of the main physical quantities characterizing the thermal effect of the Qinghai-Xizang Plateau， which is affected by the surface temperature， air temperature， heat exchange coefficient and wind speed.However， different heat exchange coefficient （CH）calculation methods give SH with different values.To solve this， in this study， three CH calculation methods are used to calculate the SH using the conventional meteorological observation data from 89 stations on the Qinghai-Xizang Plateau for 1984 -2020.The SHs obtained by different CH algorithms are verified using in situ SH observations from 5 flux stations.The results indicate that the SH calculated by Chen and Weng （1984） （CH-CW） scheme is the best in terms of the smallest bias between the caculations and observaitons.The long-term trend of SH calculated with CH-CW scheme and the characteristics and causes of the long-term change over the Qinghai-Xizang Plateau are analyzed using PLFIM piecewise linear fitting model.The results show that the trend of the annual SH over the Qinghai-Xizang Plateau have turning points at 1999 and 2009.The SH decreased from 1984 to 1999， increased significantly from 1999 to 2009， and showed a weak downward trend from 2009 to 2020.The turning points of SH trend in summer are 2000 and 2010， respectively.The turning points of SH trend in other seasons are the same as the annual SH.The overall trend of SH in spring， summer and autumn is consistent with the annual trend， characterized by "weakened - strengthened - weakened".The SH in winter continued to increase from 2009 to 2020， but with a much weaker trend compared with that from 1999 to 2009.Attribution analysis shows that the decrease of annual SH from 1984 to 1999 is mainly attributed to the decrease of wind speed； the increase of annual SH from 1999 to 2009 was mainly due to the increase of surface-air temperature difference caused by the significant increase of surface temperature， and less to the slow down of wind speed increase.From 2009 to 2020， the significant increase of wind speed led to a decrease of CH， and the relative faster increase of air temperature compared to the increase of the surface temperature lead to a decrease in the surface-air temperature difference， both of which contributed to the decrease of the annual average SH during this period， with the negative contribution from the surface-air temperature difference much lager than that from the CH change.…”
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Medical Data over Sound—CardiaWhisper Concept by Radovan Stojanović, Jovan Đurković, Mihailo Vukmirović, Blagoje Babić, Vesna Miranović, Andrej Škraba

“…While the main focus is on the transmission of analog data, digital data transmission is also illustrated. …”
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State preparation by shallow circuits using feed forward by Harry Buhrman, Marten Folkertsma, Bruno Loff, Niels M. P. Neumann

“…This model fits naturally between quantum algorithms in the NISQ era and full-fledged fault-tolerant quantum computation. …”
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Safety profile of ramelteon from the perspective of physicians and pharmacists: a 20-year real-world pharmacovigilance analysis based on the FAERS database by Jianhong Wu, Yujie Zhou, Linghe Qiu, Liang Liu, Fei Wang, Lili Zhen, Na Li

“…The main SOCs included psychiatric disorders, general disorders and administration site conditions, and nervous system disorders. …”
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Di George syndrome: early diagnosis in pediatric practice by A. A. Pavlikov, I. M. Melnikova, T. I. Pakhomova, N. P. Myakin

“…Due to the fact that an increasing number of patients with 22q11.2DS survive to adulthood, the development of algorithms for the prognosis of this disease is important.…”
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AI in Medical Questionnaires: Innovations, Diagnosis, and Implications by Xuexing Luo, Yiyuan Li, Jing Xu, Zhong Zheng, Fangtian Ying, Guanghui Huang

“… This systematic review aimed to explore the current applications, potential benefits, and issues of artificial intelligence (AI) in medical questionnaires, focusing on its role in 3 main functions: assessment, development, and prediction. …”
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Sensitive Multispectral Variable Screening Method and Yield Prediction Models for Sugarcane Based on Gray Relational Analysis and Correlation Analysis by Shimin Zhang, Huojuan Qin, Xiuhua Li, Muqing Zhang, Wei Yao, Xuegang Lyu, Hongtao Jiang

“…In this study, we established three experimental fields, which were planted with three main sugarcane cultivars in Guangxi, China, respectively, implementing a multi-gradient fertilization design with 39 plots and 810 sampling grids. …”
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Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data by T. Radke, S. Fuchs, C. Wilms, I. Polkova, I. Polkova, I. Polkova, M. Rautenhaus, M. Rautenhaus

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DIGITAL TOOLS FOR MATCHING QUALIFICATIONS TO THE LEVELS OF THE NATIONAL QUALIFICATIONS FRAMEWORK by Volodymyr Kovtunets, Sergiy Londar, Serhii Melnyk, Oles Kovtunets

“…Therefore, a user should be capable of forming at most four matrices of judgments and computing the main eigenvectors with some level of accuracy. The maximal dimension of matrices is four (for example it's true for the Ukrainian case). …”
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Mathematical model of a lightweight three-axle off-road vehicle construction for Arctic zone of Russia by I. E. Agureev, V. N. Bondarenko

“…The work uses numerical methods to solve the equations of the constructed model, which enables to gradually weaken the accepted assumptions and build more general calculation algorithms. The main integration method for ensuring the stability of solutions is the multi-step Adams method, which, with the correct choice of step, ensures the necessary stability of the solution over sufficiently long model times. …”
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Helicobacter pylori eradication treatment for gastric carcinoma prevention in asymptomatic or dyspeptic adults: systematic review and Bayesian meta-analysis of randomised controlle... by Teruhiko Terasawa, Isao Miyashiro, Katsuaki Kato, Takaki Yoshikawa, Reo Takaku, Hiroshi Nishida

“…Eradication algorithms were heterogeneous, and unsuccessful eradication and reinfection were frequently observed. …”
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A systematic review and meta-analysis of utility-based quality of life in chronic kidney disease treatments. by Melanie Wyld, Rachael Lisa Morton, Andrew Hayen, Kirsten Howard, Angela Claire Webster

“…., University of Sheffield, producing the lowest estimates. The main limitations of this study were that treatment assignments were not random, that only transplant had longitudinal data available, and that we calculated EuroQol Group EQ-5D scores from SF-36 and SF-12 health survey data, and therefore the algorithms may not reflect EQ-5D scores measured directly.…”
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Pharmacological correction of hepatotoxicity induced by anti-tuberculosis drugs by D. S. Sukhanov, E. V. Timofeev, Y. S. Alekseeva, K. A. Nagornova

“…The article presents the mechanisms of action of the main hepatoprotective drugs, provides the evidence base of their effectiveness, and also notes their shortcomings. …”
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Research on Characteristics and Influencing Factors of High Temperature Disaster Risk in Wuhan Based on Local Climate Zone by Shujing GUO, Li ZHANG

“…Then Landsat 8 remote sensing images are processed with SAGA GIS software, Google Earth Pro software, and Random Forest algorithm to classify the urban development area of Wuhan into 17 local climate zone (LCZ) types based on the remote sensing image classification method of World Urban Database and Access Portal Tools (WUDAPT). …”
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What happens between first symptoms and first acute exacerbation of COPD – observational study of routine data and patient survey by Alex Bottle, Alex Adamson, Xiubin Zhang, Benedict Hayhoe, Jennifer K Quint

“…Prescriptions for the main classes of medication were noted for the year following diagnosis. …”
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Machine learning models for prediction of lymph node metastasis in patients with gastric cancer: a Chinese single-centre study with external validation in an Asian American populat... by Qian Li, Yuan Tian, Wei Peng, Shangcheng Yan, Weiran Yang, Zhuan Du, Ming Cheng, Renwei Chen, Qiankun Shao, Mengchao Sheng, Yongyou Wu

“…Objective To develop and validate machine learning (ML)-based models to predict lymph node metastasis (LNM) in patients with gastric cancer (GC).Design Retrospective cohort study.Setting Second Affiliated Hospital of Soochow University.Participants A total of 500 inpatients from the Second Affiliated Hospital of Soochow University, collected retrospectively between 1 April 2018 and 31 March 2023, were used as the training set, while 824 Asian patients from the Surveillance, Epidemiology and End Results database comprised the external validation set.Main outcome measures Prediction models were developed using multiple ML algorithms, including logistic regression, support vector machine, k-nearest neighbours, naive Bayes, decision tree (DT), gradient boosting DT, random forest and artificial neural network (ANN). …”
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