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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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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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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Management and outcome of whole-spine epidural abscesses – institutional case series and systematic review by Erik Schulz, Linda Bättig, Felix C. Stengel, Lorenzo Bertulli, Nader Hejrati, Maximilian Wegener, Carol Strahm, Benjamin Martens, Martin N. Stienen, Stefan Motov

“…Larger multicenter studies are needed to establish reliable treatment algorithms.…”
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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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Nondestructive estimation of leaf chlorophyll content in banana based on unmanned aerial vehicle hyperspectral images using image feature combination methods by Weiping Kong, Weiping Kong, Lingling Ma, Huichun Ye, Huichun Ye, Jingjing Wang, Chaojia Nie, Binbin Chen, Xianfeng Zhou, Wenjiang Huang, Zikun Fan

“…Comprehensive analyses of the linear relationships between all constructed two-pair feature combinations and LCC indicated that the ratio of mean to modified red-edge sample ratio index (MEA/MSRre) stood out (R2 = 0.745, RMSE = 2.17). …”
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Integrated metabolomic-lipidomic profiling reveals novel biomarkers and therapeutic targets for alcohol use disorder with cognitive impairment by Li Shi, Li Shi, Li Shi, Xiaofang Chen, Bojie Zhou, Bojie Zhou, Shanghao Yang, Shanghao Yang, Qinglin Ou, Qinglin Ou, Xuhui Zhou, Xuhui Zhou

“…Cognitive function was assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Machine learning algorithms (Random Forest and LASSO regression) were employed for biomarker selection, and pathway analysis was performed using MetaboAnalyst 5.0.ResultsThe multi-omics platform detected 117 differentially expressed molecules (11 metabolites and 106 lipids) with high diagnostic accuracy (mean AUC=0.92 ± 0.03). …”
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Exploring the Global and Regional Factors Influencing the Density of <i>Trachurus japonicus</i> in the South China Sea by Mingshuai Sun, Yaquan Li, Zuozhi Chen, Youwei Xu, Yutao Yang, Yan Zhang, Yalan Peng, Haoda Zhou

“…Leveraging advanced machine learning algorithms and causal inference, our robust experimental design uncovered nine key global and regional factors affecting the distribution of <i>T. japonicus</i> density. …”
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Evaluating Leaf Water Potential of Maize Through Multi-Cultivar Dehydration Experiments and Segmentation Thresholding by Shuanghui Zhao, Yanqun Zhang, Pancen Feng, Xinlong Hu, Yan Mo, Hao Li, Jiusheng Li

“…The analysis screened published VIs highly correlated with <i>Ψ_leaf</i> and constructed a model for <i>Ψ_leaf</i> estimation based on three algorithms—partial least squares regression (PLSR), random forest (RF), and multiple linear stepwise regression (MLR)—for each cultivar and all three cultivars. …”
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Predictive Modelling of nephrotoxicity from vancomycin and piperacillin-tazobactam (VPT) combination and vancomycin as monotherapy: an artificial intelligence (AI) Approach by Dr Winnie Lee, Mr William J Bolton, Mr Richard C Wilson, Professor Pantelis Georgiou, Professor Alison H Holmes, Dr Nina Zhu, Dr Timothy M Rawson

“…Results: Our preliminary data from the binomial regression and random forest models indicate that the best model was random forest, with a mean AUC score of 0.886, which showed an increase in AKI with increased vancomycin exposure. …”
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An optimized approach for predicting water quality features and a performance evaluation for mapping surface water potential zones based on Discriminant Analysis (DA), Geographical... by Abhijeet Das

“…In the research, performance of Dr-WQI method, that generates a mean value of 85.36, indicating good to poor water quality. …”
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Classification of Individuals With COVID-19 and Post–COVID-19 Condition and Healthy Controls Using Heart Rate Variability: Machine Learning Study With a Near–Real-Time Monitoring C... by Carlos Alberto Sanches, Andre Felipe Henriques Librantz, Luciana Maria Malosá Sampaio, Peterson Adriano Belan

“…Classification models were developed using supervised machine learning algorithms (decision tree, support vector machines, k-nearest neighbor, and neural networks) and evaluated through cross-validation. …”
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Application of machine learning for the analysis of peripheral blood biomarkers in oral mucosal diseases: a cross-sectional study by Huiyu Yao, Zixin Cao, Liangfu Huang, Haojie Pan, Xiaomin Xu, Fucai Sun, Xi Ding, Wan Wu

“…Future research should focus on validating these findings in larger cohorts and exploring alternative machine-learning algorithms to improve diagnostic accuracy further.…”
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