Development and validation of a machine learning-based nomogram for survival prediction of patients with hilar cholangiocarcinoma after curative-intent resection by Yubo Ma, Qi Li, Zhenqi Tang, Kangpeng Li, Chen Chen, Jianjun Lei, Dong Zhang, Zhimin Geng

“…Based on the decision curve analysis, the nomogram had a good clinical application value, outperforming both the TNM staging system and the Bismuth-Corlette classification. …”
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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

“…Original studies published in English, including both journal and conference papers, were included, while reviews, narrative reviews, systematic reviews, and meta-analyses were excluded. …”
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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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Features of Statistical Accounting of Allergic Diseases in Children — Evidence from Moscow by Anita R. Denisova, Alexander B. Malahov, Alexandr N. Pampura, Elena A. Vishneva, Margarita A. Soloshenko, Nikoloz M. Gaboshvili, Leyla S. Namazova-Baranova

“…The differences in the incidence rates of allergic diseases revealed by a comparative analysis of data from various sources — UMIAS and FSO No. 12 — indicate the need to improve both the system of statistical registration of incidence and the development of modern algorithms for early diagnosis and dynamic monitoring of children with allergies.…”
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Divergent crop mapping accuracies across different field types in smallholder farming regions by Xin Huang, Anton Vrieling, Yue Dou, Xueying Li, Andrew Nelson

“…This study addresses these gaps by categorizing crop fields into different types and assessing mapping accuracy for both major and minor crops within each field type. …”
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Medical Data over Sound—CardiaWhisper Concept by Radovan Stojanović, Jovan Đurković, Mihailo Vukmirović, Blagoje Babić, Vesna Miranović, Andrej Škraba

“…It then enables a range of control and visualization functions for both local and remote users. Emphasis is placed on describing the system architecture and its key components, as well as the software methodologies used for signal decoding on the receiver side, where several algorithms are implemented using open-source, platform-independent technologies, such as JavaScript, HTML, and CSS. …”
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GAB-YOLO: a lightweight deep learning model for real-time detection of abnormal behaviors in juvenile greater amberjack fish by Mingxin Liu, Mingxin Liu, Chun Zhang, Cong Lin, Cong Lin

“…With the growing global population and economic development, the demand for sashimi has increased, presenting both new opportunities and challenges for aquaculture. …”
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Machine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep by Fernando Amarilho-Silveira, Ignacio De Barbieri, Elly A. Navajas, Jaime Araujo Cobuci, Gabriel Ciappesoni

“…The dataset was randomly partitioned into two subsets: one for training (80%) the algorithms and the other for direct validation (20%). …”
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Energy-Accuracy Trade-Offs for Resistive In-Memory Computing Architectures by Saion K. Roy, Naresh R. Shanbhag

“…Resistive in-memory computing (IMC) architectures currently lag behind SRAM IMCs and digital accelerators in both energy efficiency and compute density due to their low compute accuracy. …”
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Machine learning prediction model for functional prognosis of acute ischemic stroke based on MRI radiomics of white matter hyperintensities by Yayuan Xia, Linhui Li, Peipei Liu, Tianxu Zhai, Yibing Shi

“…Radiomic models for the three ROIs were developed using six machine learning algorithms. The clinical model was built by identifying clinical risk factors through univariate and multivariate logistic regression analyses. …”
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Combining a Risk Factor Score Designed From Electronic Health Records With a Digital Cytology Image Scoring System to Improve Bladder Cancer Detection: Proof-of-Concept Study by Sandie Cabon, Sarra Brihi, Riadh Fezzani, Morgane Pierre-Jean, Marc Cuggia, Guillaume Bouzillé

“…With this second data set, the combination of both models obtained areas under the curve of 0.82 on the training set and 0.83 on the test set, demonstrating the value of combining risk factor–based and image-based models. …”
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THEORETICAL AND METHODOLOGICAL ASPECTS OF USING INFORMATION ANDCOGNITIVE TECHNOLOGIES IN THE TRAINING OF TRANSPORT SPECIALISTS by Lavrentieva Olena, Krupskyi Oleksandr

“…In the professional training of students, certain information-cognitive technologies can be applied both with and without the use of computer systems. …”
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The UK Biobank mental health enhancement 2022: Methods and results. by Katrina A S Davis, Jonathan R I Coleman, Mark Adams, Gerome Breen, Na Cai, Helena L Davies, Kelly Davies, Alexandru Dregan, Thalia C Eley, Elaine Fox, Jo Holliday, Christopher Hübel, Ann John, Aliyah S Kassam, Matthew J Kempton, William Lee, Danyang Li, Jared Maina, Rose McCabe, Andrew M McIntosh, Sian Oram, Marcus Richards, Megan Skelton, Fenella Starkey, Abigail R Ter Kuile, Laura M Thornton, Rujia Wang, Zhaoying Yu, Johan Zvrskovec, Matthew Hotopf

“…It includes established measures, such as the PHQ-9 for current depression and CIDI-SF for lifetime panic, as well as bespoke questions. Algorithms and R code were developed to facilitate analysis.…”
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Association of composite dietary antioxidant index and endometriosis risk in reproductive—age women: a cross-sectional study using big data-machine learning approach by Wenxin Chen, Kui Xiao, Chenyu Zhou, Jiajia Cheng, Zixuan Zeng, Fang Zhang

“…Among the various components of the CDAI, changes in vitamin A, vitamin E, and carotene were independently associated with the occurrence of EM, while both LASSO and RF machine learning algorithms consistently identified selenium and carotene as significant factors. …”
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Marine soundscape forecasting: A deep learning-based approach by Shashidhar Siddagangaiah

“…Despite the rapid development of anomaly detection algorithms and deep-learning models for forecasting, their application to marine soundscapes remains unexplored. …”
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Multi-omics analysis identifies SNP-associated immune-related signatures by integrating Mendelian randomization and machine learning in hepatocellular carcinoma by Qingyan Kou, Zhichao Wu, Wenbin Zhao, Zhenyuan Liu, Shengxian Qiao, Qiang Mu, Xu Zhang

“…Machine learning analysis was performed on the genes identified through Mendelian randomization (MR) and survival association analysis, using 101 algorithms to construct a robust prognostic model. A novel riskScore model was developed by integrating genetic, clinical, and immune cell infiltration data. …”
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Unraveling the oxidative stress landscape in diabetic foot ulcers: insights from bulk RNA and single-cell RNA sequencing data by Jialiang Lin, Linjuan Huang, Weiming Li, Haijun Xiao, Mingmang Pan

“…Drug prediction highlighted Thymoquinone and Erlotinib as potential therapeutic candidates. Machine learning algorithms (SVM-RFE, LASSO and RF) identified BCL2 and 和FOXP2 as candidate hub DORGs for DFU diagnosis. …”
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Three-dimensional linkage analysis with digital PCR for genome integrity and identity of recombinant adeno-associated virus by Tam Duong, Michele Firmo, Chien-Ting Li, Bingnan Gu, Peng Wang

“…We have designed the three amplicon sites targeting both ends of the viral genome, as well as the center of key therapeutic gene of interest (GOI). …”
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Surgical interventions in velopharyngeal dysfunction: comparative perceptual speech and nasometric outcomes for three techniques by Ryan Instrum, Agnieszka Dzioba, Anne Dworschak-Stokan, Murad Husein

“…We hope to aid in the eventual creation of treatment algorithms for VPD, allowing practitioners to tailor surgical technique selection to patient factors. …”
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Mapping Gridded GDP Distribution of China Based on Remote Sensing Data and Machine Learning Methods by Saimiao Liu, Wenliang Liu, Yi Zhou, Shixin Wang, Futao Wang, Zhenqing Wang

“…Therefore, based on the remote sensing data of land use and nighttime light, this study developed two methods: the factor averaging method (FAM) and grid averaging method (GAM), and used Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms to jointly construct the spatial model of GDP, so as to produce China’s 1 km gridded GDP in 2020. …”
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