THE CLINICAL CONFERENCE AS GUIDANCE TO FORMING INTERNS’ PROFESSIONAL COMPETENCIES DURING FORMATION IN THE UMSA’S DEPARTMENT OF POSTGRADUATED EDUCATION OF DENTISTS by T.P. Skripnikova, M.V. Khrebor, Ju. I. Sylenko, O.A. Pisarenko

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Machine learning-based prognostic prediction for acute ischemic stroke using whole-brain and infarct multi-PLD ASL radiomics by Zhenyu Wang, Chaojun Jiang, Xianxian Zhang, Tianchi Mu, Qingqing Li, Shu Wang, Congsong Dong, Yuan Shen, Zhenyu Dai, Fei Chen

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Evaluating and Forecasting the Probability of Lightning Occurrence in Rasht City by Afsaneh Ghasemi, Jamil Amanollahi

“…Therefore, the probability of lightning occurrence in the future is higher than non-occurrence of lightning. Besides, among the three tree, CART, CHAID and C5, the CART and C5 trees had less satisfactory indices lacking the highest accuracy and precision in predicting lightning in future. …”
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AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector by Şenda Yıldırım, Ahmet Deniz Yücekaya, Mustafa Hekimoğlu, Meltem Ucal, Mehmet Nafiz Aydin, İrem Kalafat

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Deep learning radiomics nomogram predicts lymph node metastasis in laryngeal squamous cell carcinoma by Yun Liang, Yun Liang, Min He, Wenqing Chen, Wenqing Chen, Lizhen Li, Lizhen Li, Yumeng Dong, Yumeng Dong, Gang Liang, Hui Huangfu, Zengyu Jiang, Zengyu Jiang, Sheng He, Sheng He, Sheng He

“…The optimal DLR model was combined with significant clinical imaging features from CT scans to develop the predictive nomogram for LNM in LSCC.ResultsThe nomogram, under receiver operating characteristic (ROC) curve analyses, yielded areas under the curve (AUC) values of, respectively, 0.934 and 0.864 for training and validation sets, significantly higher than clinical imaging features (0.832 and 0.817), conventional radiomics (0.861 and 0.818), and DLR (0.913 and 0.864), indicating that it was significantly more accurate in predicting LNM in LSCC patients. Additionally, decision curve analysis found that the nomogram had significantly higher clinical utility than the other 3 models.ConclusionThe predictive nomogram, combining clinical imaging and DLR features, is able to accurately identify LNM in LSCC patients, providing valuable information for non-invasive LN staging and personalized treatment approaches.…”
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Dynamic and interpretable deep learning model for predicting respiratory failure following cardiac surgery by Man Xu, Hao Liu, Anran Dai, Qilian Tan, Xinlong Zhang, Rui Ding, Chen Chen, Jianjun Zou, Yongjun Li, Yanna Si

“…Feature selection was conducted via the Least Absolute Shrinkage and Selection Operator (LASSO) and Boruta algorithms. Five machine learning models, including logistic regression, multilayer perceptron, extreme gradient boosting, categorical boosting, and deep neural network (DNN), were trained using preoperative and intraoperative variables. …”
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Collaborative management of water-agriculture-energy-ecology nexus for increasing carbon sequestration to sustainable development: A case study in inland river Northwest China by Xiaoyu Tang, Yue Huang, Xiaohui Pan, Yunan Ling, Chanjuan Zan, Jiabin Peng, Xi Chen, Tie Liu

“…In dry years, hydropower generation decreased by 30.02 %, agricultural economic benefits increased by 4.36 %, and carbon sequestration increased by 3.99 %. These findings help decision-makers gain insight into the interrelationships between water utilization, agricultural irrigation, hydropower generation, and ecological restoration and make decisions for collaborative management of the WAEE nexus, contributing to achieving carbon neutrality and ecosystem sustainability in arid areas.…”
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Artificial Intelligence in Pediatric Orthopedics: A Comprehensive Review by Andrea Vescio, Gianluca Testa, Marco Sapienza, Filippo Familiari, Michele Mercurio, Giorgio Gasparini, Sergio de Salvatore, Fabrizio Donati, Federico Canavese, Vito Pavone

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Development and validation of web-based, interpretable predictive models for sepsis and mortality in extensive burns by Shi-Qi Wang, Shi-Qi Wang, Kan Qiu, Qi-Rui Zheng, Bing-Jie Zhou, Ming-Yu Li, Hai-Yan Zhong, Yong Chen, Si-Ming Yuan, Si-Ming Yuan

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Exploring ribosome biogenesis in lung adenocarcinoma to advance prognostic methods and immunotherapy strategies by Zipei Song, Yuheng Wang, Miaolin Zhu, Pengpeng Zhang, Zhihua Li, Xin Geng, Xincen Cao, Jianan Zheng, Jianwei Tang, Liang Chen

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Predicting Index Trend Using Hybrid Neural Networks with a Focus on Multi-Scale Temporal Feature Extraction in the Tehran Stock Exchange by Mohammad Osoolian, Ali Nikmaram, Mahdi Karimi

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Interpretable noninvasive diagnosis of tuberculous pleural effusion using LGBM and SHAP: development and clinical application of a machine learning model by Bihua Yao, Xingyu Yu, Liannv Qiu, Er-min Gu, Siyu Mao, Lei Jiang, Jijun Tong, Jianguo Wu

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Data fusion-based improvements in empirical regression and machine learning for global daily ∼ 8 km resolution sea surface nitrate estimation and interpretation by Aifen Zhong, Difeng Wang, Fang Gong, Jingjing Huang, Zhuoqi Zheng, Xianqiang He, Qing Zhang, Qiankun Zhu

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Predicting the risk of postoperative avascular necrosis in patients with talar fractures based on an interpretable machine learning model by Jian Zhang, Jian Zhang, Jian Zhang, Jihai Xu, Jihai Xu, Jiapei Yu, Jiapei Yu, Jiapei Yu, Hong Chen, Hong Chen, Xin Hong, Songou Zhang, Xin Wang, Xin Wang, Chengchun Shen, Chengchun Shen, Chengchun Shen

“…Six machine learning algorithms were employed to construct the prediction models. …”
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Sistem Akuaponik untuk Peternakan Lele dan Tanaman Kangkung Hidroponik Berbasis IoT dan Sistem Inferensi Fuzzy by Fachrul Rozie, Iwan Syarif, Muhammad Udin Harun Al Rasyid, Edi Satriyanto

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Longitudinal CE-MRI-based Siamese network with machine learning to predict tumor response in HCC after DEB-TACE by Nan Wei, René Michael Mathy, De-Hua Chang, Philipp Mayer, Jakob Liermann, Christoph Springfeld, Michael T Dill, Thomas Longerich, Georg Lurje, Hans-Ulrich Kauczor, Mark O. Wielpütz, Osman Öcal

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Flexible Configurable Modular Neural Network-Based OFDM Receiver by A. B. Sergienko, P. V. Apalina, A. D. Lebedinskaya

“…Variants of the second perceptron were trained for three quadrature modulation (QAM) constellations: 4QAM, 16QAM, and 64QAM.Conclusion. …”
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New framework for the development of clinical guidelines in Russia by D. V. Blinov, E. S. Akarachkova, A. S. Orlova, E. V. Kryukov, D. I. Korabelnikov

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Prevention of fetal brain injury in category II tracings by Masahiro Nakao, Michael G. Ross, Shoichi Magawa, Satoshi Toyokawa, Kiyotake Ichizuka, Naohiro Kanayama, Shoji Satoh, Nanako Tamiya, Akihito Nakai, Keiya Fujimori, Tsugio Maeda, Akira Oka, Hideaki Suzuki, Mitsutoshi Iwashita, Tomoaki Ikeda

“…In rapid progression cases, physicians' decisions to deliver the fetus occurred at ~BDecf 8 mEq/L, whereas the “decisions” did not occur until BDecf reached 12 mEq/L in slow progression cases. …”
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Molecular feature-based classification of retroperitoneal liposarcoma: a prospective cohort study by Mengmeng Xiao, Xiangji Li, Fanqin Bu, Shixiang Ma, Xiaohan Yang, Jun Chen, Yu Zhao, Ferdinando Cananzi, Chenghua Luo, Li Min

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