Quality of life and physical working capacity in pulmonary sarcoidosis by A. L. Gudim, L. B. Postnikova, V. A. Kostrov, A. A. Mironov, N. I. Kubysheva

“…In patients with low QOL, CPET can be used to determine the PWC and mechanisms of its reduction. The presented diagnostic algorithm will optimize the choice of therapy for patients with sarcoidosis.…”
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Frailty in older adults patients: a prospective observational cohort study on subtype identification by Zhikai Yang, Chen Ji, Ting Wang, Wei He, Yuhao Wan, Min Zeng, Di Guo, Lingling Cui, Hua Wang

“…The logistic regression model exhibited robust predictive capability, with an AUC (Area under curve) of 0.88. …”
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A low-cost autonomous portable poultry egg freshness machine using majority voting-based ensemble machine learning classifiers by Jirayut Hansot, Wongsakorn Wongsaroj, Thaksin Sangsuwan, Natee Thong-un

“…The proposed machine learning model is an ensemble machine learning algorithm, which integrates predictions obtained from several individual classifiers like Random Forest, Decision Trees, Support Vector Machine, Naïve Bayes, k-Nearest Neighbors and Logical Regression to make a final prediction. …”
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Comparing the Potential of Near- and Mid-Infrared Spectroscopy in Determining the Freshness of Strawberry Powder from Freshly Available and Stored Strawberry by Da Wang, Wenwen Wei, Yanhua Lai, Xiangzheng Yang, Shaojia Li, Lianwen Jia, Di Wu

“…The best model was determined as the SPA-LS-SVM model based on MIR spectra, which had the residual prediction deviation (RPD) value of 11.198 and the absolute difference between root-mean-square error of calibration and prediction (AB_RMSE) value of 0.505. …”
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Investigating imperceptibility of adversarial attacks on tabular data: An empirical analysis by Zhipeng He, Chun Ouyang, Laith Alzubaidi, Alistair Barros, Catarina Moreira

“…Adversarial attacks are a potential threat to machine learning models by causing incorrect predictions through imperceptible perturbations to the input data. …”
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A Predictor-Corrector Method for Solving Equilibrium Problems by Zong-Ke Bao, Ming Huang, Xi-Qiang Xia

“…One step serves to predict the next point; the other helps to correct the new prediction. …”
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Guidelines for the management of emergencies and critical illness in pediatric and adult patients with sickle cell disease by Armand Mekontso Dessap, Stephane Dauger, Mehdi Khellaf, Maite Agbakou, Sophie Agut, François Angoulvant, Jean-Benoît Arlet, Cécile Aubron, Florent Baudin, Florence Boissier, Nicolas Bounaud, Pierre Catoire, Jérôme Cecchini, Djamila Chaiba, Anthony Chauvin, Richard Chocron, Benedicte Douay, Delphine Douillet, Narcisse Elenga, Olivier Flechelle, Ségolène Gendreau, Sybille Goddet, Jeremy Guenezan, Anoosha Habibi, Claire Heilbronner, Bérengère Koehl, Pierrick Le Borgne, Philippe Le Conte, Annick Legras, Michael Levy, Bernard Maitre, Mathieu Oberlin, Mehdi Oualha, Nicolas Peschanski, France Pirenne, Corinne Pondarre, Jérôme Rambaud, Keyvan Razazi, Geoffroy Rousseau, Aurélie Schirmann, Isabelle Thuret, Ruddy Valentino, Guillaume Voiriot, Barbara Villoing, Marion Grimaud, Sandrine Jean

“…The section on transfusion therapy addressed indications and methods of transfusion, as well as the diagnosis and prediction of delayed hemolytic transfusion reactions. …”
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Adaptive Optimization of Quantized Perturbation Coefficients for Fiber Nonlinearity Compensation by M. Malekiha, D. V. Plant

“…In this paper, we propose and experimentally verify the adoption of a decision-directed least mean square algorithm for optimization and complexity reduction of PB-NLC equalizer. …”
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Heart failure and atrial fibrillation: updates and perspectives by V. N. Larina, I. K. Skiba, A. S. Skiba, V. G. Larin, E. A. Vartanyan

“…The article discusses modern approaches and features of management, as well as predicting the risk of complications in patients with cardiac arrhythmias, in particular, atrial fibrillation, and the potential of using conservative and interventional treatment methods for heart failure (HF), taking into account updates and achievements in clinical practice. …”
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Data-Driven Approach for Intelligent Classification of Tunnel Surrounding Rock Using Integrated Fractal and Machine Learning Methods by Junjie Ma, Tianbin Li, Roohollah Shirani Faradonbeh, Mostafa Sharifzadeh, Jianfeng Wang, Yuyang Huang, Chunchi Ma, Feng Peng, Hang Zhang

“…The proposed GBDT-BO model shows promise for predicting surrounding rock quality and guiding dynamic tunnel excavation and support.…”
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Characterization of Hazelnut Trees in Open Field Through High-Resolution UAV-Based Imagery and Vegetation Indices by Maurizio Morisio, Emanuela Noris, Chiara Pagliarani, Stefano Pavone, Amedeo Moine, José Doumet, Luca Ardito

“…Three supervised binary classification algorithms were used to build models capable of predicting the status of the tree quadrant, using the VIs as predictors. …”
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An automated hip fracture detection, classification system on pelvic radiographs and comparison with 35 clinicians by Abdurrahim Yilmaz, Kadir Gem, Mucahit Kalebasi, Rahmetullah Varol, Zuhtu Oner Gencoglan, Yegor Samoylenko, Hakan Koray Tosyali, Guvenir Okcu, Huseyin Uvet

“…This study proposes an automated system that uses convolutional neural networks (CNNs) to detect potential fracture areas and predict fracture conditions, aiming to outperform traditional object detection-based systems. …”
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Subgrouping suicidal ideations: an ecological momentary assessment study in psychiatric inpatients by Stephanie Homan, Zachary Roman, Anja Ries, Prabhakaran Santhanam, Sofia Michel, Anna-Marie Bertram, Nina Klee, Carlo Berther, Sarina Blaser, Marion Gabi, Philipp Homan, Hanne Scheerer, Michael Colla, Stefan Vetter, Sebastian Olbrich, Erich Seifritz, Isaac Galatzer-Levy, Tobias Kowatsch, Urte Scholz, Birgit Kleim

“…Abstract Background Suicidal ideation (SI) is one of the strongest predictors of suicide attempts, yet reliable prediction models for suicide risk remain scarce. A key challenge is that SI can fluctuate over time, potentially reflecting different subgroups that may offer important insights for suicide risk prediction. …”
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Machine learning model for differentiating malignant from benign thyroid nodules based on the thyroid function data by Quan Zhou, Lihua Zhang, Nan Xiang, Lele Zhang, Fuqiang Ma, Fengchang Yu, Shenhui Lv, Zhilin Lu, He-Rong Mao

“…FT4, TPOAB and FT3 were validated as the top three features in the Gradient Boosting model.Conclusions This study innovatively developed a predictive model for benign and malignant TNs based on the Gradient Boosting Decision Tree algorithm. …”
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LYN and CYBB are pivotal immune and inflammatory genes as diagnostic biomarkers in recurrent spontaneous abortion by Zhuna Wu, Qiuya Lin, Zhimei Zhou, Yajing Xie, Li Huang, Liying Sheng, Qirong Shi, Yumin Ke

“…The performance of the predictive model was evaluated using a Nomo plot. We further confirmed the expression levels and diagnostic value of key genes by performing immunohistochemistry (IHC) in clinical tissue samples. …”
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Clinical subtypes identification and feature recognition of sepsis leukocyte trajectories based on machine learning by ShengHui Miao, YiJing Liu, Min Li, Jing Yan

“…Incorporating early ICU baseline variables into an XGBoost algorithm enables effective prediction of high-mortality risk subgroups (AUC > 0.8). …”
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基于粗糙集与神经网络的齿轮箱故障诊断 by 刘慧玲, 潘宏侠, 王爱玉

“…A method of combining the rough sets and neural network based on the condition attributes discretization and reduct ion algorithm is proposed to fault diagnosis. …”
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Device Modeling Based on Cost-Sensitive Densely Connected Deep Neural Networks by Xiaoying Tang, Zhiqiang Li, Lang Zeng, Hongwei Zhou, Xiaoxu Cheng, Zhenjie Yao

“…By incorporating cost-sensitive learning methods, the proposed model focus more on the critical data that determines device characteristics, leading to accurate prediction of key device characteristics under various parameters. …”
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Transcriptome analysis provides new insights into the berry size in ‘Summer Black’ grape under a two-crop-a-year cultivation system by Peiyi Ni, Shengdi Yang, Yunzhang Yuan, Chunyang Zhang, Hengliang Zhu, Jing Ma, Shuangjiang Li, Guoshun Yang, Miao Bai

“…Moreover, based on the results of interactive analysis of TO-GCN and transcriptional regulation prediction of L1–L3 genes, we constructed a unique hierarchical regulatory network for the heat stress regulation of berry size. …”
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Identification of neutrophil extracellular trap-related biomarkers in ulcerative colitis based on bioinformatics and machine learning by Jiao Li, Yupei Liu, Zhiyi Sun, Suqi Zeng, Caisong Zheng

“…Gevokizumab, canakinumab and carboxylated glucosamine were predicted as potential therapeutic drugs for UC.ConclusionBy combining three machine learning algorithms and bioinformatics, this research identified three hub genes that could serve as novel targets for the diagnosis and therapy of UC, which may provide valuable insights into the mechanism of NETs in UC and potential related therapies.…”
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