Multivariate discriminant analysis of the electrocardiogram by Polina A. Sakharova, Vyacheslav A. Balandin

“…The article presents a study of heart rate variability using multivariate discriminant analysis. …”
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Spatio-temporal heterogeneity of the snow cover from data of the penetrometer SnowMicroPen by A. Y. Komarov, Y. G. Seliverstov, P. B. Grebennikov, S. A. Sokratov

“…Te paper presents the results of studies aimed at investigation of the spatial and temporal variability of snow coverstructure on the basis of strength values and its variations obtained by means of the high-resolution penetrometer SnowMicroPen. …”
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Analysis of vegetation coverage changes and driving forces in the source region of the yellow river by Kaining Yu, Caijia Yang, Tao Wu, Yifeng Zhai, Shixiong Tian, Yuqing Feng

“…Notably, the interaction between precipitation and temperature with other variables exhibited the strongest explanatory power, with q-values exceeding 0.5. …”
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Effect of atorvastatin on the most important mechanisms of arrhythmogenesis in patients with ST-elevated myocardial infarction by V. E. Oleynikov, M. V. Lukianova, E. V. Dushina, Yu. A. Barmenkova

“…Aim. To study the effect of the 48-week atorvastatin therapy on the mechanisms of arrhythmogenesis, determined during daily ECG monitoring, in patients with ST-elevated myocardial infarction (STEMI).Material and methods. …”
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Individual typological approach to the analysis of the body function of medical students by N. P. Setko, O. M. Zhdanova, A. G. Setko

“…Aim. Rationale of the individual typological approach in the analysis of the body function of medical students.Material and methods. …”
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Cardiac remodeling in patients with heart failure with mildly reduced ejection fraction and metabolic disorders: association with biomarkers and autonomic nervous system parameters by E. A. Lyasnikova, A. I. Gareeva, V. K. Muslimova, E. S. Zhabina, S. N. Kozlova, M. Yu. Sitnikova, E. V. Shlyakhto

“…Aim. The high prevalence of obesity in a cohort of patients with heart failure and mildly reduced ejection fraction (HFmrEF) determines the relevance of clarifying the role of biomarkers and autonomic imbalance in myocardial remodeling, taking into account metabolic risk factors.Material and methods. …”
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Comparing tacrolimus level monitoring in peripheral blood mononuclear cells and whole blood within one year after kidney transplantation: a single-center, prospective, observationa... by Jia You, Jia You, Jia You, Rui Chen, Rui Chen, Yuhui Chai, Xue Wang, Wenmin Xie, Yunyun Yang, Kaile Zheng, Kaile Zheng, Kaile Zheng, Lizhi Chen, Zhuo Wang, Xuebin Wang, Xuebin Wang, Xuebin Wang

“…This study aimed to compare tacrolimus intra-patient variablity (IPV), allograft function, and de novo donor-specific anti-HLA antibody (dnDSA) status between PBMC-based and whole-blood tacrolimus monitoring methods to assess whether PBMC monitoring provides greater clinical utility.MethodsThis single-center, prospective, observational, non-interventional study enrolled kidney transplant recipients between November 2021 and February 2023. …”
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Mathematical model of the formation of the basic statistical sample for evaluating the level of the digital competence of lecturers by Svetlana V. Avilkina, Marina A. Bakuleva, Nadezhda P. Kleynosova

“…The received values of levels of mastering different digital competences are aggregated on each indicator of a linguistic variable in summary values, which can be used as basic statistical sampling.Results. …”
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Predicting habitat suitability of Illicium griffithii under climate change scenarios using an ensemble modeling approach by Anubhav Bhuyan, Amal Bawri, Bhrigu Prasad Saikia, Shilpa Baidya, Suhasini Hazarika, Bijay Thakur, Vivek Chetry, Bidya Sagar Deka, Pangkhi Bharali, Amit Prakash, Kuladip Sarma, Ashalata Devi

“…The aim of the study is to identify key environmental variables influencing the current distribution of I. griffithii and to predict the potential distribution under current and future climatic scenarios (SSP245 and SSP585). …”
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Characterization of Monotone Sequences of Positive Numbers Prescribed by Means by János T. Tóth, Ferdinánd Filip, Szilárd Svitek, Zuzana Václavíková

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Radiomics-Based Classification of Clear Cell Renal Cell Carcinoma ISUP Grade: A Machine Learning Approach with SHAP-Enhanced Explainability by María Aymerich, Alejandra García-Baizán, Paolo Niccolò Franco, Mariña González, Pilar San Miguel Fraile, José Antonio Ortiz-Rey, Milagros Otero-García

“…While histopathological evaluation remains the gold standard for grading, non-invasive methods, such as radiomics, offer potential for automated classification. This study aims to develop a radiomics-based machine learning model for the ISUP grade classification of ccRCC using nephrographic-phase CT images, with an emphasis on model interpretability through SHAP (SHapley Additive exPlanations) values. …”
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Diabetes mellitus type 2: the relationship of baseline clinical, laboratory and echocardiographic parameters with long-term major adverse cardiovascular events by I. A. Bondar, A. A. Demin, D. V. Grazhdankina

“…The plasma level of the N-terminal propeptide of natriuretic hormone B-type (NT-proBNP) was determined. The variability of fasting blood glucose and intraday glycemic variability were measured by calculating the standard deviation (SD) and the coefficient of variation (CV) of at least 3 blood glucose values for 3 days. …”
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Coronary calcium associated with changes in instrumental and humoral markers of sympathetic activity in patients with non-obstructive coronary atherosclerosis by E. V. Grakova, K. V. Kopeva, A. N. Maltseva, A. S. Dasheeva, K. V. Zavadovsky, A. M. Gusakova, A. V. Svarovskaya, I. N. Vorozhtsova, E. L. Antsifirova, Yu. L. Shadrina

“…Aim. To study the associations between sequential factors of the 10-year coronary heart disease (CHD) risk index MESA, heart rate variability (HRV), molecular markers of sympathetic activity and the presence or absence of calcium in the coronary arteries (CA) in patients with non-occlusive coronary atherosclerosis.Materials and methods. …”
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Estimates of the statistical correlation between the extreme ice pressure patterns with various spatial resolution by S. V. Klyachkin

“…Ice pressure is characterized with significant spatial variability. Study of this variability with the help of in situ observations is rather difficult, because (1) the instrumental measurements are expensive and technically complicated, and, hence, the amount of such measurements is little, and (2) the visual observations have pre-dominantly qualitative character and depend significantly on the observer’s experience. …”
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Phenotype diversity and relationship among kopyor coconut in Jember Regency, Indonesia by Sukendah, Mukhamad Ainul Yaqin, Agus Sulistyono

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CLINICAL RELEVANCE OF AMBULATORY BLOOD PRESSURE MONITORING IN WOMEN WITH METABOLIC SYNDROME IN II TRIMESTER OF PREGNANCY by O. A. Lee

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RISK FACTORS OF ARRHYTHMIAS IN PATIENTS WITH ACUTE DECOMPENSATION OF CHRONIC HEART FAILURE by N. V. Larionova, A. M. Shutov, M. V. Menzorov, E. V. Efremova, V. V. Kasalinskaya

“…The values of heart rate variability, exceeding the “risk-sharing point of death” did not improve on 10 day treatment. …”
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Integrating traditional and non-traditional model risk frameworks in credit scoring by Hendrik A. du Toit, Willem D. Schutte, Helgard Raubenheimer

“…Practical validation tests are proposed to enable transparency of model input variables in the validation process of ML models. …”
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Advanced Machine Learning Techniques for Predicting Concrete Compressive Strength by Mohammad Saleh Nikoopayan Tak, Yanxiao Feng, Mohamed Mahgoub

“…This paper develops a machine learning model for compressive strength prediction using mix design variables and curing age from a “Concrete Compressive Strength Dataset” obtained from the UCI Machine Learning Repository. …”
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Predicting Aboveground Carbon Storage in Different Types of Forests in South Subtropical Regions Using Machine Learning Models by Jiarun Liu, Zihang Yang, Lin Li, Xiaoxue Chu, Shiguang Wei, Juyu Lian

“…The model with the best generalization ability was selected to calculate SHAP values for each predictor. The XGB model demonstrated superior performance across all forest types, with R2 values ranging from 0.898 to 0.974. …”
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