Modified Kumaraswamy seasonal autoregressive moving average models with exogenous regressors for double-bounded hydro-environmental data. by Aline Armanini Stefanan, Murilo Sagrillo, Bruna G Palm, Fábio M Bayer

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FIVE YEAR PROGNOSIS IN PATIENTS WITH CORONARY HEART DISEASE AND COMORBIDITIES: DATA FROM MULTICENTER STUDY IN INGUSHETIA REPUBLIC by P. O. Ugurchieva, R. T. Didigova, Z. O. Ugurchieva, M. N. Mamedov

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Petri graph neural networks advance learning higher order multimodal complex interactions in graph structured data by Alma Ademovic Tahirovic, David Angeli, Adnan Tahirovic, Goran Strbac

“…Abstract Graphs are widely used to model interconnected systems, offering powerful tools for data representation and problem-solving. However, their reliance on pairwise, single-type, and static connections limits their expressive capacity. …”
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Advancing public health through Spatial Data Infrastructures: a review of global practices, governance and policy recommendations by Victor Mayowa Adeleye, Patrick Ashinze, Teslim Okeoyo, Veronica El Nabbout, Emmanuel Chileshe Phiri, Gargi Gautam, Collins Chukwunonso Ezea, Alhazan Amir Adebayo, Halima Ali, Ekemode Oluwadamilola Monsurat

“…After screening, 75 articles were excluded for being non-SDI specific, editorial, or abstract-only. Data extraction focused on SDI governance and public health outcomes, and thematic analysis was used to assess the impact on disease surveillance, healthcare access, and data sharing. …”
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Efficient Explainable Models for Alzheimer’s Disease Classification with Feature Selection and Data Balancing Approach Using Ensemble Learning by Yogita Dubey, Aditya Bhongade, Prachi Palsodkar, Punit Fulzele

“…Early diagnosis of Alzheimer’s disease is critical for better management and treatment outcomes, but it remains a challenging task due to the complex nature of the disease. Clinical data, including a range of cognitive, functional, and demographic variables, play a crucial role in Alzheimer’s disease classification. …”
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Poroelasticity and Fluid Flow Modeling for the 2012 Emilia-Romagna Earthquakes: Hints from GPS and InSAR Data by Massimo Nespoli, Maria Elina Belardinelli, Adriano Gualandi, Enrico Serpelloni, Maurizio Bonafede

“…Modeling results are then compared with postseismic InSAR and GPS displacement time series: the InSAR data consist of two SBAS series presented in previous works, while the GPS signal was detected adopting a variational Bayesian independent component analysis (vbICA) method. …”
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Arctic Sea Ice Surface Temperature Retrieval from FengYun-3A MERSI-I Data by Yachao Li, Tingting Liu, Zemin Wang, Mohammed Shokr, Menglin Yuan, Qiangqiang Yuan, Shiyu Wu

“…In this study, we developed an applicable single-channel algorithm to retrieve ISTs from MERSI-I data. The algorithm accounts for the following challenges: (1) the wide range of incidence angle; (2) the unstable snow-covered ice surface; (3) the variation in atmospheric water vapor content; and (4) the unique spectral response function of MERSI-I. …”
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Data from multi-national aging cohorts show dysregulated metabolic pathways in people with physio-cognitive decline by Yi-Long Huang, Wei-Ju Chang, Chao-Hsiung Lin, Shu Zhang, Yukiko Nishita, Rei Otsuka, Wei-Ju Lee, Chih-Kuang Liang, Ming-Yueh Chou, Li-Ning Peng, Hidenori Arai, Luigi Ferrucci, Liang-Kung Chen

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Profiling disease experience in patients living with brain aneurysms by analyzing multimodal clinical data and quality of life measures by Sebastian R. Reder, Jochen Hardt, Marc A. Brockmann, Carolin Brockmann, Suam Kim, Melina Kawulycz, Marie Schulz, Sven Rainer Kantelhardt, Katja Petrowski, Sabine Fischbeck

“…Two sub-profiles highlighted trauma-induced impairments, functional disabilities from LV, and persistent anxiety. …”
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XGBoost-based machine learning model combining clinical and ultrasound data for personalized prediction of thyroid nodule malignancy by Wenhan Li, Wenhan Li, Yajing Zhou, Ziyu Luo, Ziyu Luo, Miao Tan, Miao Tan, Rui Yin, Jianhui Li, Jianhui Li

“…Nowadays, machine learning (ML) offers advanced capabilities to handle high-dimensional data and complex patterns. This study aimed to develop an ML model integrating clinical data and ultrasound features to improve personalized prediction of TN malignancy.MethodsData from 2,014 patients with TNs (2018.01–2024.01) were retrospectively analyzed, with 1,612 in the training set and 402 in the test set. …”
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Global biome changes over the last 21 000 years inferred from model–data comparisons by C. Li, C. Li, A. Dallmeyer, J. Ni, M. Chevalier, M. Willeit, A. A. Andreev, X. Cao, L. Schild, L. Schild, B. Heim, M. Wieczorek, U. Herzschuh, U. Herzschuh, U. Herzschuh

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Analysis of Digital Life Effect of Residents’ Trust Based on Multivariate Discrete Choice Model by Fan Zeng, Xiao Cao, Huan-Ming Zhang

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Gaussian process modeling and multi-step prediction for time series data in wireless sensor network environmental monitoring by Yan CHEN, Zi-jian WANG, Ze ZHAO, Dong LI, Li CUI

“…For time series data collected from WSN environmental monitoring applications,a novel multi-step prediction method based on Gaussian process model was proposed.The method could make prediction for future environmental monitoring data.Kernel functions were used to describe data properties in the Gaussian process model.Kernel functions for environmental monitoring data were constructed through the EMD(empirical mode decomposition)technique and analysis of data inherent physical properties.And the constructed kernel functions were capable of describing the data change mode.Extensive experiments for multi-step prediction performance comparison test were performed on three kinds of data sets using over 20 000 environmental monitoring data records.Experimental results show that the average prediction accuracy of the Gaussian process multi-step prediction method can be increased by 20% than compared prediction methods.The prediction method can be applied to future environmental parameters trend analysis,early warning for abnormal environmental events and other scenes.…”
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Gaussian process modeling and multi-step prediction for time series data in wireless sensor network environmental monitoring by Yan CHEN, Zi-jian WANG, Ze ZHAO, Dong LI, Li CUI

“…For time series data collected from WSN environmental monitoring applications,a novel multi-step prediction method based on Gaussian process model was proposed.The method could make prediction for future environmental monitoring data.Kernel functions were used to describe data properties in the Gaussian process model.Kernel functions for environmental monitoring data were constructed through the EMD(empirical mode decomposition)technique and analysis of data inherent physical properties.And the constructed kernel functions were capable of describing the data change mode.Extensive experiments for multi-step prediction performance comparison test were performed on three kinds of data sets using over 20 000 environmental monitoring data records.Experimental results show that the average prediction accuracy of the Gaussian process multi-step prediction method can be increased by 20% than compared prediction methods.The prediction method can be applied to future environmental parameters trend analysis,early warning for abnormal environmental events and other scenes.…”
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The development of models of an analytical data processing system for monitoring information security of an informatization object using cloud infrastructure by V. A. Sizov, A. D. Kirov

“…The following limitations are suggested: limitation on the time of making a decision on an incident; limitation on the degree of quality of analysis of information security events by the analytical data processing system and limitation on the compatibility of data analysis functions with data types about information security events. …”
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Validity and Reliability of 2D Video Analysis for Swimming Kick Start Kinematics by Ivan Matúš, Bibana Vadašová, Tomáš Eliáš, Łukasz Rydzik, Tadeusz Ambroży, Wojciech Czarny

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The first high-quality chromosome-level genome of Parupeneus biaculeatus using HiFi and Hi-C data by Zhisen Luo, Murong Yi, Xiaodong Yang, Zihao Luo, Xiafang Li, Changping Jiang, Bin Kang, Liangliang Huang, Hung-Du Lin, Xiongbo He, Yunrong Yan

“…A total of 22,490 protein-coding genes were predicted, of which 98.37% were functionally annotated. Repeat analysis revealed that 34.83% of the genome consists of repetitive sequences. …”
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Prospects for the Integration of Google Trends Data and Official statistics to Assess social Comfort and Predict the Financial situation of the Population by M. V. Shakleinaa, M. I. Volkova, K. I. Shaklein, S. R. Yakiro

“…Google Trends data analysis methods are based on the development of an integrated approach to the semantic search for information about the components of social comfort, which reduces the share of author’s subjectivity; methodology of primary processing, considering the principles of comparability, homogeneity, consistency, relevance, description of functions and models necessary for the selection and adjustment of search queries. …”
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Barriers and facilitators to data quality of electronic health records used for clinical research in China: a qualitative study by Hongling Chu, Nan Li, Yiming Zhao, Kaiwen Ni, Lin Zeng

“…The interviews were audiorecorded and transcribed verbatim. Data analysis was performed using the inductive thematic analysis approach.Results The main themes included factors related to healthcare systems, clinical documentation, EHR systems and researchers. …”
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Discourse 8-D Thinking as the Object of Research and Training by Irina Oukhvanova

“… The article rooted in the tradition of Discourse Linguistics deals with the  8-dimension model of discourse organization and production discussed with reference to three key sources: (1) the Tartu Semiotic School with its focus on the notion of semiosphere (Yuri Lotman) as a ground for revealing the semiosis of communicative signs in functioning; (2) the Causal-Genetic Approach to discourse modelling (Irina Oukhvanova / Oukhvanova-Shmygova) as a  ground for reconstructing and classifying the  causes of the  inherent discourse elements production open to become constructive elements of discourse on micro, meso, and macrolevels of its functioning, and (3) the approach to discourse organization built in the field of Discourse Linguistics  / la linguistique du discours (Dominique Maingueneau) as a ground for linguistic approaches to discourse analysis. …”
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