Unveiling psychobiological correlates in primary Sjögren’s syndrome: a machine learning approach to determinants of disease burden by László V. Módis, László V. Módis, András Matuz, András Matuz, Zsófia Aradi, Ildikó Fanny Horváth, Antónia Szántó, Antal Bugán

“…This study aimed to evaluate the predictive weight of different factors in determining both objective and subjective disease burden using machine learning (ML) models.Methods117 pSS patients, whose biological (blood cell counts, complement activity, IgG, RF, SSA, SSB), psychological (personality traits, depression, anxiety, basic self-esteem assessed via self-reported questionnaires), and social (socioeconomic status and social support) measures were collected in a composite database. …”
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A Novel Electrical Load Forecasting Model for Extreme Weather Events Based on Improved Gated Spiking Neural P Systems and Frequency Enhanced Channel Attention Mechanism by Yuanshuo Guo, Jun Wang, Yan Zhong, Tao Wang, Zeyuan Sui

“…First, optimized variational mode decomposition (VMD) is used to decompose the load series and the sub-sequences are combined with relevant features, to form the different input sequences of the prediction model. …”
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Developing a Topographic Model to Predict the Northern Hardwood Forest Type within Carolina Northern Flying Squirrel (Glaucomys sabrinus coloratus) Recovery Areas of the Southern A... by Andrew Evans, Richard Odom, Lynn Resler, W. Mark Ford, Steve Prisley

“…We recorded overstory species composition and terrain variables at 338 points, to construct a robust, spatially predictive model. …”
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Synthesis, characterization, pyrolysis and thermokinetic studies of an energetic ionic liquid: 4-amino-1-methyl-1,2,4-triazolium nitrate by Santhosh G, Supriya N, Vijayalakshmi K. P, Deepthi Thomas

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Determinants Factors in Predicting Life Expectancy Using Machine Learning by B. Kouame Amos, I. V. Smirnov

“…At the end of our study, we concluded that the variables that best explain life expectancy are adult mortality, infant mortality, percentage of expenditure, measles, under-five mortality, polio, total expenditure, diphtheria, HIV / AIDS, GDP, longevity of 1.19 years, resource composition, and schooling. …”
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Risk Assessment Approach of Toll Road Operator by A. Yu. Talavirya, M. B. Laskin

“…All such factors are modeled in the AnyLogic environment as random variables with a rich choice of distribution functions and their parameters.Results. …”
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An Empirical Analysis of the Interdependencies Between Investment, Economic Growth, and Fiscal Performance: Evidence from Romania by Moldovan Nicoleta Claudia, Spulbar Cristi, Ene Simona Maria, Racataian Raluca Ioana

“…The analysis of how these variables influence each other, both in the long and short term, was conducted using the econometric Vector Error Correction Model (VECM). …”
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INDICATORS AND REGIONAL PLANNING: PROMULGATE A COHERENCE by Iuli Nascimento

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The Impact of Students' Motivational Drive and Attitude toward Online Learning on Their Academic Engagement during the Emergency Situation by Audi Yundayani, Yatha Yuni, Fiki Alghadari

“…In addition, the Confirmatory Factor Analysis (CFA) method was employed to assess the reflective measurement models. This included the internal consistency (Cronbach's alpha, composite reliability), the convergent validity encompassed indicator reliability and average variance extracted (AVE), and the discriminant validity conducted using the cross-loadings approach and the Fornell-Larcker criterion. …”
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A MATHEMATICAL APPROACH TO INVESTMENT WITH CHARGE ON BALANCE AND VOLUNTARY CONTRIBUTIONS UNDER WEIBULL MORTALITY FORCE FUNCTION by Edikan Edem Akpanibah, Peter Benneth, Ase Matthias Esabai

“…To achieve this, there is need to develop an optimal portfolio which considers the volatility of the stock market price consisting of one risk-free asset and a risky asset which follows the Heston volatility model (HVM). Also, the portfolio considers additional voluntary contributions (AVC) by members, tax on the stock market price, charge on balance (CB), and the mortality risk of the pension scheme members (PSM) modeled by the Weibull mortality force function.  …”
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Critical Factors Governing the Frictional Coefficient in Mg Alloys—Learn From Machine Learning by Negar Bagherieh, Moslem Noori, Dongyang Li, Meisam Nouri

“…The collected data is then used to train models for the following two scenarios: (i) COF prediction using composition, processing parameters, and tribological variables; (ii) COF prediction using mechanical properties (hardness, yield strength, ultimate tensile strength, ductility, and elastic modulus), and tribological variables. …”
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Contextualizing GCM Biases in Low‐Cloud Coverage: The Role of Large‐Scale Conditions by Hamish Lewis, Gilles Bellon

“…Here, we quantify the contribution of GCM LCC biases from both the macrophysical low‐cloud parameterizations, and the GCM simulation of large‐scale meteorological conditions, which control a large portion of observed LCC variability. We use a machine‐learning model trained to predict observed relationship between LCC and large‐scale conditions to perform the bias decomposition within nine CMIP6 GCMs in both coupled and atmosphere‐only configurations. …”
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Prediction of the digestibility and digestible energy content of hay for horses using an enzymatic degradability method by D. Andueza, W. Martin-Rosset

“…The incorporation of chemical composition variables as independent variables into the prediction models did not result in a meaningful improvement in the model results obtained from dCS and dCO. …”
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Experimental study on DEM parameters calibration for organic fertilizer by the particle swarm optimization − backpropagation neural networks by Fandi Zeng, Limin Liu, Yinzeng Liu, Hongbin Bai, Chunxiao Li, Zhihuan Zhao

“…The previously identified important variables were optimized by the Central Composite Design test. …”
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Low-cycle fatigue life prediction method for stud connectors based on interpretable machine learning by Jianan Pan, Xiaoling Liu, Bing Wang, Ying Liu

“…However, traditional theoretical formulas and experimental methods suffer from limitations such as low accuracy and individual variability. This study aims to develop a high-precision prediction model for low-cycle fatigue life using machine learning methods, providing a new approach for material performance evaluation. …”
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Biomod2 for evaluating the changes in the spatiotemporal distribution of Locusta migratoria tibetensis Chen in the Qinghai-Tibet Plateau under climate change by Rulin Wang, Nier Wu, Zhaopeng Shi, Chao Li, Na Jiang, Chun Fu, Mingtian Wang

“…[Method] Utilizing 68 geographical distribution points of L. migratoria tibetensis, in conjunction with 6 environmental variables, a composite model was developed employing the Biomod2 software package to simulate potential shifts in the spatial distribution of L. migratoria tibetensis under future climate scenarios. …”
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Assessing Digital Performance of Public Services in the EU: E-Governance and Technology Integration by Oana Ramona Lobonț, Cristina Criste, Alexandra Ioana Vintilă, Andreea Florentina Crăciun, Nicoleta Claudia Moldovan

“…To evaluate the digital performance of the European Union regions, the analysis employs the following methods: (i) Principal Component Analysis to construct a composite Institutional Quality Index based on governance indicators, (ii) Gaussian graphical models to assess the relationships between digital intensity, institutional quality, and e-government highlighting the interconnections between variables, (iii) Data Envelopment Analysis to measure the relative efficiency of public service delivery, and (iv) cluster dendrograms to identify the clusters of countries with similar performance levels and challenges. …”
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Prediction of Temperature Distribution on an Aircraft Hot-Air Anti-Icing Surface by ROM and Neural Networks by Ziying Chu, Ji Geng, Qian Yang, Xian Yi, Wei Dong

“…Two models, AlexNet combined with Proper Orthogonal Decomposition (POD-AlexNet) and multi-CNNs with GRU (MCG), are proposed by comparing several classic neural networks. …”
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Microbial biomass – not diversity – drives soil carbon and nitrogen mineralization in Spanish holm oak ecosystems by Elisa Bruni, Jorge Curiel Yuste, Lorenzo Menichetti, Omar Flores, Daniela Guasconi, Bertrand Guenet, Ana-Maria Hereș, Aleksi Lehtonen, Raisa Mäkipää, Marleen Pallandt, Leticia Pérez-Izquierdo, Etienne Richy, Mathieu Santonja, Boris Tupek, Stefano Manzoni

“…For this reason, most models predicting soil organic matter (SOM) dynamics at the ecosystem level do not explicitly describe the role of microorganisms as mediators of SOM decomposition. …”
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Spatial distribution and habitat preference of sarcosaprophagus Calliphoridae (Diptera) in the Brazilian Northeast, with notes on the utilization of different animal baits by ANA BEATRIZ L. DE ASSIS, TACIANO M. BARBOSA, RICARDO JOSÉ P. SOUZA E GUIMARÃES, RENATA A. GAMA

“…On the other hand, native taxa have a more restricted distribution, except for the species Cochliomyia macellaria (Fabricius, 1775). The variables that most influenced the models were precipitation and wind. …”
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