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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Luffa–Ni/Al layered double hydroxide bio-nanocomposite for efficient ibuprofen removal from aqueous solution: Kinetic, equilibrium, thermodynamic studies and GEP modeling by Soheil Tavassoli, Afsaneh Mollahosseini, Saeed Damiri, Mehrshad Samadi

“…In addition, a powerful data-driven model, namely gene expression programming (GEP), was employed to provide an explicit formula relating input variables to removal efficiency, highlighting the potential of LF@ppy@LDH for water purification and environmental remediation.…”
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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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Improving the qualifications of doctors on the base of intelligent decision support systems by О. Стенін, В. Пасько, О. Лісовиченко, В. Солдатов

“…This article proposes the structure and composition of the IDSS of medicinal decisions, for which it is proposed to use a combined approach based on the frame structure of the knowledge model using production rules. …”
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Geochemical evaluation of black limestones in the Early Cretaceous platform carbonates of Gümüşhane (NE Turkey): An insight into petroleum source rock potential and palaeoenvironme... by Çiğdem Saydam Eker, Uğur Volkan Arı, Selçuk Alemdağ

“…These results indicate that the examined limestones have variable source rock potential, ranging from low to high. …”
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Use of Population-Based Compartmental Modeling and Retinol Isotope Dilution to Study Vitamin A Kinetics and Total Body Stores among Ghanaian Women of Reproductive Age by Michael H Green, Veronica Lopez-Teros, Joanne Balmer Green, Georg Lietz, Sika M Kumordzie, Anthony Oxley, Ahmed D Fuseini, K Winifred Nyaaba, Emily Becher, Jennie N Davis, K Ryan Wessells, Seth Adu-Afarwuah, Reina Engle-Stone, Marjorie J Haskell

“…Methods: Women (n = 89) ingested a dose of [2H6]retinyl acetate and blood samples (3/woman) were collected from 6 h to 91 d, with all participants sampled at 14 d, about half at either 21 or 28 d, and each at one other time. Composite data (plasma retinol fraction of dose; FDp) were analyzed using Simulation, Analysis and Modeling software to obtain kinetic parameters, TBS, and other state variables as well as model-derived values for the RID composite coefficient FaS. …”
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From pixels to planning: scale-free active inference by Karl Friston, Karl Friston, Conor Heins, Tim Verbelen, Lancelot Da Costa, Lancelot Da Costa, Tommaso Salvatori, Dimitrije Markovic, Dimitrije Markovic, Alexander Tschantz, Magnus Koudahl, Christopher Buckley, Christopher Buckley, Thomas Parr

“…This model generalizes partially observed Markov decision processes to include paths as latent variables, rendering it suitable for active inference and learning in a dynamic setting. …”
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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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Laboratory Modeling of the Bazhenov Formation Organic Matter Transformation in a Semi-Open System: A Comparison of Oil Generation Kinetics in Two Samples with Type II Kerogen by Anton G. Kalmykov, Valentina V. Levkina, Margarita S. Tikhonova, Grigorii G. Savostin, Mariia L. Makhnutina, Olesya N. Vidishcheva, Dmitrii S. Volkov, Andrey V. Pirogov, Mikhail A. Proskurnin, Georgii A. Kalmykov

“…Differences between the samples were detected in hydrocarbon generation endurance (5 and 8 days), n-alkane composition, and C27/C29 and DBT/Phen. A hypothesis about the influence of kerogen variability and mineral matrix on oil production was made. …”
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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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INDICATORS AND REGIONAL PLANNING: PROMULGATE A COHERENCE by Iuli Nascimento

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Design and Optimization of Friction Winch for 25 MW Airborne Wind Energy Systems by Lizhao NIU, Kuo YIN, Chonghui LEI

“…Using the central composite experimental design method, a response surface model was established for the drum's stress, mass and main design structural parameters. …”
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Analisis Kemampuan Literasi Informasi antara Anggota Ikatan Pustakawan Pelajar dan Non Anggota Pada Siswa SMAN 5 Malang Berdasarkan Model Empowering Eight by Marshanda Anta Azzarah, Adi Prasetyawan

“…The main deficiency for both groups of students, whether DLC members or non-members, lies in determining the topic for paper composition. The weakness lies in the application of the empowering eight information literacy model to determine the topic. …”
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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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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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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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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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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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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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