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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Response surface optimization of sedimentation efficiency for sustainable green microalgae harvesting using automated non-invasive methods by Amr M. Ayyad, Eladl G. Eltanahy, Mervat H. Hussien, Dina A. Refaay

“…Sealed vessels and smaller culture volumes further enhanced sedimentation efficiency. RSM predictive models achieved high accuracy (adjusted R2 > 99%). …”
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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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The effect of internal migration on regional growth in Italy: a dynamic spatial panel data analysis by Francesca Centofanti, Roberto Basile, Francesca Licari, Jacopo Pitari

“…The analysis considers this heterogeneity, estimating various specifications of the dynamic spatial model and controlling for the endogeneity of migration variables through a control function approach. …”
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Optimization of chlorpheniramine maleate (CPM) delivery by response surface methodology – four component modeling using various response times and concentrations of chitosan-alanin...

“…The release behavior of drug was affected by preparation variables. A central composite design was used to evaluate and optimize the effect of preparation variables; chitosan concentration (X1), percentage of crosslinker (X2), concentration of drug (X3) and release time (X4) on the cumulative amount of drug release, Y in solutions of pH = 2.0 and pH = 7.4, respectively. …”
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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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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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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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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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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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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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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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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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