Fitting Parameters Research Articles

Modeling the Interface Between Phases in Dense Polymer‐Carbon Black Nanoparticle Composites by Dielectric Spectroscopy: Where Are We Now and What are the Opportunities?

AbstractThe macroscopic properties of polymer nanocomposites (PNC) rely largely on the interphase between the polymer chains and the filler particles. One significant difficulty to solve this issue is to quantitatively model the structure‐property correlations due to the interfacial region in these complex materials. While dielectric spectroscopy (DS) measurements are routinely used to characterize the effective permittivity of filled polymers, fitting standard effective medium models and mixing equations to these data remains notoriously difficult to interpret. This is due to the absence of explicit reference to internal length scales characterizing the interfaces in the PNC. As an illustrative example, a two‐level homogenization framework is proposed which enables the extraction of useful information on the impact of a thin interphase confined on a nanometer length scale based on broadband DS data. This model leads to new ways of tuning the interphase so as to optimize the material's response to electric field, a situation relevant for electromagnetic shielding. This approach provides guidance on how to observe directly and experimentally the actual properties of the interface between the phases (as opposed to model‐based inference). Aside from its secure physical foundation in the theory of effective medium, a significant advantage of this approach is that a genetic algorithm (GA) technique applied to this physics‐based model enables the uniqueness of the fit parameters to be considered, as the GA method is robust in terms of finding globally optimum solutions, therefore placing confidence in non‐universal values of the percolation exponents. Recent work in physics‐informed machine learning indicates that the effective dielectric properties of PNC with many degrees of freedom due to their complex morphology can be described by considering only a few degrees of freedom describing the interface features between the phases in these composites.

Age-Related Differences in Physical Fitness and Performance of an “Ability Test” among Firefighters

This study’s primary objective was to examine the differences in body composition, abdominal strength, absolute and relative power, handgrip strength, one repetition maximum for squat and bench press, and the maximum count of push-up and sit-up repetitions executed within a minute across different age cohorts of firefighters. Furthermore, this study aimed to evaluate the age-related differences in firefighters’ completion times of six firefighting tasks. Eighty-four male volunteer firefighters (age 33.79 ± 6.97 years) were grouped into three age categories, 20–30 years, 31–40 years, and 41–50 years, and underwent the aforementioned evaluations. One-way analysis of variance (MANOVA) revealed that age exerts a statistically significant influence (p < 0.001) on body fat percentage, waist circumference, and waist-to-hip ratio. Furthermore, age significantly affected the overall time of the ability test (p < 0.001) and the duration required to accomplish each individual task (p < 0.001). Additionally, age significantly affected abdominal strength, relative power (as measured by the step test), and the maximum count of push-up and sit-up repetitions performed within a minute. These outcomes support earlier research indicating an age-associated decrement in physical fitness parameters among firefighters. It is recommended that firefighters prioritize maintaining strength and endurance of the abdominal muscles, upper body muscular endurance, and a healthy body weight. The emphasis on specific muscular groups is essential for improving task performance within this profession.

Axial Compression Damage Model and Damage Evolution of Crumb Rubber Concrete Based on the Energy Method

The current constitutive model and damage evolution law of crumb rubber concrete (CRC) were obtained by fitting and changing parameters based on the normal concrete model. However, this model does not accurately reflect the characteristics of the material. In this paper, we studied the energy dissipation in the failure process of CRC to derive the constitutive model and damage evolution law of CRC based on the energy method. Four substitution rates of 5%, 10%, 15%, and 20% were selected, and the rubber concrete prism was prepared by replacing the natural fine aggregate with the same volume of crumb rubber aggregate. After that, uniaxial compressive tests were conducted. The energy lost due to the damage was calculated and analyzed, and the energy method was used to establish the damage evolution law and damage model of the crumb rubber concrete. The results demonstrated that the Guo Zhenhai damage model, which is based on the energy method, can more effectively explain the crumb rubber concrete stress–strain full curve, and the energy consumed as a result of the damage exhibits a growing and then reducing pattern with the increase in rubber doses. When the energy-based method is used, the Guo Zhenhai damage evolution model is similar to the damage evolution law calculated using the SIR damage evolution model. During uniaxial compression damage, rubber concrete with various rubber dosages demonstrated varying energy absorption in different deformation phases. When the rubber particle content was 10%, the energy absorption capacity of the specimen was 6.9% higher than that of normal concrete.

A generalized time-domain constitutive finite element approach for viscoelastic materials

Despite the existence of time domain finite element formulations for viscoelastic materials, there are still substantial ways to improve the analysis. To the authors’ knowledge, the formulation of the problem is always done with respect to a single constitutive relation and so limits the implementer to a single scheme with which to model relaxation. Furthermore, all current constitutive relations involve the finding of fitting parameters for an analytical function, which is a sufficiently painful process to warrant the study of best fitting procedures to this day. In contrast, this effort is the first full derivation of the two dimensional problem from fundamental principles. It is also the first generalization of the problem, which frees users to select constitutive relations without re-derivation or re-expression of the problem. This approach is also the first approach to the problem that could lead to the elimination of constitutive relations for representing relaxation in viscoelastic materials. Following, the full derivation, several common constitutive relations are outlined with analysis of how they may best be implemented in the generalized form. Several expressions for viscoelastic terms are also provided given linear, quadratic, and exponential interpolation assumptions.

A reaction–diffusion coupled kinetic model for hydrothermal acid pretreatment of wheat straw

Hydrothermal acid pretreatment is a promising method to overcome the recalcitrance of lignocellulosic biomass for subsequent efficient enzymatic hydrolysis and biofuel production. However, little is known about heterogeneous catalysis reactions involving the proton diffusion in the pore of lignocellulosic biomass coupled with the hydrolysis of hemicellulose and cellulose catalyzed by proton. In this study, a reaction–diffusion coupled kinetic model for hydrothermal acid pretreatment of wheat straw was developed based on the visualization of particle geometry and pore features. The increase rate of average proton concentration in the pores is higher at a higher temperature or a smaller particle size. A mathematical model was successfully developed to describe the time courses of average proton concentration dictated by initial acid concentration and an unsteady diffusion fitting parameter. The kinetic parameters of each reaction step for hydrothermal acid pretreatment of wheat straw were obtained by fitting experimental data. The activation energies for the hydrolysis of slow-hydrolyzing cellulose, fast-hydrolyzing cellulose, and hemicellulose were determined of 937.8 kJ·mol−1, 90.7 kJ·mol−1, and 39.8 kJ·mol−1, respectively, indicating the temperature dependence of slow-hydrolyzing cellulose with a high crystalline index is strongest. The prediction accuracy of this model can be further improved at higher stirring speeds and initial acid concentrations. Additionally, the model demonstrates high prediction accuracy across three different particle sizes. Generally, this model can gratifyingly correlate data of formation and degradation of monosaccharides and furfural in the hydrothermal acid pretreatment of wheat straw with fair accuracy.

Energy harvesting‐enabled energy‐efficient routing using spotted hyena optimization in wireless sensor network

SummaryWith the continuous proliferation of sensing technology, it has become possible to utilize energy harvesting (EH)‐enabled sensor nodes for a variety of applications. However, conventional wireless sensor networks (WSNs), that is, those without EH‐enabled nodes, still have limited applicability due to their limited battery resources. Further, the utilization of EH‐enabled nodes in the network not only imposes a financial burden on the user but also limits its performance due to its dependence on environmental conditions. To address this concern, in this paper, we propose the EH‐enabled energy‐efficient routing (EHEER) technique for green communication in WSN. The predominant concern being addressed in this paper is the selection of cluster head (CH), which helps in gathering, aggregating and forwarding the data from the cluster‐based routing paradigm. We use the spotted hyena optimizer (SHO) algorithm for optimizing the fitness parameters for CH selection, namely, energy ratio, distance considerations, node density, load balancing and the network's average energy. We use EH‐enabled nodes in the network strategically so as to keep control over the costs incurred in the network. The simulation outcomes empirically prove the efficacy of the proposed work, as it effectively increases the network stability and operational period by a huge margin as compared to the existing techniques.

RETRACTED: Safety climate, quality of care, adherence to and compliance with standard precautions among nurses: Structural equation modeling.

The aim was to investigate the interrelationships of nurses' safety climate, quality of care, and adherence to and compliance with standard precautions (SPs). Investigations about nurses' safety climate and quality care and their association with adherence to and compliance with SPs remain remarkably scant across literature, specifically among developing countries like the Philippines. Cross-sectional design and structural equation modeling (SEM) approach while complying with STROBE guidelines. Participant nurses were recruited using convenience sampling (n=870). Four validated self-report instruments were used to collect data from February to August 2022. Spearman rho, SEM, mediation, and path analyses were employed for data analysis. The emerging model showed acceptable model fit parameters. The safety climate positively influenced the quality of care and adherence to and compliance with SPs. Quality of care directly affected adherence to SPs, while adherence to SPs directly affected compliance with SPs. The quality of care mediated the relationship between safety climate and adherence to SPs. Whereas adherence to SPs mediated the relationships between safety climate and compliance with SPs and the quality of care and compliance with SPs. Nurses' safety climate directly affected the quality of care and SPs adherence and compliance. The quality of care mediated the impact of safety climate on SPs adherence. Finally, SPs adherence demonstrated a mediating effect among quality of care, safety climate, and SPs compliance. Nursing policymakers and administrators can use the findings to design strategic policies and sustainable in-service educational courses fostering and maintaining nurses' safety climate, quality of care, and SPs adherence and compliance.

Green synthesis of silver nanoparticles using olive mill wastewater and olive stones extract and testing their antimicrobial activities against Escherichia coli and Staphylococcus epidermidis

Abstract Plant mediated synthesis of silver nanoparticles is eco-friendly and of low cost. The synthesis involves a reduction of silver ions and is controlled by several independent factors. In this work, silver nanoparticles (AgNPs) were successfully synthesized using olive stone extract (OSE) and olive mill wastewater (OMW) extract. The nanoparticle synthesis was monitored using the plasmon resonance observed in the UV–Vis absorption spectrum, in which a Voigt profile was fitted. The peak wavelength (λ 0), the peak area (A), and the Full Width at Half Maximum (FWHM) were the fitting parameters and were used as the response. The independent factors were the incubation temperature, the incubation time, the silver nitrate, extract, and sodium hydroxide concentrations. The influence of these factors was investigated ‘two factor at a time’, using interaction plots. Strong interaction was observed between all factors, with sodium hydroxide to have a crucial role. The optimum conditions for silver nanoparticle formation were a) OSE (1% v/v), AgNO3 (2 mM), and NaOH (0.2 mM), and b) OMW (2% v/v), AgNO3 (1 mM), and NaOH (7.9 mM), showing an absorption maximum at 414 nm, and 410 nm, respectively. The mean diameter of AgNPs using OMW, measured with Transmission Electron Microscopy was 12.87 ± 4.84 nm. Both types of AgNPs showed antibacterial action against Staphylococcus epidermidis (S. epidermidis) and Escherichia coli (E. coli), using the broth microdilution assay. Both nanoparticle types inhibited bacterial growth up to one dilution higher than reference samples.

Dynamic Model Inference of Gene Regulatory Network based on Hybrid Parallel Genetic Algorithm and Threshold Qualification Method

Gene regulation is the process by which various substances in cells regulate the behaviour of gene expression, thereby controlling almost all cellular activities. Therefore, studying gene regulation not only helps to uncover the internal laws governing life processes but also plays a crucial role in predicting, diagnosing, treating, and designing drugs for genetic diseases. By utilizing multi-source biological information such as gene expression profiles, transcription factor information, and protein interaction data, a network model can be developed to depict the regulatory relationships between genes, facilitating further research. To address the limitations of traditional gene regulatory network construction methods, a novel dynamic model has been created by combining hybrid genetics and threshold restriction. This model comprises two parts: solution space reduction and parameter fitting. During solution space reduction, singular value decomposition is employed to define a mathematically feasible gene regulatory network, reducing unnecessary calculations. Subsequently, the control genes of each gene are constrained within a certain range using threshold limitation, enhancing computational efficiency while adhering to bioinformatics principles. In the parameter fitting phase, parallel genetic algorithms are utilized to expediently optimize the entire solution space. The mountain climbing method is then applied to solve problems meticulously within a limited scope, improving calculation accuracy. In this study, this approach was applied to establish genetic regulatory systems for complex skin melanoma and type 2 diabetes. Through comparison with actual networks, the validity of the method was confirmed. Compared to traditional genetic and particle swarm optimization methods, the effectiveness of the proposed method was verified. This paper models the intricate mechanism of gene regulation and elucidates the regulatory process involving genes, proteins, and small biological molecules in greater detail than other models, aligning more closely with intracellular dynamics laws.

On Generalized Additive Models for Representation of Solar EUV Irradiance

AbstractIn the context of space weather forecasting, solar EUV irradiance specification is needed on multiple time scales, with associated uncertainty quantification for determining the accuracy of downstream parameters. Empirical models of irradiance often rely on parametric fits between irradiance in several bands and various solar indices. We build upon these empirical models by using Generalized Additive Models (GAMs) to represent solar irradiance. We apply the GAM approach in two steps: (a) A GAM is fitted between FISM2 irradiance and solar indices F10.7, Revised Sunspot Number, and the Lyman‐α solar index. (b) A second GAM is fit to model the residuals of the first GAM with respect to FISM2 irradiance. We evaluate the performance of this approach during Solar Cycle 24 using GAMs driven by known solar indices as well as those forecasted 3 days ahead with an autoregressive modeling approach. We demonstrate negligible dependence of performance on solar cycle and season, and we assess the efficacy of the GAM approach across different wavelengths.

Optimization of magnetic coupling mechanism of dynamic wireless power transfer based on NSGA-II algorithm

Optimization of magnetic coupling mechanism is an important way to improve the performance of a dynamic wireless power transfer system. Inspired by the common radial magnetic core for circular coils, a new radial magnetic core for rectangular coils is adopt. Through simulation and experimental results comparison, which has higher coupling coefficient with the same core area. Combined with the magnetic circuit analysis, the magnetic flux leakage and conduction regions are divided into magnetic fluxes with different shapes, which magnetic resistances are calculated respectively. Based on the simulation results, parameter distributions of fluxes under different conditions are obtained. Therefore, the expressions of the coupling coefficient k of the adopt magnetic cores and coils and the design parameters of coils and cores are obtained. Taking the maximum k and the minimum rate of change of coupling coefficient with 100 mm displacement as the optimization objectives, a multi-objective optimization solution is carried out by using NSGA-II algorithm. The coil optimization scheme is obtained and verified by experiments. k and Δk are 0.442 and 6.8% respectively, and the errors are less than 5%. In the optimization process, there is no simulation model constructed. The optimization modeling combined of magnetic field segmentation method and parameter fitting has lower complexity and calculation time of optimization.

The generalized Kelvin chain-based model for an orthotropic viscoelastic material

We propose a constitutive material model to describe the rheological (viscoelastic) mechanical response of timber. The viscoelastic model is based on the generalized Kelvin chain applied to the orthotropic material and is compared to the simple approach given by standards. The contribution of this study consists of the algorithmization of the viscoelastic material model of the material applied to the orthotropic constitutive law and implementation into the FEM solver. In the next step, the fitting of the input parameters of the Kelvin chain is described, and at least a material model benchmark and comparison to the approach given by standards were done. The standardized approach is based on the reduction of the material rigidity at the end of the loading period using a creep coefficient, whereas the loading history state variables are not considered when establishing the result for a specific time step. The paper presents the benefits of the rheological model. It also demonstrates the fitting algorithm based on particle swarm optimization and the least squares method, which are essential for the use of the generalized Kelvin chain model. The material model based on the orthotropic generalized Kelvin chain was implemented into the FEM solver for the shell elements. This material model was validated on the presented benchmark tasks, and the influence of the time step size on the accuracy of model results was analyzed.

Exploring the Correlation between Physical Fitness and Kinematic Parameters in Butterfly Stroke among Physical Education Students

Exploring the Correlation between Physical Fitness and Kinematic Parameters in Butterfly Stroke among Physical Education Students

The effects of acute self-myofascial release and various stretching techniques on selected physical fitness parameters in recreational athletes

The effects of acute self-myofascial release and various stretching techniques on selected physical fitness parameters in recreational athletes

Results of an interlaboratory study on the working curve in vat photopolymerization

The working curve informs resin properties and print parameters for stereolithography, digital light processing, and other photopolymer additive manufacturing (PAM) technologies. First demonstrated in 1992, the working curve measurement of cure depth vs radiant exposure of light is now a foundational measurement in the field of PAM. Despite its widespread use in industry and academia, there is no formal method or procedure for performing the working curve measurement, raising questions about the utility of reported working curve parameters. Here, an interlaboratory study (ILS) is described in which 24 individual laboratories performed a working curve measurement on an aliquot from a single batch of PAM resin. The ILS reveals that there is enormous scatter in the working curve data and the key fit parameters derived from it. The measured depth of light penetration Dp varied by as much as 7x between participants, while the critical radiant exposure for gelation Ec varied by as much as 70x. This significant scatter is attributed to a lack of common procedure, variation in light engines, epistemic uncertainties from the Jacobs equation, and the use of measurement tools with insufficient precision. The ILS findings highlight an urgent need for procedural standardization and better hardware characterization in this rapidly growing field.

Holistic evaluation of involute gear surfaces using 3D point cloud inversion

Gear users desire increased performance from their products and are interested in predicting the behavior (vibration, loss, service life, etc), and gear manufacturers are interested in adjusting machining parameters to manufacture even better gears. Both require proficient metrology. The evaluation standards were originally written to classify the manufacturing capability of machine tools and as tolerances tighten they may not provide enough insight into the functional performance of the manufactured parts. Furthermore, the advantage of modern coordinate and gear measuring machines is not fully taken into account when applying standardized measurement and evaluation strategies. Improved analysis tools can increase the relevant information gained from current measurement capabilities and enable more useful functional characteristics to be specified by designers and manufacturers. A key improvement is to move from profile and helix line traces to flank surfaces and corresponding fitted form elements. This is achieved by the introduced method. We present an inversion algorithm that fits dimension, form, and pose, and is applicable with current methods through freezing of specific fitting parameters. The method fits a synthetic point cloud with output results that are accurate to the 7th digit, or in practical terms are numerically equal. The inversion method can increase the understanding of error contributions and the functional performance of gears.

A machine learning approach for determining temperature-dependent bandgap of metal oxides utilizing Allen–Heine–Cardona theory and O’Donnell model parameterization

To evaluate the high temperature sensing properties of metal oxide and perovskite materials suitable for use in combustion environments, it is necessary to understand the temperature dependence of their bandgaps. Although such temperature-driven changes can be calculated via the Allen–Heine–Cardona (AHC) theory, which assesses electron–phonon coupling for the bandgap correction at given temperatures, this approach is computationally demanding. Another approach to predict bandgap temperature-dependence is the O’Donnell model, which uses analytical expressions with multiple fitting parameters that require bandgap information at 0 K. This work employs data-driven Gaussian process regression (GPR) to predict the parameters employed in the O’Donnell model from a set of physical features. We use a sample of 54 metal oxides for which density functional theory has been performed to calculate the bandgap at 0 K, and the AHC calculations have been carried out to determine the shift in the bandgap at non-zero temperatures. As the AHC calculations are impractical for high-throughput screening of materials, the developed GPR model attempts to alleviate this issue by predicting the O'Donnell parameters purely from physical features. To mitigate the reliability issues arising from the very small size of the dataset, we apply a Bayesian technique to improve the generalizability of the data-driven models as well as quantify the uncertainty associated with the predictions. The method captures well the overall trend of the O’Donnell parameters with respect to a reduced feature set obtained by transforming the available physical features. Quantifying the associated uncertainty helps us understand the reliability of the predictions of the O’Donnell parameters and, therefore, the bandgap as a function of temperature for any novel material.

Technical note: Workload and transmission data for mobile C-arm fluoroscopy in gastrointestinal endoscopy.

Mobile C-arms may be used in fixed locations, and it is recommended that qualified experts evaluate structural shielding. To assess clinical workload distributions for mobile C-arms used in gastrointestinal endoscopy and determine the Archer equation parameters for the C-arm beam spectra. Consecutive (30 months) gastrointestinal endoscopic procedures on two Cios Alpha systems (Siemens) were retrospectively analyzed. X-ray tube voltage, tube current-time product, reference point air kerma (Ka,r), air kerma-area product (PKA), and fluoroscopic time were examined. The primary beam half-value layer (HVL) was measured with an ionization chamber and aluminum 1100 plates. Stray radiation fraction at 1m from a scattering source (ACR R/F phantom) was directly measured. Monte Carlo (Geant4) simulation was performed to calculate the transmission of broad X-ray beams through lead, concrete, gypsum, and steel, with X-ray HVLs matching those of the C-arm X-ray beam. The transmission data were fitted to the Archer equation. The number of procedures (3509) was equivalent to 13.48 procedures per room per week. Dose quantities were 54.8 mGy (Ka,r), 18.3Gy∙cm2 (PKA), and 7.8 min (fluoroscopic time) per procedure. X-ray beam irradiation events were recorded for 2906 (82.8%) procedures with 160,009 events, whose mA-minute weighted tube voltage was 91.0kV and the workload was 0.68mA-minute per procedure. The two rooms had a significant difference in the number of procedures per week, 17.3 (29) [mean (maximum)] and 9.6 (16), respectively. The stray radiation fraction was 9.7×10-4 (80kV) and 1.25×10-3 (120kV). Transmission fitting parameters were provided for the tube voltage (on average, 90kV; high end, 120kV) of the C-arm. This work provides workload and transmission data for mobile C-arm fluoroscopy in gastrointestinal endoscopy, which indicates a need for structural shielding evaluation of the procedure rooms.

New Theoretical Model to Describe Carrier Multiplication in Semiconductors: Explanation of Disparate Efficiency in MoTe2 versus PbS and PbSe.

We present a theoretical model to compute the efficiency of the generation of two or more electron-hole pairs in a semiconductor by the absorption of one photon via the process of carrier multiplication (CM). The photogeneration quantum yield of electron-hole pairs is calculated from the number of possible CM decay pathways of the electron and the hole. We apply our model to investigate the underlying cause of the high efficiency of CM in bulk 2H-MoTe2, as compared to bulk PbS and PbSe. Electronic band structures were calculated with density functional theory, from which the number of possible CM decay pathways was calculated for all initial electron and hole states that can be produced at a given photon energy. The variation of the number of CM pathways with photon energy reflects the dependence of experimental CM quantum yields on the photon energy and material composition. We quantitatively reproduce experimental CM quantum yields for MoTe2, PbS, and PbSe from the calculated number of CM pathways and one adjustable fit parameter. This parameter is related to the ratio of Coulomb coupling matrix elements and the cooling rate of the electrons and holes. Large variations of this fit parameter result in small changes in the modeled quantum yield for MoTe2, which confirms that its high CM efficiency can be mainly attributed to its extraordinary large number of CM pathways. The methodology of this work can be applied to analyze or predict the CM efficiency of other materials.

Interrelations of physical state parameters and biological age of students in the process of physical education

Background and Study Aim. The objective of the study is to investigate the relationships of students' biological age (BA) with the parameters of functional and physical fitness (PF) in the process of adaptation to standard (university) and experimental physical education programs. Material and Methods. The studies were conducted at the National University of Telecommunications of Ukraine and involved 140 students aged 17-18 years old, comprising 80 boys and 60 girls. We recorded 55 indices of students' functional state and physical fitness, and determined their Body Mass Index (BMI). The experimental data were processed using methods of correlation, dispersion, and regression analysis. Mathematical models describing the relationships between BMI and key indicators of students' physical well-being were developed. Results. The impact of the program on 17-18-year-old students is marked by a decrease in BMI, an increase in the levels of physical fitness and overall well-being among both the experimental group of girls and boys. Additionally, there is a reduced correlation between BMI and the parameters of their physical health. The key indices of functional state and physical fitness determining the BMI of male and female students were identified. Multiple regression models were developed to illustrate the dependence of male and female BMI on somatic and functional indices. Quantitative characteristics of the degree of partial and total impact of key indices on male and female BMI were provided. It was found that the key indicators of students' physical state (PS) structure, which exerted the most significant influence on BMI, differ between young men and women. In young men, the key indicators influencing BMI include the following: Vital capacity (VC) accounts for 51.9% of BMI variation; self-perceived health status (SPHS) contributes 15.3%; static balancing (SB) affects 8.5%; inspiration breath-hold time (IBH) influences 8.4%; expiration breath-hold time (EBH) has a 0.8% impact; systolic blood pressure (SBP) plays a role with 4.6%; the number of sit-ups in 30 seconds (SU30s) is associated with 0.9%; and left hand muscle strength has a 0.6% influence. In girls, the key indicators are VC (34.5% of BMI variation), SBP (23.1%), and SB (7.9%). Conclusions. The experimental program of increased motor activity leads to an increase in the functional and motor capabilities of students, while simultaneously decreasing their BMI. Among the parameters studied, changes in vegetative parameters have the greatest impact on reducing students' BMI during the annual cycle of long-term adaptation to physical loads. In contrast, changes in somatic parameters and physical fitness parameters have a lesser influence on students' BMI. The calculated equations of multiple regression serve as model characteristics that reflect the relationships between BMI and the key indicators of functional state and physical fitness in 17-18-year-old students. These equations can be utilized to estimate, model, and predict the BMI of 17-18-year-old students of different genders when altering the parameters of their physical state in various conditions of daily life and activity.