Fit To Data Research Articles

Solvent-free synthesis of citronellyl acetate using Fermase: Optimization, kinetics and RSM studies

Citronellyl acetate is a flavor ester with a fresh fruit aroma and a fruity, sweet flavor. The current study illustrates the synthesis of citronellyl acetate via a transesterification reaction of citronellol and vinyl acetate in a solvent-free system with the help of biocatalyst Fermase. The effect of various parameters affecting the reaction, including reactant mole ratio, enzyme loading, temperature, and agitation speed, was studied to obtain the maximum conversion of citronellol. Under optimized conditions with a 1:10 mole ratio of citronellol to vinyl acetate, 3% (w/w) enzyme loading with respect to citronellol, 300 RPM agitation speed, and 60 °C temperature, a maximum conversion of 99.8% was achieved in 2 h reaction time. The enzyme was reused for six reaction cycles, retaining 85% of its activity. Various kinetic models for enzymatic reactions were explored and validated. The ping-pong bi-bi model with both substrate inhibition gave the best fit for the experimental data. Optimization studies were also conducted using the response surface methodology to observe the interaction of various parameters.

Population pharmacokinetic analysis of quizartinib in patients with newly diagnosed FLT3-internal-tandem-duplication-positive acute myeloid leukemia.

The population pharmacokinetics (PK) of quizartinib and its pharmacologically active metabolite AC886 have been previously described in healthy volunteers (HV) and relapsed/refractory (R/R) FLT3-internal-tandem-duplication-positive (FLT3-IDT-positive) acute myeloid leukemia (AML) patients receiving quizartinib monotherapy. In this analysis, we characterized the population PK of quizartinib and AC886 in newly diagnosed FLT3-ITD-positive AML patients receiving standard induction and consolidation chemotherapy as background treatment, using data from the Phase 3 QuANTUM-First trial and 12 earlier studies. Quizartinib PK were best described by a three-compartment model with sequential zero- and first-order absorption and first-order elimination. A two-compartment model with first-order metabolite formation and first-order elimination best fitted AC886 data. The PK of both moieties showed large interindividual variability (approximately 70% coefficient of variation for systemic clearances). The use of strong cytochrome P450 3A (CYP3A) inhibitors had the largest impact on exposure, increasing the steady-state area under the curve during the dosing interval (AUCss) by 1.8-fold. This is consistent with observations in HV and R/R AML patients and confirms the need for dose adjustments during coadministration. A novel finding in newly diagnosed AML patients was the phase-dependent change in steady-state quizartinib exposure: dose-normalized AUCss values were 0.6-fold during induction, similar during consolidation, and 1.4-fold during continuation compared to R/R AML patients receiving quizartinib monotherapy. The present analysis highlighted the comparison of quizartinib and AC886 PK between newly diagnosed AML patients and previously studied populations, informed dose modifications needed with strong CYP3A inhibitors, and supported the use of derived individual exposure metrics in separate exposure-response analyses.

A mathematical framework for the statistical interpretation of biological growth models

Biological entities are inherently dynamic. As such, various ecological disciplines use mathematical models to describe temporal evolution. Typically, growth curves are modelled as sigmoids, with the evolution modelled by ordinary differential equations. Among the various sigmoid models, the logistic, Gompertz and Richards equations are well-established and widely used for the purpose of fitting growth data in the fields of biology and ecology. The present paper puts forth a mathematical framework for the statistical analysis of population growth models. The analysis is based on a mathematical model of the population–environment relationship, the theoretical foundations of which are discussed in detail. By applying this theory, stochastic evolutionary equations are obtained, for which the logistic, Gompertz, Richards and Birch equations represent a limiting case. To substantiate the models of population growth dynamics, the results of numerical simulations are presented. It is demonstrated that a variety of population growth models can be addressed in a comparable manner. It is suggested that the discussed mathematical framework for statistical interpretation of the joint population-environment evolution represents a promising avenue for further research.

Radar Sensor Data Fitting for Accurate Linear Sprint Modelling

Background: Accurate linear sprint modelling is essential for evaluating athletes’ performance, particularly in terms of force, power, and velocity capabilities. Radar sensors have emerged as a critical tool in capturing precise velocity data, which is fundamental for generating reliable force-velocity (FV) profiles. This study focuses on the fitting of radar sensor data to various sprint modelling techniques to enhance the accuracy of these profiles. Forty-seven university-level athletes (M = 23, F = 24; 1.75 ± 0.1 m; 79.55 ± 12.64 kg) participated in two 40 m sprint trials, with radar sensors collecting detailed velocity measurements. This study evaluated five different modelling approaches, including three established methods, a third-degree polynomial, and a sigmoid function, assessing their goodness-of-fit through the root mean square error (RMSE) and coefficient of determination (r2). Additionally, FV metrics (Pmax, F0, V0, FVslope, and DRF) were calculated and compared using ANOVA. Results: Significant differences (p < 0.001) were identified across the models in terms of goodness-of-fit and most FV metrics, with the sigmoid and polynomial functions demonstrating superior fit to the radar-collected velocity data. Conclusions: The results suggest that radar sensors, combined with appropriate modelling techniques, can significantly improve the accuracy of sprint performance analysis, offering valuable insights for both researchers and coaches. Care should be taken when comparing results across studies employing different modelling approaches, as variations in model fitting can impact the derived metrics.

Comparison of intermediate-range order in GeO2 glass: Molecular dynamics using machine-learning interatomic potential vs reverse Monte Carlo fitting to experimental data.

The short-range order and intermediate-range order in GeO2 glass are investigated by molecular dynamics using machine-learning interatomic potential trained on abinitio calculation data and compared with the reverse Monte Carlo fitting of neutron diffraction data. To characterize the structural differences in each model, the total/partial structure factors, coordination number, ring size and shape distributions, and persistent homology analysis were performed. These results show that although the two approaches yield similar two-body correlations, they can lead to three-dimensional models with different short- and intermediate-range ordering. A clear difference was observed especially in the ring distributions; RMC models exhibit a broad distribution in the ring size distribution, while neural network potential molecular dynamics yield much narrower ring distributions. This confirms that the density functional approximation in the abinitio calculations determines the preferred network assembly more strictly than RMC with simple coordination constraints even when using multiple diffraction data.

Trends in physical fitness among Lithuanian adolescents aged 11–17 years between 1992 and 2022

BackgroundPhysical fitness is an excellent marker of general health and performance. We aimed to calculate trends in physical fitness among Lithuanian adolescents between 1992 and 2022.MethodsUsing a repeated cross-sectional design,...

Porous biochars derived from brewery waste for the treatment of Cr(VI)-contaminated water.

The use of brewery waste for the removal of pollutants such as chromium has rarely been studied. In the present work, the removal of hexavalent chromium (Cr(VI)) from aqueous solutions was evaluated by brewer's spent grain (BSG), brewing sewage sludge (BSS), and their mixture (MIX), which were obtained from the Bedele Brewery Share Company, Ethiopia. BSG with acid and heat treatment at 600 °C was selected during the preliminary screening experiments and further characterized via FTIR, XRD, and SEM. An adsorption experiment was carried out in batches to study the effectiveness of adsorbents in removing Cr(VI) under different conditions. Factors affecting adsorption, including pH, contact time, adsorbent dosage, and initial Cr(VI) concentration, were analyzed and optimized. The best conditions for the highest efficiency in removing Cr(VI) were a contact time of 7 h, initial solution pH of 2, initial Cr(VI) concentration of 40 mg/L, and adsorbent dose of 2 g/L. The pseudo-second-order (PSO) model, which suggests chemisorption of Cr(VI) on the surface of the adsorbent, describes the kinetics of Cr(VI) removal by the adsorbent (R2 = 0.9570). The Freundlich isotherm was a good fit for the experimental equilibrium adsorption data. The BSG biochar was found to have an approximate adsorption capacity of 31.87 mg/g for Cr(VI). The ability to recycle adsorbents suggests that BSG biochar could be effectively used to treat Cr(VI) in wastewater. As a result, converting industrial waste into useful material is cost effective and beneficial for the protection of the environment. More research is recommended to study how well this adsorbent works in real wastewater samples and during the column adsorption process.

DarkMatters: A powerful tool for WIMPy analysis

We introduce a new software package, DarkMatters, which has been designed to facilitate the calculation of all aspects of indirect dark matter detection of WIMPs in astrophysical settings. Two primary features of this code are the improvement in performance compared to existing tools, and higher levels of accuracy when determining radio synchrotron emission associated with WIMP annihilations, both of which are enabled by the employment of a set of modern and novel numerical techniques. The code also includes functionality for a multi-wavelength set of output products including gamma-ray, radio and neutrino fluxes which can be saved in common formats used by the astronomical community, such as the FITS data file format. The calculations may be tailored to work with a wide range of astrophysical target structures, from dwarf galaxies to galaxy clusters, and the configuration of the underlying calculations is managed by a set of key–value dictionary entries that are easy to understand and use. The code base is publicly accessible through an online repository with a permissive MIT source code licence.

Effect of Time and Stress on Creep Damage Characteristics of Cement-Based Materials

In the realm of daily life, ensuring the safety of building structures and civil engineering projects remains a paramount research focus. The creep properties of materials significantly influence their long-term loading process. Specifically, creep load and creep time are pivotal factors that impact material creep damage, thereby playing a crucial role in assessing the safety of engineering endeavors and estimating aspects such as housing construction. This study undertakes creep damage tests on cement-based materials, subjecting them to varying creep loads and creep times, and subsequently conducts uniaxial compression tests on the specimens post-creep damage. The refined Nishihara model is employed for data fitting, facilitating the construction of a creep damage time-stress model. Concurrently, a Neural Network model is utilized to validate the experimental data. The findings indicate that both steady-state creep strain and steady-state creep rate exhibit discernible trends relative to creep load and creep time, effectively mirroring the alterations in creep damage experienced by the specimens. The refined Nishihara model proves adept at predicting and equating creep damage under diverse creep loads and creep times. Similarly, the trained Neural Network model demonstrates capability in measuring and estimating various creep damages. The study successfully explored the correlation between creep time and creep load, enabling the simulation of long-term creep damage within a shorter creep time and facilitating an analysis of its physical and mechanical properties, which is pivotal in predicting the safety of large-scale engineering projects. Concurrently, it advances research on material damage equivalence, offering insights and theoretical groundwork for developing a system to assess material damage equivalence under various damage conditions.

A new modeling approach for microplastic drag and settling velocity

Understanding microplastics' (MPs') transport and settling behaviors in aquatic environments is crucial for devising effective management strategies. This study contributes a novel modeling framework to develop accurate and interpretable drag and velocity models for MPs using machine learning techniques. It achieves faster model creation and improved accuracy than traditional methods like theoretical analysis and data fitting. The framework demonstrates high predictive accuracy across different MP types (1D, 2D, 3D, and mixed), with a coefficient of determination CD = 0.86–0.95 for the drag models and CD = 0.92–0.95 for the velocity models. Compared with best-performing empirical approaches, the new drag models exhibit an average reduction in root mean square error (RMSE) by 59% and mean absolute error (MAE) by 62%. Similarly, the velocity models show a mean decrease in RMSE and MAE by 27% and 25%, respectively. Moreover, the framework outperforms commonly used symbolic regression methods, reducing errors by 18%–27%. The sensitivity analysis reveals that the relative density difference and the dimensionless diameter are essential for predicting the settling of all MP types, while the effective shape parameters vary across different MP categories. By providing accurate predictions of MPs' settling dynamics, this study offers insights for developing targeted mitigation strategies to reduce MPs' environmental impacts.

Annular cement sheath characterization and hydraulic aperture assessment through single- and two-phase seepage tests

The integrity of well barriers is critical throughout the life cycle of oil and gas wells, with annular cement as a key component in maintaining the structural integrity of the barrier system. In this study, an annular cemented section with a well-defined microannulus was systematically constructed and characterized, and both single- and two-phase seepage tests were conducted to gain a deeper understanding of how relative permeability impacts the estimation of hydraulic aperture in potential leakage pathways. The test cell design allows modification of the microannulus gap size by changing the pressure applied inside the inner casing. This enables studying different microannulus sizes throughout the conducted tests. The single-phase seepage tests showed consistent measurements of hydraulic aperture for both gas and water, despite variations in fluid type and applied pressure, with surface roughness likely affecting the non-linear behavior of the hydraulic aperture under stress. The two-phase seepage tests demonstrated a clear relationship between breakthrough pressure and microannulus size, with the van Genuchten model providing a better fit for relative permeability and capillary pressure data. Breakthrough time experiments revealed that initial permeability estimations were significantly lower than those obtained from single-phase tests; however, once relative permeability at breakthrough was accounted for, the estimated absolute permeability values aligned closely with the single-phase experimental data. These findings offer valuable insights into the complex interactions between cement sheath integrity and gas migration, potentially contributing to developing new well integrity assessment technologies, including tracer gas below the well barrier element, as well as insights relevant for both gas migration and sustained casing pressure analysis.

Quantifying solid volume of stacked eucalypt timber using detection-segmentation and diameter distribution models

Accurate timber quantification is essential in forestry and the timber industry, impacting harvest planning, processing, pricing, and overall supply chain management. Traditional methods for estimating the volume of stacked timber, often reliant on manual measurements, are time-consuming and prone to error. This research aims to develop an accurate procedure for estimating the volume of stacked eucalypt timber in yards. The proposed procedure combines automatic log detection and diameter distribution models. Automatic log detection was achieved using advanced computer vision techniques, specifically the You Only Look Once version 9 (YOLOv9) model, which automates the identification and counting of individual logs within a stack. We used stem diameter distribution models to estimate total stack volume based on log counts and probability densities. This approach ensures high accuracy and efficiency, significantly reducing the time and effort required for volume estimation. The dataset used for this study includes diameter measurements from a pre-harvest inventory of eucalypt trees aged 6 and 7 years, alongside videos of stacked timber. The YOLOv9 model was trained to detect logs from these videos, achieving high precision in object detection and segmentation tasks. Performance metrics such as Box Precision, Box Recall, and mean Average Precision (mAP) were used to evaluate the model's effectiveness. The results indicate that the model generalizes well to new data, with high accuracy in both validation and test sets. Among the distribution models evaluated, the generalized extreme value (GEV) distribution provided the best fit for the stem diameter data, allowing for accurate volume predictions. This procedure, which integrates automatic log detection with diameter distribution models, offers a scalable solution applicable to large and complex timber stacks. Finally, a repository was established to allow users to test the proposed method. Future works will focus on refining the model's accuracy and expanding its applicability across different species, forest production and log conditions.

Research on the Elderly Fitness Fall Risk Prediction Model Based on Decision Tree

As the global aging process intensifies, the issue of falls has become a common health risk in the daily lives of the elderly, especially in physical fitness activities. This article establishes a fall risk prediction model using a decision tree algorithm based on fitness data of the elderly and analyzes the relationship between various health factors and fall risk. The study found that features such as heart rate variability, blood sugar levels, and exercise duration have significant impacts on fall prediction. Through the analysis of the ROC curve and PR curve of the model, the results show that the model performs well in the prediction accuracy and risk identification of fall events. The research in this article not only provides a predictive tool for the safety of the elderly in fitness but also provides a theoretical basis and practical guidance for preventing falls in the elderly and formulating personalized safe exercise plans.

Parthenium hysterophorus invasive weed valorization into biochar for removal of pharmaceuticals and personal care products: Competitive adsorption analysis via batch and fixed–bed column systems

The proliferation of invasive weeds and emergence of new contaminants have become significant global concern, threatening human–health and environment. This investigation assesses performance of Parthenium hysterophorus weed–derived biochar (HPC and KPC) for removing acetaminophen (ACT) and metronidazole (MET) in single (ACT/MET) and multi–component (ACT + MET) systems in batch and column modes. Synthesized HPC and KPC were characterized through SEM, EDX, XRD, porosimetry, TGA and FTIR analyses. HPC displayed micropores, featuring surface area of 166.62 m2/g, while KPC exhibited mesopores with 146.16 m2/g surface area. The investigation in batch study includes exploring operating conditions like contact time (0–180 min), concentration (0.5–20 mg/L), biochar dose (0.25–4 g/L) and pH (2–12). At optimal conditions (1 h, pH 6.5 and 1 g/L dose), HPC achieved maximum sorption capacity 48.72 mg/g for MET and 32.10 mg/g for ACT, and KPC exhibited 18.41 mg/g for MET and 16.54 mg/g for ACT in batch mode. According to findings, pseudo–second–order and Langmuir models provided accurate–fit for batch experimental data. Furthermore, study also investigated influence of column operating conditions such as bed depth (3–9 cm), flow rate (0.25–1.00 L/h) and concentration (5–20 mg/L). In column, HPC outperformed KPC in removing ACT and MET, with breakthrough data fitting Thomas model. Both systems exhibit synergistic effect in simultaneously removing ACT and MET from multi–pollutant mixture. In summary, converting invasive weeds into biochar and employing it to remove emerging contaminants provides sustainable approach to protecting aquatic environment and invasive weeds management.

A maximum log-likelihood based data fusion model for estimating household’s vehicle purchase decision

ABSTRACT The growing adoption of electric vehicles offers a potential opportunity to reduce transportation sector carbon footprint. In our research, we studied vehicle purchase behavior with emphasis on alternative fuel vehicles using the vehicle purchase dataset ‘MaritzCX New Vehicle Customer Study.’ This study consisted of a two-level modeling approach. In the first level, purchasing of a new car was estimated based on consumers socio-economic characteristics. In the second level, the vehicle purchase decision was examined with a two-dimensional dependent variable – vehicle type and fuel type. We employed an innovative data fusion approach that probabilistically links records from MaritzCX with records from National Household Travel Survey with the objective of identifying new independent variables affecting the decision process while maximizing data fit. The final model included a host of independent variables from four different categories: vehicle-, economic-, demographic-, and spatial characteristics. Finally, the model results were employed to conduct an elasticity analysis.

Model-independent description of B→Dπℓν decays

We introduce a new parametrization of B→Dπℓν form factors using a partial-wave expansion and derive bounds on the series coefficients using analyticity and unitarity. This is the first generalization of the model-independent formalism developed by Boyd, Grinstein, and Lebed for B→Dℓν to semileptonic decays with multihadron final states, and enables data-driven form-factor determinations with robust, systematically improvable uncertainties. Using this formalism, we extract the form-factor parameters for B→D2*(→Dπ)ℓν decays in a model-independent way from fits of data from the Belle Experiment. We find that the semileptonic data are compatible with the presence of two poles in the Dπ S-wave channel, which is the scenario preferred by nonleptonic decays and unitarized chiral perturbation theory. Published by the American Physical Society 2024

Fitting COVID-19 Incidences in Kenya Using Fractional Polynomials and Linear Splines

The study provides an in-depth analysis of COVID-19 infections in Kenya, aiming to model the non-linear trajectory of daily cases. The research explores two statistical techniques: fractional polynomials and linear splines, to fit the growth of infection rates over time. COVID-19, which first appeared in Kenya in March 2020, exhibited fluctuating trends in daily infections. The study utilizes infection data collected from March 13, 2020, to June 6, 2021. Descriptive statistics and exploratory data analysis revealed significant variability in daily cases, with the infection trajectory characterized by multiple waves. Fractional polynomial models, known for their flexibility in fitting non-linear relationships, were evaluated at varying degrees to identify the best model for COVID-19 incidence trends. The analysis showed that a second-degree fractional polynomial with powers (1, 2) provided the most accurate fit for the data. The closed test algorithm was applied to confirm the model's suitability. Additionally, linear spline models were employed, partitioning the data into segments and fitting linear splines at each knot point. The model with 19 knots demonstrated superior performance based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), outperforming the fractional polynomial model. The comparison of the two methods concluded that linear splines provided a more precise fit for the infection data, capturing the complex nature of COVID-19's spread in Kenya. The study's findings offer critical insights into the infection dynamics and can aid policymakers in resource allocation and mitigation planning during pandemics. The study recommends further analysis by incorporating more covariates and extending the models to other countries for a comparative understanding of pandemic management strategies.

Observation of an order change during the course of a reaction in a kinetic study of the reduction of trans-dicyanodibromoaurate(III) by octacyanotungstate(IV) in an acidic medium

AbstractThe reduction of dibromodicyanoaurate(III) ion by octacyanotungstate(IV) ions was studied under near stoichiometric conditions in an acidic medium. An order change occurred during the course of the reaction. The reaction was first order in Au(CN)2(Br)2– and second order in W(CN)84– for the first half-life, but after ca. 60% conversion became first order in both reactants. The order change is ascribed to two different AuII intermediates that form during the reaction course. A third order rate constant of k3 = 7.80 × 105 M−2 min−1 and a second order rate constant of k2 = 108 M−1 min−1 was found at [H+] = 1.633 × 10–3 M, an ionic strength of I = 0.11 M (NaBr) and at 25.0 ± 0.1 °C for the reaction. The reaction rate decreases with increasing [H+] in the reaction mixture at low pH and an experimental pKa of 1.80 ± 0.03 was found at 25.0 ± 0.1 °C for the deprotonation of HW(CN)83–. The reaction rate is independent of [Br−] and decrease with addition of W(CN)83– in the reaction mixture. Activation parameters have been obtained by a least-squares fit of the temperature data directly to the Eyring equation. Graphical abstract

Data Quality in the Fitting of Approximate Models: A Computational Chemistry Perspective.

Empirical parametrization underpins many scientific methodologies including certain quantum-chemistry protocols [e.g., density functional theory (DFT), machine-learning (ML) models]. In some cases, the fitting requires a large amount of data, necessitating the use of data obtained using low-cost, and thus low-quality, means. Here we examine the effect of using low-quality data on the resulting method in the context of DFT methods. We use multiple G2/97 data sets of different qualities to fit the DFT-type methods. Encouragingly, this fitting can tolerate a relatively large proportion of low-quality fitting data, which may be attributed to the physical foundations of the DFT models and the use of a modest number of parameters. Further examination using "ML-quality" data shows that adding a large amount of low-quality data to a small number of high-quality ones may not offer tangible benefits. On the other hand, when the high-quality data is limited in scope, diversification by a modest amount of low-quality data improves the performance. Quantitatively, for parametrizing DFT (and perhaps also quantum-chemistry ML models), caution should be taken when more than 50% of the fitting set contains questionable data, and that the average error of the full set is more than 20 kJ mol-1. One may also follow the recently proposed transferability principles to ensure diversity in the fitting set.

Adsorptive processes applied to the defluorination of groundwater for human consumption

Contamination of groundwater by fluoride ions can occur through both natural and anthropogenic activities, such as the discharge of industrial waste containing this compound. Thus, effective fluoride removal from groundwater is essential to ensure safe drinking water. This study evaluated the performance of adsorption techniques for defluoridating groundwater in Rio Grande do Sul, Brazil. Preliminary tests were conducted using synthetic solutions with a fluoride concentration of 5 mg.L−1, applying several adsorbents. Additionally, an ultrasonic process was used to synthesize an adsorbent from activated alumina pre-treated with carbon (AACP) and modified with ZnCl₂ (AA-ZnCl₂). The AACP and AA-ZnCl2 were characterized through BET, EDS, scanning electron microscopy, X-ray diffraction (XRD), and FT-IR analysis. A Central Composite Design and response surface methodology were applied to optimize adsorption efficiency, focusing these factors: pH and adsorbent dosage. Kinetic and isotherm adsorption tests were conducted for both AACP and AA-ZnCl₂. The results showed that AACP achieved fluoride removal efficiencies of 65.4 % in synthetic solutions and 38.6 % in groundwater. The AA-ZnCl₂ demonstrated superior performance, removing over 98 % of fluoride in synthetic solutions and 55.4 % in groundwater, across a pH range of 4 to 10, with an optimal solid dosage of 3 g.L−1. For an initial fluoride concentration of 5 mg.L−1, a removal efficiency of 97.4 % was achieved within 5 min of contact time. The kinetic adsorption data were best described by the pseudo-second-order model, while the Freundlich isotherm model provided the best fit for the adsorption isotherm data. The findings in this work indicate hat ZnCl₂-modified activated alumina, synthesized with ultrasonic assistance, is highly effective for defluoridating groundwater for safe human consumption being an alternative method to be implemented in an industrial scale.