Kolmogorov-Smirnov Statistical Test Research Articles

Designing a Human Resource Empowerment Model for Pharmacists in Pharmacies in Tehran

Background: One of the most effective strategies to gain a competitive advantage in the current business environment is to enhance the efficiency of employees within organizations. The purpose of this research was to design a human resource empowerment model for pharmacists in pharmacies in Tehran. Methods: The research's statistical population includes two qualitative and quantitative components. In the qualitative part, 35 elite pharmacists from universities, industry, and the Ministry of Health were included, while the quantitative part involved a statistical sample of 220 individuals. For data analysis, the Grandad process of Kolmogorov-Smirnov statistical tests, independent one-sample t-test, exploratory factor analysis, confirmatory factor analysis, and structural equation modeling were used. SPSS 25 and AMOS 24 software were used to analyze the research data Results: The findings showed that in the final development model of empowering pharmacists and personnel in Tehran courts, causal factors include psychological characteristics and training. The core category includes cultural and moral competence, as well as professional and technical competence. Background factors include commitment and skill. Intervening factors include moral values. Strategies include efficiency and speed. The consequences include creativity, innovation, and social responsibility.Results: The findings showed that in the final development model of empowering pharmacists and personnel in Tehran courts, causal factors include psychological characteristics and training. The core category includes cultural and moral competence, as well as professional and technical competence. Background factors include commitment and skill. Intervening factors include moral values. Strategies include efficiency and speed. The consequences include creativity, innovation, and social responsibility. Conclusion: It can be concluded that continuous training workshops should be organized for pharmacists in Tehran pharmacies to enhance their specialized knowledge. Periodic in-service courses should be provided to these individuals based on their professional activities. The organization should consider social activities and social behaviors as focal points.

Generative models for synthetic data generation: application to pharmacokinetic/pharmacodynamic data.

The generation of synthetic patient data that reflect the statistical properties of real data plays a fundamental role in today's world because of its potential to (i) be enable proprietary data access for statistical and research purposes and (ii) increase available data (e.g., in low-density regions-i.e., for patients with under-represented characteristics). Generative methods employ a family of solutions for generating synthetic data. The objective of this research is to benchmark numerous state-of-the-art deep-learning generative methods across different scenarios and clinical datasets comprising patient covariates and several pharmacokinetic/pharmacodynamic endpoints. We did this by implementing various probabilistic models aimed at generating synthetic data, such as the Multi-layer Perceptron Conditioning Generative Adversarial Neural Network (MLP cGAN), Time-series Generative Adversarial Networks (TimeGAN), and a more traditional approach like Probabilistic Autoregressive (PAR). We evaluated their performance by calculating discriminative and predictive scores. Furthermore, we conducted comparisons between the distributions of real and synthetic data using Kolmogorov-Smirnov and Chi-square statistical tests, focusing respectively on covariate and output variables of the models. Lastly, we employed pharmacometrics-related metric to enhance interpretation of our results specific to our investigated scenarios. Results indicate that multi-layer perceptron-based conditional generative adversarial networks (MLP cGAN) exhibit the best overall performance for most of the considered metrics. This work highlights the opportunities to employ synthetic data generation in the field of clinical pharmacology for augmentation and sharing of proprietary data across institutions.

Negative Binomial Three Parameter Lindley Distribution and Its Properties

Many researchers have proposed mixed distributions as one of the most important methods for obtaining new probability distributions. Several studies have shown that mixed Negative Binomial distributions fits count data better than Poisson and Negative Binomial distribution itself. In this paper, we introduce a mixed distribution by mixing the distributions of negative binomial and three Parameter Lindley distribution. This new distribution has a thick tail and may be considered as an alternative for fitting count data with over dispersion. The parameters of the new distribution are estimated using MLE method and properties studied. Special cases of the new distribution and also identified. A simulation study carried out shows that the ML estimators give the parameter estimates close to the parameter when the sample is large, that is, the bias and variance of the parameter estimates decrease with increase in sample size showing the consistent nature of the new compound distribution. The study also compares the performance of the new distribution over distributions of Poisson, Negative Binomial, Negative Binomial oneParameter Lindley Distribution, Negative Binomial two Parameter distribution, three parameter Lindley distribution using a real count over dispersed dataset and the results shows that Negative Binomial three parameter Lindley distribution gave the smallest Kolmogorov Smirnov test statistic, AIC and BIC as compared to other distributions, hence the new distribution provided a better fit compared to other distributions under study for fitting over dispersed count data.

A Closer Look at the Statistical Behavior of a Chaotic System with Message Inclusion for Cryptographic Applications

One technique, especially in chaos-based cryptographic applications, is to include the message in the evolution of the dynamical system. This paper aims to find out if and to what extent the statistical behavior of the chaotic system is affected by the message inclusion in its dynamic evolution. The study is illustrated by the dynamical system described by the logistic map in cryptographic applications based on images. The evaluation of the statistical behavior was performed on an original scheme proposed. The Monte Carlo analysis of the applied Kolmogorov–Smirnov statistical test revealed that the dynamical system in the processing scheme with message inclusion does not modify its proper statistical behavior (revealed by definition relation). This was possible due to the proposed scheme designed. Namely, this scheme contains a decision switch which, supported by an appropriate choice of the magnitude of the scaling factor, ensures that the values of the dynamical system are maintained in the definition domain. The proposed framework for analyzing the statistical properties and for preserving the dynamical system behavior is one main contribution of this research. The message inclusion scheme also provides an enhancement with cryptographic mixing functions applied internally; the statistical behavior of the dynamical system is also analyzed in this case. Thus, the paper contributes to the theoretical complex characterization of the dynamical system considering also the message inclusion case.

Vector Autoregressive Model Using Non-Normal Copula Errors

This article focuses on a bivariate vector autoregressive model of lag 1 with non-normal errors. We propose a bivariate error distribution using two identical marginals through a copula function. The copula used for the study is the Farlie-Gumbel-Morgenstern copula, which is considered to be one of the efficient class of copula in describing the dependence between two random variables. The model parameters are estimated using the method of inference functions for margins, and the finite sample properties of the model is illustrated through simulation studies. A real life example is considered to illustrate the applications of the proposed model. The adequacy of the copula model is examined using the two sample version of kolmogorov smirnov test statistic.

Comparative Performance Analysis of Filtering Methods for Removing Baseline Wander Noise from an ECG Signal

ECG signals play a vital role in the diagnosis of cardiovascular conditions. However, they often suffer from the effects of various noise sources, including baseline wandering, respiratory artifact noise, power line interference and electrode motion artifacts. To overcome these challenges, it is imperative to implement low-frequency signal noise reduction strategies. Such strategies aim to significantly improve the quality of ECG signals, thus promoting more accurate and reliable diagnosis of cardiovascular disorders. This paper conducts a comparative analysis to assess the effectiveness of commonly used filtering and wavelet techniques in reducing Baseline Wander (BW) noise within ECG signals generated by the influence of breathing or electrode movements. It is common to observe the selection and evaluation of only one particular technique in the existing literature. In contrast, this study aims to provide a comprehensive comparative analysis, providing insight into the performance and relative merits of different techniques. Our research uses both filtering and Discrete Wavelet Transform (DWT) techniques in baseline noise removal. In this context, a reference point is established utilizing noise-free signals and a meticulous investigation of the wavelet-based approach that most effectively eliminates the resulting noise is provided. Subsequently, we assess the reference input and output signal via Signal-to-Noise Ratio (SNR) and Kolmogorov–Smirnov statistical test measurements. The most important contribution of this work to the scientific community resides in the comprehensive examination of IIR/FIR-based and wavelet method-based filtering methods capable of yielding the highest SNR levels across various ECG signals with various types of BW noise. Additionally, the effectiveness of the Chebychev-II filter in BW noise removal is highlighted. Our study was conducted using the MATLAB platform and code command lines were shared to facilitate the reproduction of our study by other researchers. It is considered that this study will be an important reference in the selection of effective techniques for removing BW noise within ECG signals.

Search for Optimal Parameters in the Control Structure of a Surgical System for Soft Tissue Operations Based on In Vitro Experiments on Cardiovascular Tissue

The surgical robots currently used in cardiac surgery are equipped with a remote center of motion (RCM) mechanism that enables the required spherical workspace. The dynamics model of the surgical robot’s RCM mechanism presented in this work includes a direct current (DC) motor, an optimal proportional–integral–derivative (PID) controller, and a LuGre friction model that takes into account the Stribeck effect and surface deformation. A finite element method (FEM) analysis of transients was carried out using the energy hypothesis of von Mises with an optimal input signal from the mechatronic system with a PID controller obtained using the Runge–Kutta differentiation method in the Dormand–Prince ordinary differential equations variant (ODE45). Five criteria were adopted for the objective function: the safety factor related to the stress function in the time-varying strength problem, the first natural frequency related to stiffness and the resonance phenomenon, the buckling coefficient in the statics problem related to stability, the static factor of safety, and the displacement of the operating tip. The force inputs to the dynamics model were derived from in vitro force measurements on cardiovascular tissue using a force sensor. The normality of the statistical distribution of the experimental data was confirmed using the Kolmogorov–Smirnov statistical test. The problem of multi-criteria optimization was solved using the non-sorter genetic algorithm (NSGA-II), the finite element method, and the von Mises distortion energy hypothesis. Velocity input signals for the transient dynamics model were obtained from a second in vitro experiment on cardiovascular tissue using the minimally robotic invasive surgery (MIRS) technique. An experienced cardiac surgeon conducted the experiment in a modern method using the Robin Heart Vision surgical robot, and a system of four complementary metal–oxide–semiconductor (CMOS) optical sensors and ariel performance analysis system (APAS-XP 2002) software were used to obtain the endoscopic tool trajectory signal. The trajectory signal was accurate to ±2 [mm] in relation to the adopted standard, and it was smoothed using the Savitzky–Golay (SG) polynomial smoothing, whose parameters were optimally selected using the Durbin–Watson (DW) statistical test.

Evaluation of the Effect of Educational Bureaucracy on School Administration: A Blunting School Climate for Teachers

This study aims to determine the relationship between School Climate, Organizational Commitment and Educational Bureaucracy (coercive & enabling) to examine it according to some variables. In the study, relational survey model, which is one of the quantitative research methods, was used. The data were collected from a total of 280 teachers working in public primary and secondary schools in Gebze during the 2022-2023 academic year. Statistical analysis of the data was carried out with SPSS 26.0. ANOVA and t-tests were applied to the items to test the effect of dependent variables. Kolmogorov-Smirnov test statistics were applied and it was found that the answers given by the participants showed homogeneous distribution (p>0.05). Skewness and Kurtosis values were evaluated to obtain normal distribution values. Based on the results, it was concluded that there is a negative, low and significant relationship between School Climate, Organizational Commitment and Coercive Bureaucracy. It was seen that there is a positive, moderate and significant relationship between School Climate, Organizational Commitment and Enabling Bureaucracy. In addition, significant differences were found between the answers given to the scale items according to teachers' gender, age, institution and field.

Automated Shape and Process Parameter Optimization for Scaling Up Geometrically Non-Similar Bioreactors

Scaling bioprocesses remains a major challenge. Since it is physically impossible to increase all process parameters equally, a suitable scale-up strategy must be selected for a successful bioprocess. One of the most widely used criteria when scaling up bioprocesses is the specific power input. However, this represents only an average value. This study aims to determine the Kolmogorov length scale distribution by means of computational fluid dynamics (CFD) and to use it as an alternative scale-up criterion for geometrically non-similar bioreactors for the first time. In order to obtain a comparable Kolmogorov length scale distribution, an automated geometry and process parameter optimization was carried out using the open-source tools OpenFOAM and DAKOTA. The Kolmogorov–Smirnov test statistic was used for optimization. A HEK293-F cell expansion (batch mode) from benchtop (Infors Minifors 2 with 4 L working volume) to pilot scale (D-DCU from Sartorius with 30 L working volume) was carried out. As a reference cultivation, the classical scale-up approach with constant specific power input (233 W m−3) was used, where a maximum viable cell density (VCDmax) of 5.02·106 cells mL−1 was achieved (VCDmax at laboratory scale 5.77·106 cells mL−1). Through the automated optimization of the stirrer geometry (three parameters), position and speed, comparable cultivation results were achieved as in the small scale with a maximum VCD of 5.60·106 cells mL−1. In addition, even on the pilot scale, cell aggregate size distribution was seen to strictly follow a geometric distribution and can be predicted with the help of CFD with the previously published correlation.

The influence of profitability, liquidity, and capital structure on firm value

This study aims to examine the influence of profitability, liquidity, and capital structure on the value of companies listed on the Indonesian Stock Exchange (IDX) during the period 2018-2021. The population used in this study were food and beverage sub-sector manufacturing companies listed on the IDX over the four-year period from 2018 to 2021, comprising 42 companies. The sample obtained was 17 manufacturing companies listed on the IDX during the 2018-2021 period. The research method used in this study is the quantitative method with multiple linear regression analysis tools, where quantitative data is a study method that is based on conjunctive data. This study uses descriptive statistical tests, Kolmogorov Smirnov statistical tests, multiple linear regression tests, and hypothesis testing with the help of the SPSS 23 program. The results of this study show that profitability partially has a positive and significant effect on the firm’s value, and the variables of liquidity and capital structure simultaneously have no effect on the firm’s value.

Awareness, attitudes towards plagiarism, and acceptance of using Turnitin in academic writings among undergraduate students

The purpose of this study was to examine the association of awareness of plagiarism, attitudes towards plagiarism and acceptance of using Turnitin among undergraduate students in a public university. The study was conducted on 143 undergraduate students at Universiti Teknologi MARA, Negeri Sembilan campus. Data was collected using an online questionnaire in the Google Form platform. These students took the Academic Writing subject. The ordinal data were collected using the 5-point Likert Scale. By studying the normality of the data using Kolmogorov-Smirnov and Shapiro-Wilk statistical tests, the association between awareness of plagiarism, attitudes towards plagiarism, and acceptance of using Turnitin, can be made. The tests compared the sample's scores to a normally distributed set of scores with the same mean and standard deviation. This paper presents the major findings from non-parametric correlations (Spearman’s rank-order and Kendall Tau-b) and suggests future research direction in increasing undergraduates’ awareness of plagiarism and awareness of using Turnitin when submitting their academic assignments. To curb plagiarism among undergraduates in an academic writing course, the authors propose a plagiarism policy within academic institutions, strict sanctions on plagiarism, and teach students how to avoid plagiarism and the use of Turnitin.

Pre- and Postnatal Determinants Shaping the Microbiome of the Newborn in the Opinion of Pregnant Women from Silesia (Poland).

Pre- and postnatal factors influence the formation of the newborn's microbiome as early as birth and the intrauterine period has a substantial impact on the composition of the baby's gastrointestinal microbiota and its subsequent development. This study intends to measure pregnant women's knowledge of the importance of microbiota for the health of the newborn. The sample was selected based on defined inclusion and exclusion criteria. The assessment of women's knowledge was assessed by the Kolmogorov-Smirnov and Kruskal-Wallis statistical tests. This study population comprised 291 adult pregnant women with a mean age of 28.4 ± 4.7 years. A total of 34% (n = 99), 35% (n = 101), and 31.3% (n = 91) were at the 1-3 trimester, respectively. The results showed that 36.4% of the women were aware that the intrauterine period changes the makeup of the gastrointestinal microbiota, whereas 5.8% exhibited awareness of the composition of the child's normal gut microbiota. Most of the women surveyed-(72.1%)-know that colonization of the tract occurs as early as the birth period. Women with student status (those who will pursue higher education in the future) and those who had given birth to the most children exhibited higher levels of knowledge.

Analytical chemistry kernel library for spectroscopic profiling data

The kernel is a powerful mathematical tool in spectroscopic profiling data analysis. This paper gives a theoretical and systematic formulation for various kernel types. An open-source Python toolkit (ackl, “analytical chemistry kernel library”) is developed accordingly. Highlights of the toolkit include: (1) It designs a unified API and implements totally 31 kernel types (e.g., linear, poly, Gaussian, Matern, Cauchy, Sorensen, wavelet, Fejér, etc.), which is by far the most comprehensive kernel library. (2) It provides a tailored hyper-parameter optimization mechanism for each kernel, which suits the spectroscopic profiling data properties. (3) It designs a set of 16 evaluation metrics (e.g., classification accuracy, F1 score, MANOVA test statistic, Kolmogorov-Smirnov test statistic, Cohen effect size, Fisher's discriminant ratio, computational cost, etc.) to compare different kernels in discriminative tasks. Finally, we conducted spectroscopic profiling case studies using this tool and summarized a general guideline for kernel selection.

Multiscale modeling of ammonia adsorption on zinc sulfate-doped activated carbon: Sensitivity analysis, parameter identification, and model validation

In this study four models are developed and validated to describe the dynamic adsorption of ammonia on zinc sulfate-doped activated carbon, ranging from simple single-scale to complex multiscale models. They are based on equations for mass balance, thermodynamics, hydrodynamics, and adsorption kinetics. Since the values of the parameters are needed for the implementation of the models, the doped activated carbon is characterized and ammonia isotherms measurements are performed at three different temperatures, i.e., 288, 303, and 313 K. A method based on parameter sensitivity analysis is used to evaluate the estimability of the unknown parameters of the Toth isotherm equation. Experimental breakthrough curves were then measured at various ammonia concentrations and gas flow rates. These curves were used to determine the overall mass transfer and axial dispersion coefficients, as well as the effective and intracrystalline diffusion, involved in the model equations. The models were implemented and solved using Comsol Multiphysics®. The study showed that the adsorption process was limited by the diffusion of ammonia and adsorption on the zinc sulfate crystal. The models were validated using breakthrough curves different from those used for parameter identification. The agreement between model predictions and measurements was evaluated using performance indices and confirmed by a Kolmogorov-Smirnov statistical test.

Processing speed test: Results from a Japanese normative sample of healthy participants compared with a US normative sample

BackgroundThe Processing Speed Test (PST), a validated iPad®-based cognitive screening test for MS, has been applied to the cognitive assessment of Japanese MS patients using US normative data. MethodsTo develop PST normative data from Japanese healthy volunteers and compare the PST score distribution between Japanese and US healthy volunteers, 254 healthy Japanese-speaking volunteers were enrolled and stratified by age (20–65 years). Potential participants with a Mini-Mental State Examination score < 27 were excluded. PST raw scores (total correct) were from the Japan cohort and compared with age-restricted US normative data and propensity score-matched data created by matching sex, age, and educational level from a published study of 428 healthy participants. PST score distributions and standardized z-scores were compared using t-test and Kolmogorov–Smirnov test statistics. ResultsThe mean age of the Japan cohort was 44.1 years. The PST scores of Japanese volunteers were significantly different from those of the age-restricted (mean ± SD 61.8 ± 10.1 vs 53.7 ± 10.8; p < 0.001) and the propensity score-matched US cohort (62.1 ± 10.1 vs 53.3 ± 10.6; p < 0.001). ConclusionRegression analyses centered on US normative data could underestimate disease severity in Japanese MS patients, suggesting that separate normative data should be considered for each population sample.

Maximum $$\log _q$$ likelihood estimation for parameters of Weibull distribution and properties: Monte Carlo simulation

The maximum $${\log }_q$$ likelihood estimation method is a generalization of the known maximum $$\log $$ likelihood method to overcome the problem for modeling non-identical observations (inliers and outliers). The parameter q is a tuning constant to manage the modeling capability. Weibull is a flexible and popular distribution for problems in engineering. In this study, this method is used to estimate the parameters of Weibull distribution when non-identical observations exist. Since the main idea is based on modeling capability of objective function $$\rho (x;\varvec{\theta })=\log _q\big [f(x;\varvec{\theta })\big ]$$ , we observe that the finiteness of score functions cannot play a role in the robust estimation for inliers. The properties of Weibull distribution are examined. In the numerical experiment, the parameters of Weibull distribution are estimated by $$\log _q$$ and its special form, $$\log $$ , likelihood methods if the different designs of contamination into underlying Weibull distribution are applied. The optimization is performed via genetic algorithm. The modeling competence of $$\rho (x;\varvec{\theta })$$ and insensitiveness to non-identical observations are observed by Monte Carlo simulation. The value of q can be chosen by use of the mean squared error in simulation and the p value of Kolmogorov–Smirnov test statistic used for evaluation of fitting competence. Thus, we can overcome the problem about determining of the value of q for real data sets.

Efficiency and effectiveness of global onshore wind energy utilization

The progress of onshore wind expansion varies globally, as often expressed by a country’s installed capacity and capacity factor. However, installed capacity and capacity factor do not allow conclusions about how effectively and efficiently countries use their spatially diverse wind potential. It is unknown how far wind energy expansion is and whether the countries use the available potential in the best possible way. Thus, this study aims to quantify and compare the wind potential use effectiveness and efficiency for the 40 countries with the highest installed capacity in 2021. A global wind turbine site dataset and the wind farm potential index were used for estimating the suitability of current wind turbine sites. The wind potential use efficiency was calculated by comparing the (1) distributions of the wind farm potential index at wind turbine sites with (2) distributions of the wind farm potential index of the countries as a whole by the Kolmogorov-Smirnov test statistic. The capacity-to-suitable-area ratio defined the wind potential use effectiveness. The results reveal that 81.9% of the global onshore wind turbine fleet operates at suitable sites. Simultaneous occurrences of high effectiveness and efficiency are not given in any country: China, Brazil, and Italy use their wind potential more efficiently than many other countries. The USA and India adequately consider the wind resource efficiency for wind turbine siting. Germany has the highest effectiveness despite low efficiency. The results reveal how critical it is to quantify the progress of wind energy expansion based on the effectiveness and efficiency, not only the installed capacity and capacity factor.

Uniform inference for value functions

We propose a method to conduct uniform inference for the (optimal) value function, that is, the function that results from optimizing an objective function marginally over one of its arguments. Marginal optimization is not Hadamard differentiable (that is, compactly differentiable) as a map between the spaces of objective and value functions, which is problematic because standard inference methods for nonlinear maps usually rely on Hadamard differentiability. However, we show that the map from objective function to an Lp functional of a value function, for 1≤p≤∞, are Hadamard directionally differentiable. As a result, we establish consistency and weak convergence of nonparametric plug-in estimates of Cramér–von Mises and Kolmogorov–Smirnov test statistics applied to value functions. For practical inference, we develop detailed resampling techniques that combine a bootstrap procedure with estimates of the directional derivatives. In addition, we establish local and uniform size control of one-sided tests which use the resampling procedure. Monte Carlo simulations assess the finite-sample properties of the proposed methods and show accurate empirical size and nontrivial power of the procedures. Finally, we apply our methods to the evaluation of a job training program using bounds for the distribution function of treatment effects.

Determining the Relationship between Stress Management and the Level of Perceived Job Stress and the Quality of Services Provided by Midwives Working in the Maternity Department of Hospitals in Hamedan Province in 2022

Background and Objective: One of the important problems of midwifery is high job stress and its consequences. Having the ability to manage stress is one of the basic requirements of this profession that can reduce midwifery mistakes in the workplace. Although job stress exists in all jobs, midwives face higher levels of stress due to the nature of their job, and this issue can lead to a decrease in service quality. The present study was conducted to determine the relationship of stress management with the level of perceived job stress and the quality of services provided by midwives working in the maternity department of hospitals in Hamedan province, Iran. Materials and Methods: The statistical population of this descriptive cross-sectional study consisted of 210 midwives working in hospitals with obstetrics and gynecology departments in Hamedan province. The random sampling method was used to select the samples. The required data for this study were collected using a demographic information form, Cobin's Stress Management Questionnaire, Hospital Stress Scale, and Maternity Care Quality in Maternity Unit Scale. Data were analyzed in Stata-13 software using Kolmogorov-Smirnov statistical tests and multivariate linear regression. The significance level was considered less than 0.05. Results: The findings showed that 81.4% of midwives had a high level of stress management ability. Moreover, 65.7% of midwives perceived moderate-level job stress, and 79% of midwives provided good quality services in the delivery room. Based on the results, was a moderate and inverse relationship between stress management and perceived job stress (beta=-0.47), and a moderate and direct relationship between stress management and the quality of provided services (beta=0.56). It was also found that perceived job stress had a weak and inverse relationship with the quality of provided services (beta=-0.25). Conclusion: The results revealed that having the ability to manage stress and control job stress could improve the quality of services provided by midwives. It is necessary for midwife managers to improve the process of providing quality services by holding stress management workshops.

The Effect of Aerobic Training and Octopamine Supplement on Gene Expression Levels of VEGF and PDGFR in the Heart Tissue of Rats Exposed to Deep Fried Oil

Background: Deep-fried oils (DFO) produce toxins that endanger people's health. Using herbal supplements along with exercise training can help improve health. The purpose of this study was to investigate the effect of aerobic training (T) and octopamine (O) supplementation on VEGF and PDGFR gene expression levels in the heart tissue of rats poisoned with DFO. Materials and Methods: In this experimental study, 25 male Wistar rats were purchased and placed in 5 groups of 5 animals, including: 1) control, 2) DFO, 3) DFO+T, 4) DFO+O and 5) DFO+T+O. Over a course of 4 weeks, rats in groups 2 to 5 received DFO by gavage, and rats in groups 3 and 5 ran on the treadmill 5 times a week with moderate intensity; also, rats in groups 4 and 5 received 81 μmol/kg octopamine supplement intraperitoneally 5 days a week. The Kolmogorov-Smirnov statistical test, independent samples t-test and two-way analysis of variance were used to analyze the research findings (p<0.05). Results: DFO had a significant effect on increasing the gene expression levels of VEGF (p=0.001) and PDGFR (p=0.002); training had a significant effect on the reduction of VEGF (p=0.001) and PDGFR (p=0.012); also, octopamine consumption had a significant effect on the reduction of VEGF (p=0.001) and PDGFR (p=0.045). Conclusion: It seems that aerobic training and octopamine consumption alone have anti-inflammatory effects in the heart tissue of DFO-poisoned rats, however they do not have significant anti-inflammatory interactive effects.