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Particle swarm optimizer is a well-known efficient population and control parameter-based algorithm for global optimization of different problems. This paper focuses on a new and primary sample for PSO, which is named phasor particle swarm optimization (PPSO) and is based on modeling the particle control parameters with a phase angle ( θ ), inspired from phasor theory in the mathematics. This phase angle ( θ ) converts PSO algorithm to a self-adaptive, trigonometric, balanced, and nonparametric meta-heuristic algorithm. The performance of PPSO is tested on real-parameter optimization problems including unimodal and multimodal standard test functions and traditional benchmark functions. The optimization results show good and efficient performance of PPSO algorithm in real-parameter global optimization, especially for high-dimensional optimization problems compared with other improved PSO algorithms taken from the literature. The phasor model can be used to expand different types of PSO and other algorithms. The source codes of the PPSO algorithms are publicly available at https://github.com/ebrahimakbary/PPSO .

Over the past years, many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems. This study presents a new optimization method based on an unusual geological phenomenon in nature, named Geyser inspired Algorithm (GEA). The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process. The efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005, CEC 2014, CEC 2017, and real-parameter benchmark functions. Moreover, GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness. In addition, to demonstrate the applicability and robustness of GEA, a comprehensive investigation is performed for a fair comparison with other standard optimization methods. The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms, including ABC, BBO, PSO, and RCGA. Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea .

Purpose The purpose of this study was to investigate the determinants of purchase intentions for utilitarian and hedonic counterfeit sporting goods among Muslim’ consumers. The moderating effect of income and status consumption was also examined. Design/methodology/approach Questionnaires were distributed among male undergraduate university students who were consumers of sports goods. A total of 360 valid and usable questionnaires were collected and analyzed through descriptive and inferential statistics. Findings The results showed that perceived risk, personal gratification, religiosity, information susceptibility and normative susceptibility all have a positive and significant effect on negative attitudes toward counterfeit sports goods. The effect of a negative attitude toward counterfeit goods was also negative and significant for the intention to purchase utilitarian and hedonistic goods. Income was not a moderator in the relationship between negative attitudes and the intention to purchase utilitarian and hedonic goods. However, the moderating effect of status consumption on the intention to purchase both utilitarian and hedonic goods was significant. Research limitations/implications The use of a male-only student sample potentially limits the generalization of the findings, but not the underlying relationships between the variables. Efforts to discourage or prevent the purchase of counterfeit sporting goods should highlight the risk and reduced personal gratification associated with purchasing counterfeit products, while also appealing to a person’s faith-based ethics. Originality/value This study produces a novel model explaining the purchase intentions for utilitarian and hedonic counterfeit sporting goods. This model can inform the development of anti-counterfeiting strategies in Muslim countries.

Background One of the key aspects of fair access to healthcare is access to affordable medications, which is essential for almost every government. Exploring and estimating the effects of the Social Determinants of Health (SDH) on affordability is an important step to clarify and picture the equity in health and targets for policy making. This paper attempts to estimate these effects on medication affordability in Iran. Methods We used data from the Iran Households Survey (IHS) in a 26-year period from 1997 to 2022. We developed a panel data econometric model to estimate the effects of Socioeconomic Determinants (SEDs) on medication expenses and three ratios, including: medication expense/ health costs, income, and all costs, as affordability indices. We employ Fixed Effect (FE) estimators, Random Effect (RE) estimators, Panel Tobit (PT) estimators, and Maximum Likelihood Estimators (MLE) for this purpose. Results the mean medication expenses for each household are about 1.45E + 07 IRR adjusted in 2021(1400 Persian calendar) inflation rate. The Concentration Index (CI) is about 0.12. The median of the medication expenses/ health expenses is about 33.5%. We don’t realize that inequality for this ratio between provinces (CI = -0.01), increase prevalence of rare disease, age of households’ head, increase the share of food and housing expenses, and increase family size can increase household expenses and financial burden. Instead of getting a transfer payment as a cash subsidy, having insurance can reduce medication costs. Conclusion This study uses provincial panel data to explore socioeconomic determinants of medication affordability, incorporating novel variables like rare disease prevalence, internet access, government support, and pharmacist population. Results show minimal inequality in medication costs among wealthier provinces, with no differences in the medication-to-health expense ratio. Older age, larger household size, and rare disease prevalence hinder affordability, while private insurance and government subsidies improve access. No significant correlations were found between affordability and national insurance or availability. Future research should investigate the causal mechanisms and occupational and educational differences.

PurposeThe purpose of the study was to compare young adult responses to rational and emotional sports product advertisements. The moderating roles of product type and gender were also examined.Design/methodology/approachThe study utilized a 2 (advertising appeal: emotional vs rational) ×2 (gender: male vs female)×2 (sport product type: utilitarian vs hedonic) research design. Data were collected from 160 sport product users. The collected data were analyzed using three-way analysis of variance (ANOVA).FindingsThe results indicated that emotional advertising has a greater impact on the attitude toward advertising and the intention to purchase sports products. The results also revealed that rational advertising appeals have more impact for utilitarian sports products and that emotional advertising appeals are more effective for hedonic sports products. However, there were no significant effects for gender as a moderating variable.Originality/valueThis is the first study to examine responses to rational and emotional appeals in sports product advertisements.

In this paper, we firstly derive a general expression for the entries of the mth (m∈ℕ) power for two certain types of tridiagonal matrices of arbitrary order. Secondly, we present a method for computing the positive integer powers of the anti-tridiagonal matrix corresponding to these matrices. Also, we give Maple 18 procedures in order to verify our calculations.

PurposeThe aim of this paper is to investigate the interaction effect that sponsor-team congruence and team fan (home/rival team) have on the influence of different types of sports sponsorship (joint, corporate social responsibility [CSR]-linked and conventional sponsorship) on fans' attitudes and purchase intentions toward the sponsor.Design/methodology/approachTwo experimental studies were conducted on 391 and 297 participants. The data gathered underwent analysis through five multivariate general linear model analyses.FindingsThe study found that CSR-linked sponsorship had the strongest positive impact and the weakest negative impact on the attitude and purchase intention of home team supporters and the attitude of rival team supporters. Nonetheless, the sponsor-team congruence did not significantly moderate the relationship. Additionally, the research demonstrated that the fan identity of both home and rival teams moderates the impact of sports sponsorships on attitude and purchase intention.Originality/valuePrevious research has studied the effectiveness of sponsorship format types independently. This is the first research comparing sponsorship formats regarding fan type and congruence.

Many important engineering optimization problems require a strong and simple optimization algorithm to achieve the best solutions. In 2020, Rao introduced three non-parametric algorithms, known as Rao algorithms, which have garnered significant attention from researchers worldwide due to their simplicity and effectiveness in solving optimization problems. In our simulation studies, we have developed a new version of the Rao algorithm called the Fully Informed Search Algorithm (FISA), which demonstrates acceptable performance in optimizing real-world problems while maintaining the simplicity and non-parametric nature of the original algorithms. We evaluate the effectiveness of the suggested FISA approach by applying it to optimize the shifted benchmark functions, such as those provided in CEC 2005 and CEC 2014, and by using it to design mechanical system components. We compare the results of FISA to those obtained using the original RAO method. The outcomes obtained indicate the efficacy of the proposed new algorithm, FISA, in achieving optimized solutions for the aforementioned problems. The MATLAB Codes of FISA are publicly available at https://github.com/ebrahimakbary/FISA .

The optimization problems are becoming more complicated, requiring new and efficient optimization techniques to solve them. Many bio-inspired meta-heuristic algorithms have emerged in the last decade to solve these complex problems as most of these algorithms may be trapped into local optima and could not effectively solve all types of optimization problems. Hence, researchers are still trying to develop new and better optimization algorithms. This paper introduces a novel biologically-based optimization algorithm called circulatory system-based optimization (CSBO). CSBO is modeled based on the function of the body's blood vessels with two distinctive circuits, i.e. pulmonary and systemic circuits. The proposed CSBO algorithm is tested on a wide variety of complex functions of the real world and validated with the standard meta-heuristic algorithms. The results indicate that the CSBO algorithm successfully achieves the optimal solutions and avoids local optima. Note that the source code of the CSBO algorithm is publicly available at http://www.optim-app.com/projects/csbo .

Tridiagonal matrices appear frequently in mathematical models. In this paper, we derive the positive integer powers of special tridiagonal matrices of arbitrary order.