Background and Objectives: This paper explores the realm of optimization by synergistically integrating two unique metaheuristic Algorithms: the Wild Horse Optimizer (WHO) and the Fireworks Algorithm (FWA). WHO, inspired by the behaviors of wild horses, demonstrates proficiency in global exploration, while FWA emulates the dynamic behavior of Fireworks, thereby enhancing local exploitation. The goal is to harness the complementary strengths of these Algorithms, achieving a harmonious balance between exploration and exploitation to enhance overall optimization performance.Methods: The study introduces a novel hybrid metaheuristic Algorithm, WHOFWA, detailing its design and implementation. Emphasis is placed on the Algorithm's ability to balance exploration and exploitation. Extensive experiments, featuring a diverse set of benchmark optimization problems, including general test functions and those from CEC 2005, CEC 2019, and 2022, assess WHOFWA's effectiveness. Comparative analyses involve WHO, FWA, and other metaheuristic Algorithms such as Reptile Search Algorithm (RSA), Prairie Dog Optimization (PDO), Fick’s Law Optimization (FLA), and Ladybug Beetle Optimization (LBO).Results: According to the Friedman and Wilcoxon signed-rank tests, for all selected test functions, WHOFWA outperforms WHO, FWA, RSA, PDO, FLA, and LBO by 42%, 55%, 74%, 71%, 48%, and 52%, respectively. Finally, the results derived from addressing real-world constrained optimization problems using the proposed Algorithm demonstrate its superior performance when compared to several well-regarded Algorithms documented in the literature.Conclusion: In conclusion, WHOFWA, the hybrid metaheuristic Algorithm uniting WHO and FWA, emerges as a powerful optimization tool. Its unique ability to balance exploration and exploitation yields superior performance compared to WHO, FWA, and benchmark Algorithms. The study underscores WHOFWA's potential in tackling complex optimization problems, making a valuable contribution to the realm of metaheuristic Algorithms.

The nature of business and investment activities is such that earning a return requires risk tolerance. Choosing a stock portfolio is a difficult and difficult task that the investor sees in the face of the many and varied choices that she must choose as one of the best methods. The present study deals with the problem of stock portfolio optimization according to the Value at Risk based intelligent Fireworks Algorithm and compares it with Particle Swarm Optimization Algorithm with the historical simulation method using MATLAB software. The parameters of meta-heuristic Algorithms were adjusted by Taguchi method using MINITAB software. Not suspended, used. For reliability of the study, generalized Dickey-Fuller test and Phillips-Prone test were used. To evaluate the accuracy of the Conditional Value at Risk model, the kupiec proportion of failure test, Christoffersen independence test and Conditional coverage test are used. A comparison was also made between the models by Lopez test. The execution time of the Particle Swarm Optimization was less than that of the Fireworks Algorithm at all three levels of confidence, but the convergence speed of the Fireworks Algorithm was faster than that of the Particle Swarm Optimization at all levels. Findings showed that the Value at Risk model using the Fireworks Algorithm, despite the longer execution time due to better convergence speed and higher rank of Lopez test has a more appropriate validity for stock portfolio optimization.

Peoples of Persian culture celebrate the last Wednesday of the year on its eve, the Tuesday night, according to the Persian calendar. Named Chaharshanbe-Soori, literally 'red Wednesday', the festivity is held on a day from 12 to 19 of March. The event precedes Nowruz (the Persian New Year) and comprises several traditions of which setting up bonfires and jumping over them is an integral part. Red refers to fire, itself symbolizing brightness, purity, life, and health in ancient Persia.1 The origin of the festivity goes back to a Zoroastrian tradition circa 1725 BC.

Fireworks activity causes short-term air pollution. The festival of Diwali is celebrated with firecrackers in India during October/November every year. The ambient air quality was assessed in Vadodara city, Gujarat state of India during Diwali festival for the consecutive years 2009, 2010, and 2011. During the festival day, the average concentration of PM10 was increased 35 times compared with a normal day before Diwali. Similarly, concentration of SO2 has increased 23 times and NOx has increased 3 times on the festival day. The diurnal pattern of the above pollutants showed an increase in the night. Air quality index calculated for 24 h average during Diwali 2009 in two stations exceeded a value of 125 indicating severe air pollution in Vadodara city. It is also confirmed that the yearly pollution load is getting increased in Vadodara city as the air quality index value has a gradual increase from 2009 to 2011. The good correlation coefficient between different pollutants confirmed the firework as the prime source of pollution. Based on PM10 concentration, the highest mortality rate was found to be 30.4 %. The short-term high accumulation of PM10 is a matter of serious concern for city dwellers as it can penetrate deep into the lungs and cause many respiratory and cardiovascular diseases.

Devaluation of assets in the future is one of the most important investment concerns that has led investors to choose the set of assets that have the lowest risk and highest return. The present study deals with the problem of stock portfolio optimization according to the Conditional Value at Risk based on the new and intelligent Fireworks Algorithm and compares it with genetic Algorithm with the historical simulation method using MATLAB software. The parameters of meta-heuristic Algorithms were adjusted by Taguchi method using MINITAB software. Not suspended, used. For reliability of the study, generalized Dickey-Fuller test and Phillips-Prone test were used. To evaluate the accuracy of the Conditional Value at Risk model, the kupiec proportion of failure test, Christoffersen independence test and Conditional coverage test are used. A comparison was also made between the models by Lopez test. Findings showed that at %95 and %99 confidence levels, the conditional risk value model using the Fireworks Algorithm has a suitable and reliable validity for measuring market risk and optimizing the stock portfolio.

The present research was designed to examine the efficacy of the Fireworks interventional program of anger management training on parents. One hundred forty one parents (78 fathers and 63 mothers) volunteered to participate in the training program. A total of 7 workshops were held for the participants (4 groups of fathers and 3 groups of mothers). Parents were instructed the Fireworks anger management program for seven sessions, each session two hours per week. Data was collected utilizing State-Trait Anger Expression Inventory (Spielberger, 1998). The findings, based on dependent t-test, revealed that anger temperament and anger expression were decreased. Also, anger management training had a significant impact on the increase of anger control-in and anger control-out. In addition, independent t-test revealed that there is a significant difference between fathers and mothers in anger reaction, anger temperament, and anger control-in, and anger control-out.

Man made accidents while handling explosives in Fireworks industries cause loss of human lives and infrastructure due to collapse of walls which indicates that no fully safe manufacturing and construction procedures are followed in these industries. Hence, an attempt is made to suggest a cost effective construction by conducting analytical studies on brick masonry with strengthening measures using ANSYS and the results presented. It was observed that, by providing RCC vertical bands at door jambs, the resistance of brick masonry against accidental overloading can be improved considerably so that progressive collapse of the entire structure can be avoided.

Background: Iranian people celebrate the last Wednesday eve of the year as Chaharshanbe Suri and use explosives and firecrackers as part of this festival.Objectives: This study concerned Fireworks injuries related to the Chaharshanbe Suri festival in Tabriz, a provincial capital in East Azerbaijan, Iran, to evaluate the epidemiology and provide data to prevent and manage these accidents.Patientsand Methods: This study was comprised of patients who were treated at the emergency department of Tabriz Sina hospital with burn injuries related to Fireworks. The patients’ demographics, time of the accidents, locations of the burns, and the type of explosive were recorded. Descriptive statistics were used to analyze the data.Results: Forty-nine patients were seen in the emergency department. The mean age was 17.16  9.1, ranged from 3 to 36. Forty patients were male (81.6%) and 9 were female (18.4%). Twenty-one patients had second degree burns (47.7%), 10 patients had first degree burns (22.7%), 9 patients had third degree burns (20.5%), and 4 patients had first and second degree burns (9.1%).Conclusions: The study shows that walking in the streets or driving with open windows can be dangerous in Iran during Charshanbe suri.

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic genetic Algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic Algorithm and multi-objective optimization goals. The genetic Algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the Algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.