In this paper, a new bio-inspired metaheuristic algorithm called Giant Armadillo Optimization (GAO) is introduced, which imitates the natural behavior of giant armadillo in the wild. The fundamental inspiration in the design of GAO is derived from the hunting strategy of giant armadillos in moving towards prey positions and digging termite mounds. The theory of GAO is expressed and mathematically modeled in two phases: (i) exploration based on simulating the movement of giant armadillos towards termite mounds, and (ii) exploitation based on simulating giant armadillos’ digging skills in order to prey on and rip open termite mounds. The performance of GAO in handling optimization tasks is evaluated in order to solve the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that GAO is able to achieve effective solutions for optimization problems by benefiting from its high abilities in exploration, exploitation, and balancing them during the search process. The quality of the results obtained from GAO is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that GAO presents superior performance compared to competitor algorithms by providing better results for most of the benchmark functions. The statistical analysis of the Wilcoxon rank sum test confirms that GAO has a significant statistical superiority over competitor algorithms. The implementation of GAO on the CEC 2011 test suite and four engineering design problems show that the proposed approach has effective performance in dealing with real-world applications.

The first step in the design phase of the Brushless Direct Current (BLDC) motor is the formulation of the mathematical framework and is often used due to its analytical structure. Therefore, the BLDC motor design problem is considered to be an optimization problem. In this paper, the analytical model of the BLDC motor is presented, and it is considered to be a basis for emphasizing the optimization methods. The analytical model used for the experimentation has 78 non-linear equations, two objective functions, five design variables, and six non-linear constraints, so the BLDC motor design problem is considered as highly non-linear in electromagnetic optimization. Multi-objective optimization becomes the forefront of the current research to obtain the global best solution using metaheuristic techniques. The bio-inspired multi-objective grey wolf optimizer (MOGWO) is presented in this paper, and it is formulated based on Pareto optimality, dominance, and archiving external. The performance of the MOGWO is verified on standard multi-objective unconstraint benchmark functions and applied to the BLDC motor design problem. The results proved that the proposed MOGWO algorithm could handle nonlinear constraints in electromagnetic optimization problems. The performance comparison in terms of Generational Distance, inversion GD, Hypervolume-matrix, scattered-matrix, and coverage metrics proves that the MOGWO algorithm can provide the best solution compared to other selected algorithms. The source code of this paper is backed up with extra online support at and .

Various authors have put their sincere efforts into proposing ratio estimators for estimating the population's mean and variance under different situations and sampling methods. But the problem arises when data is unstable, imprecise, ambiguous, incomplete and vague. In such situations, classical methods of estimation do not yield precise results, as these methods are not meant for such problems. Given this difficulty, Neutrosophic statistics are the only alternative as it deals with indeterminacy. So in this study, we have proposed a generalized Neutrosophic robust ratio type estimator which can be used to provide good results in such situations, as well as in the case of the presence of outliers. For the evaluation point of view, we have made use of real data set and simulation study to check the efficacy of our suggested estimators over the mentioned existed estimators.

We characterize the generalized Chebyshev polynomials of the second kind (Chebyshev-II), and then we provide a closed form of the generalized Chebyshev-II polynomials using the Bernstein basis. These polynomials can be used to describe the approximation of continuous functions by Chebyshev interpolation and Chebyshev series and how to efficiently compute such approximations. We conclude the paper with some results concerning integrals of the generalized Chebyshev-II and Bernstein polynomials.Comment: Change the title (Tschebyscheff to Chebyshev), and adding few comments. Adding the Journal reference

This study aimed to investigate the therapeutic potential of scopoletin in ulcerative colitis, with a primary focus on its impact on crucial inflammatory pathways and immune responses. A male mouse model of DSS-induced colitis was employed with six distinct groups: a control group, a group subjected to DSS only, three groups treated with varying scopoletin doses, and the final group treated with dexamethasone. The investigation included an assessment of the effects of scopoletin on colitis symptoms, including alterations in body weight, Disease Activity Index (DAI), and histopathological changes in colonic tissue. Furthermore, this study scrutinized the influence of scopoletin on cytokine production, PPARγ and NF-κB expression, NLRP3 inflammasome, and the composition of intestinal bacteria. Scopoletin treatment yielded noteworthy improvements in DSS-induced colitis in mice, as evidenced by reduced weight loss and colonic shortening (p < 0.05, < 0.01, respectively). It effectively diminished TNF-α, IL-1β, and IL-12 cytokine levels (p < 0.01, p < 0.05), attenuated NLRP3 inflammasome activation and the associated cytokine release (p < 0.05, p < 0.01), and modulated the immune response by elevating PPARγ expression while suppressing NF-κB pathway activation (p < 0.05, p < 0.01). Additionally, scopoletin induced alterations in the gut microbiota composition, augmenting beneficial Lactobacillus and Bifidobacteria while reducing E. coli (p < 0.05). It also enhanced tight junction proteins, signifying an improvement in the intestinal barrier integrity (p < 0.05, < 0.01). Scopoletin is a promising therapeutic agent for managing ulcerative colitis, showing benefits that extend beyond mere anti-inflammatory actions to encompass regulatory effects on gut microbiota and restoration of intestinal integrity.