As smart cities expand rapidly, the demand for strong fire protection has become even more essential. A significant challenge lies in ensuring that fire detection systems integrate seamlessly into our modern infrastructures. Leveraging multisensor systems can yield reliable data on potential fires. Particularly in smart buildings, the effectiveness of multisensor fire detection algorithms becomes paramount. This research introduces a fuzzy logic-driven method that harnesses the power of smoke, flame, and temperature sensors. While smoke, flames, and elevated temperatures are primary fire indicators, they can manifest concurrently or sequentially. We have amalgamated various fuzzy logic rules (IF-THEN structures) to gauge the intensity of fires. When a fire ignites, the sensors spring into action, identifying its source and promptly notifying users via the Internet and GSM modems. Moreover, they relay the fire’s precise geographic coordinates to fire departments. The fire’s status will be consistently updated on a dedicated online portal. It was observed from our results that the system proficiently sends fire alerts to residents, and the fire status undergoes regular updates at 45-second intervals. This refreshing is initiated by the identification of a designated percentage of smoke and flame. These outcomes validate the system’s effectiveness in enhancing the precision and responsiveness of fire detection capabilities.

The multiobjective (MO) optimizers show great promise in solving constrained engineering structural problems. This paper introduces a MO version of the Brown Bear Optimization (BBO) algorithm, inspired by the foraging behavior of brown bears. The proposed Multiobjective Brown Bear Optimization (MOBBO) algorithm is applied to five structural optimization problems, including 10‐bar, 25‐bar, 60‐bar, 72‐bar, and 942‐bar trusses, aiming to minimize both mass and maximum nodal deflection simultaneously. Comparative evaluations against six benchmark algorithms demonstrate MOBBO’s superior convergence, solution diversity, and effectiveness in addressing highly constrained problems. The hypervolume (HV) and inverted generational distance (IGD) metrics place MOBBO in first rank according to the Friedman test, with an average standard deviation of 0.0002. Moreover, the spacing‐to‐extent (STE) and generational distance (GD) metrics rank MOBBO second. The final Friedman rank test highlights MOBBO’s overall dominance, achieving a first rank. Best Pareto plots, diversity graphs, and box plot analyses further suggest MOBBO’s superior performance and convergence compared to existing algorithms. Therefore, the MOBBO algorithm can be effectively applied to various MO optimization tasks in industry, offering refined global optimization solutions and contributing valuable insights to the field of MO algorithms.

Vegetable tannery wastewater, highly laden with recalcitrant organics, is not easily treatable through biological processes. This study focuses on the use of response surface methodology in optimizing a coagulation-flocculation process for pretreatment of vegetable tannery wastewater. This study also assessed the possibility of replacing chemical coagulants such as aluminum sulphate with green alternatives such as cassava starch and orange peel powder. The effects of coagulant dosage and pH on three key wastewater quality parameters (chemical oxygen demand (COD) and total suspended solids (TSS)’s removal efficiencies as well as sludge volume index (SVI)) were also assessed. Quadratic models developed for all the three responses were adequate. The optimal conditions were attained at a pH of 3.17 and a dosage of 2.76 g/L for cassava starch coagulant, pH of 3.74 and a dosage of 5.16 g/L for orange peel powder coagulant, and pH of 6.09 and a dosage of 11.60 g/L for aluminum sulphate. The COD and TSS removal efficiencies as well as SVI achieved under these optimal conditions were 37.25%, 73.95%, and 14.80 mL/g, respectively, for cassava starch coagulant; 17.97%, 66.08%, and 19.87 mL/g, respectively, for orange peel powder coagulant; and 38.51%, 76.06%, and 29.57 mL/g, respectively, for aluminum sulphate. The outperformance of cassava starch over orange peel powder and its comparable results with aluminum sulphate makes the former a more environment-friendly alternative to aluminum sulphate for treatment of tannery wastewater.

A nine-compartment deterministic cholera model was formulated, and the model describes interactions between human, Vibrio cholerae bacteria, and the enviroment that warrant the interaction. Realities and socioeconomic burden influence spread and control mechanism of the disease. The model investigated some effective ways of hindering cholera outbreak and spread. The existence and uniqueness of solution of the system of equations that the model comprises were ascertained. The basic reproduction number R 0 of the model was obtained using “next-generation matrix” method, and the most sensitive parameters were identified using “normalised forward sensitivity index” method. Three controls, hygiene consciousness denoted by X1, cholera vaccine X2, and cholera awareness programme X3, were chosen. Optimal control theory is applied to ascertain the level of effect of the controls in reducing susceptible, exposed, infected individuals and causative pathogen population. Pontryagin’s maximum principle is used to prove the optimal solution of the model, and the optimal system was derived and numerically solved. Simulations were made with graphs that show the effects of the controls on susceptible, exposed, infected, and Vibrio cholerae population. The findings are that simultaneous application of the three controls can be one of the fast and effective ways of controlling cholera. If two controls are to be selected, hygiene consciousness and vaccine are the best combination.

The aim of this paper is to present an original approach for ranking of DEA-efficient DMUs based on the cross efficiency and analytic hierarchy process (AHP) methods. The approach includes two basic stages. In the first stage using DEA models the cross efficiency value of each DEA-efficiency DMU is specified. In the second stage, the pairwise comparison matrix generated in the first stage is utilized to rank scale of the units via the one-step process of AHP. The advantage of this proposed method is its capability of ranking extreme and nonextreme DEA-efficient DMUs. The numerical examples are presented in this paper and we compare our approach with some other approaches.

We present a novel hybrid algorithm based on particle swarm optimization (PSO) and simulated annealing (SA) for the design of two-dimensional recursive digital filters. The proposed method, known as SA-PSO, integrates the global search ability of PSO with the local search ability of SA and offsets the weakness of each other. The acceptance criterion of Metropolis is included in the basic algorithm of PSO to increase the swarm’s diversity by accepting sometimes weaker solutions also. The experimental results reveal that the performance of the optimal filter designed by the proposed SA-PSO method is improved. Further, the convergence behavior as well as optimization accuracy of proposed method has been improved significantly and computational time is also reduced. In addition, the proposed SA-PSO method also produces the best optimal solution with lower mean and variance which indicates that the algorithm can be used more efficiently in realizing two-dimensional digital filters.

This paper formulates a search model that gives the optimal search plan for the problem of finding a discrete random walk target in minimum time. The target moves through one of n-disjoint real lines in R : we have n-searchers starting the searching process for the target from any point rather than the origin. We find the conditions that make the expected value of the first meeting time between one of the searchers and the target finite. Furthermore, we show the existence of the optimal search plan that minimizes the expected value of the first meeting time and find it. The effectiveness of this model is illustrated using numerical example.

Let denote the class of square matrices containing in each row and in each column exactly 1’s. The minimal value of , for which the behavior of the permanent in is not quite studied, is . We give a simple algorithm for calculation of upper magnitudes of permanent in and consider some extremal problems in a generalized class , the matrices of which contain in each row and in each column nonzero elements , , and and zeros.

Application of soft computing techniques for various metamaterial designs and optimizations is an emerging field in the microwave regime. In this paper, a global optimization technique, namely, particle swarm optimization (PSO), is used for the design and optimization of a square split ring resonator (SSRR) having a resonant frequency of 2.4 GHz. The PSO optimizer yields the structural parameters, which is further simulated and validated with the optimized value. This optimized structure results in the mutual coupling reduction in a microstrip antenna array designed for wireless application.