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In this paper, a new human-based metaheuristic algorithm called Carpet Weaving Optimization (CWO) is introduced, which is inspired by human skills when weaving a carpet. The main source of inspiration in the design of CWO is taken from the communication between the carpet weaver and the map reader who try to weave a carpet according to the given pattern. The theory of CWO is stated and then mathematically modeled based on the simulation of the carpet weaving process. The effectiveness of CWO in optimization has been assessed across twenty-three standard benchmark functions encompassing unimodal, high-dimensional multimodal, and fixed-dimensional multimodal categories. The optimization outcomes underscore CWO's capability to yield favorable results across various optimization challenges, adeptly navigating between exploration, exploitation, and achieving a balanced search process. Comparative analysis against twelve rival algorithms demonstrates CWO's superior performance, consistently outshining competitors across all twenty-three benchmark functions and securing the top rank as the premier optimizer. Moreover, the efficacy of CWO in real-world applications has been scrutinized through its optimization of four engineering design quandaries. Simulation findings corroborate CWO's commendable performance in real-world and engineering contexts, as evidenced by its capacity to deliver superior values for design variables and objective functions compared to competing algorithms. [ABSTRACT FROM AUTHOR]

This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the unique hunting behavior of frilled lizards in their natural habitat. FLO draws its inspiration from the sit-and-wait hunting strategy of these lizards. The algorithm's core principles are meticulously detailed and mathematically structured into two distinct phases: (i) an exploration phase, which mimics the lizard's sudden attack on its prey, and (ii) an exploitation phase, which simulates the lizard's retreat to the treetops after feeding. To assess FLO's efficacy in addressing optimization problems, its performance is rigorously tested on fifty-two standard benchmark functions. These functions include unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions, as well as the challenging CEC 2017 test suite. FLO's performance is benchmarked against twelve established metaheuristic algorithms, providing a comprehensive comparative analysis. The simulation results demonstrate that FLO excels in both exploration and exploitation, effectively balancing these two critical aspects throughout the search process. This balanced approach enables FLO to outperform several competing algorithms in numerous test cases. Additionally, FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems, further validating its robustness and versatility in solving real-world optimization challenges. Overall, the study highlights FLO's superior performance and its potential as a powerful tool for tackling a wide range of optimization problems. [ABSTRACT FROM AUTHOR]

This research article concentrates on process conditions in addition to improving color selections in polymer compounders and developing more accurate simulation models. The feed rate (FR), temperature (T) and screw speed (SS) are three processing variables that the research investigates using general trends (GTs) and Box–Behnken design (BBD) response surface methodology. The identical set of processing settings was tweaked at three separate phases independently of one another. This study uses the experimental design to investigate process parameters' optimization while holding all other parameters constant. This design was given the name GT. To develop this design and its statistical optimization, this study used the software of the design expert method. A regression model was run in this design, which displayed collective as well as individual effects of the parameters on color images. The values of tri-stimulus color with the best optimization had the smallest proper color variance (dE*). To obtain information on pigment characteristics, an SEM image analysis was conducted, which aids in improving future designs and overcoming manufacturing issues that affect color fluctuation properties and waste reduction for various chemical grades, both of which enhance environmentally friendly processes. [ABSTRACT FROM AUTHOR]

Tunisia economic relies heavily on an agriculture key driver. Unfortunately, it yet faces challenges in water management in particularly southern regions having scarce rainfall. Inefficiencies in regular irrigation practized for date palm is addressed by this study. We propose here a smart irrigation system. Leveraging Internet of Things (IoT) and Artificial Intelligence (AI), we have conducted a solution for the Sahara oases. This was done by deploying an IoT sensor network for real-time environmental monitoring. AI algorithms have been used for data processing, enabling precise water predictions for date palms. Results showcase a substantial reduction in water usage, improved crop health, and enhanced yield. The study introduces a novel smart irrigation approach for southern Tunisia. A water optimization has resulted through remote control and monitoring via a cloud-based wireless communication system. Beyond advancing smart agriculture, the research offers crucial implications for mitigating water stress in arid regions, contributing to the transformation of agricultural landscapes. Smart irrigation systems emerge as a viable solution for optimizing water use and avoiding both over- and under-irrigation. [ABSTRACT FROM AUTHOR]

Organizational citizenship behavior (OCB) is vital for continuous organizational growth and development. OCB also plays an evident role in advancing the quality of health care services and practicing OCB in hospitals can be useful. Although OCBs' contributing factors have been studied in Jordan in earlier researches, the utilization of OCB in health sector is still a new topic. Therefore, this article aimed to investigate the impact of employees' gender and age on OCB at Jordanian governmental hospitals using a fuzzy approach. A convenient sample of 126 employees working in three main governmental hospitals in the north of Jordan has been taken. The collected data include linguistic terms that suffer from uncertainty which, in turn, cannot be dealt with traditional numerical values. This study indicates that gender impact on OCB has statistically significant differences at α =.05 in favor of males with the total score of 0.011% as far as altruism, courtesy, and civic virtue are concerned. Similarly, the effect of age factor on OCB is statistically significant differences at α =.05 in relation to courtesy, sportsmanship, and civic virtue with the total score of 0.27%. Finally, the results provide baseline data for further studies that may pay more attention toward employees' demographics in OCB. This study can support mangers and leaders holding dominant positions in public sector health care organizations. [ABSTRACT FROM AUTHOR]