A Novel Swarm Optimization Algorithm Based on Hive Construction by Tetragonula carbonaria Builder Bees

Proton Exchange Membrane (PEM) fuel cells represent a promising clean energy technology due to their high efficiency, environmental sustainability, and wide applicability in transportation and stationary power systems. Accurate parameter extraction from PEM fuel cell models is critical for reliable performance prediction, control, and optimization. However, this task is challenging because of the nonlinear, multimodal, and highly coupled characteristics of fuel cell models. To address this challenge, this paper proposes an Enhanced Elk Herd Optimizer (EEHO), incorporating a novel best-bull–guided differential reproduction mechanism to improve search accuracy, convergence speed, and robustness. The proposed enhancement enables a portion of offspring solutions to be generated by perturbing the global best solution using scaled differences between randomly selected herd members. This mechanism strengthens exploitation around promising regions while maintaining population diversity and preventing premature convergence. The EEHO is applied to extract seven unknown parameters of PEM fuel cell models by minimizing the sum of squared errors between experimental and simulated voltage data. The effectiveness of the proposed method is validated using two commercial PEM fuel cell stacks, namely a 250 W stack and a BCS 500 W stack. Extensive comparative evaluations against the conventional Elk Herd Optimizer and several well-established methods demonstrate that the EEHO achieves superior performance in terms of accuracy, convergence speed, robustness, and statistical consistency. The proposed algorithm attains lower error values, faster convergence, and more stable performance across multiple independent runs. Furthermore, the extracted parameters produce highly accurate voltage and power characteristics, closely matching experimental observations. The results confirm that the proposed EEHO provides an efficient, reliable, and robust optimization framework for PEM fuel cell parameter extraction and offers strong potential for broader applications in energy system modeling, intelligent optimization, and renewable energy optimization problems. Quantitatively, the proposed EEHO achieved a significant reduction in the averages of the Sum of Squared Errors (SSE) of up to 24.96% and 23.29% compared with the conventional EHO for the 250 W stack and a BCS 500 W stack, respectively, demonstrating its superior accuracy in parameter estimation. To further validate the robustness and generalization capability of the proposed EEHO, two additional commercial PEM fuel cell datasets, of Ballard Mark V and Modular SR-12, are investigated and compared against several state-of-the-art optimization algorithms. The results, supported by Wilcoxon and Friedman statistical tests and boxplot analyses, confirm that EEHO consistently achieves superior accuracy, stability, and convergence reliability across different operating conditions.

Prevention of menstrual migraine with perimenstrual transdermal 17-β-estradiol: a randomized, placebo-controlled, double-blind crossover study

Optimizing multi-chiller dispatch in HVAC system using equilibrium optimization algorithm

Metaheuristic optimization algorithms continue to be essential for solving complex real-world problems, yet existing methods often struggle with balancing exploration and exploitation across diverse problem landscapes. This paper proposes a novel nature-inspired metaheuristic optimization algorithm named the Painted Wolf Optimization (PWO) algorithm. The main inspiration for the PWO algorithm is the group behavior and hunting strategy of painted wolves, also known as African wild dogs in the wild, particularly their unique consensus-based voting rally mechanism, a behavior fundamentally distinct from the social dynamics of grey wolves. In this innovative process, pack members explore different areas to find prey; then, they hold a pre-hunting voting rally based on the alpha member to determine who will begin the hunt and attack the prey. The efficiency of the proposed PWO algorithm is evaluated by a comparison study with other well-known optimization algorithms on 33 test functions, including the Congress on Evolutionary Computation (CEC) 2017 suite and different real-world engineering design cases. Furthermore, the algorithm’s performance is further tested across a spectrum of optimization problems with extensive unknown search spaces. This includes its application within the field of cybersecurity, specifically in the context of training a machine learning-based intrusion detection system (ML-IDS), achieving an accuracy of 0.90 and an F-measure of 0.9290. Statistical analyses using the Wilcoxon signed-rank test (all p<0.05) indicate that the PWO algorithm outperforms existing state-of-the-art algorithms, providing superior solutions in diverse and unpredictable optimization landscapes. This demonstrates its potential as a robust method for tackling complex optimization problems in various fields. The source code for the PWO algorithm is publicly available at https://github.com/saeidsheikhi/Painted-Wolf-Optimization.

The purposes of this study were 1) to develop an instructional model integrating WebQuest Learning Approach and Reflective Practice to enhance English critical reading ability of undergraduate students, 2) to investigate the effectiveness of the instructional, and 3) to investigate undergraduate students? English critical reading ability development learning with the instructional model. The research and development processes were divided into four phases: 1) studying of the significance of the problem and learning approaches, 2) developing of the instructional model integrating WebQuest Learning Approach and Reflective Practice, 3) Studying the effectiveness of the instructional model integrating WebQuest Learning Approach and Reflective Practice, and 4) Revising and developing the instructional model integrating WebQuest Learning Approach and Reflective Practice. The sample consisted of 11 second-year undergraduate students majoring in English as a foreign language at the Faculty of Language, Arts, and Humanities in a university in Cambodia. The sample were purposively selected and studied with the newly developed instructional model for 10 weeks, total of 30 hours. The instrument was the English critical reading ability test. Quantitative statistic used in this study was Wilcoxon Signed-ranks test. Qualitative data were analyzed using content analysis. The results showed that: 1. The instructional model integrating WebQuest Learning Approach and Reflective Practice consisted of five learning steps: 1) Generating skepticism, 2) Assigning the Quest, 3) Exploring the Quest, 4) Reflecting the Quest and 5) Concluding the Quest. 2. A Wilcoxon Signed-Ranks test indicated the sample (were taught with the instructional model integrating WebQuest Learning Approach and Reflective Practice) had significantly higher critical reading ability mean score in the posttest compared to those in the pretest at .05 level of significance. 3. The sample (were taught with the instructional model integrating WebQuest Learning Approach and Reflective Practice) resulted in the higher development of the English critical reading ability after experienced with the instruction.

Introduction: Complication in pregnancy can be endangering both mother and fetus. Pregnant woman and her family must be able to recognize risk factors of the complication. Family support make pregnant woman feel more confident, happy and ready to face her pregnancy. The aimed of this study was to learn the influences of social family support toward anticipatory behavior of pregnancies sign’s alert in primigravida.Methods: A quasy experimental purposive sampling design was used in this study. There were 20 respondents who met the inclusion criteria. They were divided into 2 groups, intervention and control groups, each comprising 10 respondents. The independent variable in this study was social family support and the dependent variable was the anticipatory behavior of pregnancies sign’s alert in primigravida. Data were collected by using questionnaire, observation and structured interview and then analyzed by using Wilcoxon Signed Rank Test and Mann Whitney U Test with significance level α≤0.05.Results: The result showed that the social family support influencing the knowledge of pregnant woman with significant level p=0.011, social family support also influencing the attitude of pregnant woman with significant level p=0.008 and the action of pregnant woman p=0.017. There were difference knowledge, attitude and action between intervention and control groups after treatment with significant level p=0.000 for knowledge, p=0.015 for attitude and p=0.002 for action.Conclusion: It can be concluded that social family support influence anticipatory behavior (knowledge, attitude and action) of pregnancies sign’s alert in primigravida. Further studies should be concern to the other factors that influencing the anticipatory behavior of pregnancies sign’s alert in primigravida, both of internal and external factors.

Dispersed foraging slime mould algorithm: Continuous and binary variants for global optimization and wrapper-based feature selection

In hypothesis testing, it is common to observe a shift in both location and scale simultaneously. Location-scale tests are designed to handle such situations, and combining location and scale test statistics is a popular approach among statisticians. Lepage proposed a combination of the Wilcoxon and Ansari-Bradley tests, respectively, for location-shift and scale-shift alternatives, which is effective for distributions with medium tails and symmetric shapes. To improve power, we replace the standard ranks with recently proposed relative ranks in the Wilcoxon test within the frame of Lepage’s statistic. This modification enhances power for a wider range of distributions, including symmetric, asymmetric, short-tailed, medium-tailed, and long-tailed densities. We conducted Monte Carlo simulations to compare the power performance of our suggested test to the classical Lepage test and another location-scale Cucconi test across various continuous distributions. Our results indicate that the proposed test offers significantly higher power and a more stable significance level, making it a strong contender among location-scale tests. To further illustrate the practical application of our test, we analyzed four real-world biomedical datasets. These examples demonstrate that the suggested test provides a reliable solution for the two-sample independent location-scale testing problem.

Purpose: to verify the effects of tongue pressure exercises against the incisive papilla, used both alone and in combination with functional swallowing training, on the electrical activity of the suprahyoid muscles and the self-perception of aesthetic changes in the submandibular region. Chi-square, Fisher’s exact, Mann-Whitney, Kruskal Wallis, and Wilcoxon tests were used. The significance level was set at 5%. Methods: an experimental, analytical, prospective study on 27 women, aged 30 to 78 years, divided into two treatment groups (G1 and G2), and a control group (CG). Individuals were submitted to 8 weeks of training, in which G1 performed only tongue pressure exercises against the incisive papilla, and G2 performed the same exercises in combination with swallowing training, while CG was not submitted to any intervention. The suprahyoid muscle electrical activity was obtained with surface electromyography at the beginning and end of the study when subjects also answered a self-perception questionnaire on possible aesthetic results. Pearson’s chi-square, Fisher’s exact, Mann-Whitney, Kruskal Wallis, and Wilcoxon tests were used. The significance level was set at 5%. Results: statistically significant differences were found between the initial and final electrical activity in both G1 and G2, which was not found in CG. There was no significant difference between the groups regarding their self-perception of visual or muscle improvements. Conclusion: both strategies analyzed were equally effective to increase suprahyoid muscle recruitment, though with no impact on the self-perception of aesthetic changes in the submandibular region.

Swarm intelligence optimization algorithm has been proved to perform well in the field of parameter optimization. In order to further improve the performance of intelligent optimization algorithm, this paper proposes an improved and adaptive tunicate swarm algorithm (IMATSA) based on tunicate swarm algorithm (TSA). IMATSA improves TSA in the following four aspects: population diversity, local search convergence speed, jumping out of local optimal position, and balancing global and local search. Firstly, IMATSA adopts Tent map and quadratic interpolation to initialize population and enhance the diversity. Secondly, IMATSA uses Golden-Sine algorithm to accelerate the convergence of local search. Thirdly, in the process of global development, IMATSA adopts Levy flight and the improved Gauss disturbance method to adaptively improves and coordinates the ability of global development and local search. Then, this paper verifies the performance of IMATSA based on 14 benchmark functions experiment, ablation experiment, parameter optimization experiments of Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT), Wilcoxon signed rank test and image multi-threshold segmentation experiment with the performance metrics are convergence speed, convergence value, significance level P-value, Peak Signal-to-Noise Ratio (PSNR) and Standard Deviation (STD). Experimental results show that IMATSA performs better in three kinds of benchmark functions; each component of IMATSA has a positive effect on the performance; IMATSA performs better in parameter optimization experiments of SVM experiment and GBDT; there is significant difference between IMATSA and other algorithms by Wilcoxon signed rank test; in image segmentation, the performance is directly proportional to the number of thresholds, and compared with other algorithms, IMATSA has better comprehensive performance.

In this paper, a new optimization algorithm called the search and rescue optimization algorithm (SAR) is proposed for solving single‐objective continuous optimization problems. SAR is inspired by the explorations carried out by humans during search and rescue operations. The performance of SAR was evaluated on fifty‐five optimization functions including a set of classic benchmark functions and a set of modern CEC 2013 benchmark functions from the literature. The obtained results were compared with twelve optimization algorithms including well‐known optimization algorithms, recent variants of GA, DE, CMA‐ES, and PSO, and recent metaheuristic algorithms. The Wilcoxon signed‐rank test was used for some of the comparisons, and the convergence behavior of SAR was investigated. The statistical results indicated SAR is highly competitive with the compared algorithms. Also, in order to evaluate the application of SAR on real‐world optimization problems, it was applied to three engineering design problems, and the results revealed that SAR is able to find more accurate solutions with fewer function evaluations in comparison with the other existing algorithms. Thus, the proposed algorithm can be considered an efficient optimization method for real‐world optimization problems.

Introduction: Tuberculosis bacterial can cause fibrosis tissue in lung. Fibrosis decrease elasticity and compliance of lung as a result, there will be air ventilation and oxygenation disorder, thus it will decrease peak expiratory flow rate (PEFR). The objective of this study was to analyze the effect of active cycle of breathing technique on raised peak expiratory flow rate (PEFR). Method: A quasy experimental purposive sampling design was used in this study. There were 14 sample who met in inclusion criteria divided into 7 subjects for intervention group and 7 subject for control group. Data were analyzed by using Paired t-Test, Independent t-Test, Wilcoxon Signed Rank Test, and Mann Whitney U Test with significance level α≤0.05. Result: The result showed that active cycle of breathing had significance level on increased peak expiratory flow rate (PEFR) (p=0.001). Discussion: It can be concluded that active cycle of breathing has effect on raise peak expiratory flow rate. Further study are recommended to measure the effect of active cycle of breathing on raised maximum midexpiratory flow rate (MMFR50-75%).

Lack of self-care independence is a phenomenon that appears on an outpatient at the sumberglagah hospital, Mojokerto. Most of the patients have less independence in self-care efforts in leprosy, causing a faster rate of patients to the disability process. The data explained that the demonstration method can improve self-care independence. This research was aimed to analyze the influence of health education with demonstration method in self care independent for disability 2nd grade of leprosy. Design used in this research was quasy experimental pre post test designed. The sampling technique used was purposive sampling. Samples were taken from those suitable with inclusion criteria, with total samples were 16 people. The independent variable was intervention of health education with demonstration method and the dependent variable was self care independence on leprosy measured by questioner and observation instrument of independence. Data were analyzed by Wilcoxon signed rank test and Mann Whitney test significance a &lt; 0,05. Result showed that treatment group has significance level p=0.016 and control group has significance level p=0.102 and the result of Mann whitney test showed p 0.001. This study describes the demonstration method can improve the level of self-care independence of disabled leprosy patients in 2nd grade. Demonstration method can help leprosy patients in terms of habit and routine care for themselves independently, to prevent further disability. This method can also be offered at the hospital sumberglagah to keep providing health education demonstration method in outpatient leprosy patients. It can also be a solution to decrease amount of disabled leprosy patients in Indonesia.

The efficacy of eye care for ventilated patients: outline of an experimental comparative research pilot study

This paper introduces a novel meta-heuristic algorithm named Rhinopithecus Swarm Optimization (RSO) to address optimization problems, particularly those involving high dimensions. The proposed algorithm is inspired by the social behaviors of different groups within the rhinopithecus swarm. RSO categorizes the swarm into mature, adolescent, and infancy individuals. Due to this division of labor, each category of individuals employs unique search methods, including vertical migration, concerted search, and mimicry. To evaluate the effectiveness of RSO, we conducted experiments using the CEC2017 test set and three constrained engineering problems. Each function in the test set was independently executed 36 times. Additionally, we used the Wilcoxon signed-rank test and the Friedman test to analyze the performance of RSO compared to eight well-known optimization algorithms: Dung Beetle Optimizer (DBO), Beluga Whale Optimization (BWO), Salp Swarm Algorithm (SSA), African Vultures Optimization Algorithm (AVOA), Whale Optimization Algorithm (WOA), Atomic Retrospective Learning Bare Bone Particle Swarm Optimization (ARBBPSO), Artificial Gorilla Troops Optimizer (GTO), and Harris Hawks Optimization (HHO). The results indicate that RSO exhibited outstanding performance on the CEC2017 test set for both 30 and 100 dimension. Moreover, RSO ranked first in both dimensions, surpassing the mean rank of the second-ranked algorithms by 7.69% and 42.85%, respectively. Across the three classical engineering design problems, RSO consistently achieves the best results. Overall, it can be concluded that RSO is particularly effective for solving high-dimensional optimization problems.