Precise models predicting fuel cell performance under different operating conditions require accurate parameter identification in a proton exchange membrane fuel cell (PEMFC). Most traditional parameter estimation methodologies depend on optimization algorithms which are limited in their efficiency, convergence speed, and robustness. Typically, existing algorithms fail to achieve a balance between precision and computational efficiency, leading to suboptimal modeling of the complex, nonlinear behavior of PEMFCs. In this paper, we present the two-stage differential evolution (TDE) algorithm, which fills these gaps by using a new mutation strategy that improves solution diversity and speeds up convergence. Seven critical unknown parameters (\({\xi }_{1},{\xi }_{2},{\xi }_{3},{\xi }_{4},\beta ,{R}_{C},\) and \(\lambda\)) in PEMFC models are identified by using the proposed TDE algorithm. The optimization process is to minimize the sum of squared errors (SSE) between the experimentally measured and predicted cell voltages. TDE resulted in a 41% reduction in SSE (minimum SSE of 0.0255 compared to 0.0432), a 92% improvement in maximum SSE, and over 99.97% reduction in standard deviation compared to the HARD-DE algorithm. Furthermore, TDE was shown to be 98% more efficient than HARD-DE, with a runtime of 0.23 s, compared to HARD-DE’s runtime of 11.95 s. Extensive testing of these advancements was performed on six commercially available PEMFC stacks over twelve case studies, and I/V and P/V characteristics were confirmed to be consistent with experimental data. The results show that TDE has better accuracy, robustness and computational efficiency than the other methods, and therefore TDE can be used as a real time PEMFC parameter estimation tool.

The use of proton exchange membrane fuel cells (PEMFCs) in sustainable energy applications depends on their high efficiency levels along with their ability to produce low emissions and operation without noise. The optimization of PEMFC design variables faces difficulties because of the complex nonlinear relationships which exist between activation overpotential and concentration overpotential and internal resistance. The optimization methods PSO, DE and WOA face three major setbacks which include their delayed convergence rates as well as their sensitiveness to initial parameter settings and their tendency to lock onto sub-optimal solutions. The study presents the Parrot Optimizer (PO) as a new metaheuristic algorithm which derives its inspiration from the adaptive behaviors of Pyrrhura Molinae parrots to overcome current optimization challenges. The PO serves to optimize six PEMFC stack design variables for BCS 500 W, Nedstack 600 W PS6, SR-12 W, Horizon H-12, Ballard Mark V, and STD 250 W. The research performs an extensive comparison between nine advanced algorithms to analyze their performance against PSO, DE, WOA, Rabbit Optimization Algorithm (ROA), Flamingo Herd Optimization (FHO), Arithmetic Optimization Algorithm (AOA), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO) and Bat Algorithm (BA). The objective function Sum of Squared Error (SSE) for stack voltage is minimized using different algorithms for comparative analysis. Simulation results for I–V and V–P characteristics aligned closely with experimental data under varying temperature and pressure conditions. PO achieved the lowest Mean SSE values across all cases, with values of 0.025519, 0.275211, 0.242413, 0.102915, 0.148632, and 0.283774 for the BCS 500 W, Nedstack 600 W PS6, SR-12 W, Horizon H-12, Ballard Mark V, and STD 250 W stacks, respectively. Additionally, PO demonstrated the fastest runtime (RT) in all cases, with values as low as 0.116855 s for the Horizon H-12 stack. The results indicate that PO delivers better performance than existing algorithms because it reaches the lowest Sum of Squared Error for stack voltage outputs across every test scenario. I–V and V–P characteristic simulations match experimental results across different temperature and pressure values which proves the theoretical value and practical usage of PO in solving nonlinear optimization problems. The study demonstrates PO as a dependable optimization method which improves PEMFC design processes while enhancing operational reliability through future research that includes real-time control and algorithm combination and system scalability.

Today, reliable power delivery and increasing demand are important issues in modern power systems. Flexible Alternating Current Transmission Systems (FACTS) devices are used to control transmission line parameters to increase power transfer and stability. Nevertheless, the problem of determining the optimal placement and sizing of these devices is still challenging, as the placement and sizing of the devices affects generation costs, power losses, voltage stability, and system reliability. This study proposes the Fata Morgana Algorithm (FATA), an optimization algorithm inspired by the natural process of mirage formation to optimize placement and sizing of FACTS devices in an IEEE 30 bus system with wind turbine integration. The FATA algorithm is evaluated against recently developed and improved optimization techniques, such as rime-ice formation phenomenon based Improved RIME (IRIME) Algorithm, Newton–Raphson-Based Optimization (NRBO), Resistance Capacitance Algorithm (RCA), Krill Optimization Algorithm (KOA), and Grey Wolf Optimizer (GWO), across multiple optimization objectives: reduction in generation cost, reduction in power loss and combined generation cost plus power loss, termed as Gross cost function. Results obtained show that FATA consistently outperforms the other algorithms in terms of convergence and solution quality, offering a robust approach to solving single objective optimization problems. FATA theoretically provides a good balance between exploration and exploitation, and produces better global solutions. It practically increases power system efficiency by lowering operational costs and losses and improving stability. Results indicate that the FATA algorithm produced minimum generation cost of 807.0405 $/h, which is 0.088–0.426% less than the competing algorithms. It also reduced power losses to 5.5917 MW, which is 1.095–6.781% less than other methods. For gross cost minimization, the FATA algorithm achieved a minimum gross cost of 1366.3727 $/h, which is 0.4799% better than the next best algorithm and 3.2261% better than the worst. The results also show that FATA is robust in solving complex optimization problems in power systems, and it provides significant improvements in run time and convergence efficiency. The main advantage for readers is that FATA provides a reliable and efficient way to optimize power systems. Future work could also investigate the application of FATA in real time, as well as in larger power networks with more renewable energy sources.

Background: Attention-Deficit/Hyperactivity Disorder (ADHD) represents a widely prevalent and heterogeneous neurodevelopmental condition in pediatric populations, often exhibiting a substantial propensity to persist into adulthood. ADHD is a multifaceted disorder that resists straightforward diagnostic tests. Clinicians must invest substantial time and effort to secure an accurate diagnosis and implement effective treatment. ADHD diagnosis is primarily based on psychiatric tests, as there is currently no clinically utilized objective diagnostic tool. Nonetheless, several studies in have documented endeavors to create objective instruments designed to assist in the diagnostic process of ADHD, aiming to enhance diagnostic accuracy and reduce subjectivity. Method: This research endeavor sought to establish an objective diagnostic modality for ADHD through the utilization of electroencephalography (EEG) signal analysis. With the use of innovative deep learning techniques, this research seeks to improve the diagnosis of ADHD using EEG data. To capture complex patterns in EEG data, this study proposes a double-augmented attention mechanism ResNet-based model. Using an autoencoder for feature extraction, the Reptile Search Algorithm for feature selection, and a modified ResNet architecture for model training comprise the technique. Results: AUC, F1-score, accuracy, precision, recall, and other standard classifiers like Random Forest and AdaBoost were utilized to compare the model’s performance. By a wide margin, the proposed ResNet model outperforms the traditional models with a 99.42% accuracy, 99.03% precision, 99.82% recall, and 99.42% F1-score. Conclusions: ROC AUC score of 0.99 for the model underscores its remarkable capability to differentiate between children with and without ADHD, thereby minimizing misclassification errors and improving diagnostic precision.

In the era of widespread misinformation, detecting fake news has become a crucial challenge, particularly on social media platforms. This paper introduces an optimized approach for Fake News Detection, combining BERT and GloVe embeddings with Principal Component Analysis (PCA) and attention mechanisms, enriched by social and temporal features for more effective text representation. Leveraging the CIC Truth Seeker Dataset 2023, we applied SHAP for feature selection and interpretability, ensuring transparency in the model’s predictions. Our methodology achieved a remarkable accuracy of 99.9% using a Random Forest classifier, showcasing the efficacy of this optimized hybrid approach. The integration of interpretability techniques such as LIME and SHAP provides deeper insights into the model’s decisions, making it a reliable tool for combating misinformation. This novel approach offers a robust and transparent solution to the growing threat of fake news, contributing significantly to the integrity of online information and public discourse on platforms like Twitter X.