Training artificial neural networks is often challenged by large search spaces, slow convergence, and premature stagnation in local optima. To address these challenges, this work introduces an Enhanced Bat Algorithm (EBA) that incorporates multiple improvement strategies to enhance search robustness and ensure stable convergence. The proposed EBA is applied to optimize Gaussian centers within radial basis function (RBF) networks. Beyond optimizing Gaussian centers, we introduce a joint RBF neural-network training mechanism that simultaneously determines RBF widths and synaptic weights through a new optimization technique fulfilled by Optimum Weight Vector (OWV), the Adam optimizer, and L_2 regularization to improve generalization. Evaluation on 30 benchmark optimization functions in very high dimensional demonstrates that EBA delivers the best performance, achieves the lowest average mean error across all categories, and significantly outperforms well-known optimization algorithms including BA, PSO, GA, ACO, ALO, CSO, FO, and GWO—often by several orders of magnitude. In image recognition tasks, the proposed RBF + EBA + Adam + OWV classifier achieves state-of-the-art performance on four datasets: 97.1
The aim of this research is to introduce a numerical technique relying on the shifted Jacobi polynomials (SJPs) and the Laplace transform (LT) for anatomizing a class of the fractional variational problems (FVPs). The derivative in this problem is in the sense of the Caputo. To anatomizing FVP, first, we make an approximation for the Caputo operator ((c) D( t)(& varsigma;)m(t)) using the SJPs with unknown parameters. Then, applying LT, we access m(t) and put it in the study problem. The process of this method and the Lagrange multiplier method reduces the study problem to a system of nonlinear algebraic equations. In this system, unknown parameters are calculated by solving the obtained system, and it gives m(t). Finally, some illustrative examples are presented to demonstrate the accuracy and validity of the proposed scheme.
Friction stir spot welding (FSSW) is a popular technique for solid-state welding of both weldable and non-weldable materials. As part of this investigation, Al6063-T6 weld specimens were strengthened with silicon carbide (SiC, ∼2 wt.%, average particle size ≈ 10 μm) and graphite (Gr, ∼1 wt.%, average particle size ≈ 8 μm) particles. The current study focused on optimizing the multi-objective FSSW process to achieve the optimal combination of process parameters for stronger welds. The Taguchi L16 orthogonal array was used to design experiments on process factors, including the dwell time, tool shoulder diameter, and tool pin length. Tensile-shear strength and flash height were the two output quality characteristics measured. Multi-objective optimization was performed using a hybrid measurement of alternatives and ranking based on the Compromise Solution (MARCOS)-Taguchi technique. The significance of parameters was determined utilizing the analysis of variance technique, and a confirmatory test verified the optimality of the results. The optimal combination of parameters achieved by Taguchi was also confirmed by the MARCOS method, indicating that both approaches can be used with high reliability to optimize weld quality characteristics. It was observed that the maximum weld strength was achieved with a 2 mm pin length, a 15 mm shoulder diameter, a 1000 rpm rotation speed, and a 9 s dwell time. Pin length had a significant impact on weld quality, followed by rotation speed, shoulder diameter, and dwell time. The presence of SiC and Gr reinforcement improved microhardness and tensile-shear strength. The MARCOS-Taguchi approach effectively optimized FSSW process parameters, with confirmatory testing validating the results. MARCOS reduced the prediction error to 0.3% compared to 4.1% in Grey Relational Analysis (GRA) and 3.9% in Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).
The rapid rise in industrial activity has intensified heavy metal contamination of freshwater, with Pb2+, Hg2+, and Cd2+ being among the most toxic and persistent pollutants. In this study, cerium-doped zinc oxide (Ce-doped ZnO) nanoparticles were synthesized via a simple precipitation route, structurally characterized by XRD, FE-SEM/EDX, BET, and DLS, and evaluated for their simultaneous adsorption of Pb2+, Hg2+, and Cd2+ ions from aqueous media. XRD results confirmed the formation of hexagonal wurtzite ZnO with crystallite size decreasing from 36.1 nm for pure ZnO to 32.3 nm and 31.9 nm for 2 % and 4 % Ce-doped samples, respectively. BET analysis revealed a significant surface area enhancement (33.6 m(2)/g for 4 % Ce-ZnO), which contributed to improved adsorption performance. Batch adsorption experiments demonstrated that 4 % Ce-ZnO achieved maximum removal efficiencies of 94 % (Pb2+), 87 % (Hg2+), and 95 % (Cd2+) under optimized conditions (pH 6.5, 0.025 g adsorbent, 30 mg/L metal ion concentration, and 220 min contact time). Kinetic modeling revealed that the Elovich model best described the adsorption process (R-2 > 0.93), suggesting chemisorption as the dominant mechanism, while Langmuir isotherm fitting indicated monolayer adsorption with maximum capacities (q(max)) of 20.94, 17.54, and 19.98 mg/g for Pb2+, Hg2+, and Cd2+, respectively. Thermodynamic analysis confirmed the spontaneous (Delta G < 0) and exothermic (Delta H < 0) nature of adsorption, with a slight shift toward non-spontaneity for Hg and Cd at higher temperatures. Regeneration studies showed that Ce-doped ZnO retained >80 % efficiency after seven reuse cycles, highlighting its operational stability. Overall, Ce-doped ZnO nanoparticles represent an inexpensive, scalable, and reusable adsorbent with strong potential for industrial wastewater treatment and sustainable heavy metal remediation.
This study comparatively evaluates kiwifruit and citrus cultivation using both Material Flow Cost Accounting (MFCA) and Traditional Cost Accounting (TCA) frameworks to identify hidden inefficiencies and evaluate cost-sustainability trade-offs. All financial data are expressed in US dollars (USD) using an average exchange rate of 1 USD = 145000 IRR (reference year: 2019). By focusing on two prominent crops in Iranian agriculture, the study highlights key differences in resource allocation, profitability, and environmental sustainability. Kiwifruit cultivation demonstrated higher profitability, with a net profit of $18900.26 ha(-1) compared to $8194.03 ha(-1) for citrus. This advantage is driven by Kiwifruit's superior yield of 22862.6 kg ha(-1) and gross value of production, which significantly outweigh its higher labor and input costs. Conversely, citrus offers a more resource-efficient option, requiring 451.1 h ha(-1) of labor, compared to 1446.1 h ha(-1) for Kiwifruit, and lower potassium and farmyard manure costs. From an environmental perspective, nitrate leaching and biocide emissions were the most significant contributors to negative impacts for both crops. Kiwifruit exhibited higher environmental costs, particularly in nitrate leaching ($5.68 ha(-1)) and ammonia emissions, highlighting the need for improved nutrient management practices. The findings underline that Kiwifruit is a viable option for large-scale farmers capable of managing its high labor and environmental costs, while citrus is more suitable for small-scale farmers seeking sustainability and lower input demands. The application of Material Flow Cost Accounting revealed critical inefficiencies, such as high labor intensity and nutrient losses, which traditional accounting systems may overlook. These results provide actionable insights for policymakers and farmers, emphasizing the need for targeted interventions to enhance both economic viability and environmental sustainability. For Kiwifruit cultivation, strategies such as mechanization and integrated pest management are recommended to balance profitability with ecological responsibility. For citrus, optimizing nutrient application and pest control could further improve its cost-efficiency and environmental profile. This comparative analysis contributes to the growing body of literature on sustainable agricultural practices, demonstrating the utility of Material Flow Cost Accounting in refining resource use and decision-making in crop production systems.
