High-dimensionality is one of the major problems which affect the quality of the quantitative structure-activity relationship (QSAR) modelling. Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. In this paper, four new transfer functions were adapted to improve the exploration and exploitation capability of the BGOA in QSAR modelling of influenza A viruses (H1N1). The QSAR model with these new quadratic transfer functions was internally and externally validated based on MSEtrain, Y-randomization test, MSEtest, and the applicability domain (AD). The validation results indicate that the model is robust and not due to chance correlation. In addition, the results indicate that the descriptor selection and prediction performance of the QSAR model for training dataset outperform the other S-shaped and V-shaped transfer functions. QSAR model using quadratic transfer function shows the lowest MSEtrain. For the test dataset, proposed QSAR model shows lower value of MSEtest compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed QSAR model is an efficient approach for modelling high-dimensional QSAR models and it is useful for the estimation of IC50 values of neuraminidase inhibitors that have not been experimentally tested.
There is an increasing concern about incorporating green criteria into production planning approaches. Production planning models that ignore green parameters may generate outcomes that are unfriendly to the environment. The relevant literature has suggested a flourishing trend towards the integration of green parameters into production planning approaches. The earlier reviews have most commonly analyzed the green production planning approaches from an "energy efficiency" perspective. Literature on the integration of other green criteria is also available. However, such studies are rarely reviewed. Along with "energy efficiency," the study in hand reviews the production planning strategies from another green perspective which is "low-carbon emissions." The first objective of this study is to review the medium and short-term production planning approaches from the aforementioned green criteria and provide a classification scheme. Second, new research avenues are identified to facilitate researchers in incorporating green schemes in production planning approaches. This study explored various databases for articles published on green production planning approaches. Consequently, 84 articles published between 2011 and 2022 were considered for the review. This review pointed out that most of the studies on green production planning considered "energy efficiency" and studies on "carbon emissions" were overlooked. Furthermore, green concepts were mostly integrated into the short-term production planning level and comparatively few studies were found for the medium-term. This study will help researchers to analyze green production planning in terms of modeling approaches, objective functions, uncertainties, solution approaches, etc.
CO2 levels in the atmosphere are growing as a result of the burning of fossil fuels to meet energy demands. The introduction of chemical looping combustion (CLC) as an alternative to traditional combustion by transporting oxygen emphasizes the need to develop greener and more economical energy systems. Metal oxide, also defined as an oxygen carrier (OC), transports oxygen from the air to the fuel. Several attempts are being made to develop an OC with a reasonable material cost for superior fuel conversion and high oxygen transport capacity (OTC). This study aims to synthesize a potential OC using the wet impregnation method for the CLC process. Thermogravimetric analysis (TGA) was used to determine the cyclic redox properties using 5% CH4/N2 and air as reducing and oxidizing gases, respectively. The 10CuPA-based OC retained a high OTC of about 0.0267 mg O2/mg of OC for 10 cycles that was higher than 10CuA-based OC. Furthermore, the oxygen transfer rate for 10CuPA-based OC was relatively higher compared to 10CuA-based OC over 10 cycles. In comparison to 10CuA-based OC, the 10CuPA-based OC presented a steady X-ray diffraction (XRD) pattern after 10 redox cycles, implying that the phase was stably restored due to praseodymium-modified γ alumina support.
One of the recently developed metaheuristic algorithms, the coyote optimization algorithm (COA), has shown to perform better in a number of difficult optimization tasks. The binary form, BCOA, is used in this study as a solution to the descriptor selection issue in classifying diverse antifungal series. Z-shape transfer functions (ZTF) are evaluated to verify their efficiency in improving BCOA performance in QSAR classification based on classification accuracy (CA), the geometric mean of sensitivity and specificity (G-mean), and the area under the curve (AUC). The Kruskal-Wallis test is also applied to show the statistical differences between the functions. The efficacy of the best suggested transfer function, ZTF4, is further assessed by comparing it to the most recent binary algorithms. The results prove that ZTF, especially ZTF4, significantly improves the performance of the original BCOA. The ZTF4 function yields the best CA and G-mean of 99.03% and 0.992%, respectively. It shows the fastest convergence behaviour compared to other binary algorithms. It takes the fewest iterations to reach high classification performance and selects the fewest descriptors. In conclusion, the obtained results indicate the ability of the ZTF4-based BCOA to find the smallest subset of descriptors while maintaining the best classification accuracy performance.
The horse herd optimization algorithm (HOA), one of the more contemporary metaheuristic algorithms, has demonstrated superior performance in a number of challenging optimization tasks. In the present work, the descriptor selection issue is resolved by classifying different essential oil retention indices using the binary form, BHOA. Based on internal and external prediction criteria, Z-shape transfer functions (ZTF) were tested to verify their efficiency in improving BHOA performance in QSPR modelling for predicting retention indices of essential oils. The evaluation criteria involved the mean-squared error of the training and testing datasets (MSE), and leave-one-out internal and external validation (Q2). The degree of convergence of the proposed Z-shaped transfer functions was compared. In addition, K-fold cross validation with k = 5 was applied. The results show that ZTF, especially ZTF1, greatly improves the performance of the original BHOA. Comparatively speaking, ZTF, especially ZTF1, exhibits the fastest convergence behaviour of the binary algorithms. It chooses the fewest descriptors and requires the fewest iterations to achieve excellent prediction performance.