Iranian Journal of Science and Technology, Transactions A: Science最新文献

This paper aims to identify and evaluate the CSFs in the acceptance of blockchain technology (BCT) in Iranian Rubber Industry and rank the influential parts (IPs) of BCT in the Industry by the opinions of 120 experts and simultaneously applying fuzzy Delphi and SWARA (Step-wise Weight Assessment Ratio Analysis) and ARAS (A new additive ratio assessment). A developed model of the CSFs and IP of the BCT in the industry was proposed. The results indicate that 10 CSF from the 31 CSF have a very strong impact on the 9 parts. In the following, the weight of the CSFs was extracted. The analysis showed that the sixth CSFs, (F6: Cyber-Security, Trustable Collaborations and Reliability) was placed in first priority. Also, the 8th CSFs (F8: Experience and Knowledge), was ranked in last. At the end of the research, 9 IPs of the BCT in the Industry were evaluated. The final analysis proves that that the first part (A1: Customer Segments) is ranked first, and the eights part (A8; Key Partners) is ranked last. The proposed integrated conceptual model of this research can be very useful in identifying and evaluating the CSFs of BCT and the valuable IPs of this technology in the rubber industry and other related industries.

The optimization of machine learning models to air quality index (AQI) prediction through hyperparameter tuning is the main emphasis of this study. To help with health risk awareness and regulatory actions, AQIs are essential for evaluating and informing the public about air quality. To improve AQI predictions, machine learning models such as Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP) have been used in the literature. Hyperparameters have a major impact on these models performance and must be carefully adjusted for best outcomes. In this paper, DT, RF, XGBoost, LightGBM, and MLP were fine-tuned using a variety of hyperparameter Optimization approaches, including Grid Search Cross-Validation (GridSearchCV), Random Search Cross-Validation (RandomSearchCV), Simulated Annealing (SA), and Genetic Algorithms (GA). The article provides a thorough analysis of different techniques, highlighting their effects on model efficiency and forecast accuracy. The results provide insightful information about the efficacy of various machine learning models and hyperparameter tuning strategies in the context of air quality forecasting. According to experimental findings, ensemble models such as XGBoost and LightGBM fared better than others in terms of computational efficiency and prediction accuracy. As compared to grid-based searches, evolutionary tuning techniques like GA and SA produced better parameter sets faster. The study concludes that XGBoost provides the best overall results for AQI forecasting when tweaked via GA, and that appropriate hyperparameter optimization greatly enhances model performance. These results offer important information for creating reliable prediction models that will aid in environmental monitoring and policy formation.

We study the commutativity of prime ring (mathfrak {R}) on the action of permuting maps says, permuting ((alpha ,beta )) n-derivations, and permuting generalized ((alpha ,beta )) n-derivations. In this paper, we also generalized the result of Ashraf (Southeast Asian Bull. Math. 38:321–332, 2014), Theorem 2.6). Further, we also investigate some more results on the mentioned permuting maps with the help of traces.

Microbiologically influenced corrosion (MIC) poses serious troubles in the oil and gas industry, resulting in both economic and environmental issues. The use of biocides and anti-corrosion materials is the main solution to control the bio-corrosion. Recently, nanomaterials have been introduced as a new generation of both biocides and anti-corrosions effective in controlling the bio-corrosion. In this study, the MIC effect of Clostridium and Citrobacter as an APB consortia was studied by electrochemical methods, and the anti-corrosion effects of graphene oxide and gamma alumina powder were conducted on ASTM106 grade B stainless steel alloy. The corrosion morphology was studied using surface analysis methods. The corrosion behavior of steel in different mediums including the presence and absence of Acid Producing Bacteria (APB) and the nanostructures was investigated via electrochemical impedance spectroscopy and Tafel polarization techniques. Corrosion test result indicated that the addition of the nanostructure increased the charge transfer resistance, resulting in the reduction of corrosion rate by 81–87 percent. This is attributed to preventing the production of biofilm by the nanostructure and the formation of a protective film in the presence of graphene oxide Nano-sheets. The optimized concentration for both was determined as 0.5 mg/mL.

This study deals with efficient numerical schemes for solving singularly perturbed Volterra–Fredholm integro-differential equations. On the layer adapted Shishkin mesh, the numerical solution is calculated using the upwind finite difference scheme for the differential part and the quadrature rule for the integral parts. The method proves to be first-order convergent in the maximum norm. Then, using a post-processing technique we significantly enhance the accuracy from first-order to second order. Further, a hybrid scheme on the nonuniform mesh is also constructed and analyzed whose solution converges uniformly, independent of the perturbation parameter, and directly gives second order accuracy. The convergence analysis is carried out for all the schemes and the theoretical results are validated through some numerical tests. Finally, a comparison of the computational cost taken shows the efficiency of the proposed work.

Hexavalent chromium (Cr (VI)), resulting from several industrial activities, has become a significant environmental concern due to its high levels in water, soil, and plant tissues. This study aimed to assess the ability of white sorghum (Poaceae) to tolerate and remediate Cr (VI)-contaminated soil. Plants were cultivated in plastic pots containing a mixture of (1:1:1) agricultural sand, soil, and compost, and were treated with different concentrations of chromium (0 PPM, 20 PPM, and 60 PPM). The main results revealed that chromium predominantly accumulated in the roots, with an accumulation rate of 184 mg/kg of dry matter. The bioaccumulation factor (BF) exceeded 1, accompanied by a high biomass production (12 g within 30 days). This indicates the capacity of sorghum seedlings to absorb and retain significant amounts of Cr (VI), likely through SO₄²⁻ and HPO₄²⁻ membrane transporters. Increased Cr concentrations induced a significant rise in cell electrolyte leakage, indicating the loss of cell membrane integrity. Additionally, exposure to chromium resulted in a significant increase in total soluble sugars and proline, as well as heightened activities of catalase (CAT) and peroxidase (POD) which serve to protect cellular components and mitigate damage from reactive oxygen species. The results from the correlation analysis and principal component analysis revealed a high degree of alignment with the analyses of individual traits, thereby validating the interrelationships among the parameters studied. These findings indicate that the tested sorghum variety can thrive in Cr (VI)-contaminated environments, effectively accumulate chromium in its roots, and convert it to its trivalent form, emphasizing its potential for phytoremediation. However, it is essential to ascertain the concentration of heavy metals in plants remains within recommended safe thresholds, particularly for plants that may enter the food chain.

In this article, a new class of basis functions called generalized Laguerre polynomials is introduced to solve equations in the form of the non-linear time fractional reaction-diffusion equation. To obtain a new numerical method, the fractional derivative in the equation is replaced using Caputo’s definition. Then, using operational matrices of fractional and ordinary derivatives, in order to improve the presented numerical method, the method of generalized Laguerre polynomials (GLPs) is introduced. The basis of obtaining an approximate solution in this method is that the approximate solution is considered as a linear combination of these basic functions in terms of coefficients and control parameters. By obtaining the values of coefficients and control parameters optimally using the method of Lagrange cofficients, the approximate solution is obtained. The advantage of this method is to use a small number of basis functions to obtain satisfactory results. On the other hand, the accuracy of the method is improved by increasing the number of sentences of basic functions. In the end, with the aim of confirming the correctness of the numerical results and proving the efficiency, we will test the GLPs method by presenting two examples.

In this paper, we utilize a convergence termed weighted convergence, which is expressed via weighted density. This extends asymptotic (also known in the literature as natural density and linear density) and logarithmic densities. We employ this to express and prove Korovkin-type theorems for sequences of operators that are monotone and sublinear (msLOs). To demonstrate the practical relevance of our results, illustrative examples that satisfy the conditions of our theorems are included. The rate of convergence for these msLOs is analysed using the modulus of continuity.

Let (mathcal {X}) be a certain Banach space of analytic functions on the open unit disk (mathbb {D}) in the complex plane. Let p be an analytic polynomial and let (M_p) denote the operator of multiplication by p. Under certain conditions on p, we characterize the structure of the operator S such that (M_pS=SM_p). We determine the commutant of direct sum of certain multiplication operators on direct sum of certain spaces of functions. Assume that (psi) is an analytic automorphism of the unit disk, we also characterize the commutant of (M_{p(psi )}) under certain conditions on p.

Selective propane dehydrogenation is one of the commercialized technologies for the production of propylene and hydrogen. Here, alkaline (K) modified Pt-Sn /γ-Al₂O₃ catalysts were prepared via sequential incipient wetness impregnation method for the propane dehydrogenation reaction. They were characterized by BET, TEM, TPR, TPD-NH₃, CO-dispersion, and TPO analysis’s. The catalytic performance towards propane dehydrogenation was assessed at an hourly gas space velocity of 10,000 cm³ (g.cat)⁻¹ h^− 1 at 580 °C up to 5 h time on stream at an absolute pressure of 1 bar. Results showed that the parent Pt-Sn/γ-Al₂O₃ catalyst exhibits 54.2% propane conversion at 580 °C with a poor selectivity towards propylene of 83.8%. Incorporation of K element generally increased selectivity towards propylene and decreased propane conversion. For a constant Pt and Sn content of 0.4 and 0.8 wt%, respectively, different catalysts with a K element content of 0.0, 0.3, 0.8, 1.5 and 2.5 wt% were synthesized. Optimal catalytic performance was observed for a 0.8 wt% K element content. Compared to the Pt-Sn/γ-Al₂O₃ sample, the optimum catalyst resulted in a 48.2% increase in propylene yield and a minimal decrease of only 3.6% in propane conversion.