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

Pre-starlike functions hold a significant place in the theory of analytic functions, and various inequalities related to the coefficients of these functions play a critical role in understanding their geometric properties. In our work, Fekete-Szegö type coefficient inequalities are obtained for the class of pre-starlike functions. These results generalize some previously known properties of pre-starlike functions and present new inequalities valid for a broader class of these functions. Additionally, for the some special values of parameters several corollaries are presented.

Interrupted time series (ITS) models play an important role in directing research on the effects of planned or unexpected interventions on data analysis. The ITS analysis is an increasingly popular technique for evaluating public health interventions, their effects on diseases, and their capacity to make flexible predictions about various consequences. Hence, the first-order interrupted autoregressive model is proposed with the autocorrelated skew-Normal innovation. This is motivated by the issue of identifying a potential probabilistic model for the nonlinear time series with intervention for clinical data sets. By classifying before and after the intervention stages and examining changes during the intervention, the analysis attentively estimates the effects of the intervention. The ECME algorithm for iteratively estimating the parameters is described, and the observed information matrix is derived analytically. A preferred aspect of the data analysis is the evaluation of robustness of estimates in statistical models and the local impact of small perturbations. Therefore, the local influence analysis of the considered model is thoroughly examined while taking into account three perturbations. To evaluate the performance of proposed methods, some simulation data sets considering the ECME estimates are presented to show the robustness of estimates against influential observations. Finally, the proposed process is executed favorably to model the new cases of COVID-19 time series in the Czech Republic, with some goodness of fit benchmarks, allowing for both the applicability of the proposed process and the influence of diagnostic analysis.

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.

This article aims at exploring various geometric properties of entire functions, followed by some results related to differential equations and special functions. It focuses on the monotonicity of the normalized Miller-Ross function and further identifying adequate conditions for it to belong to certain subclasses of starlike functions. Additionally, the article provides various relations to ensure that the solution of a certain second-order differential equation is subordinate to the Janowski function. These findings are illustrated through a variety of examples involving certain entire functions.

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.

In this paper, A-statistical approximation of Lupaş (p, q)-Bernstein-Kantorovich operators based on Riemann type integrals for (1le q<p<infty) is discussed. This work extends the research conducted by Iliyas et al. (Filomat 36(15):5221–5240, 2022; 10.2298/FIL2215221I) on Lupaş (p, q)-Bernstein-Kantorovich operators based on Jackson and Riemann-type integrals for (1le q<p<infty .) We also emphasize the convergence condition for this sequence of operators for (1le q<p<infty) and investigate the convergence estimate for the functions by these operators. In this study, both A-statistical convergence theorems and the corresponding rates of A-statistical convergence are established. These results are derived using the notions of A-statistical convergence, the rate of A-statistical convergence, and the modulus of smoothness. Additionally, we present an example showing that while the Lupaş Bernstein-Kantorovich operators, constructed via Riemann-type (p, q)-integrals, exhibit statistical convergence to (mathcal {F}(x)), the classical Korovkin theorem does not hold in the conventional sense. In this work, we also employ the concept of statistical convergence to approximate all strictly monotonic positive functions (mathcal {F} in mathcal {C}[0,1]) using the Lupaş Bernstein-Kantorovich operators constructed through the Jackson integral. Graphical analysis highlighting convergence and flexibility presented for theoretical consistency.

Carbon Dioxide (CO₂) emissions have contributed to the rise in global warming. Therefore, there is a need to implement techniques to facilitate carbon capture and storage (CCS). This study assessed the feasibility of utilizing carbon pricing for revenue generation of carbon dioxide (CO₂) storage project of a reservoir in an onshore field of the Niger Delta Basin, Southern Nigeria. This study developed an economic (deterministic) model from technically simulated gas model data. Three economic indicators were analyzed the “NPV (Net Present Value)”, “IRR (Internal Rate of Return)” and the Payback time for a projection of 15-year project timeframe. The input parameters include the capital expenditure (CAPEX), operational expenditure (OPEX), discounted rate, volume of CO₂ stored and “CCT (Carbon Credit Tax)”. The reservoir stores approximately 1.72 million tonnes of CO₂ per annum at a carbon credit price of 40 dollars per tonne ($40/ ton) as an initial assumption. The finding reveals it will take 4.62 years for the project to recover the initial cost of investment at a profitable IRR of 18.7% and a positive NPV of $46.6 million. Assessing the NPV sensitivity by varying the carbon credit tax reflects that at $33.4/ton the project will attain break-even point. Thus, higher carbon credit tax resulted in a potential increase of the NPV, IRR and payback time. This reflects a strong relationship between the economic indicator and the carbon credit tax.

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.