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In the present work, the nonlinear equation for a single-diode design of a photovoltaic cells is introduced. The mathematical model based on False Position Method (FPM) was used to determine the parameters of the voltage of the solar cell device based on the electrical equivalent circuit. The False Position Method currently presents to demonstrate the non-linear electrical behavior of this device. The proposed method is tested to solve the nonlinear example and the obtained results are used.

In this paper, we introduce a new approximation of the cumulative distribution function of the standard normal distribution based on Tocher's approximation. Also, we assess the quality of the new approximation using two criteria namely the maximum absolute error and the mean absolute error. The approximation is expressed in closed form and it produces a maximum absolute error of 4.43 × 10 − 10 , while the mean absolute error is 9.62 × 10 − 11 . In addition, we propose an approximation of the inverse cumulative function of the standard normal distribution based on Polya approximation and compare the accuracy of our findings with some of the existing approximations. The results show that our approximations surpass other the existing ones based on the aforementioned accuracy measures.

In this paper, a new strategy for the design of fuzzy logic controllers (FLC) is proposed. This strategy is based on the optimization, by the particle swarm optimization (PSO) Algorithm and by sine-cosine based swarm optimization (SCSO) Algorithm, of the input-output gains and the geometric forms of the triangular membership functions of the proposed FLC. This latter is obtained by the minimizing a cost function based on combining two criterions, the Integral Time Absolute Error (ITAE) and Integral Absolute Error (IAE). A comparison, between the conventional FLC and the proposed FLC is carried out. The PSO-FLC shows a remarkable improvement in the performances of the controlled induction motor.

Introduction. Recently, permanent magnet synchronous motors (PMSMs) have become essential in various high-performance applications, including electric vehicles and renewable energy systems. However, traditional control methods, such as PI controllers, often struggle to handle dynamic operating conditions and external disturbances, resulting in torque ripple and stability issues. Problem.The main issue with existing control strategies is their inability to maintain accurate torque control and system stability under fluctuating loads and varying motor parameters, which negatively impacts performance in real-world applications. Goal. This paper proposes a robust hybrid control strategy that integrates sliding mode control (SMC) with proportional resonant control (PRC), enhanced by Luenberger and Kalman observers. The goal is to improve torque stability, reduce errors, and optimize performance in PMSM drive systems.Methodology. The proposed method combines SMC and PRC to form an SMC-PRC controller, with Luenberger and Kalman observers integrated for effective load torque estimation.Results. The simulation experiments were carried out to compare the effectiveness of the proposed control strategy with that of traditional PI controllers. The results revealed that the SMC-PRC approach offers a notable improvement in overall control performance, including reduced tracking error, enhanced dynamic response, and better stability. Furthermore, the proposed method achieved faster settling times and maintained robust operation under varying system conditions. Scientific novelty. This work introduces a hybrid control approach that combines SMC and PRC with advanced state estimation techniques, providing a robust and efficient solution to PMSM control.Practical value. The proposed method is highly beneficial for applications under dynamic operating conditions, such as electric vehicles and renewable energy systems, improving system efficiency and stability. References 40, tables 7, figures 10.

This review article attempts to evaluate the accuracy of intraocular lens power calculation formulae in short eyes. A thorough literature search of PubMed, Embase, Cochrane Library, Science Direct, Scopus, and Web of Science databases was conducted for articles published over the past 21 years, up to July 2021. The mean absolute error was compared by using weighted mean difference, whereas odds ratio was used for comparing the percentage of eyes with prediction error within ±0.50 diopter (D) and ±1.0 D of target refraction. Statistical heterogeneity among studies was analyzed by using Chi-square test and I2 test. Fifteen studies including 2,395 eyes and 11 formulae (Barrett Universal II, Full Monte method, Haigis, Hill-RBF, Hoffer Q, Holladay 1, Holladay 2, Olsen, Super formula, SRK/T, and T2) were included. Although the mean absolute error (MAE) of Barrett Universal II was found to be the lowest, there was no statistically significant difference in any of the comparisons. The median absolute error (MedAE) of Barrett Universal II was the lowest (0.260). Holladay 1 and Hill-RBF had the highest percentage of eyes within ±0.50 D and ±1.0 D of target refraction, respectively. Yet their comparison with the rest of the formulae did not yield statistically significant results. Thus, to conclude, in the present meta-analysis, although lowest MAE and MedAE were found for Barrett Universal II and the highest percentage of eyes within ±0.50 D and ±1.0 D of target refraction was found for Holladay 1 and Hill-RBF, respectively, none of the formulae was found to be statistically superior over the other in eyes with short axial length.

Purpose This paper aims to achieve accurate maximum power from solar photovoltaic (PV), its five parameters need to be estimated. This study proposes a novel optimization technique for parameter estimation of solar PV. Design/methodology/approach To extract optimal parameters of solar PV new optimization technique based on the Jellyfish search optimizer (JSO). The objective function is defined based on two unknown variables and the proposed technique is used to estimate the two unknown variables and the rest three unknown variables are estimated analytically. Findings In this paper, JSO is used to estimate the parameters of a single diode PV model. In this study, eight different PV panels are considered. In addition, various performance indices, such as PV characteristics, such as power-voltage and current-voltage curves, relative error (RE), root mean square error (RMSE), mean absolute error (MAE) and normalized mean absolute error (NMAE) are determined using the proposed algorithm and existing algorithms. The results for different solar panels have been obtained under varying environmental conditions such as changing temperature and constant irradiance or changing irradiance and constant temperature. Originality/value The proposed technique is new and provides better results with minimum RE, RMSE, NMAE, MAE and converges fast, as depicted by the fitness graph presented in this paper.

Malaria is an endemic disease in India and targeted to eliminate by the year 2030. The present study is aimed at understanding the epidemiological patterns of malaria transmission dynamics in Assam and Arunachal Pradesh followed by the development of a malaria prediction model using monthly climate factors. A total of 144,055 cases in Assam during 2011–2018 and 42,970 cases in Arunachal Pradesh were reported during the 2011–2019 period observed, and Plasmodium falciparum (74.5%) was the most predominant parasite in Assam, whereas Plasmodium vivax (66%) in Arunachal Pradesh. Malaria transmission showed a strong seasonal variation where most of the cases were reported during the monsoon period (Assam, 51.9%, and Arunachal Pradesh, 53.6%). Similarly, the malaria incidence was highest in the male population in both states (Asam, 55.75%, and Arunachal Pradesh, 51.43%), and the disease risk is also higher among the > 15 years age group (Assam, 61.7%, and Arunachal Pradesh, 67.9%). To predict the malaria incidence, Bayesian structural time series (BSTS) and Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors (SARIMAX) models were implemented. A statistically significant association between malaria cases and climate variables was observed. The most influencing climate factors are found to be maximum and mean temperature with a 6-month lag, and it showed a negative association with malaria incidence. The BSTS model has shown superior performance on the optimal auto-correlated dataset (OAD) which contains auto-correlated malaria cases, cross-correlated climate variables besides malaria cases in both Assam (RMSE, 0.106; MAE, 0.089; and SMAPE, 19.2%) and Arunachal Pradesh (RMSE, 0.128; MAE, 0.122; and SMAPE, 22.6%) than the SARIMAX model. The findings suggest that the predictive performance of the BSTS model is outperformed, and it may be helpful for ongoing intervention strategies by governmental and nongovernmental agencies in the northeast region to combat the disease effectively.

The chemical production process is tedious due to the integration of different types of equipment and variables. Designing the controller is crucial in the chemical industry due to the interactive and non-linear system behaviour. An intelligent autonomous controller can improve the operating efficiency of the industry. Although several controllers have been developed, different system failures are frequently reported. Hence, controllers such as proportional integral derivative (PID), fuzzy logic controller (FLC), and hybrid fuzzy PID (F-PID) applied in the chemical industries are critically reviewed in the paper. Initially, the PID controller-based approaches are reviewed for different purposes in the chemical industry. After that, the FLC-based controllers-based papers are reviewed. In order to satisfy the issues in both controllers, the H-PID controllers have been reviewed. This review paper will provide an effective solution for operation control in the chemical industry under different operating conditions.

Alternative refrigerants for R410A, which is most commonly used in building cooling and heating, are being requested with a global warming potential of 750 or lower and these regulations will continue to be implemented. At the current stage, several candidates are being discussed, but these will not be the final solution. What is certain is the R410A replacement refrigerant will be proposed in the form of mixed refrigerants. To figure out the thermodynamic properties of the mixed refrigerant, VLE data must be obtained first. In this study, we performed VLE experiments on a representative mixed refrigerant (R32-R125) to verify our newly constructed VLE experiment facility, and compared the results with the REFPROP 10 and the results of other researchers. The experiment was conducted at a temperature of 273.15 K to 313.15 K, and the maximum MAPE for pressure was found to be 0.0178 % and the maximum MAE for composition was found to be 0.0029. In the future, the facility will be used to present VLE test data with new mixed refrigerants as candidate replacement refrigerants for R410A.

To control the systems which operate in a repetitive mode is known to be a difficult task. For example, systems like robotic manipulators are used for doing the repetitive assigned task. This can be overcome by the iterative learning control (ILC) method. In this article, the DC motor model is discussed for controlling the speed of the motor by the design of an ILC algorithm, and its performance is compared with the conventional PID algorithm. The ILC algorithm is implemented for the minimum and non-minimum phase systems. The resulting response, absolute and integral absolute errors are plotted. The Z-N tuning method is used to implement PID control. The performance of the DC motor has been evaluated by comparing the Integral Square Error (ISE), Integral Absolute Error (IAE), and Integral Time Absolute Error (ITAE) with the conventional PID controller.