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There are three standard equivalent circuit models of solar cells in the literature—single-diode, double-diode, and triple-diode models. In this paper, first, a modified version of the single diode model, called the Improved Single Diode Model (ISDM), is presented. This modification is realized by adding resistance in series with the diode to enable better power loss dissipation representation. Second, the mathematical expression for the current–voltage relation of this circuit is derived in terms of Lambert’s W function and solved by using the special trans function theory. Third, a novel hybrid algorithm for solar cell parameters estimation is proposed. The proposed algorithm, called SA-MRFO, is used for the parameter estimation of the standard single diode and improved single diode models. The proposed model’s accuracy and the proposed algorithm’s efficiency are tested on a standard RTC France solar cell and SOLAREX module MSX 60. Furthermore, the experimental verification of the proposed circuit and the proposed solar cell parameter estimation algorithm on a solar laboratory module is also realized. Based on all the results obtained, it is shown that the proposed circuit significantly improves current–voltage solar cell representation in comparison with the standard single diode model and many results in the literature on the double diode and triple diode models. Additionally, it is shown that the proposed algorithm is effective and outperforms many literature algorithms in terms of accuracy and convergence speed.

Parameters of the solar cell equivalent circuit models have a significant role in assessing the solar cells’ performance and tracking operational variations. In this regard, estimating solar cell parameters is a difficult task because cells have nonlinear current-voltage characteristics. Thus, a fast and accurate optimization algorithm is usually required to solve this engineering problem effectively. This paper proposes two hybrid variants of honey badger algorithm (HBA) and artificial gorilla troops optimizer (GTO) to estimate solar cell parameters. The proposed algorithms minimize the root mean square error (RMSE) between measurement and simulation results. In the first variant, GTO is used to determine the initial population of HBA, while in the second variant, HBA is used to determine the initial population of GTO. These variants can efficiently improve convergence characteristics. The proposed optimization algorithms are applied for parameter estimation of different equivalent circuit models of solar cells and various photovoltaic (PV) modules. Namely, the proposed algorithms test three solar cell equivalent models: single-diode, double-diode, and triple-diode equivalent circuit models. Different photovoltaic modules are investigated, such as the RadioTechnique Compelec (RTC) France solar cell, Solarex’s Multicrystalline 60 watts solar module (MSX 60), and the Photowatt, France solar panel (Photo-watt PWP 201). In addition, the applicability of the proposed optimization algorithms is verified using obtained results from a commercial solar module called Shell Monocrystalline PV module (SM55) with different irradiation and temperature levels. The good results of the proposed algorithms show that they can efficiently improve convergence speed and the accuracy of the obtained results than other algorithms used for parameter estimation of PV equivalent circuit models in the literature, particularly in terms of the values of the RMSE and statistical tests. In addition, the parameters estimated by the proposed methods fit the simulation data perfectly at different irradiance and temperature levels for the commercial PV module.

With the increase in the penetration of battery electric vehicles (BEVs) all over the world, utilities should start considering their increased demand as part of their electric demand. Generally, the literature lacks works that consider the impact of transportation electrification on the reliability of the power system. Thus, this paper proposes a new mechanism for reliability assessment including BEVs, with both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes. Three charging strategies: uncontrolled, controlled unidirectional, and controlled bidirectional are considered in this paper to model the interactions between the transportation and electric power systems. A dynamic stochastic consumption model for a fleet of BEVs is developed to be used in the reliability assessment for the distribution networks. This dynamic model takes into consideration the variability and uncertainty of different trip purposes, starting and ending trip times, as well as the corresponding battery consumption in weather conditions. Furthermore, it is composed of two sequential submodels: travel behavior and battery depletion. The first submodel considers trip-related information while the second considers battery-depleted energy. Simulation results on a benchmark test system show the negative impacts of uncontrolled charging on the power system’s reliability. However, they also show that controlled charging can significantly reduce or mitigate these impacts.

Oral health is a key contributor to a person’s overall health and well-being. Oral microbiota can pose a serious threat to oral health. Thus, the present study aimed to develop a cinnamon oil (CO)-loaded nanoemulsion gel (NEG1) to enhance the solubilization of oil within the oral cavity, which will enhance its antibacterial, antifungal, and analgesic actions against oral microbiota. For this purpose, the CO-loaded nanoemulsion (CO-NE) was optimized using I-optimal response surface design. A mixture of Pluracare L44 and PlurolOleique CC 497 was used as the surfactant and Capryol was used as the co-surfactant. The optimized CO-NE had a globule size of 92 ± 3 nm, stability index of 95% ± 2%, and a zone of inhibition of 23 ± 1.5 mm. This optimized CO-NE formulation was converted into NEG1 using 2.5% hydroxypropyl cellulose as the gelling agent. The rheological characterizations revealed that the NEG1 formulation exhibited pseudoplastic behavior. The in vitro release of eugenol (the marker molecule for CO) from NEG1 showed an enhanced release compared with that of pure CO. The ex vivo mucosal permeation was found to be highest for NEG1 compared to the aqueous dispersion of CO-NE and pure cinnamon oil. The latency reaction time during the hot-plate test in rats was highest (45 min) for the NEG1 sample at all-time points compared with those of the other tested formulations. The results showed that the CO-NEG formulation could be beneficial in enhancing the actions of CO against oral microbiota, as well as relieving pain and improving overall oral health.

Burn wound healing is a complex process that involves the repair of injured tissues and the control of infection to diminish the scar formation, pain, and discomfort associated with such injuries. The aim of this research was to formulate and optimize a self-nanoemulsion drug delivery system based on the use of coconut oil and loaded with simvastatin. Coconut oil possesses antiinflammatory and antibacterial activity, and simvastatin has interesting properties for promoting the wound-healing process because it increases the production of the vascular endothelial growth factor at the site of injury. The Box–Behnken design was employed for the optimization of the coconut oil–simvastatin self-nanoemulsion drug delivery system. The prepared formulations were characterized according to globular size and their activity in the healing of burn wounds by assessing the mean wound diameter and level of interlukin-6 in experimental animals. Additionally, the antimicrobial activity of the prepared formulations was assessed. The nanoemulsion was considered adequately formed when it had droplets of between 65 and 195 nm. The statistical design proved the important synergistic effect of coconut oil and simvastatin for burn wound management in their synergistic potentiation of wound closure and their anti-inflammatory and antimicrobial effects. The optimum formulation achieved up to a 5.3-fold decrease in the mean burn wound diameter, a 4.25-fold decrease in interleukin-6 levels, and a 6-fold increase in the inhibition zone against Staphylococcus aureus when compared with different control formulations. Therefore, the designed nanoemulsions containing a combination of coconut oil and simvastatin could be considered promising platforms for the treatment of chronic and burn wounds.

Despite the development and implementation of several MRI techniques for breast density assessments, there is no consensus on the optimal protocol in this regard. This study aimed to determine the most appropriate MRI protocols for the quantitative assessment of breast density using a personalized 3D-printed breast model. The breast model was developed using silicone and peanut oils to simulate the MRI related-characteristics of fibroglandular and adipose breast tissues, and then scanned on a 3T MRI system using non-fat-suppressed and fat-suppressed sequences. Breast volume, fibroglandular tissue volume, and percentage of breast density from these imaging sequences were objectively assessed using Analyze 14.0 software. Finally, the repeated-measures analysis of variance (ANOVA) was performed to examine the differences between the quantitative measurements of breast volume, fibroglandular tissue volume, and percentage of breast density with respect to the corresponding sequences. The volume of fibroglandular tissue and the percentage of breast density were significantly higher in the fat-suppressed sequences than in the non-fat-suppressed sequences (p < 0.05); however, the difference in breast volume was not statistically significant (p = 0.529). Further, a fat-suppressed T2-weighted with turbo inversion recovery magnitude (TIRM) imaging sequence was superior to the non-fat- and fat-suppressed T1- and T2-weighted sequences for the quantitative measurement of breast density due to its ability to represent the exact breast tissue compositions. This study shows that the fat-suppressed sequences tended to be more useful than the non-fat-suppressed sequences for the quantitative measurements of the volume of fibroglandular tissue and the percentage of breast density.

The integration of distributed generation (DG) into the power grid has increased in recent years due to its techno-economic benefits for utilities and consumers. However, due to the fact that distribution systems were not originally designed to accommodate such DG units, many challenges are being faced by utilities to seamlessly integrate them into their systems. One of the critical challenges is their effect on protection system settings and coordination. The DG units will affect the pickup current settings of the protection relays, coordination between the primary and secondary relays, and even the direction of the fault current. Failing to consider DG’s effect on the protection system may lead to serious equipment damage or system failure, causing huge financial setbacks for utilities. To that end, this work proposes a new dynamic approach to optimally allocate different types of DG units over the planning horizon. The objective is to minimize the overall costs of the system while taking into consideration the intermittent nature of renewable DG and the impacts on the protection system. Simulation results have been developed on a typical distribution system to prove the effectiveness of the proposed approach.