In this paper, a multi-objective allocation and scheduling of wind turbines and electric vehicle parking lots are performed in an IEEE 33-bus radial distribution network to reach the minimum annual costs of power loss, purchased grid energy, wind energy, PHEV energy, battery degradation cost, and network voltage deviations. Decision variables, such as the site and size of wind turbines and electric parking lots in the distribution system, are found using an improved golden jackal optimization (IGJO) algorithm based on Rosenbrock’s direct rotational (RDR) strategy. The results showed that the IGJO finds the optimal solution with a lower convergence tolerance and a better (lower) objective function value compared to conventional GJO, the artificial electric field algorithm (AEFA), particle swarm optimization (PSO), and manta ray foraging optimization (MRFO) methods. The results showed that using the proposed method based on the IGJO, the energy loss cost, grid energy cost, and network voltage deviations were reduced by 29.76%, 65.86%, and 18.63%, respectively, compared to the base network. Moreover, the statistical analysis results proved their superiority compared to the conventional GJO, AEFA, PSO, and MRFO algorithms. Moreover, considering vehicles battery degradation costs, the losses cost, grid energy cost, and network voltage deviations have been reduced by 3.28%, 1.07%, and 4.32%, respectively, compared to the case without battery degradation costs. In addition, the results showed that the decrease in electric vehicle availability causes increasing losses for grid energy costs and weakens the network voltage profile, and vice versa.

Loss of viability is one of the most important problems during starter culture production. Previous research has mostly focused on the production process of bacterial starters, but there are few studies about cellular protein deterioration causing cell defectiveness during storage. In the present study, we investigated the influence of storage temperature (−21, 4, 35°C) on the cellular protein modifications which may contribute to the senescence of freeze-dried Acetobacter senegalensis.

In this paper, explore the effectiveness of a new Wide Area Fuzzy Power System Stabilizer (WAFPSS), optimized using the Exponential Distribution Optimization (EDO) algorithm, and applied to an IEEE three-area, six-machine power system model. This research primarily focuses on assessing the stabilizer’s capability to dampen inter-area oscillations, a critical challenge in power grid operations. Through extensive simulations, the study demonstrates how the WAFPSS enhances stability and reliability under a variety of operational conditions characterized by different communication delay patterns. The application of the proposed stabilizer on this specific IEEE model provides a detailed insight into its performance in real-world scenarios, illustrating its adaptability and effectiveness in managing dynamic disturbances. The simulation results reveal that the proposed WAFPSS achieves significant reductions in the Integral Time Squared Error (ITSE), with improvements of 94.1%, 97.02%, and 98.18% in three distinct cases, showcasing its superior damping capability and robustness. The findings indicate that the advanced optimization techniques provided by the EDO algorithm significantly improve the stabilizer’s response, ensuring robust power system performance. This integration of WAMS with sophisticated control systems using fuzzy logic presents a strategic solution to managing the complexities faced by modern power networks, optimizing their stability in the face of increasing renewable integration and fluctuating demand.

In this paper, the optimal design of a hybrid energy system (HES), consisting of photovoltaic technology integrated with fuel cells (HPV/FC) and relying on hydrogen storage, is performed to meet the annual demand of a residential complex to find the minimum total net present cost (TNPC), while observing the reliability constraint as the energy-not-supplied probability (ENSP) and considering real meteorological data of the Kuala Lumpur city in Malaysia. The decision variables include the size of system components, which are optimally determined by an improved artificial ecosystem-based optimization algorithm (IAEO). The conventional AEO is improved using the dynamic lens-imaging learning strategy (DLILS) to prevent premature convergence. The results demonstrated that the decrease (increase) of the reliability constraint leads to an increase (decrease) in the TNPC, as well as the cost of electricity (COE). For a maximum reliability constraint of 5%, the results show that the TNPC and COE obtained USD 2.247 million and USD 0.4046 million, respectively. The superior performance of the IAEO has been confirmed with the AEO, particle swarm optimization (PSO), and manta ray foraging optimization (MRFO), with the lowest TNPC and higher reliability. In addition, the effectiveness of the hydrogen tank efficiency and load changes is confirmed in the hybrid system design.

Background: Women in study areas suffered from the problems of caesarean delivery (CD), low birth weight (LBW), and macrosomia. Objective: To investigate how gestational weight gain (GWG) influences the effect of the pre-pregnancy body mass index (BMI) on the risks of CD, LBW, and macrosomia in urban and rural areas in a city of Iran. Methods: We used 767 and 612 eligible subjects from the public health care centers in urban and rural areas respectively. Results: The risk of CD increased from 74% to 2.62-fold in urban and from 62% to 2.15-fold in rural areas, and the risk of macrosomia increased from 58% to 2.35-fold in urban and from 47% to 96% in rural areas, among obese women compared to normal weight women who gained above median GWG. The risk of LBW increased from 38% to 92% in urban and from 49% to 97% in rural areas among lean women compared to normal weight women who gained below median GWG. Conclusion: These findings strongly support the need to reform adequate pre-pregnancy weight and GWG against the risks of CD and macrosomia among overweight and obese women, and against the risk of LBW among lean women in both areas. Keywords: Body mass index, gestational weight gain, caesarean delivery, low birth weight, macrosomia