Optimal expansion of medium-voltage power networks is a common issue in electrical distribution planning. Minimizing total cost of the objective function with technical constraints and reliability limits, make it a combinatorial problem which should be solved by optimization algorithms. This paper presents a new hybrid simulated annealing and tabu search algorithm for distribution network expansion problem. Proposed hybrid algorithm is based on tabu search and an auxiliary simulated annealing algorithm controls the tabu list of the main algorithm. Also, another auxiliary simulated annealing based algorithm has been added to local searches of the main algorithm to make it more efficient. The numerical results show that the method is very accurate and fast comparing with the other algorithms.  

Choosing suitable points to establish daily neighborhood markets, as service units, is an important task for urban planners in the field of location sciences. It is related to facilitating urban access and controlling urban traffic. The purpose of this research is to locate neighborhood day markets in Zabol, considering proper distribution to enhance accessibility and improve intra-city travel demand. The research method used in this study is descriptive and analytical. Initially, the positive and negative factors influencing location were identified and examined. Then, through data collection, information layer preparation, classification, internal valuation of the layers, and weighting and overlaying of information layers using fuzzy logic in a GIS environment, the lands of Zabol were prioritized for creating neighborhood day markets. Six favorable points for locating day markets were identified. Furthermore, considering the characteristics of each selected point and calculating the accessibility of the forty neighborhoods in Zabol, the forbidden search algorithm was applied to solve the hub location problem. The software provided the best possible answer after 100 iterations, with a final weight of 531, within a time frame of 11 seconds. The algorithm’s findings indicate that points 1, 2, 4, and 5 are the most suitable for locating day markets.

Background: Wireless sensor networks include a set of non-rechargeable sensor nodes that interact for particular purposes. Since the sensors are non-rechargeable, one of the most important challenges of the wireless sensor network is the optimal use of the energy of sensors. The selection of the appropriate cluster heads for clustering and hierarchical routing is effective in enhancing the performance and reducing the energy consumption of sensors. Aim: Clustering sensors in different groups is one way to reduce the energy consumption of sensor nodes. In the clustering process, selecting the appropriate sensor nodes for clustering plays an important role in clustering. The use of multistep routes to transmit the data collected by the cluster heads also has a key role in the cluster head energy consumption. Multistep routing uses less energy to send information. Methods: In this paper, after distributing the sensor nodes in the environment, we use a Teaching-Learning-Based Optimization (TLBO) algorithm to select the appropriate cluster heads from the existing sensor nodes. The teaching-learning philosophy has been inspired by a classroom and imitates the effect of a teacher on learner output. After collecting the data of each cluster to send the information to the sink, the cluster heads use the Tabu Search (TS) algorithm and determine the subsequent step for the transmission of information. Findings: The simulation results indicate that the protocol proposed in this research (TLSIA) has a higher last node dead than the LEACH algorithm by 75%, ASLPR algorithm by 25%, and COARP algorithm by 10%. Conclusion: Given the limited energy of the sensors and the non-rechargeability of the batteries, the use of swarm intelligence algorithms in WSNs can decrease the energy consumption of sensor nodes and, eventually, increase the WSN lifetime.

Due to COVID-19 disease, children of all ages seem susceptible to COVID-19,thus, preventing children from COVID-19 disease is a serious duty. One of the preventive strategies in children is the behavior of mask-wearing. Recently, many parents have stated concerns to doctors and social media about whether masks may be harmful for their children. Incorrect news about the negative effects of face masks has also been increasing. Therefore, this letter aims at answering the most common and most important parents' worries about children’, s wearing face masks.

In this paper, the problem of simultaneous stabilization of a collection of nonlinear systems is proposed by using the Takagi-Sugeno (TS) fuzzy modeling and synthesis approach. In practice, due to the uncertainty in the system model, failure modes, or different operating points, we often encounter the problem of simultaneous stabilization. The TS fuzzy model has been considered for the controller design of complex nonlinear systems as it provides a simple and effective concept. This problem refers to the stabilization of some nonlinear systems by utilizing a single nonlinear controller. Both state feedback and output feedback controllers are designed based on the PDC concept and by solving some linear matrix inequalities (LMIs). Moreover, to reduce the conservatisms related to using the common Lyapunov function and restrictive equality conditions in the design method, Finsler’s lemma has been employed. Finally, three numerical examples are provided to show that the proposed controllers stabilize well-considered nonlinear systems.

Hypothesis: The porosity of silica-based chromium catalys t s is one of the important issues in the petrochemical indus t ry due to the high catalytic activity attracted by university and indus t rial researchers. One of the mos t important parameters that s t rengthens the s t ructure of the hydrogel is the aging process that is considered in this s t udy. By performing the aging operation on the primary silica hydrogel, the size of the initial particles increases and the surface area decreases. By doing the drying process, the pore volume of silica is reduced due to the capillary force. With changing the solvent from water to alcohols and ethyl acetate, the pore volume of the silica support also increases. This is due to the reduction in surface tension between the silica wall and the water in the cavities. Methods: The formation of silica support using sol-gel method and the parameters affecting it have been inves t igated. Also, in the drying s t ep by azeotropic dis t illation method, five solvents such as water, 1-propanol, 2-propanol, 1-butanol and ethyl acetate were used. FTIR, SEM and BET analyses were used to identify silica supports. Finding: The effect of organic solvents on pore volume was inves t igated,so that all hydrogels were synthesized under the same conditions, while different solvents were used in the drying s t ep by azeotropic dis t illation method. The use of different organic solvents did not show significant changes in the specific surface area of the silica support but caused significant changes in the its pore volume. The results showed that the morphology of the silica support was improved by solvent replacement using organic solvents and the bes t morphology and crack-free s t ructure of the ethyl acetate solvent were reported. The highes t catalytic activity of 80 kgPE/gCr. h was obtained for slurry polymerization with silica support dried by ethyl acetate solvent.

Tazkirat al-Awliyā, written by Farīd ud-Dīn Attār (1145-1221) is a book about the life of famous Sufis and their miraculous deeds (Karāmāts). The miracles performed by saints and mentioned by Attār are categorized in the present article which attempts to study the relationship between the Karāmāts and miracles of the prophets.

In this paper, we introduce a Takagi-Sugeno (TS) fuzzy model which is derived from a typical Multi-Layer Perceptron Neural Network (MLP NN). At first, it is shown that the considered MLP NN can be interpreted as a variety of TS fuzzy model. It is discussed that the utilized Membership Function (MF) in such TS fuzzy model, despite its flexible structure, has some major restrictions. After modifying the MF, we introduce a TS fuzzy model whose MFs are tunable near and far from focal points, separately. To identify such TS fuzzy model, an incremental learning algorithm, based on an efficient space partitioning technique, is proposed. Through an illustrative example, the methodology of the learning algorithm is explained. Next, through two case studies: approximation of a nonlinear function for a sun sensor and identification of a pH neutralization process, the superiority of the introduced TS fuzzy model in comparison to some other TS fuzzy models and MLP NN is shown.