Considering the importance of solidarity as a theoretical and political structure and its connection with social harmony, acceptability and political legitimacy, the present research has conceptualized and formulated the problem of solidarity in the context of Iran's Multi-ethnic society through the mediation of existing studies. . In the current research, the hybrid method has been used. The sample and statistical population of the research includes selected qualitative studies that have investigated the issue of solidarity and its dimensions in Iran from different aspects and perspectives in the period from 1372 to 1398. After searching in different databases, 500 related articles, books and research reports were identified and finally 54 sources were selected to perform the meta-combination method. Based on the synthesis of studies, solidarity in the Multi-ethnic society of Iran has found a problematic situation. The results of the research confirm that the coordinates of solidarity in Iranian society are based on structural contexts and conditions. The main field in which the issue of solidarity in Iran should be established is the acceptance of a reality called "reliability and stability of cultural diversity". Based on the meta-combination of studies, the coordinates of solidarity in Iran's Multi-ethnic society, due to "intercultural affinities", "disruption and non-inclusion in ethnic policies", "instability and imbalance of development", "inefficiency of ethnic political management" and "identification disorder". In the meantime, "cultural globalization", "taking role models from ethnic elites", "geopolitics of ethnic areas and transnational interventions" and "ethnic extension" are considered as intervening conditions. Despite the fact that ethnic and religious diversity is recognized as a central sign in the constitution, with this definition, political agents have sometimes resorted to strategies that social suffering and extroversion has formed in ethnic communities.

Multi-label classification aims at assigning more than one label to each instance. Many real-world Multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel local search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm optimization algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of Multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

Introduction: Aquatic animal products are considered very important food items in the food basket regarding their high calorie, protein, and omega-3 unsaturated fat. Many aquatic animals, including fish, may always be infected with pathogenic microorganisms at various stages, from hunting to purchasing. Eventually, these factors would cause severe poisoning in humans. The present study investigates the registration and identification of Staphylococcus aureus’, s toxic genes isolated from tilapia using the Multiplex PCR technique. Materials and Methods: The sample size included 42 subjects (21 fresh fish samples and 21 frozen fish samples). First, after preparing the samples, Staphylococcus aureus was isolated and examined as surface culture in Baird-Parker medium. Next, a Coagulase test was performed, and the enterotoxin gene was identified by DNA extraction. Finally, gene sequencing was carried out automatically and systematically. Results: The results suggested that 92. 9% of the total samples (21 fresh tilapia and 18 frozen fish) had no Staphylococcus aureus, and 7. 1% of the samples were infected with Staphylococcus aureus. The coagulase test results also suggested that all three frozen fish samples were coagulase positive. Examining enterotoxin-producing genes (SEA, SEB, SEC, SED, SEE, and SEG) among three frozen samples infected with Staphylococcus bacteria revealed SEA enterotoxin gene only in one frozen sample. Conclusion: The results showed that due to the low cost and much shorter time required to identify toxic Staphylococcus aureus genes using Multiplex PCR, this method is highly effective in studying the genotypes of Staphylococcus isolates. Therefore, by using this method, one can improve the food health level in the society.

Many tubular joints commonly found in offshore jacket structures are Multi-planar. Investigating the effect of loaded out-of-plane braces on the values of the stress concentration factor (SCF) in offshore tubular joints has been the objective of numerous research works. However, due to the diversity of joint types and loading conditions, several quite important cases still exist that have not been studied thoroughly. Among them are internally ring-stiffened Multi-planar XT-joints subjected to axial loading. In the present research, data extracted from the stress analysis of 81 finite element (FE) models, verified using experimental test results, was used to study the effects of geometrical parameters on the chord-side SCFs in Multi-planar tubular XT-joints reinforced with internal ring stiffeners subjected to two types of axial loading. Parametric FE study was followed by a set of the nonlinear regression analyses to develop four new SCF parametric equations for the fatigue analysis and design of axially loaded Multi-planar XT-joints reinforced with internal ring stiffeners.

Identification of the exact location of an exon in a DNA sequence is an important research area of bioinformatics. The main issues of the previous signal processing techniques are accuracy and robustness for the exact locating of exons. To address the mentioned issues, in this study, a method has been proposed based on deep learning. The proposed method includes a new preprocessing, a new mapping method, and a Multi-scale modified and hybrid deep neural network. The proposed preprocessing method enriches the network to accept and encode genes at any length in a new mapping method. The proposed Multi-scale deep neural network uses a combination of an embedding layer, a modified CNN, and an LSTM network. In this study, HMR195, BG570, and F56F11.4 datasets have been used to compare this work with previous studies. The accuracies of the proposed method have been 0.982, 0.966, and 0.965 on HMR195, BG570, and F56F11.4 databases, respectively. The results reveal the superiority and effectiveness of the proposed hybrid Multi-scale CNN-LSTM network.

Central ignition is one of the main ideas of inertial confinement fusion in which the target is irradiated symmetrically and uniformly by laser beams. The study of target layer design using hydrodynamic codes is very important in improving target performance and fuel gain. Therefore, in the present study, we investigated the optimization of a typical spherical target with a polystyrene ablator by using MultiIFE hydrodynamic code. Considering the remarkable physical properties of diamond, it has been used to optimize the ablator layer. This target was irradiated with symmetrical laser beams with 1. 7 MJ total pulse energy and peak power of about 600 TW. The results show that the optimal thickness of the diamond is about 18 , m. A diamond ablator increases the absorbed laser energy at the target surface by about 16%. Increasing the absorbed energy leads to an increase of about 4% in the maximum temperature of the ions, and as a result, the fuel burn fraction increases by about 1%. Eventually, fuel gain increases by about 9%.

In this study, a robust H_2/H_∞ Multi-objective state-feedback controller and tracking design are presented for a mobile two-wheeled inverted pendulum (MTWIP). The proposed control has to track the desired angular velocity while keeping the mobile two-wheeled inverted pendulum balanced. First, error of output states are added to the dynamic of system for better tracking control. And uncertainties of parameters are defined by affine parameters. Next, Takagi-Sugeno (T-S) fuzzy model is used for estimating the uncertainty of nonlinear model parameters. Robust H_2/H_∞ controller is designed and analyzed for each local linear subsystem of mobile two-wheeled inverted pendulum by using a linear matrix inequalities method. To sum up, in order to calculate the whole dynamic of system from each local linear subsystem, weight average defuzzifer method is used and the total controller is designed and analyzed according to parallel distribute compensation. The simulation indicate that the proposed scheme has high accuracy, robustness, good tracking, fast transient responses and lower control effort for a mobile two-wheeled inverted pendulum despite the uncertainties and external disturbance.