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In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.

Further as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.

As per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.

In this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).

In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.

Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.

In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

In this research, the main purpose is to use a suitable structure to predict the trading signals of the stock market with high accuracy. For this purpose, two models for the analysis of technical adaptation were used in this study.

It can be seen that support vector machine (SVM) is used with particle swarm optimization (PSO) where PSO is used as a fast and accurate classification to search the problem-solving space and finally the results are compared with the neural network performance.

Based on the result, the authors can say that both new models are trustworthy in 6 days, however, SVM-PSO is better than basic research. The hit rate of SVM-PSO is 77.5%, but the hit rate of neural networks (basic research) is 74.2.

In this research, two approaches (raw-based and signal-based) have been developed to generate input data for the model: raw-based and signal-based. For comparison, the hit rate is considered the percentage of correct predictions for 16 days.

This study aims to investigate the impact of new technologies on parameters of organizational behavior and evaluate their determining role of technology maturity and readiness of staff in the digital readiness.

This study has obtained an integrated model of technology’s effect on staff’s organizational behavior considering digital readiness level by using system dynamics is developed. In this model, the effects of new technologies entry on organizational behavior variables are analyzed in different layers, and the result of this impact on the consequent of a bank organizational behavior and each indicator is examined separately in different scenarios. In determining the indicators and their significant coefficients, the viewpoints of banking experts and professionals in organizational behavior have been considered.

As a result of our surveys, five technology effects, without intermediaries, were obtained, which are automation, learning, streamlining repetitive jobs, addiction to technology and reducing face-to-face contact. Each of these factors would make a chain of side effects. In a way that, ultimately, their positive or negative effects on productivity and consequently on organization profits appear. The result indicates technology has effects on important behavioral factors such as stress, motivation, organization values and personal satisfaction. Indicators, which are formed by positive or negative factors, are being upgraded or downgraded. Therefore, managing negative cycles and developing positive cycles can be considered as one of the major banking concerns for controlling IT effects on its organizational behavior of human resources.

There is little academic remarkable literature on clarifying the effects of digitalization on employee's behavior in an organization, this research offers managers and organizations a model of influential factors that need to be taken into account by managers when they encounter new technologies. This study’s proposed analysis is useful to improve the efficiency and productivity of the organization, and alongside this, it is effective for the digital transformation process. This study fills previous research gaps in the academic context related to the practical studies that relied on digital maturity.

The purpose of this paper is to maximize the total demand covered by the established additive manufacturing and distribution centers and maximize the total literal weight assigned to the drones.

Disaster management or humanitarian supply chains (HSCs) differ from commercial supply chains in the fact that the aim of HSCs is to minimize the response time to a disaster as compared to the profit maximization goal of commercial supply chains. In this paper, the authors develop a relief chain structure that accommodates emerging technologies in humanitarian logistics into the two phases of disaster management – the preparedness stage and the response stage.

Solving the model by the genetic and the cuckoo optimization algorithm (COA) and comparing the results with the ones obtained by The General Algebraic Modeling System (GAMS) clear that genetic algorithm overcomes other options as it has led to objective functions that are 1.6% and 24.1% better comparing to GAMS and COA, respectively.

Finally, the presented model has been solved with three methods including one exact method and two metaheuristic methods. Results of implementation show that Non-dominated sorting genetic algorithm II (NSGA-II) has better performance in finding the optimal solutions.

In the present research, location and routing problems, as well as the supply chain, which includes manufacturers, distributor candidate sites and retailers, are explored. The goal of addressing the issue is to reduce delivery times and system costs for retailers so that routing and distributor location may be determined.

By adding certain unique criteria and limits, the issue becomes more realistic. Customers expect simultaneous deliveries and pickups, and retail service start times have soft and hard time windows. Transportation expenses, noncompliance with the soft time window, distributor construction, vehicle purchase or leasing, and manufacturing costs are all part of the system costs. The problem's conceptual model is developed and modeled first, and then General Algebraic Modeling System software (GAMS) and Multiple Objective Particle Swarm Optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGAII) algorithms are used to solve it in small dimensions.

According to the mathematical model's solution, the average error of the two suggested methods, in contrast to the exact answer, is less than 0.7%. In addition, the performance of algorithms in terms of deviation from the GAMS exact solution is pretty satisfactory, with a divergence of 0.4% for the biggest problem (N = 100). As a result, NSGAII is shown to be superior to MOSPSO.

Since this paper deals with two bi-objective models, the priorities of decision-makers in selecting the best solution were not taken into account, and each of the objective functions was given an equal weight based on the weighting procedures. The model has not been compared or studied in both robust and deterministic modes. This is because, with the exception of the variable that indicates traffic mode uncertainty, all variables are deterministic, and the uncertainty character of demand in each level of the supply chain is ignored.

The suggested model's conclusions are useful for any group of decision-makers concerned with optimizing production patterns at any level. The employment of a diverse fleet of delivery vehicles, as well as the use of stochastic optimization techniques to define the time windows, demonstrates how successful distribution networks are in lowering operational costs.

According to a multi-objective model in a three-echelon supply chain, this research fills in the gaps in the link between routing and location choices in a realistic manner, taking into account the actual restrictions of a distribution network. The model may reduce the uncertainty in vehicle performance while choosing a refueling strategy or dealing with diverse traffic scenarios, bringing it closer to certainty. In addition, two modified MOPSO and NSGA-II algorithms are presented for solving the model, with the results compared to the exact GAMS approach for medium- and small-sized problems.

This study aims to investigate a locating-routing-allocating problems and the supply chain, including factories distributor candidate locations and retailers. The purpose of this paper is to minimize system costs and delivery time to retailers so that routing is done and the location of the distributors is located.

The problem gets closer to reality by adding some special conditions and constraints. Retail service start times have hard and soft time windows, and each customer has a demand for simultaneous delivery and pickups. System costs include the cost of transportation, non-compliance with the soft time window, construction of a distributor, purchase or rental of a vehicle and production costs. The conceptual model of the problem is first defined and modeled and then solved in small dimensions by general algebraic modeling system (GAMS) software and non-dominated sorting genetic algorithm II (NSGAII) and multiple objective particle swarm optimization (MOPSO) algorithms.

According to the solution of the mathematical model, the average error of the two proposed algorithms in comparison with the exact solution is less than 0.7%. Also, the algorithms’ performance in terms of deviation from the GAMS exact solution, is quite acceptable and for the largest problem (N = 100) is 0.4%. Accordingly, it is concluded that NSGAII is superior to MOSPSO.

In this study, since the model is bi-objective, the priorities of decision makers in choosing the optimal solution have not been considered and each of the objective functions has been given equal importance according to the weighting methods. Also, the model has not been compared and analyzed in deterministic and robust modes. This is because all variables, except the one that represents the uncertainty of traffic modes, are deterministic and the random nature of the demand in each graph is not considered.

The results of the proposed model are valuable for any group of decision makers who care optimizing the production pattern at any level. The use of a heterogeneous fleet of delivery vehicles and application of stochastic optimization methods in defining the time windows, show how effective the distribution networks are in reducing operating costs.

This study fills the gaps in the relationship between location and routing decisions in a practical way, considering the real constraints of a distribution network, based on a multi-objective model in a three-echelon supply chain. The model is able to optimize the uncertainty in the performance of vehicles to select the refueling strategy or different traffic situations and bring it closer to the state of certainty. Moreover, two modified algorithms of NSGA-II and multiple objective particle swarm optimization (MOPSO) are provided to solve the model while the results are compared with the exact general algebraic modeling system (GAMS) method for the small- and medium-sized problems.

In spite of the importance of food intake in weight management and preventing chronic diseases, it remains difficult to predict how anxious people change their eating behaviour in exposure to bad or good moods. The purpose of the study was to investigate the interaction effect of anxiety and different moods on food intake and blood pressure in healthy women students.

A total of 82 women university students (18-30 years) participated in the study. Subjects completed a valid anxiety questionnaire at baseline to measure trait and state anxiety scores, then they were randomly divided into two groups to watch comedy and drama movies for mood induction. After watching, some snacks were presented, and then energy intake and blood pressure were measured.

Students who suffered from severe state anxiety, consumed more energy from food when they watched a dramatic movie (p = 0.014). Subjects who suffered from moderate level of state anxiety and watched a dramatic movie experienced more systolic and diastolic blood pressure compared with subjects who suffered from moderate state anxiety but watched the comedy (p = 0.043 and p = 0.041, for systolic and diastolic blood pressure respectively). More diastolic blood pressure was shown among students who watched the drama movie and suffered from a severe level of trait anxiety (p = 0.049).

Electrocardiography and stroke volume measurement were not used.

Our findings showed blood pressure elevation in anxious people when they experienced bad feeling such as sadness, and they also consumed more energy from food. Both of these factors are related to the occurrence of chronic disorders such as cardiovascular diseases.

Nowadays, organizations are facing fast markets’ changing, competition strategies, technological innovations and accessibility of information. In such highly dynamic situations, many factors must be coordinated to realize effective decision-making. In addition, the definition of organizational intelligence is as follows: intellectual ability to answer organizational issues and focus on the unification of human and mechanical abilities for solving problems. This paper aims to investigate important factors (organizational learning, knowledge management and e-learning systems) that influence organizational intelligence.

Data have been collected from 290 personnel of tax administration of East Azarbaijan, Iran. For measuring the model’s elements, a questionnaire has been proposed. Surveys have been reviewed by experts with significant experiences in the organizational intelligence field. For statistical analysis of questionnaires, the statistical package social sciences 25 and SMART-partial least squares 0.3 have been used.

Findings from the study verify the validity of the design for an organizational intelligence assessment. The outcomes indicate that e-learning systems positively affected organizational intelligence. In addition, they show that the influence of knowledge management and organizational learning on organizational intelligence is important.

Organizational intelligence’s multidimensional nature makes it a very useful and essential management tool. Therefore, it provides beneficial results for the organizations’ managers to study the important factors affecting it.

This chapter demonstrates that women challenge oppressive gender relations by engaging in active agency at different levels. Iranian women’s struggles for gender equality show a critical consciousness of the politics of local male domination and an indigenous contestation of the cultural practices which sanction injustices against women.

This chapter is based on the findings and analysis of the book, Women, Power and Politics in 21st Century in Iran. It is the result of the political and personal experiences of a number of Iranian women academics, journalist and activists who live and work in Iran.

Based on the updated findings and new statistical data, this chapter argues that women, despite their high level of education and activism, continue to face gender inequality, in particular in the sphere of employment.

This chapter is intended to counter the often inaccurate and misleading impressions put forward by the media, politicians and some academics in the West when they talk about Iranian women. Within the broader feminist theoretical positioning, the aim of this chapter is to contribute to the debate on essentialism and the stereotype of Iranian women as submissive Muslim women without agency.

Feminist knowledge production is diverse. Nonetheless, consideration of the historical and geographical locations of feminist knowledge production is vital to our understanding of the complex processes of women’s liberation. Thus, Iranian women’s voices are important to what is traditionally understood as feminism.