n. mansouri

Cloud computing provides computing resources like software and hardware as a service by the network for several users. Task scheduling is one of the main problems to attain cost-effective execution. The main purpose of task scheduling is to allocate tasks to resources so that it can optimize one or more criteria. Since the problem of task scheduling is one of the Nondeterministic Polynomial-time (NP)-hard problems, meta-heuristic algorithms have been widely employed for solving task scheduling problems. One of the new bio-inspired meta-algorithms is Seagull Optimization Algorithm (SOA). In this paper, we proposed an energy-aware and cost-efficient SOA-based Task Scheduling (SOATS) algorithm. The aims of proposed algorithm to make a trade-off between five objectives (i.e., energy consumption, makespan, cost, waiting time, and load balancing) using a fewer number of iterations. The experiment results by comparing with several meta-heuristic algorithms (i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Whale Optimization Algorithm (WOA)) prove that the proposed technique performs better in solving task scheduling problem. Moreover, we compared the proposed algorithm with well-known scheduling Cost-based Job Scheduling (CJS), Moth Search Algorithm based Differential Evolution (MSDE), and Fuzzy-GA (FUGE). In the heavily loaded environment, the SOATS algorithm improved energy consumption and cost saving by 10 and 25%, respectively.

Sand and gravel product plants are among the significant sources of dust pollutants. This study was conducted to estimate dust concentrations released from these plants in a mineral complex in the southwest of Tehran.

Initially, the amount of silt and moisture content of the samples taken from these plants were determined according to the American Society for Testing and Materials C136 and D2216 methods, respectively. Accordingly, the rates of particulate matter emissions from these plants were determined by the AP-42 dust emission estimation methods published by the United States Environmental Protection Agency. Next, a Gaussian model was used to estimate the particulate matter concentrations in the surrounding residential areas. Finally, the simulated concentrations were compared with the United States Environmental Protect Agency and World Health Organization standards.

Results showed that hauling operations, with producing 70%, 86%, and 90% of total PM2.5, PM10 and total suspended particulates, respectively, were the major sources of dust emission in the sand and gravel product plants. The lowest dust emission was related to stockpiling handling, producing 0.24%, 0.33%, and 0.16% of the total PM2.5, PM10 and total suspended particulates. The results of the presented model indicated that 24-hour average concentrations of PM2.5, PM10, and total suspended particulates produced by mining activities were about 36, 183, and 690 µg/m3 in the working zone and less than 30, 100, and 400 µg/m3 beyond the mineral complex boundary, respectively. Thus, annual average dust concentrations were negligible. The concentrations of PM2.5 and PM10 produced by these plants in the mineral complex ambient air were higher than the standard average values recommended by the United States Environmental Protect Agency and World Health Organization. However, the concentrations of PM2.5 and PM10 from these plants in the residential areas around the complex, were below the standard limits proposed by the Environmental Protection Agency.

Sand and gravel mining activities increased the concentrations of particulate matter in the air of the surrounding areas and, to some extent, farther cities. PM2.5 and PM10 resulting from the sand and gravel mining activities could damage the workers in the mineral complex. They exceeded the 24-hour average permissible limits proposed by the United States Environmental Protection Agency about 1 and 33 µg/m3, respectively. This study showed the necessity of changing the industrial policies adopted to decrease dust emission rates. The results of this study can help the air pollution experts develop proper strategies for improving the air quality in the vicinity of surface mines.

Task scheduling is one of the fundamental issues that attract the attention of lots of researchers to enhance cloud performance and consumer satisfaction. Task scheduling is an NP–hard problem that is challenging due to the several conflicting objectives of users and service providers. Therefore, meta-heuristic algorithms are the more preferred option for solving scheduling problems in a reasonable time. Although many task scheduling algorithms are proposed, existing strategies mainly focus on minimizing makespan or energy consumption while ignoring other performance factors. In this paper, we propose a new task scheduling algorithm based on the Discrete Pathfinder Algorithm (DPFA) that is inspired by the collective movement of the animal group and mimics the guidance hierarchy of swarms to find hunt. The proposed scheduler considers five objectives (i.e., makespan, energy consumption, throughput, tardiness, and resource utilization) as cost functions. Finally, different algorithms such as Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Grasshopper Optimization Algorithm (GOA), and Bat Algorithm (BA), are used for comparison. The experimental results indicate that the proposed scheduling algorithm can improve up to 9.16%, 38.44%, 3.59%, and 3.44% the makespan in comparison with FA, BA, PSO, and GOA, respectively. Moreover, the results show dramatic improvements in terms of resource utilization, throughput, and energy consumption.

Today, many scientific problems require high computational power and storage space. Cloud computing is a model for easy access to different resources such as storage space with minimal need for service provider interaction. The cloud environment has been used for many benefits, but security and privacy issues are important challenges due to outsourcing. On the other hand, task scheduling is a fundamental issue in distributed systems such as cloud computing. Because there are several tasks to be performed that require different resources while resources are limited. Therefore, cloud tasks must be intelligently scheduled so that system performance and provider profitability are maximized. To solve this challenge, various techniques such as gradient-based algorithms for continuous and single-model problems are common. In cloud computing, due to the large search space and complex nature, these algorithms may not provide a suitable solution. Efficient meta-heuristic techniques can deal with these problems and find near-optimal solutions in a reasonable time. In this paper, a security-based scheduling algorithm using an improved Particle Swarm Optimization algorithm is presented. The improved algorithm uses multi adaptive learning to provide diversity in a population. Therefore, a good balance between exploration and exploitation. The proposed task scheduling algorithm simultaneously considers five parameters (i.e., round trip time, load, energy consumption, cost, and security) to provide load balancing and reduce energy consumption. The proposed algorithm is implemented using the CloudSim simulator and compared with the relevant strategies (i.e., CJS, OTSS, GTSA, and JSSS). The simulation results show that the proposed algorithm, considering the characteristics of tasks and resources, has significant efficiency and effectiveness in the cloud environment, especially at high workloads.

The purpose of this study was to investigate the status of HSE indices in a number of organizations affiliated with Tehran Municipality.

In this cross-sectional study, the study population included all organizations affiliated with Tehran Municipality HSE performance was assessed through an 18-item checklist. The content validity of this checklist was examined by obtaining the opinions of experts in the field. The data of this study were analyzed using the SPSS software version 22.

The results of this study showed that three organizations, namely Suburbs Bus Company, Urban Planning Organization, and Terminals and Parks Organization had the best HSE performance with indexes of 63.6%, 57.485 and 52.59%, respectively. Among the 18 items of HSE performance, the HSE policy had the highest score (68.6%).

Based on the findings of this study, the value of the HSE index was not complete in any of the fields. The results of this study showed that although the foundation of the HSE system was implemented in many units of Tehran Municipality, it is still a long way before the desired point is reached. Therefore, it is necessary to formulate and implement appropriate action plans and to evaluate the effectiveness of control measures to improve the HSE indicators.

This study aims to present a sustainable environmental model using the life cycle sustainability assessment (LCSA) method in Shazand Petrochemical Company. To determine the evaluation indices, two Delphi and DEMATEL-FTOPSIS questionnaires were distributed among 27 and 8 experts in the field of sustainable environmental management. The environmental, social, economic, environmental-economic, socio-environmental and socio-economic factors were selected as the main criteria based on the previous studies. Data analysis was performed using the DEMATEL-FTOPSIS approach. The values of the determined indices were specified by this study, and the management of the company was prepared. The indicated that the BOD/COD output from the company with the value of 2.181 has the highest effectiveness. Moreover, the index of having short - and long-term planning for local sustainable development with the value of 2.416 had the highest influence. Identification of powerful, strategic and high-quality contractors to enter a long-term contract with them in order to get the competitive advantage in the value chain of the company with the value of 0.569 was found to be the best strategy. Eventually, the sustainable management model for petrochemical industry was presented based on the output results. In this model, indices, prioritizing improvement plans and assessing life cycle sustainability were identified as the most important factors among others.

Although the relation between schizotypy and depression has been completely approved but limited attention has been dedicated to rumination as a mediator for this relation. Therefore, this research was conducted aimed to identify the relation between schizotypy and depression according to mediator role of rumination. For this purpose, 200 students in Kharazmi University (130 females and 70 males) were selected using stage cluster sampling in a correlation-descriptive survey. Data gathering tools were schizotypal personality questionnaire (SPQ), Beck depression inventory (BDI- II), and rumination reply scale (RRS). Data were analyzed by SPSS using Pearson correlation and regression analysis. Findings showed that there is significant relation between schizotypy, depression and rumination (P<0.05). Also, there is significant relation between schizotypy, depression and rumination components, namely brooding and reflection (P<0.05). Finally, results indicated that rumination and its components could be trivial mediator for relation between schizotypy and depression (p<0.05). Findings showed that individuals with high score of schizotypy represent more depression symptoms due to rumination mechanism

In this study, a probability- based methodology is formulated to evaluate quantitatively andsystematically the overtopping risk of earth dams. This study considers the overtopping phenomenainduced by the occurrence of flood and wind effects. The risk analysis based models for overtoppingconsist of random modeling of the flood generation, wind dynamic aspects, reservoir routing, reservoirgeometry, inflow discharge, outflow discharge, dam height, initial water surface and correction factorfor increasing water induced by wind. For calibrating the results of simulation and modeling, Vanakdam in Charmahal Bakhtiari is used as a case study. The case study was employed to demonstrate howthe total risk of overtopping over a practical case could be estimated based on the proposedmethodology. Annual maximum series of peak flow discharge and wind velocity of Vanak dam areused to analyze extreme flood and wind with different frequencies by using hydrological frequencyanalysis software (HYFA). Risk analysis results show that considering uncertainties of randomparameters affects significantly the dam reliability. Also overtopping risk of Vanak dam is notsensitive to increasing initial water surface. By increasing uncertainty of inflow flood, overtoppingrisk is increased.Risk overtopping increases, while the uncertainty of reservoir geometry increased.Also dam reliability is decreased by malfunction of spillway’s gates. Based on this analysis both theeffect of wind and flood together influence the overtopping risk of earth dam.