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A boiling surface with different initial roughness and under various nanoparticles volume fractions was studied in present work.

Develop a correlation and sensitivity analysis.

The results showed that for small (7.3 nm) and much larger (about 2,000 nm) surface roughness, compared to nanoparticle size of around 25 nm, the heat transfer rate of nanofluid diminishes relative to that of base fluid. The results also demonstrated that the boiling heat transfer rate is reduced by increasing the concentration of nanoparticles. For larger boiling surface roughness (480 nm) and nanoparticles volume fractions of less than 0.1 Vol.%, the value of heat transfer increases with the increase of nanoparticles concentration; and for those of more than 0.1 Vol.%, heat transfer rate decreases by adding more nanoparticles, significantly.

Finally, an equation was presented for estimating the wall superheat and the C_sf coefficient in terms of mentioned parameters.

This paper aims to quantify the impact of craft worker shortage on construction project safety performance.

A database of 50 North American construction projects completed between 2001 and 2014 was compiled by taking information from a research project survey and the Construction Industry Institute Benchmarking and Metrics Database. The t-test and Mann-Whitney test were used to determine whether there was a significant difference in construction project safety performance on projects with craft worker recruiting difficulty. Poisson regression analysis was then used to examine the relationship between craft worker recruiting difficulty and Occupational Safety and Health Administration Total Number of Recordable Incident Cases per 200,000 Actual Direct Work Hours (TRIR) on construction projects.

The result showed that the TRIR distribution of a group of projects that reported craft worker recruiting difficulty tended to be higher than the TRIR distribution of a group of projects with no craft worker recruiting difficulty (p-value = 0.004). Moreover, the average TRIR of the projects that reported craft worker recruiting difficulty was more than two times the average TRIR of projects that experienced no craft recruiting difficulty (p-value = 0.035). Furthermore, the Poisson regression analysis demonstrated that there was a positive exponential relationship between craft worker recruiting difficulty and TRIR in construction projects (p-value = 0.004).

The projects used to construct the database are heavily weighted towards industrial construction.

There have been significant long-term gains in construction safety within the USA. However, if recent craft shortages continue, the quantitative analyses presented herein indicate a strong possibility that more safety incidents will occur unless the shortages are reversed. Innovative construction means and methods should be developed and adopted to work in a safe manner with a less qualified workforce.

The Poisson regression model is the first model that quantifiably links project craft worker availability to construction project safety performance.

Entrepreneurship is known to be important to the economy, and many scholars across the globe have researched it from a number of viewpoints. Currently, there is a need for an academic study to explore this area by combining sustainability value creating practices and the efforts of current entrepreneurs towards the said target, particularly in the case of the agricultural sector. While the entrepreneurship studies have mostly focused on the determinants of entrepreneurial opportunity recognition, few studies have attempted to analyze the factors influencing the entrepreneurial alertness (EA) of students, especially in relation to agricultural students. To fill this gap, this work investigated the impact of human and social capital on EA among the students of agricultural higher education in Iran.

The sample consisted of 254 agricultural students in higher education from Ilam province in the Islamic Republic of Iran, selected by the stratified random sampling method for the study. Modeling of structural equations was used in inferential statistics.

According to the results of the trial, human resources and social capital (SC) have been seen to have a strong, optimistic and measurable impact on EA. Key findings also show that human capital (HC) has an indirect, optimistic and important effect on EA through the mediator role of SC. Establishing higher education science teams, groups, networks and associations can foster opportunities to create and develop relationships and communication between agricultural students and entrepreneurs.

These findings illustrate the value of human and social resources in fostering entrepreneurship alertness among Iranian students of agricultural higher education. Considering the research results, the authors recommend some theoretical and realistic implications and suggestions for ways of promoting and increasing EA among farm students to encourage sustainable growth of agricultural careers in western Iran.

The main objective of this study is identification of the key factors in planning digital content marketing (DCM) strategy in line with the corporate's main marketing objectives in the B2B sector.

In order to identify the different content types and their corresponding marketing goals, content analysis method was served to analyze the content of Instagram pages of 24 top-ranked corporates from three different industries. SPSS version 22 was used to investigate the significant difference levels and the mean ranks of identified content types.

The findings uncovered the twelve content types which are commonly published by the corporates in line with their main marketing goals in the B2B sector. Furthermore, the results revealed the most valuable content types from the B2B audiences' viewpoint and the most efficient content types in persuading audiences to participate in conversations.

This study sheds some light on the ambiguous facets of DCM in the B2B sector, and its findings is useful as the starting point for the scholars who intend to investigate the various aspects of DCM and for the practitioners who work in the related fields.

This research offers a novel contribution to using Instagram as a DCM platform in the B2B sector. Also it contributes to identifying the main factors in communicating to B2B audiences through DCM.

Despite the significant potential of digital content marketing (DCM) to establish public and professional awareness, especially in uncertain situations, no previous research has investigated how to plan business-to-business DCM to help firms and society get through a crisis. Thus, this study aims to offer an integrative framework for providing valuable information for managing uncertainty, particularly during the pandemic crisis.

Through the lens of business awareness, this research explores relevant content types that can help firms and society get through the pandemic crisis. For this, the systematic review of 52 articles appearing in publication outlets for more than one decade (2010 to 2021) was conducted.

Based on the findings from the literature review, this paper identified two main categories of valuable content types for firms and society during the pandemic, namely, business-centered content types to enhance industrial environment awareness and human-centered content types to raise emotional awareness during the pandemic crisis.

To the best of the authors’ knowledge, this research delivers the first scientific article that focuses on presenting an integrative framework for providing valuable content types helping firms and society to manage uncertainty.

In hot and dry climates, air conditioning accounts for a large portion of total energy consumption; therefore, this paper aims to investigate the impact of sol-air temperature and ground temperature on the loss of cooling energy in hot and dry regions of Iran.

In line with this objective, the values of sol-air temperature along different directions and ground temperature at different depths were assessed with respect to climatic data of Yazd City. The impact of sol-air temperature and ground temperature on the rate of heat loss was investigated. So, energy loss of the walls aligned to four primary directions was calculated. This process was repeated for a 36 m² building with three different shape factors. All analyses were conducted for the period from May to September, during which buildings need to be cooled by air conditioners.

Numerical analyses conducted for hot and dry climate show that sol-air temperature leads to a 41-17 per cent increase in the wall’s energy loss compared with ambient temperature. Meanwhile, building the wall below the surface leads to a significant reduction in energy loss. For example, building the wall 400 cm below the surface leads to about 74.8-79.2 per cent energy saving compared with above ground design. The results also show that increasing the direct contact between soil and building envelope decreases the energy loss, so energy loss of a building that is built 400 cm below the surface is 53.7-55.3 per cent lower than that of a building built above the surface.

The impact of sol-air temperature and ground temperature on the cooling energy loss of a building in hot and dry climate was investigated. Numerical analysis shows that solar radiation increases heat loss from building envelope. Soil temperature fluctuations decrease with depth. Heat loss from building envelope in an underground building is lower than that from building envelope in a building built above the ground. Three different shape factors showed that sol-air temperature has the maximum impact on square-shaped plan and minimal impact on buildings with east-west orientation.

The purpose of this study is to develop a novel and practical series-parallel inventory-redundancy allocation system in a green supply chain including a single manufacturer and multiple retailers operating in several positions without any conflict of interests. The manufacturer first produces multi-product and then dispatches them to the retailers at different wholesale prices based on a common replenishment cycle policy. In contrast, the retailers sell the purchased products to customers at different retail prices. In this way, the manufacturer encounters a redundancy allocation problem (RAP), in which the solution subsequently enhances system production reliability. Furthermore, to emphasize on global warming and human health concerns, this paper pays attention both the tax cost of industrial greenhouse gas (GHG) emissions of all produced products and the limitation for total GHG emissions.

The manufacturer intends not only to maximize the total net profit but also to minimize the mean time to failure of his production system using a RAP. To achieve these objectives, the max-min approach associated with the solution method known as the interior point method is utilized to maximize the minimum (the worst) value of the objective functions. Finally, numerical experiments are presented to further demonstrate the applicability of the proposed methodology. Sensitivity analysis on the green supply chain approach is also performed to obtain more insight.

The computational results showed that increasing the number of products and retailers might lead into a substantial increase in the total net profit. This indicated that the manufacturer would feel adding a new retailer to the green supply chain strongly. Moreover, an increase in the number of machines provides significant improvement in the reliability of the production system. Furthermore, the results of the performed sensitivity analysis on the green approach indicated that increasing the number of machines has a substantial impact on both the total net profit and the total tax cost. In addition, not only the proposed green supply chain was more efficient than the supply chain without green but also the proposed green supply chain was very sensitive to the tax cost of GHG emission rather than the number of machines.

In summary, the motivations are as follows: the development of a bi-objective series-parallel inventory-RAP in a green supply chain; proposing a hybrid inventory-RAP; and considering the interior point solution method. The novel method comes from both theoretical and experimental techniques. The paper also has industrial applications. The advantage of using the proposed approach is to generate additional opportunities and cost effectiveness for businesses and companies that operate utilizing the green supply chain under an inventory model.

Today’s, supply chain production and distribution of products to improve the customer satisfaction in the shortest possible time by paying the minimum cost, has become the most important challenge in global market. On the other hand, minimizing the total cost of the transportation and distribution is one of the critical items for companies. To handle this challenge, this paper aims to present a multi-objective multi-facility model of green closed-loop supply chain (GCLSC) under uncertain environment. In this model, the proposed GCLSC considers three classes in case of the leading chain and three classes in terms of the recursive chain. The objectives are to maximize the total profit of the GCLSC, satisfaction of demand, the satisfactions of the customers and getting to the proper cost of the consumers, distribution centers and recursive centers.

Then, this model is designed by considering several products under several periods regarding the recovery possibility of products. Finally, to evaluate the proposed model, several numerical examples are randomly designed and then solved using non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm. Then, they are ranked by TOPSIS along with analytical hierarchy process so-called analytic hierarchy process-technique for order of preference by similarity to ideal solution (AHP-TOPSIS).

The results indicated that non-dominated ranked genetic algorithm (NRGA) algorithm outperforms non-dominated sorting genetic algorithm (NSGA-II) algorithm in terms of computation times. However, in other metrics, any significant difference was not seen. At the end, to rank the algorithms, a multi-criterion decision technique was used. The obtained results of this method indicated that NSGA-II had better performance than ones obtained by NRGA.

This study is motivated by the need of integrating the leading supply chain and retrogressive supply chain. In short, the highlights of the differences of this research with the mentioned studies are as follows: developing multi-objective multi-facility model of fuzzy GCLSC under uncertain environment and integrating the leading supply chain and retrogressive supply chain.

The purpose of this study is simulation of of polymer electrolyte membrane fuel cell. Proton-exchange membrane fuel cells are promising power sources for use in power plants and vehicles. These fuel cells provide a high level of energy efficiency at low temperature without any pollution. The convection inside the cell plays a key role in the electrochemical reactions and the performance of the cell. Accordingly, the transport processes in these cells have been investigated thoroughly in previous studies that also carried out functional modeling.

A multi-phase model was used to study the limitations of the reactions and their impact on the performance of the cell. The governing equations (conservation of mass, momentum and particle transport) were solved by computational fluid dynamics (CFD) (ANSYS fluent) using appropriate source terms. The two-phase flow in the fuel cell was simulated three-dimensionally under steady-state conditions. The flow of water inside the cell was also simulated at high-current density.

The simulation results suggested that the porosity of the gas diffusion layer (GDL) is one of the most important design parameters with a significant impact on the current density limitation and, consequently, on the cell performance.

This study was mainly focused on the two-phase analysis of the steady flow in the fuel cell and on investigating the impacts of a two-phase flow on the performance of the cell and also on the flow in the GDL, the membrane and the catalyst layer using the CFD.

This paper aims to examine total annual cost from economic view mixed materials heat exchangers based on three optimization algorithms. This study compares the use of three optimization algorithms in the design of economic optimization shell and tube mixed material heat exchangers.

A shell and tube mixed materials heat exchanger optimization design approach is expanded based on the total annual cost measured by dividing the costs of the heat exchanger to area of surface and power consumption. In this study, optimization and minimization of the total annual cost is considered as the objective function. There are three types of exchangers: cheap, expensive and mixed. Mixed materials are used in corrosive flows in the heat exchanger network. The present study explores the use of three optimization techniques, namely, hybrid genetic-particle swarm optimization, shuffled frog leaping algorithm techniques and ant colony optimization.

There are three parameters as decision variables such as tube outer diameter, shell diameter and central baffle spacing considered for optimization. Results have been compared with the findings of previous studies to demonstrate the accuracy of algorithms.

The present study explores the use of three optimization techniques, namely, hybrid genetic-particle swarm optimization, shuffled frog leaping algorithm techniques and ant colony optimization. This study has demonstrated successful application of each technique for the optimal design of a mixed material shell and tube heat exchanger from the economic view point.