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This study aims to investigate heat and mass transfer in a one-row heat exchanger. The required equations are obtained based on two-dimensional model analysis in a cell of the heat exchanger. By using finite difference approach, the obtained equations are solved to determine distribution of temperature and the efficiency of the heat exchanger in the case of partially wet surface. In this research, Lewis Number as unity and water vapor saturation as parabolic are assumed. Obtained results show that increase in thermal conductivity fin leads to decreasing thermal resistance; therefore, temperature changes in radial from center to out of fin are reduced and efficiency of fin increases.

In this regard, fin material plays a significant role in fin efficiency. Changes in airflow also result in an efficiency increase by temperature and relative humidity, and efficiency is decreased by airflow velocity increase, and these changes are almost linear. Moreover, the fins with more wet surface are more sensitive to changes in fin dimensions and air flow characteristics, and it is a result of conjugate heat transfer mechanism, in which latent heat transfer in the fins with more wet surface has a significant role.

Thermal property and geometry of the fin under wet conditions play a more important role than the fin under dry conditions. Changes in airflow result in an efficiency increase by temperature and relative humidity, and efficiency is decreased by airflow velocity increase, and these changes are almost linear. Fins with more wet surface are more sensitive to changes in fin dimensions and air flow characteristics.

Effects of the temperature of water supply and mass flow rate were considered in the study. The results had good agreement with actual data.

This paper aims to determine the optimum arrangement of a reverse osmosis system in two methods of plug and concentrate recycling.

To compare the optimum conditions of these two methods, a seawater reverse osmosis system was considered to produce fresh water at a rate of 4,000 m3/d for Mahyarkala city, located in north of Iran, for a period of 20 years. Using genetic algorithms and two-objective optimization method, the reverse osmosis system was designed.

The results showed that exergy efficiency in optimum condition for concentrate recycling and plug methods was 82.6 and 92.4 per cent, respectively. The optimizations results showed that concentrate recycling method, despite a 36 per cent reduction in the initial cost and a 2 per cent increase in maintenance expenses, provides 6 per cent higher recovery and 19.7 per cent less permeate concentration than two-stage plug method.

Optimization parameters include feed water pressure, the rate of water return from the brine for concentrate recycling system, type of SW membrane, feedwater flow rate and numbers of elements in each pressure vessel (PV). These parameters were also compared to each other in terms of recovery (R) and freshwater unit production cost. In addition, the exergy of all elements was analyzed by selecting the optimal mode of each system.

Interorganizational collaborations are crucial for delivering high-quality, integrated healthcare services. To maximize the benefits of these collaborative networks, effective governance structures and mechanisms must be in place. While previous studies have extensively examined organizational-level factors, such as partner capabilities and backgrounds, this study focuses on network-level factors, including collaboration structures and tie characteristics that shape effective network governance.

A systematic literature review (SLR) was conducted to identify and synthesize the key network-level factors influencing governance structures and mechanisms in healthcare networks.

The review identified 22 critical factors, categorized into three primary groups that impact network governance. These findings offer a robust foundation for developing context-sensitive governance models tailored to healthcare systems.

This study provides valuable insights for healthcare practitioners, policymakers and researchers by highlighting key factors that can improve interorganizational collaboration within healthcare systems. The findings contribute to both theory and practice, with the potential to enhance healthcare service delivery and patient outcomes.

This study is the first to systematically identify and categorize the network-level factors that influence governance structures and mechanisms in healthcare networks, providing a comprehensive and novel contribution to the field.

This study aims at enhancing the performance of a 16-stage axial compressor and improving the operating stability. The adopted approaches for upgrading the compressor are artificial neural network, optimization algorithms and computational fluid dynamics.

The process starts with developing several data sets for certain 2D sections by means of training several artificial neural networks (ANNs) as surrogate models. Afterward, the trained ANNs are applied to the 3D shape optimization along with parametrization of the blade stacking line. Specifying the significant design parameters, a wide range of geometrical variations are considered by implementation of appropriate number of design variables. The optimized shapes are analyzed by applying computational fluid dynamic to obtain the best geometry.

3D optimal results show improvements, especially in the case of decreasing or elimination of near walls corner separations. In addition, in comparison with the base geometry, numerical optimization shows an increase of 1.15 per cent in total isentropic efficiency in the first four stages, which results in 0.6 per cent improvement for the whole compressor, even while keeping the rest of the stages unchanged. To evaluate the numerical results, experimental data are compared with obtained data from simulation. Based on the results, the highest absolute relative deviation between experimental and numerical static pressure is approximately 7.5 per cent.

The blades geometry of an axial compressor used in a heavy-duty gas turbine is optimized by applying artificial neural network, and the results are compared with the base geometry numerically and experimentally.

Nosiness is an annoying behavior at the workplace that can lead to negative consequences. It is characterized by being overly curious about other people’s affairs. Specifically, this study aims to identify the factors contributing to nosiness among nurses.

We conducted an exploratory qualitative interview study involving 38 nurses in Iran. The participants were selected by purposive sampling.

We identified nine themes as the antecedents of nosiness among nurses: defamation motive, the need for certainty, the need for power, recreational motive, empathy, social comparison, the allure of the subject for the individual, having an employee-friendly workplace, and work environment and workload.

Understanding the antecedents of nosiness can help healthcare organizations curtail this phenomenon and foster a positive work environment, particularly in nursing where empathy, compassion, and attention to detail make them susceptible to nosiness.

Children and adolescents with autism spectrum disorder need special attention for their mental and physical health and education. This study aims to investigate the effects of the COVID-19 pandemic on them.

This descriptive cross-sectional study was conducted on data collected from an online survey. Respondents were the parents or caregivers of autistic children aged 3–18. It measured opinions about the favorable and unfavorable behavioral changes related to their children during the COVID-19 lockdowns.

In total, 131 respondents participated in the study. The mean age of their autistic children was 11.45 ± 4.26 years, of whom 78% were boys. Improved good behaviors during lockdowns included interaction with parents and siblings, eating healthy foods, quality of sleep, eye contact, pointing and asking for something, imitating sounds and phrases, sharing interests, reaction to emotions and empathy with family members, but sleep hours decreased.

Lockdown provided opportunities for examining the behaviors of children with autism taking into account its favorable and unfavorable effects, to improve communicating and behaving with these children.

The purpose of this paper is to evaluate the relationship between the characteristics of the audit committee and the board and profitability among the companies listed on the Tehran Stock Exchange (TSE) in Iran.

In this study, the companies listed on the TSE during the period from 2010 to 2015 are investigated. The Linear panel regression method is employed for this purpose. The independent variables of the study are composed of some corporate governance mechanisms including audit committee size, audit committee expertise, board size, board independence, chief executive officer (CEO) duality, and institutional ownership.

In spite of the fact that there does not exist any significant association between audit committee size and corporate financial performance, the results indicate that there is a positive and significant relationship between audit committee financial expertise and profitability. The authors found that the number of board members cannot affect corporate performance; moreover, duality of CEO role in Iranian companies does not affect company performance. However, the outcomes showed a positive and significant association between the proportion of outside directors on the board (board independence) and profitability at 99 percent confidence level. This implies that the role of non-executive directors in Iran is inconsistent with the stewardship theory. This is due to the fact that independent directors understand the status of business and market better than the board’s executive members. Finally, the results indicated that there is no significant association between institutional owners and Iranian companies’ performance.

The findings of this study will reveal more than ever the role of corporate governance mechanisms for society and users of financial statements because as tools on the CEO actions, they always have to pay attention to the implementation of corporate principles in the economic entity’ operation.

This is one of the most important studies that simultaneously examine the impacts of characteristics of the audit committee and the board on profitability in an emerging market, and the results of the study may give strength to Iranian as well other developing countries.

This study aims to assess and decompose the sustainable development using the 17 sustainable development goals (SDGs) in Iran in 2018, for proposing agenda-setting of public policy.

It ranks the SDGs not only in Iran but also in the region and the world to reveal the synergetic effects.

Based on the results, subaltern-populace generally suffers from the hegemonic domination of ruling elite-bourgeois, lack of strong institutions, heterogeneous policy networks and lack of advocacy role of non-governmental organizations, due to no transparency, issues in law or no rule of law, no stringent regulation, rent, suppression and Mafia, all leading to corruption and injustice.

To stop the loop of corruption-injustice, Iran should homogenize the structure of the policy network. Furthermore, the failed SDGs of the three-geographic analysis are the same in a character; all of them propose SDG 3, good health and well-being as a serious failed goal.

In this regard, strong evidence is the pandemic Coronavirus, COVID 19 since 2019, due to its highly-disastrous consequences in early 2020 where the public policymakers could not adopt policies promptly in the glob, particularly in Iran.

In Iran, in addition to this, the malfunction of health is rooted in “subjective well-being” and “traffic deaths,” respectively. Concerning the transportations system in Iran, it is underscored that it is damaging the sustainable development from all the three pillars of sustainable development including, economic, social and environmental.

Proper planning for the response phase of humanitarian relief can significantly prevent many financial and human losses. To this aim, a multi-objective optimization model is proposed in this paper that considers different types of injured people, different vehicles with determining capacities and multi-period logistic planning. This model can be updated based on new information about resources and newly identified injured people.

The main objective function of the proposed model in this paper is minimizing the unsatisfied prioritized injured people in the network. Besides, the total transportation activities of different types of vehicles are considered as another objective function. Therefore, these objectives are optimized hierarchically in the proposed model using the Lexicographic method. This method finds the best value for the first objective function. Then, it tries to optimize transportation activities as the second objective function while maintaining the optimality of the first objective function.

The performances of the proposed model were analyzed in different cases and its robust approach for different problems was shown within the framework of a case study. Besides, the sensitivity analysis of results shows the logical behavior of the proposed model against various factors.

The proposed methodology can be applied to find the best response plan for all crises.

In this paper, we have tried to use a multi-objective optimization model to guide and correct response programs to deal with the occurred crisis. This is important because it can help emergency managers to improve their plans.

Given the growing emphasis on environmental consciousness and sustainability as core principles within most companies, product-service systems are recognized as strategic approaches to achieving sustainability objectives. Consequently, understanding consumer acceptance of these systems is of paramount importance. This study seeks to explore users' perspectives on the barriers that impede the adoption of product-service systems, intending to prioritize these obstacles.

This study utilizes a social media-based approach, specifically analyzing tweets related to Zipcar, an American car rental company that exemplifies a usage-oriented product-service system. The analysis identifies the factors influencing the acceptance of this system. The study utilizes topic modeling and sentiment analysis techniques to analyze the tweets. The opportunity value of each topic is determined, aiding in the identification of topics that require improvement. Furthermore, the interrelation between topics is explored, followed by correlation analysis to assess their significance.

Eight topics strongly related to the keywords are identified. Among them, “responsiveness”, “responsibility”, and “trust” hold the highest opportunity values. The findings emphasize the importance of service providers proactively addressing the obstacles that impede consumers' willingness to adopt product-service systems. Prioritization should be given to topics with higher opportunity values.

This research uncovers the primary obstacles to adopting the product-service system by directly considering consumer opinions and providing a prioritized list of these obstacles.