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Construction industry bears a lot of casualties and accidents more than other high-risk industries annually. Thus, the use of new technologies such, as building information modeling, automatic rule checking, information technology-based safety systems in order to implement the rules and safety standards, better controls the performance of workers on site and make high coordination between operational executives, leading to create a secure environment in projects by reducing accidents. The paper aims to discuss these issues.

In this study, a researcher-designed questionnaire was distributed among 200 companies that are active in the field of construction to evaluate the effect of building information model (BIM) for safety projects and barriers to adoption. Only 70 percent of questionnaires were returned. Statistical Package for the Social Sciences analysis has been used to determine the correlation coefficient among the respondents.

The results show the factors that lead to failure in the adoption of BIM in Iran are lack of well-trained personnel, proper social infrastructure, guidance and governmental supports.

Finally, the authors presented solutions for overcoming barriers and proposed some factors leading to the successful adoption of BIM in Iran.

The purpose of this study is to compare the trend of academic project management research in Iran and the World in five-year periods with a text mining approach and TF–IDF method.

The research population consists of 1205 theses presented between 2000 and 2019 in Iranian universities. The central library website of the mentioned universities was used for data collection, and the text mining approach with the TF–IDF method was used for data analysis.

The remarkable results of this study include: Concrete structures are the most frequent among structural systems, Risk Management is the most frequent among PMBOK Knowledge Areas, Design-build (DB) system is the most frequent among Project Delivery Systems, Engineering, procurement and construction (EPC) is the most frequent among DB Project Delivery Systems, Financial Management is the most frequent among specialized construction knowledge areas, Soft Skills is the most frequent among Global Trends, Contracting Companies is the most frequent among Project Parties, Construction Projects is the most frequent among Project Areas, Power Plant and Refinery is the most frequent among Project Subjects, Optimization is the most frequent among Problem-Solving Approaches, Fuzzy Logic is the most frequent among Novel Algorithms and Motivation is the most frequent among Soft Skills.

The innovative aspect of this research is that for the first time, text mining has been used to analyze academic research on project and construction management, and also for the first time, academic research on construction industry in Iran has been compared with global research.

The purpose of this paper is to propose a new methodology that handles the issue of the dynamic behavior of customers over time.

A new methodology is presented based on time series clustering to extract dominant behavioral patterns of customers over time. This methodology is implemented using bank customers’ transactions data which are in the form of time series data. The data include the recency (R), frequency (F) and monetary (M) attributes of businesses that are using the point-of-sale (POS) data of a bank. This data were obtained from the data analysis department of the bank.

After carrying out an empirical study on the acquired transaction data of 2,531 business customers that are using POS devices of the bank, the dominant trends of behavior are discovered using the proposed methodology. The obtained trends were analyzed from the marketing viewpoint. Based on the analysis of the monetary attribute, customers were divided into four main segments, including high-value growing customers, middle-value growing customers, prone to churn and churners. For each resulted group of customers with a distinctive trend, effective and practical marketing recommendations were devised to improve the bank relationship with that group. The prone-to-churn segment contains most of the customers; therefore, the bank should conduct interesting promotions to retain this segment.

The discovered trends of customer behavior and proposed marketing recommendations can be helpful for banks in devising segment-specific marketing strategies as they illustrate the dynamic behavior of customers over time. The obtained trends are visualized so that they can be easily interpreted and used by banks. This paper contributes to the literature on customer relationship management (CRM) as the proposed methodology can be effectively applied to different businesses to reveal trends in customer behavior.

In the current business condition, customer behavior is changing continually over time and customers are churning due to the reduced switching costs. Therefore, choosing an effective customer segmentation methodology which can consider the dynamic behaviors of customers is essential for every business. This paper proposes a new methodology to capture customer dynamic behavior using time series clustering on time-ordered data. This is an improvement over previous studies, in which static segmentation approaches have often been adopted. To the best of the authors’ knowledge, this is the first study that combines the recency, frequency, and monetary model and time series clustering to reveal trends in customer behavior.

This paper seeks to present a novel approach for formation control of non‐holonomic wheeled mobile robots (WMRs). The use of a general geometrical structure has led the considered robotic team form any desired configuration. Although various methodologies have been suggested for solving such formation control problem in the literature, the proposed kinematical method of the present investigation has several advantages in terms of its robustness, tracking performance, and superior energy consumption due to the fuzzy logic scheme developed.

In an attempt to make the follower robot to assume the proper orientation, a new concept is presented which defines an appropriate heading angle. This concept is based on the natural human behavior as corresponds to situations of tracking a certain trajectory. The proposed heading angle planner is based on a two‐stage fuzzy logic system, providing appropriate heading angles for the mobile robot at each instant. In order to adjust the linear/angular velocity of the robots then, two further fuzzy controllers are devised.

The results obtained from the computer simulation studies reveal the merits as well as effectiveness of the proposed method for formation control of a group of WMRs in the presence of usual control input constraints, noisy sensor data, and external disturbances.

A novel method based on a fuzzy leader‐follower method is presented for the formation control of a group of robots.

This paper aims to find a closed-form expression for the frequency and amplitude of single-ended ring oscillators when transistors experience all regions.

In this paper, the analytical relationships presented for ring oscillator amplitude and frequency are approximately derived due to the nonlinear nature of this oscillator, taking into account the differential equation that governs the ring oscillator and its output waveform.

In the case where the transistors experience the cut-off region, the relationships presented so far have no connection between the frequency and the dimensions of the transistor, which is not valid in practice. The relationship is presented for the frequency, including the dimensions of the transistor. Also, a simple and approximately accurate relationship for the oscillator amplitude is provided in this case.

The validity of these relationships has been investigated by analyzing and simulating a single-ended oscillator in 0.18 µm technology.

The purpose of this paper is to find a closed relation for the phase noise of LC oscillators.

The governing equation of oscillators is generally a stochastic nonlinear differential equation. In this paper, a closed relation for the phase noise of LC oscillators was obtained by approximating the I–V characteristic of the oscillator with third-degree polynomials and analyzing its differential equation.

This relation expresses phase noise directly in terms of circuit parameters, including the sizes of the transistors and the bias. Next, for evaluation, the phase noise of the cross-coupled oscillator without tail current was calculated with the proposed model. In this approach, the obtained equations are expressed independently of technology by combining the obtained phase noise relation and g_m/I_D method.

A technology-independent method using the g_m/I_D method and the closed relationship is provided to calculate phase noise.

During the Covid-19 period, when human beings are socially isolated, telework is a viable solution to safeguard employees' health. Because many employees have never experienced such a working system and organizations have not planned for it before the pandemic, imposing employees to telework has adversely affected their productivity and efficiency. This study aims to identify factors affecting individuals' tendency toward teleworking during the pandemic, which can lead to practical solutions for the post-pandemic era.

Through the use of technology acceptance models, a conceptual model was designed. Data used to assess the model were cross-sectional and derived from 229 questionnaires filled out by employees in Tehran. The AMOS24 software processed the corresponding structural equation model.

The results from the cross-sectional data indicated that attitude toward telework and perceived behavioral control over the system were significantly correlated directly with the intention to telework, while perceived usefulness and perceived ease of use of telework were correlated indirectly. Therefore, the integrated model predicts behavioral intentions better than single models performed separately.

Psychological and mental health research describing adoption intentions of telework, particularly those focusing on employees, is still lacking. To the best of the authors' knowledge, this is the first study in this regard that has used a conceptual model derived from two technology acceptance models during the Covid-19 outbreak. An era in which the extent of the pandemic has forced employees to experience such working systems and thus the importance and practicality of teleworking have been more evident to nearly every individual.

Throughout human history, the occurrence of disasters has been inevitable, leading to significant human, financial and emotional consequences. Therefore, it is crucial to establish a well-designed plan to efficiently manage such situations when disaster strikes. The purpose of this study is to develop a comprehensive program that encompasses multiple aspects of postdisaster relief.

A multiobjective model has been developed for postdisaster relief, with the aim of minimizing social dissatisfaction, economic costs and environmental damage. The model has been solved using exact methods for different scenarios. The objective is to achieve the most optimal outcomes in the context of postdisaster relief operations.

A real case study of an earthquake in Haiti has been conducted. The acquired results and subsequent management analysis have effectively assessed the logic of the model. As a result, the model’s performance has been validated and deemed reliable based on the findings and insights obtained.

Ultimately, the model provides the optimal quantities of each product to be shipped and determines the appropriate mode of transportation. Additionally, the application of the epsilon constraint method results in a set of Pareto optimal solutions. Through a comprehensive examination of the presented solutions, valuable insights and analyses can be obtained, contributing to a better understanding of the model’s effectiveness.

Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.

A new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.

For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.

Obtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.

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.