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Our research explores the intricate behavior of low-carbon supply chain organizations in an ever-evolving landscape, emphasizing the profound implications of government-mandated low-carbon policies and the growing low-carbon market. Central to our exploration is applying a combined game theory model, merging Evolutionary Game Theory (EGT) with the Shapley Value Cooperative Game Theory Approach (SVCGTA).

We establish a two-tier supply chain featuring retailers and manufacturers within this novel framework. We leverage an integrated approach, combining strategic Evolutionary Game Theory and Cooperative Game Theory, to conduct an in-depth analysis of four distinct low-carbon strategy combinations for retailers and manufacturers.

The implications of our findings transcend theoretical boundaries and resonate with a trinity of economic, environmental and societal interests. Our research goes beyond theoretical constructs to consider real-world impacts, including the influence of changes in government low-carbon policies, the dynamics of consumer sensitivities and the strategic calibration of retailer carbon financing incentives and subsidies on the identified ESS. Notably, our work highlights that governments can effectively incentivize organizations to reduce carbon emissions by adopting a more flexible approach, such as regulating carbon prices, rather than imposing rigid carbon caps.

Our comprehensive analysis reveals the emergence of an Evolutionary Stability Strategy (ESS) that evolves in sync with the phases of low-carbon technology development. During the initial stages, our research suggests that manufacturers or retailers adopt low-carbon behavior as the optimal approach.

The study aims to predict a low-velocity impact on a plate reinforced with carbon nanotubes (CNTs) using machine learning models.

The first-order shear deformation plate theory (FSDT) is used to express the plate displacements filed. The Hertz nonlinear contact law is used to predict the contact between impactor and plate. Using the energy method and Hamilton’s principle, the motion equations are extracted. The six main parameters considered as inputs to machine learning models are CNTs percentage, impactor radius, plate thickness, plate length and width, CNTs distribution profile and impactor initial velocity. These input parameters are used to predict two impact targets including contact force and contact time.

As the values of the targets are continuous, the machine learning task is considered a regression problem. Therefore, this study uses different regression models to predict the targets. These regression models include linear regression, stochastic gradient descent regressor, Bayesian regression, partial least squares regression, Gaussian process regression, multilayer perceptron regressor, support vector regression and decision tree regression. To validate the effectiveness of the regression models, experiments are designed based on different evaluation metrics. The results of the experiments demonstrate that the machine learning models achieve promising performance in predicting the contact force and contact time based on the input parameters.

Due to the volume of high numerical calculations of impact mechanics to reach the response, the targets of the impact problem are predicted using a variety of machine learning methods.

Enhancing sustainability of the supply process of construction materials is challenging and requires accounting for a variety of environmental and social impacts on top of the traditional, mostly economic, impacts associated with a particular decision involved in the management of the supply chain. The economic, environmental, and social impacts associated with various components of a typical supply chain are highly sensitive to project and market specific conditions. The purpose of this paper is to provide decision makers with a methodology to account for the systematic trade-offs between economic, environmental, and social impacts of supply decisions.

This paper proposes a novel framework for sustainability assessment of construction material supply chain decisions by taking advantage of the information made available by customized building information models (BIM) and a number of different databases required for assessment of life cycle impacts.

The framework addresses the hierarchy of decisions in the material supply process, which consists of four levels including material type, source of supply, supply chain structure, and mode of transport. The application is illustrated using a case study.

The proposed framework provides users with a decision-making method to select the most sustainable material alternative available for a building component and, thus, may be of great value to different parties involved in design and construction of a building. The multi-dimensional approach in selection process based on various economic, environmental, and social indicators as well as the life cycle perspective implemented through the proposed methodology advocates the life cycle thinking and the triple bottom line approach in sustainability. The familiarity of the new generation of engineers, architects, and contractors with this approach and its applications is essential to achieve sustainability in construction.

A decision-making model for supply of materials is proposed by integrating the BIM-enabled life cycle assessment into supply chain and project constraints management. The integration is achieved through addition of a series of attributes to typical BIM. The framework is supplemented by a multi-attribute decision-making module based on the technique for order preference by similarity to ideal solution to account for the trade-offs between different economic and environmental impacts associated with the supply decisions.

Request for information (RFI) documents play a pivotal role in seeking clarifications in construction projects. However, perceived as inevitable “non-value adding” tasks, they harbour risks like schedule delays and increased project costs, underlining the importance of strategic RFI management in construction projects. Despite this, a lack of literature dissecting RFI processes impedes a full understanding of their intricacies and impacts. This study aims to bridge the gap through a comprehensive literature review, delving into RFI intricacies and implications, while emphasising the necessity for strategic RFI management to prevent project risks.

This research study systematically reviews RFI-related papers published between 2000 and 2023. Accordingly, the review discusses key themes related to RFI management, yielding best practices for industry stakeholders and highlighting research directions and gaps in the body of knowledge.

Present RFI management platforms exhibit deficiencies and lack analytics essential for streamlined RFI processing. Complications arise in building information modelling (BIM)-enabled projects due to software disparities and interoperability hurdles. The existing body of knowledge heavily relies on manual content analysis, an impractical approach for the construction industry. The proposed research direction involves automated comprehension of unstructured RFI content using advanced text mining and natural language processing techniques, with the potential to greatly elevate the efficiency of RFI processing.

The study extends the RFI literature by providing novel insights into the problemetisation with the RFI process, offering a holistic understanding and best practices to minimise adverse effects. Additionally, the paper synthesises RFI processes in traditional and BIM-enabled project settings, maps a causal-loop diagram to identify associated issues and summarises approaches for extracting knowledge from the unstructured content of RFIs. The outcomes of this review stand to offer invaluable insights to both industry practitioners and researchers, enabling and promoting the refinement of RFI processes within the construction domain.

Due to considerable contributions of the construction industry to the global carbon emissions, a great deal of attention is placed on possible incorporation of carbon footprint minimization as an important objective in the planning of construction operations. The purpose of this paper is to present a framework to estimate and minimize the carbon emissions of the concrete placing operation through identifying the optimal number of pumps and the inter-arrival time of truck mixers.

The proposed framework integrates discrete event simulation and multi-objective optimization to estimate and minimize the carbon emission, costs and production rate of the concrete placing operation. An actual construction project is used to demonstrate the application of the proposed framework. Furthermore, a sensitivity analysis is performed to investigate the sensitivity of the results to variations in modeling parameters including the ratio of idle to non-idle emission rates of equipment and the activity duration distributions.

The results of the case study highlight that variations in the number of pumps and inter-arrival time of truck mixers significantly affect the carbon emissions, cost and production rate of the concrete placing operation. Furthermore, the results of the sensitivity analysis show that variations in the ratio of idle to non-idle emission rates for pumps and truck mixers have little effects on the selected setting for the project. This is contrary to the effect of uncertainty in the activity duration distributions, which was found to be significant.

Results of this study provide an insight into the trade-off between carbon emissions, cost and production rate of the concrete placing operation.

This study aims to examine a conceptual integrated framework for small- and medium-sized enterprises’ (SMEs) export performance that comprises all potential export determinants and inhibitors. This study also incorporates and examines the potential mediators of proactiveness (business strategy), innovativeness (innovation strategy) and export marketing strategy.

His research is based on the contingency theory, resource-based and market-based view, and it provides an integrated model about the research problem. The primary data are collected through direct survey amongst active SME exporters, and three main approaches of descriptive statistics, confirmatory factor analysis and structural equation modelling are applied for data analysis.

The results show significant effects of various internal and external firms’ determinants on their export performance in Qatar. Two mediators, proactiveness (business strategy) and innovativeness (innovation strategy), have key roles in enhancing SMEs’ export as well. The final research findings have significant implications for understanding all key drivers of SME export in Qatar, and it helps policymakers, regulators and service providers to improve the current SME ecosystem and their services to SMEs. Finally, the results of this study can be extended to other emerging markets with similar economic and legal structures.

Many obstacles discourage SMEs to move internationally, especially in emerging markets. This study focuses on the capacity building to enhance SME export activities in an emerging market. Even though the latest literature in the area of export performance has focused on firms from emerging economies, studies in this area are still limited. Earlier research in this area has mostly focused solely on the determinants of export performance from either internal factors, external factors or both without using adequately potential mediating factors, which could affect export performance.

The purpose of this paper is to cover an experimental investigation of the impulse response of the foam-mass system (FMS) to unveil some of the foam dynamic behavior features needed to optimize the impact comfort of seat-occupant system. The equation of motion of the studied system is modeled as a sum of a linear elastic, pneumatic damping and viscoelastic residual forces. An identification methodology based on two separated calibration processes of the viscoelastic parameters was developed.

The viscoelastic damping force representing the foam short memory effects was modeled through the hereditary formulation. Its parameters were predicted from the free vibrational response of the FMS using iterative Prony method for autoregressive–moving–average model. However, the viscoelastic residual force resulting in the long memory effects of the material was modeled with fractional derivative term and its derivative order was predicted from previous cyclic compression standards.

The coefficients of the motion law were determined using closed form solution approach. The predictions obtained from the simulations of the impulse and cyclic tests are reasonably accurate. The physical interpretations as well as the mathematical correlations between the system parameters were discussed in details.

The prediction model combines hereditary and fractional derivative formulations resulting in short and long physical memory effects, respectively. Simulation of impulse and cyclic behavior yields good correlation with experimental findings.

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.

The purpose of this study is to understand the predominant leadership style of school leaders in Abu Dhabi. The leadership style deployed by a school leader affects the performance of the school and its pupils. Methods for identifying the leadership style of school leaders in the UAE have varied, and it is difficult to conclude what the predominant leadership style is. Some studies have sought only to identify a specific leadership style, whilst others have focussed on a particular school type. Changes and improvements cannot be made without an understanding of the baseline leadership style.

The 36-item multifactor leadership questionnaire (MLQ)5x questionnaire (Bass and Avolio, 2004) is used to quantitatively understand the full range of school leaders’ leadership styles, with 167 respondents from across both public and private schools.

School leaders predominantly exhibited transformational leadership, practising transactional leadership less frequently and rarely using laissez-faire leadership. This is a positive finding for schools in the UAE; transformational leadership has been shown to result in improved subordinate and organisational performance. Differences between school leaders in public and private schools were tested and are discussed. Dimension reduction techniques were used to assess the structure of the 36-item MLQ5x but did not provide results that met minimum requirements for acceptability. Possible reasons for this are discussed.

To the best of the author’s knowledge, this paper is the first to fully explore and baseline an understanding of the predominant leadership style amongst school leaders in the UAE, identifying the full range of leadership styles – transformation, transactional and laissez-faire – in both public and private schools.