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Steel shear walls have recently received exclusive remark. Respective of most building code requirements, design of shear wall vertical boundary elements (VBEs) and local boundary elements (LBEs) against web yielding triggers exaggerated stiffness. The extent of stiffness reduction effects in boundary elements thus calls for more exhaustive investigation. The paper aims to discuss these issues.

To this end, FEM-based push-over curves demonstrating base shear vs roof displacement, and von Mises plastic strains were scrutinized in half-scale and full-size models. Analyses were in perfect conformity with experimental data.

With reference to the AISC requirement, up to 35 percent decrease in the VBE moments of inertia could be imparted in higher levels without the ultimate load capacity nor displacement to failure being reduced. Also considered was open shear walls with reduced or minimum-design LBEs, the latter being used in continuous or abridged form. LBEs could be used with a moment of inertia 80 percent smaller than required if only used in a continuous form. The effect due to opening geometry was negligible on loading capacity but distinguished on the post-yielding buckling-induced softening.

Light-weight design of low- to medium-level steel structures against earthquake loads.

With respect to continuous walls, the results are more comprehensive than those existing in the literature in that they combine the effects due to scale and orientation (horizontal or vertical) of boundary elements. The results for open shear walls are not only comprehensive but also original in a sense that they account for the influences induced by the opening type (door or window), orientation (horizontal or vertical), and design (full-length or abridged) of boundary elements, in reduced form, on the lateral stiffness of the frame.

This study aims to explore the idea of solving the problem of squeezing nanofluid flow between two parallel plates using a novel mathematical method.

The unsteady squeezing flow is a coupled fourth-order boundary value problem with flow velocity and temperature as the desired unknowns. In the first step, the conditions that guarantee the existence of a unique solution are obtained. Then following Green’s function-based approach, an iterative method for solving the problem is developed.

The accuracy of the method is examined by comparing the obtained results with existing numerical data, indicating excellent agreement between the two. In addition, the effects of nanoparticle shape and volume fraction on the flow and heat transfer characteristics are addressed. The results reveal that although the nanoparticle shape strongly affects the temperature distribution in the squeezing flow, it only has a slight impact on the velocity field. Furthermore, the highest and lowest Nusselt numbers belong to the platelets and spherical nanoparticles, respectively.

A semi-analytical method with computational support is developed for solving the unsteady squeezing flow problem. Moreover, the existence and uniqueness of the solution are discussed for the first time.

Remotely piloted aircraft (RPA) systems have emerged as an established tool within the construction industry. Concurrent with this trend has been the rise in research on RPA, establishing this as a new field of study within the construction management domain. What is needed now is an assessment of the current state of research in this emerging discipline – its strengths and weaknesses – by which future research on RPA in construction may be guided. The purpose of this paper is to address this need.

A total of 59 peer-reviewed journal articles covering RPAs within the construction domain were systematically reviewed using a mixed-methods approach, utilizing qualitative-scientometric analyses techniques.

The results reveal a field of study in its fledgling stage, with a limited number of experts operating somewhat in isolation, from a limited number of institutions. Key publication outlets are identified, with the main focus of research being in the technical areas of remote sensing, photogrammetry and image processing.

The study benefits researchers and industry practitioners alike. For researchers, the identified gaps reveal areas of high priority in future research. For construction companies, particularly small to medium-sized businesses, the study raises awareness of the latest developments and potential applicability of RPAs in the industry.

The study exposes what is missing from current research: a broader consideration of organizational adjustments needed to accommodate RPA usage, economic analyses and impediments to wider acceptance.

The purpose of this study is to identify the drivers and future scenarios of Iran’s Social Security Organization.

The research is applied in terms of orientation and mixed in terms of methodology. In this research, the methods of theme analysis, root definitions, fuzzy Delphi and Cocoso were used. The theoretical population is the managers and senior experts of the social security organization, and the sampling method was done in a judgmental way. The tools of data collection were interviews and questionnaires. The interview tool was used to extract the main and subdrivers of the research and develop the scenarios.

Through theme analysis, 35 subdrivers were extracted in the form of economic, sociocultural, financial and investment, policy, marketing, environmental and legal themes. Due to the large number of subdrivers, these factors were screened with fuzzy Delphi. Eleven drivers had defuzzied coefficient higher than 0.7 and were selected for final prioritization. The final drivers were prioritized with the CoCoSo technique, and the two drivers of social security holdings governance and state of government revenues had the highest priority. Based on these two drivers, four scenarios of prosperity, resilient social security, unstable development and collapse have been developed.

Some of the suggestions of the research are: using the capacity of FinTechs and financial startups to invest the government revenues of the organization, using digital technologies such as business intelligence for more efficient decisions and developing corporate governance in the organization.

The purpose of this study was to investigate the inhibitive action of some green leaves on Monel 400 alloy in acidic media.

Green leaves of Mespilus japonica, Ricinus communis L and Vitis vinifera were immersed in methanol solutions, separately, and filtrated after 48 h of immersion; the obtained filtrates were examined as corrosion inhibitors of Monel 400 alloy in hydrochloric acid solution (1.0M HCl). The performance of these inhibitors was evaluated using electrochemical impedance spectroscopy and potentiodynamic polarization. The effect of temperature on corrosion behavior of Monel 400 was also studied.

The results obtained showed that all tested inhibitors performed as good corrosion inhibitors. The inhibition process is attributed to the adsorption of the inhibitors on Monel surface. The adsorption behavior was found to follow Langmiur isotherm. The inhibition efficiencies of extracts increased with increasing the concentration of each inhibitor and deceased with increasing the temperature.

Practical implication These inhibitors could have application in industries where hydrochloric solutions were used to remove the surface impurities of Monel 400.

This paper helps to find new corrosion inhibitors that are safe and eco-friendly.

Nowadays, green supply chain (SC) management acts as an important strategic issue for the manufacturers. The effective SC design requires the development of analytical models and design tools. Because of the key role of steel in the infrastructure of industries, this metal is called the development metal. Despite the importance of this industry and its economic and environmental impacts through its SC, the SC structure of this industry has been less studied at the macro level. Therefore, the purpose of this paper is twofold: first, to design the structure of a steel industry SC at three levels; and second, to find the most effective and efficient carbon dioxide emitted industry among the supplier industries of the steel industry SC in China as a case study.

In this paper, due to the relationships among different industries, DEMATEL as a multi-criteria decision-making method has been applied.

A SC structure for the steel industry has been designed at three levels. The results indicated that the industries that had the highest relationship with the steel industry are mine industry, electricity, water, and gas industry, and optical and electrical equipment industry, which were recognized as the first-level suppliers for the steel industry. On the other hand, considering the relationship among the embodied carbon dioxide emissions of various industries in China as a case study, it can be said that among the steel suppliers, the most important polluting industries, respectively, are mining industry, electricity, water, and gas industry, optical and electrical equipment industry, machinery industry, chemicals and chemical products industry and coke, refined petroleum and nuclear fuel industry.

The developed SC can help in providing the steel industries’ managers a basic model for their supplier selection problem at the macro level. This paper can also help the industrial managers to understand the causal relationships among the suppliers of their industries. Finally, this paper can help government and industries managers to discover the most polluted industrial suppliers in the steel industry.

The novelty of this study belongs to the usage of DEMATEL method based on the input-output table to discover the relationships among the industries as well as identifying the main raw material suppliers of the steel industry at three levels. Furthermore, this research discovers the relationships among the embedded carbon dioxide emission of various industries in steel SC to determine the most important polluting industries in steel SC.

The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.

The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.

A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.

The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.

The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.

This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.

This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.

The purpose of this study to introduce a new extended framework to evaluate and rank the sustainable suppliers based on the different sustainable criteria in the manufacturing companies using a new fuzzy decision-making approach.

This paper introduces a new approach using decision-making and Pythagorean fuzzy sets (PFSs) to assess the best sustainable supplier. To doing so, this study integrated the entropy, stepwise weight assessment ratio analysis (SWARA) and weighted aggregates sum product assessment (WASPAS) methods under PFSs. To calculate the criteria weights, the combined entropy-SWARA method is used to compute the objective weight and subjective weight, respectively. Furthermore, the WASPAS model is utilized to rank sustainable supplier alternatives.

The results of the analysis found that occupational health and safety systems had the highest rank among other criteria, followed by green product and eco-design, green R&D and innovation and green technology. In addition, the findings of the paper demonstrated that the extended approach was efficient and useful for selecting and evaluating the best sustainable supplier in the manufacturing companies.

Recent years have witnessed a number of studies aimed at incorporating the sustainability standards into the supplier selection problem; however, only a little research has been conducted on developing a fuzzy method for decision-making in a manner to assess and choose suppliers with high sustainability in the insurance market, encompassing the three above-mentioned sustainability criteria.