Search results

The success rate of design-build (D-B) implementation in Malaysia’s private construction projects is low compared to the nation’s public projects. Thus, the purpose of this paper is to investigate the critical success factors (CSFs) for D-B implementation in Malaysian public construction projects.

To achieve the research objectives, 111 questionnaire survey responses from construction-related professionals were collected and analysed using multivariate analysis, including t-test, analysis of variance (ANOVA) and factor analysis.

The results indicate that while the client’s briefing effectiveness remained the top CSF among many nations, the other top CSFs in Malaysia D-B construction projects vastly differed compared to those countries. Moreover, the underlying components for successful D-B projects in Malaysia include the project management team, leader competency and “stakeholder” management.

This is one of the first studies that identify the CSFs for D-B implementation in the Malaysian construction industry. The outcomes provide a valuable reference and resource for academic scholars and construction practitioners to increase D-B implementation in the Malaysian private sector.

Design-Build (DB) is known as the alternative for Design-Bid-Build in the Malaysian construction industry. For DB projects, it is critical to adopt effective decision support tool to ensure the execution of a systematic decision-making technique. This study aimed to examine the impact of a decision support tool for novice decision makers to reject or adopt DB for their construction projects.

Literature review and qualitative input from experts identified several key-selection factors pertaining to critical success factors and design-build drivers. This resulted in the development of Decision Support Tool for Design-Build (DST-DB). A quasi-experiment, which involved 382 novice decision makers in the construction industry, was conducted to test the DST-DB quantitatively. The participants were required to compare two construction projects using DST-DB and traditional decision-making methods. Multivariate analysis was performed to analyse all collected data.

The quasi-experiment data suggests that DST-DB enables significantly higher usability, likelihood, precision, confidence and satisfaction rate when compared to the traditional decision-making process. The pre- and post-surveys indicated that the DST-DB is effective in improving decision-making performance through selection factors of client-briefing, maximised resources and sharing expertise. The participants also agreed that DST-DB is easy to use and helps them to gain better understanding of the decision-making process for construction projects.

This research contributes to the existing body of knowledge through the impact of DST on the decisions of novices. The novice decision makers found that DST-DB is practically adaptable and comparatively effective for decision-making process than traditional decision-making methods. This contributes to the practical application of construction companies to provide DST-DB training to the fresh graduate employees to enhance their competencies in the decision-making process.

This paper aims to investigate the effect of lubricant viscosity model with improver on friction and lubrication of piston skirt-cylinder liner conjunction.

A dynamic calculation model is established for the piston skirt-cylinder liner conjunction of a heavy-duty commercial diesel engine, to explore the effects of two kinds of lube oil viscosity models named after polyalkyle-metacrylate-1 (PAMA1) and styrene-isoprene-copolymer (SICP) improvers on the maximum oil film viscosity, the minimum oil film thickness, the peak oil film pressure, the maximum shear rate, the friction force and the total friction power loss.

The variation trends with the crank angle of the above parameters are not changed with the difference of improvers, while obvious numerical differences are found except the maximum oil film pressure. The minimum oil film thickness and maximum shear rate of PAMA1 are larger than that of SICP, the maximum oil film viscosity of SICP is larger than that of PAMA1, which indicates that the shear-thinning effect of PAMA1 is greater, the maximum friction force on the piston of SICP is larger than that of PAMA1, and the total friction power consumption is also larger, the average friction power consumptions of SICP and PAMA1 are 385.4 and 262.8 W, respectively, with the relative difference of 31.8 per cent.

The influence of different lubricating oil additive models on the lubrication and friction of piston skirt-cylinder liner conjunction is simulated and analyzed.

Artificial Intelligence (AI) is permeating many facets of our daily lives, appearing in household appliances, cell phones and popular online apps. AI has the capacity to revolutionize teaching and learning processes in higher education institutions. The integration of AI technologies in education can lead to personalized learning experiences, improved educational quality and enhanced learning outcomes. However, the adoption of AI in higher education comes with challenges such as ethical considerations and the need to address equity and inclusion issues to ensure that all students benefit from AI advancements. This chapter considers how AI can be utilized in education, while acknowledging the challenges and finding ways to mitigate them. Useful tools include: Bespoke Learning, Intelligent Tutoring Systems, Grading, Collaborative, Learning Assistance, Research Support and Adaptive Learning. The challenges addressed are: ethical considerations, resistance to change and data security and privacy. In navigating the complexities of integrating AI in higher education, institutions must strike a balance between leveraging the transformative potential of AI technologies and addressing the ethical, social and technical challenges that accompany their implementation. By prioritizing ethical considerations, addressing resistance to change and safeguarding data security and privacy, higher education institutions can harness the benefits of AI to enhance teaching and learning practices, foster innovation and prepare students for success in the digital age.

The purpose of this paper is to investigate a five-phase permanent-magnet synchronous machine (PMSM) that features high-power density and high-fault-tolerant capability for electric vehicles (EVs).

The five-phase 20-slot/18-pole PMSM is designed by finite-element method. Two typical rotor structures which include Halbach array and rotor eccentricity are compared to achieve sinusoidal back electromotive force (EMF). The influence of slot dimensions on leakage inductance and short-circuit current is analyzed. The method to reduce eddy current loss of permanent magnets (PMs) is investigated. The machine performances under both healthy and fault conditions are evaluated. Finally, thermal behavior of the machine is studied by Ansys.

With both no-load and load performances considered, rotor eccentricity is proposed to reduce the harmonic contents of EMF. Increasing slot leakage inductance is an effective way to limit the short-circuit current. By segmenting PMs in circumferential direction, the PM eddy current loss is reduced and the machine efficiency is improved. With proper fault-tolerant control strategy, acceptable torque performance can be achieved under fault conditions. The proposed machine can safely operate under Class F insulation.

So far, many researches focus on multiphase PMSMs used in aviation fields, such as fuel pump and electric actuator. Differing from PMSMs used in aviation applications, machines for EVs require characteristics like wide speed ranges and variable operating conditions. Hence, this paper proposes a five-phase 20-slot/18-pole PMSM for EVs. The proposed design methodology is applicable to multiphase PMSMs with different slot/pole combinations.

This research aims to test the simultaneous effects of the perceived threat of COVID-19, e-health literacy, e-health access barrier, loyalty toward healthy foods in general, loyalty toward functional foods, the affordability of healthy foods in general and the affordability of functional foods on health-related quality of life (HrQoL) during the COVID-19 pandemic.

A survey with 400 respondents in Banten, Indonesia, was performed. The data were analyzed using multiple regression analysis.

The results of the research showed that HrQoL during the COVID-19 pandemic was positively affected by e-health literacy and the affordability of healthy foods in general while negatively affected by the e-health access barrier, the perceived threat of COVID-19 and loyalty toward healthy foods in general. Furthermore, HrQoL was not influenced by loyalty toward and affordability of functional foods.

This research was conducted in Banten. Due to the operational limitations during the COVID-19 pandemic, this research used a purposive sampling technique. Therefore, the next research should retest the model in different contexts and locations.

To improve HrQoL during the COVID-19 pandemic, citizens need to be educated on finding and utilizing credible online health information during the COVID-19 pandemic. Governments and health service providers should also strive to offer ease of access to credible online health information. Furthermore, the affordability of healthy foods, in general, should be managed well.

A few studies on HrQoL during COVID-19 pandemic were performed. However, there is a lack of paper that examines the role of food customer loyalty and affordability in a model of HrQoL during the COVID-19 pandemic. To the best of the authors’ knowledge, this paper is the first that involved and tested the role of food customer loyalty and affordability in a model of HrQoL during the COVID-19 pandemic.

In this letter, based on the infinitesimal transformations with respect to the generalized coordinates and generalized momentums, we obtain the definition, determining equations and structure equation of the momentum‐dependent symmetry for the systems. This study directly leads to the non‐ Noether type conserved quantity for the systems. Further we also give the inverse issue of the momentum‐dependent symmetries of the systems. However, a theory of momentum‐dependent symmetries of the nonconservative Hamiltonian systems is established. Finally, an example is discussed to illustrate the results.

The upsurge of Chinese and Indian outward foreign direct investment (FDI) raises an unanswered question about locational determinants of direct investment from the two countries. Using an unbalanced bilateral FDI database, we find that Chinese and Indian FDI are attracted to countries with large market size, low GDP growth, high volumes of imports from China or India, and low corporate tax rates. We also find important differences between China and India. While Chinese FDI is drawn to countries with open economic regimes, depreciated host currencies, better institutional environments, and English speaking status, none of these factors are important for Indian FDI. Chinese FDI is also deterred by geographic distance and OCED membership. However, neither of these has any impact on Indian FDI.

The purpose of this paper is to solve new pricing issues faced by low-carbon companies in the Yellow River Basin, which is caused by the change of key pricing factors in the mixed appliance background of Big Data and blockchain, such as product quality and carbon-emission reduction CER level (hereafter, CER level).

We choose a low-carbon supply chain with a low-carbon manufacturer and a retailer as our research object. Then, we propose that using the ineffective effect of the CER level and the quality and safety level to reflect the relationships among the CER level, the quality and safety level and the market demand is more suitable in the new environment. Based on these, we revise the demand equation. Afterwards, by using Stackelberg game, four cost-sharing situations and their pricing rules are analyzed.

Results indicated that in the four cost-sharing situations, the change trends and the magnitudes of the best retail prices were not affected by the changes of the inputs of the demand information and the traceability services costs (hereafter, DITS costs), the proportion about retailer's DITS costs undertaken by the manufacturer, the ineffective effect coefficient of the CER level and the quality and safety level and the cost optimization coefficient. However, the cost-sharing situations could affect the change magnitudes of the best revenues.

This paper has two main contributions. First, this paper proposes a demand function that is more suitable for the mixed appliance background of Big Data and blockchain. Secondly, this paper improves the cost-sharing model and finds that demand information sharing and traceability service sharing have different impacts on key pricing factors of low-carbon product. In addition, this research provides a theoretical reference for low-carbon supply chain members to formulate pricing strategies in the new background.