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The purpose of the paper is to propose a homogenization-based topology optimization method to optimize the design of variable-density cellular structure, in order to achieve lightweight design and overcome some of the manufacturability issues in additive manufacturing.

First, homogenization is performed to capture the effective mechanical properties of cellular structures through the scaling law as a function their relative density. Second, the scaling law is used directly in the topology optimization algorithm to compute the optimal density distribution for the part being optimized. Third, a new technique is presented to reconstruct the computer-aided design (CAD) model of the optimal variable-density cellular structure. The proposed method is validated by comparing the results obtained through homogenized model, full-scale simulation and experimentally testing the optimized parts after being additive manufactured.

The test examples demonstrate that the homogenization-based method is efficient, accurate and is able to produce manufacturable designs.

The optimized designs in our examples also show significant increase in stiffness and strength when compared to the original designs with identical overall weight.

Public–private partnership (PPP) projects are increasingly significant in many countries. The purpose of this paper is to assess the impact of critical success factors (CSFs) on PPP projects and comprehensively consider the interrelations and interaction among dimensions and factors to achieve a better understanding of PPP project management.

An evaluation index system for PPP projects such as the presented case study is proposed based on a literature review and a survey. Then, interpretative structural modeling is used to transform the CSFs dimension into a multi-level hierarchical model to reflect the driven-dependency relation of each dimension; the fuzzy analytic network process model optimized by moment estimation theory is used to investigate the impact of CSFs by considering their internal impact.

Regarding the project used as the case study, the driving force and dependence for driving layer and dependent layer are determined. Moreover, in driving layer, efficient and well-structured payment mechanism is the most important CSF if considering the internship and interaction among CSFs, and efficient and well-structured payment mechanism and good governance provide most positive interaction; in dependent layer, population of beneficiaries is the most important CSF if considering the internship and interaction among CSFs, and public client’s satisfaction provides most positive interaction.

This paper developed an evaluation model to explore the interrelationships of dimensions and factors and then determine the impact of CSFs. The model propose in this paper relaxes the independence assumptions of traditional methods and is more in line with reality; besides, weighting method is optimized to obtain more objective and reasonable evaluation results. Through an empirical study, the validity of the model has been verified; therefore, the study can help project stakeholders better understand the CSFs and further improve project performance.

The purpose of this study is to assess the incorporation of fiber reinforced polymer (FRP) bars in concrete as a reinforcement enhances the corrosion resistance in a concrete structure. However, FRP bars are not practically used due to a lack of standard codes. Various codes, including ACI-440-17 and CSA S806-12, have been established to provide guidelines for the incorporation of FRP bars in concrete as reinforcement. The application of these codes may result in over-reinforcement. Therefore, this research presents the use of a machine learning approach to predict the accurate flexural strength of the FRP beams with the use of 408 experimental results.

In this research, the input parameters are the width of the beam, effective depth of the beam, concrete compressive strength, FRP bar elastic modulus and FRP bar tensile strength. Three machine learning algorithms, namely, gene expression programming, multi-expression programming and artificial neural networks, are developed. The accuracy of the developed models was judged by R², root means squared and mean absolute error. Finally, the study conducts prismatic analysis by considering different parameters. including depth and percentage of bottom reinforcement.

The artificial neural networks model result is the most accurate prediction (99%), with the lowest root mean squared error (2.66) and lowest mean absolute error (1.38). In addition, the result of SHapley Additive exPlanation analysis depicts that the effective depth and percentage of bottom reinforcement are the most influential parameters of FRP bars reinforced concrete beam. Therefore, the findings recommend that special attention should be given to the effective depth and percentage of bottom reinforcement.

Previous studies revealed that the flexural strength of concrete beams reinforced with FRP bars is significantly influenced by factors such as beam width, effective depth, concrete compressive strength, FRP bars’ elastic modulus and FRP bar tensile strength. Therefore, a substantial database comprising 408 experimental results considered for these parameters was compiled, and a simple and reliable model was proposed. The model developed in this research was compared with traditional codes, and it can be noted that the model developed in this study is much more accurate than the traditional codes.

This paper aims to determine a suitable anti-wear and friction-reducing compounding additive for lithium greases (LG) by investigating the effects of three single additives potassium borate (PB), zinc dialkyl dithiophosphate and molybdenum dialkyl dithiophosphate (MoDDP) and two compound additives on the friction, wear and extreme pressure properties of LG.

The effects of the above five additives on the friction, wear and extreme pressure properties of LG were investigated using an SRV-5 friction tester. An X-ray photoelectron spectrometer was used to analyze the various elements presented on the wear surface as well as the types of compounds.

The compound additive suitable for grease consists of PB and MoDDP, which have excellent friction reduction, anti-wear and extreme pressure properties. And a boundary protection film consisting of oxide and MoS₂ is formed on the friction surface, thus improving the friction reduction and anti-wear performance of the grease.

This study can improve the anti-wear and friction-reduction performance of greases, which is of great importance in the field of industrial lubrication. The results of this paper are expected to be useful to researchers and academics of grease.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2022-0350/

The purpose of this paper is to empirically determine the key factors influencing the US consumers’ intentions to use apparel m-commerce.

An enhanced consumer’s apparel m-commerce adoption model was developed through integrating the existing e-commerce/m-commerce theories (i.e. theory of reasoned action, Technology acceptance model and diffusion of innovation theory). The investigated factors included nine independent variables – perceived usefulness (PU), perceived ease-of-use, subjective norm (SN), personal innovativeness traits, security and privacy concerns, compatibility, observability, trust and past non-store shopping experience (PE), and five control variables – age, gender, education level, income level and ethnicity. The dependent factor is consumer’s intention to use (IU) apparel m-commerce. The primary data were gathered by an online survey of US consumers via Amazon Mechanical Turk. In total, 317 eligible responses were received. The applied statistical techniques were factor analysis and multiple regression analysis.

The results show that the US consumer’s IU apparel m-commerce is significantly affected by PU, SN, compatibility and PE and education level. Overall, 67.3 percent of variation in the US consumer’s IU apparel m-commerce is explained by the developed model, which suggests a high explanatory power.

Companies should provide those functions and features on their mobile websites that enable consumers to easily find the products wanted and complete transactions efficiently. Companies should particularly target the consumers with innovativeness traits and/or those having prior non-store shopping experience. Enhancing the trust in m-commerce among the US consumers with higher education level could help companies attract more potential users. Elderly, female or lower income consumers could be the next business opportunities for apparel e-tailers.

As one of the first efforts made to understand the emerging apparel m-commerce phenomenon, this study empirically determined the key factors influencing the US consumer’s IU apparel m-commerce.

Wearable medical devices (WMDs) are improving people’s health and well-being in a noble way, as these aid in effective personal health monitoring, remote surveillance and overall illness management. Despite its wider applicability and usage, it is prevalent that users discontinue its usage, which presents an obstacle in the proliferation of such vital innovations among the masses. Therefore, relying on the expectation-confirmation model (ECM), this study aims to delve deeper to explain the factors that motivate users to continually use WMDs by incorporating novel variables, namely, health belief, health information accuracy and privacy protection.

The study proposes and tests an extended ECM perspective to predict the continuance intention (CI) of WMDs among users. By using structural equation modelling using SmartPLS, the authors tested the model on Indian people (n = 451) who had an erstwhile experience of using WMDs.

The study results show that confirmation of users’ expectations positively impacts their usefulness and satisfaction towards WMDs. Moreover, satisfaction towards WMDs is the strongest predictor of users’ CI, followed by perceived usefulness. Interestingly, personal factor such as health beliefs reveals a greater influence on perceived usefulness than technological factors like health information accuracy and privacy protection.

The study findings demonstrate the significance of using the expectation-confirmation perspective in technology-based studies in general and WMDs, in particular. This study aids by offering an integrated model of WMD’s continued usage intention for the users, in addition to practical implications for marketers and policymakers.

A paucity of research exists when understanding the predictors of CI for WMDs. This study fills this gap and adds to behavioural literature by offering a noble viewpoint involving an extended ECM perspective.

The present study aims to develop and offer a model to evaluate the customers' attitude and intention to adopt solar net metering systems (commonly called solar NMS) in a developing economy. Therefore, the research examines different factors affecting the Indian households' attitudes and intention to adopt solar NMS.

The data were collected from 247 solar NMS users from India. The structural equation modeling (SEM) technique was applied using SmartPLS 3.3.2 software to analyze the impact of various factors on their adoption intention. The conceptual model comprises environmental concern, perceived ease of use (PEOU), subjective norms, perceived usefulness (PU), attitude and behavioral intention to adopt solar NMS.

Subjective norms and environmental concerns significantly influence the PU and PEOU of solar NMS. Also, PU and PEOU significantly influence their attitude and intentions toward adopting solar NMS. Thus, the perceived social pressure and environmental concern affect their perception of solar NMS's usefulness and ease of use, leading to favorable attitudes and adoption intentions. Additionally, the solar NMS benefits the customers, society and the environment by enhancing environmental quality, compatibility with the modern lifestyle, and reducing dependency on the power grid and electricity bills. These benefits shape the customers' overall perception and increase the adoption of solar technologies.

The present research helps bridge the gaps in the existing literature by identifying (1) factors affecting customers' intention toward solar technologies in developing nations and (2) describing the significant prediction of environmental concern and subjective norms to increase solar technologies adoption.

To use the 4H-SiC material in integrated circuits for high temperature application, an accurate and simple circuit model of n-channel planar 4H-SiC MOSFET is required.

In this paper, a SPICE model of n-channel planar 4H-SiC MOSFET was built based on the device simulation results and measurement results. Firstly, a device model was simulated with Sentaurus TCAD, with measured parameters from fabricated planar 4H-SiC MOSFET previously. Then the device simulation results were analyzed and parameters for SPICE models were extracted. With these parameters, an accurate SPICE model was built and simulated.

The SPICE model exhibits the same performance as the measured results with different environment temperatures. The simulation results indicate that the maximum fitting error is 0.22 mA (7.33% approximately) at 200 °C. A common-source amplifier with this model is also simulated and the simulated gain is stable at different environment temperatures.

This paper provides a reliable modeling method for n-Channel Planar 4H-SiC MOSFET and reference value for the design of 4H-SiC high temperature integrated circuit.

The objective of this paper is to understand the benefits and utility of massive open online courses (MOOCs) as perceived by the student, vis-à-vis internship and determine the factors that influence student motivation and distraction in adoption of MOOCs.

An empirical study is conducted through a survey; data are collected through a structured questionnaire. The technology acceptance model (TAM) is used as the base framework. For data analysis, Statistical Product and Service Solutions–Analysis of Moment Structures (SPSS–AMOS) 24.0 is used.

The impact of context-specific distinctive features of MOOCs and characteristics of students on user satisfaction are examined through perceived ease of use and perceived usefulness. In the study, it is found that positive social influence and better facilitating conditions improve perceived ease of use and perceived usefulness leading to a better user satisfaction. Self-regulation positively influences self-efficacy among students while pursuing MOOCs. Contrary to the past researches, it is found that in the pandemic environment self-efficacy is not impacting perceived ease of use, perceived usefulness and satisfaction.

The findings of this study will benefit MOOCs developers and Higher Education Institutes (HEIs) in deeper understanding the significant factors affecting MOOC usage in higher education.

The study is ingrained to find the causes which will lead to user satisfaction of MOOCs by post-graduation students of B-schools in India. This is an original research and primary data has been collected for decision-making.

This study aims to research the development trend, research status, research results and existing problems of the steel–concrete composite joint of railway long-span hybrid girder cable-stayed bridge.

Based on the investigation and analysis of the development history, structure form, structural parameters, stress characteristics, shear connector stress state, force transmission mechanism, and fatigue performance, aiming at the steel–concrete composite joint of railway long-span hybrid girder cable-stayed bridge, the development trend, research status, research results and existing problems are expounded.

The shear-compression composite joint has become the main form in practice, featuring shortened length and simplified structure. The length of composite joints between 1.5 and 3.0 m has no significant effect on the stress and force transmission laws of the main girder. The reasonable thickness of the bearing plate is 40–70 mm. The calculation theory and simplified calculation formula of the overall bearing capacity, the nonuniformity and distribution laws of the shear connector, the force transferring ratio of steel and concrete components, the fatigue failure mechanism and structural parameters effects are the focus of the research study.

This study puts forward some suggestions and prospects for the structural design and theoretical research of the steel–concrete composite joint of railway long-span hybrid girder cable-stayed bridge.