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The paper's aim is to estimate the benefits and costs of China's affordable housing program, as well as to provide recommendations to this housing policy.

The Cobb‐Douglas utility function is employed to estimate the net benefits of the affordable housing policy. Both of the sunk costs and current costs are computed, and an improved housing affordability index is used to measure the levels of housing affordability in cities in China.

The total net benefits of this policy are estimated to range from $234,176.7 million to ¥271,020.4 million. The costs are divided into sunk costs and current costs, computed to be ¥447,598.63 million and ¥328,685.21 million, respectively. The supply size of affordable dwellings is far from adequate due to the low level of housing affordability in China.

The data in this study is insufficient and some information such as the income of occupiers has yet to be estimated. However, if more individual data was available, the conclusion would be confidential.

From this paper the policymakers may understand how to estimate the welfare efficiency of affordable housing policy, adjust the participant regulations and determine the supply of affordable houses.

This paper estimates the benefits and costs of China's affordable housing program as the first study in this area. The Cobb‐Douglas utility function was used in the analysis of China's housing policy.

Deep learning (DL) technologies assist manufacturers to manage their business operations. This research aims to present state-of-the-art insights on the trends and ways forward for DL applications in manufacturing operations.

Using bibliometric analysis and the SPAR-4-SLR protocol, this research conducts a systematic literature review to present a scientific mapping of top-tier research on DL applications in manufacturing operations.

This research discovers and delivers key insights on six knowledge clusters pertaining to DL applications in manufacturing operations: automated system modelling, intelligent fault diagnosis, forecasting, sustainable manufacturing, environmental management, and intelligent scheduling.

This research establishes the important roles of DL in manufacturing operations. However, these insights were derived from top-tier journals only. Therefore, this research does not discount the possibility of the availability of additional insights in alternative outlets, such as conference proceedings, where teasers into emerging and developing concepts may be published.

This research contributes seminal insights into DL applications in manufacturing operations. In this regard, this research is valuable to readers (academic scholars and industry practitioners) interested to gain an understanding of the important roles of DL in manufacturing operations as well as the future of its applications for Industry 4.0, such as Maintenance 4.0, Quality 4.0, Logistics 4.0, Manufacturing 4.0, Sustainability 4.0, and Supply Chain 4.0.

Artificial intelligence (AI) models are demonstrating day by day that they can find long-term solutions to improve wastewater treatment efficiency. Artificial neural networks (ANNs) are one of the most important of these models, and they are increasingly being used to forecast water resource variables. The goal of this study was to create an ANN model to estimate the removal efficiency of biological oxygen demand (BOD), total nitrogen (TN), total phosphorus (TP) and total suspended solids (TSS) at the effluent of various primary and secondary treatment methods in a wastewater treatment plant (WWTP).

The MATLAB App Designer model was used to generate the data set. Various combinations of wastewater quality data, such as temperature(T), TN, TP and hydraulic retention time (HRT) are used as inputs into the ANN to assess the degree of effect of each of these variables on BOD, TN, TP and TSS removal efficiency. Two of the models reflect two different types of primary treatment, while the other nine models represent different types of subsequent treatment. The ANN model’s findings are compared to the MATLAB App Designer model. For evaluating model performance, mean square error (MSE) and coefficient of determination statistics (R2) are utilized as comparative metrics.

For both training and testing, the R values for the ANN models were greater than 0.99. Based on the comparisons, it was discovered that the ANN model can be used to estimate the removal efficiency of BOD, TN, TP and TSS in WWTP and that the ANN model produces very similar and satisfying results to the APPDESIGNER model. The R-value (Correlation coefficient) of 0.9909 and the MSE of 5.962 indicate that the model is accurate. Because of the many benefits of the ANN models used in this study, it has a lot of potential as a general modeling tool for a range of other complicated process systems that are difficult to solve using conventional modeling techniques.

The objective of this study was to develop an ANN model that could be used to estimate the removal efficiency of pollutants such as BOD, TN, TP and TSS at the effluent of various primary and secondary treatment methods in a WWTP. In the future, the ANN could be used to design a new WWTP and forecast the removal efficiency of pollutants.

This paper investigates customer knowledge management activities of Taiwan’s plastic industries. The results demonstrate that the bulk of customer knowledge comes from data related to customer purchase orders and complaints. Furthermore, marketing, production, and research and development are the main departments that developed and reuse customer knowledge. The benefits derived from knowledge management for enterprises do not vary with the position of the vendor on the business scope. In addition, the benefits derived by customers from knowledge management are directly related to the benefits gained by the five major business functions, while the benefits derived from the customer knowledge management are also directly related to customer satisfaction. Summarizing the above results, an Acquisition‐Development‐Reuse (ADR) model is proposed and can provide the enterprises with a systematic reference model when the business attempts to construct a customer knowledge management system.

To support the standardized evaluation of bicyclist automatic emergency braking (AEB) systems, test scenarios, test procedures and test system hardware and software tools have been investigated and developed by the Transportation Active Safety Institute (TASI) at Indiana University-Purdue University Indianapolis. This paper aims to focus on the development of test scenarios and bicyclist surrogate for evaluating vehicle–bicyclist AEB systems.

The harmonized general estimates system (GES)/FARS 2010-2011 crash data and TASI 110-car naturalistic driving data (NDD) are used to determine the crash geometries and environmental factors of crash scenarios including lighting conditions, vehicle speeds, bicyclist speeds, etc. A surrogate bicyclist including a bicycle rider and a bicycle surrogate is designed to match the visual and radar characteristics of bicyclists in the USA. A bicycle target is designed with both leg pedaling and wheel rotation to produce proper micro-Doppler features and generate realistic motion for camera-based AEB systems.

Based on the analysis of the harmonized GES/FARS crash data, five crash scenarios are recommended for performance testing of bicyclist AEB systems. Combined with TASI 110-car naturalistic driving data, the crash environmental factors including lighting conditions, obscuring objects, vehicle speed and bicyclist speed are determined. The surrogate bicyclist was designed to represent the visual and radar characteristics of the real bicyclists in the USA. The height of the bicycle rider mannequin is 173 cm, representing the weighted height of 50th percentile US male and female adults. The size and shape of the surrogate bicycle were determined as 26-inch wheel and mountain/road bicycle frame, respectively. Both leg pedaling motion and wheel rotation are suggested to produce proper micro-Doppler features and support the camera-based AEB systems.

The results have demonstrated that the developed scenarios, test procedures and bicyclist surrogate will provide effective objective methods and necessary hardware and software tools for the evaluation and validation of bicyclist AEB systems. This is crucial for the development of advanced driver assistance systems.

The directional solidification Fe-B alloy was prepared. The microstructures and three-body abrasive wear behaviors of directional solidification alloy were investigated.

Fe-B alloy was melted in medium frequency induction furnace. The hardness was measured on HRS-150 Rockwell-hardness tester and HXD-1000TMC tester. The wear characteristic of the alloy was examined with a block-on-ring geometry. The worn surface of the alloy was investigated by scanning electron microscopy and laser scanning microscopy.

The wear weight loss and worn surface roughness increase with the increasing contact load in wear tests. When the worn surface is perpendicular to the boride growth direction, the highest hardness plane of the boride can resist abrasive effectively under the surrounding and supporting of the martensite matrix.

The relation between boride growth direction and wear direction will cause different boride breaking tendency and wear weight loss.

This paper aims to focus on the preparation of Kevlar fiber (KF) and alkaline hydrolyzed KF (KF-H) to improve the dispersed condition of polyaniline (PAn), as the aggregation of PAn would lead to some adsorption sites buried. And then the materials were used to enrich anionic dye Congo red (CR) from aqueous solution.

The materials (KF@PAn and KF-H@PAn) were designed by means of “diffusion-interfacial-polymerization” under mild condition as high affinity due to the structural properties of PAn, KF and KF-H. The dispersed degree of PAn on the surface of KF and KF-H was validated according to adsorption efficiency for CR.

The content of PAn introduced was not beyond 20 wt.%, while adsorption capacity for CR was significantly enhanced by 4–8 times (on the basis of kinetic data) according to the calculation only by the content of PAn due to KF and alkaline hydrolyzed KF exhibited almost no adsorption for CR, indicating dispersed situation of PAn coating was greatly enhanced and more active sites exposed, which was favorable for the adsorption process. Presence of NaCl would exhibit a more or less positive effect on CR uptake, suggesting the materials could be used for high salt environment.

The investigated means of dispersed degree of PAn on the surface of KF and KF-H are the further and future investigation.

This study will provide a method to improve the dispersed situation of PAn and a theoretical support to treat anionic dyes from aqueous solution especially for salt environment.

The results showed that the dispersed condition of PAn on the surface of KF and KF-H was greatly improved. According to the adsorption capacities for CR, it can be concluded that part of adsorption sites were buried due to the aggregation of PAn, and introduction of KF and KF-H, buried adsorption sites decreased greatly. This study will provide a method to decrease buried adsorption sites of PAn and a contribution for their convenient application in wastewater treatment especially for high salt environment.

Drawing on the perspectives of emotional labor, self-concept and impression management, this study presents two major findings: (1) employees' excessive reliance on impression management can bother supervisors, and (2) the effectiveness of impression management depends on how the management affects targets' attribution of characteristics to actors.

The study uses a cross-sectional design and a sample of 259 employees to investigate the antecedents of abusive supervision and, in this regard, the potential mediating effects of impression management. Through Mplus analysis, the authors specifically show that deep acting and surface acting affect impression management and that impression management activates abusive supervision.

Emotional labor is critical in triggering abusive supervision through impression management. The study specifically shows that impression management mediates two types of relationships: (1) the relationship between deep acting and abusive supervision, and (2) the relationship between surface acting and abusive supervision. The findings contribute to the abusive supervision literature by clarifying how impression management functions.

This study, by addressing how emotional labor is a potential antecedent of abusive supervision, reveals that impression management can be a mixed blessing, insofar as emotional labor can contribute to abusive supervision.

This paper aims to examine the potentials of using automated guided vehicle (AGV) technology in modular integrated construction (MiC) to realise logistics automation in module manufacturing and transport.

This paper adopts a scenario approach through three phases (i.e. scenario preparation, development and transfer), with six steps performed iteratively. The scenarios were systematically developed using a six-aspect socio-technical framework. Data were collected through a comprehensive literature review, site visits and interviews with relevant stakeholders and professionals. Implications regarding strength, weakness, opportunities and challenges and future research directions are provided.

The developed scenarios of “smart manufacturing” and “last-mile delivery” demonstrated how AGVs could be used to enhance efficiency and productivity in module manufacturing and transport. The synergies between AGVs and emerging information technologies should pave a good foundation for realising logistics automation in MiC. Future research should address: how to define the tasks of AGVs, how will the use of AGVs impact MiC practices, how to design AGV-integrated module manufacturing/transport systems and how to integrate people factors into the use of AGVs in MiC.

This paper reveals the socio-technical benefits and challenges of using AGVs in MiC.

This study extends the understanding of using logistics automation in MiC as emerging research directions, with the intention of directing scholars’ and practitioners’ interest into future exploration. It is the first attempt in its kind. Its findings could be extended to constitute a comprehensive development roadmap and prospects of automation in modular construction.

The purpose of this paper is to explore the trends of Cambodian government scholarship students’ choice of academic major; the significant association between family socioeconomic status (SES) and Science, Technology, Engineering, and Mathematics (STEM) majors, and the patterns of STEM major choices across demographic and geographical characteristics.

Descriptive analysis and cross-tabulation statistical tests were employed to analyze the data of 1,000 students drawn from the Ministry of Education, Youth and Sport scholarships database of 2018. The study employed a correlational study design.

Findings revealed that the Cambodian government scholarship students were more inclined to major in social science than science and engineering-related fields. Findings also positioned that students’ choice of STEM majors was significantly associated with family SES, gender and locality. Students from families with higher SES, male students and students from the provinces were more likely to choose STEM majors than their counterparts.

These findings point to some practical considerations on how to maximize low SES students’ enrollment in STEM majors at a higher education of Cambodia.