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Polyurethane (PUR) foam parts are traditionally manufactured using metallic molds, an unsuitable approach for prototyping purposes. Thus, rapid tooling of disposable molds using fused filament fabrication (FFF) with polylactic acid (PLA) and glycol-modified polyethylene terephthalate (PETG) is proposed as an economical, simpler and faster solution compared to traditional metallic molds or three-dimensional (3D) printing with other difficult-to-print thermoplastics, which are prone to shrinkage and delamination (acrylonitrile butadiene styrene, polypropilene-PP) or high-cost due to both material and printing equipment expenses (PEEK, polyamides or polycarbonate-PC). The purpose of this study has been to evaluate the ease of release of PUR foam on these materials in combination with release agents to facilitate the mulding/demoulding process.

PETG, PLA and hardenable polylactic acid (PLA 3D870) have been evaluated as mold materials in combination with aqueous and solvent-based release agents within a full design of experiments by three consecutive molding/demolding cycles.

PLA 3D870 has shown the best demoldability. A mold expressly designed to manufacture a foam cushion has been printed and the prototyping has been successfully achieved. The demolding of the part has been easier using a solvent-based release agent, meanwhile the quality has been better when using a water-based one.

The combination of PLA 3D870 and FFF, along with solvent-free water-based release agents, presents a compelling low-cost and eco-friendly alternative to traditional metallic molds and other 3D printing thermoplastics. This innovative approach serves as a viable option for rapid tooling in PUR foam molding.

Accurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available.

The proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China.

The results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors.

(1) The most critical influencing factors of the buildings’ cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection.

The purpose of this study is to explore the human resource development (HRD) implications of perceived higher education (HEd) leadership effectiveness on academics’ quality of worklife (QWL) in the context of COVID-19. Drawing on conservation of resource theory, this study explains the mediating role of resource adequacy (RA); then this study investigates the moderating role of COVID-19-related risk perception (CRP) on the relationship between university leadership (UL) and both academics’ turnover intention (TI) and academics’ QWL.

This study used a quantitative research exploiting the pandemic experiences and perceptions survey, PEPS, to collect data from 300 academic staff in private and public HEd institutions in Lebanon. The analyses include the test of the mediating effect of RA as well as the moderated mediation effect of CRP through regressions, PROCESS and bootstrapping.

The findings suggest that by enhancing RA, effective UL positively influences the QWL and mitigate the TI in Lebanese HEd. Furthermore, this study found that CRP weakens the direct relationship of UL on RA and the indirect effect of UL on the QWL and TI via RA such that the relationships are weakened when COVID-19 risk perception was high rather than low.

The results imply that HEd HRD professionals could think of effective human resource interventions of how to maintain good working environment where academics are facilitated to acquire high level of resources which lead to improving their QWL and mitigating the negative outcome (TIs).

To the best of the authors’ knowledge, no research has been made to investigate the moderated mediation model of the “pandemic experience and leadership perceptions” (PEPS) in the HEd sector in Lebanon, addressing academics’ experiences in business schools. This study is unique because it was conducted during the utmost pandemic outbreak (mid academic year 2021) collecting data in real time. This research contributes to the HRD literature by showing empirical evidence of the relationships in the context of Lebanese HEd institutions.

This study aims to evaluate the adoption of four types of supplier sustainability risk management (SSRM) strategies, namely, risk avoidance (RA), risk acceptance (RAC), collaboration-based risk mitigation (CBM) and monitoring-based risk mitigation (MBM) in Sri Lankan apparel and retail industries, and to investigate their effect on supply chain performance (SCP).

This study uses the dynamic capability view (DCV) to develop its hypotheses. Data collected from 89 firms were analysed using partial least square (PLS) structural equation modelling and PLS-based multiple group analysis.

Sri Lankan apparel and retail firms adopt RA and MBM strategies relatively more than CBM and RAC strategies, whereas there is no significant difference between the two industries in terms of the use of SSRM strategies. The path analysis revealed significant effects of RA and RAC strategies on SCP of both industries. The effect of CBM strategy on SCP is moderated by industry, while MBM has no significant impact.

While managing supplier sustainability risks effectively, RA and RAC strategies provide more opportunities for managers to improve SCP. In achieving SCP, CBM strategies are proven to be more effective for retail industry compared with the apparel sector. Although MBM strategies offer sustainability advantages to firms, their contribution to improving the performance of apparel and retail supply chains is not significant. This research is limited to only two industries (apparel and retail) in Sri Lanka, where the evidence for the effects of SSRM strategies is not available for other contexts.

Either the effects of the four types of SSRM strategies on SCP or the moderating effect of industry on these effects have not been empirically confirmed in the literature. Evaluating the extent to which different strategies are implemented in Sri Lankan apparel and retail industries is another significant contribution of this research. Furthermore, this study contributes by using DCV to a sustainability-based supply chain risk management research.

The purpose of this study is to develop and validate the business streamlining (BS) model proposed in 2017.

This study/paper develops and validates the qualitatively generated BS model, a conceptual model of service sourcing relationships, by testing it quantitatively. A survey was sent to chief exective officers, chief purchasing officers or facility managers in 764 private or public companies in Sweden with an annual turnover exceeding € 10m. The categories were tested and analyzed by means of factor analysis.

The BS model for managing service sourcing processes was confirmed to be significant overall, meaning that it is applicable irrespective of service sourcing context. The efficiency pursuing (EP) was found to have an interlinking role that calls for a revision of the BS model. Furthermore, the four categories tended to load pairwise.

Although this cross-sectional study confirms the relevance of the BS model for managing service-sourcing processes, further studies should examine both the relative significance of its categories in different service-sourcing contexts and why the four main categories tend to pair.

The results support that the model is flexible and adaptable to a wide range of service-sourcing circumstances. Irrespective of the relative complexity of facility management (FM) sourcing processes, the categories can be adapted to fit the service sourcing context. Thus, it can be used as a tool to analyze and facilitate strategic decision-making.

The paper validates that the BS model can represent the dynamics of different service-sourcing processes, regardless of the complexity of the context.

The surface characteristics of thermally and chemically modified wood, such as surface roughness, surface free energy (SFE) and wettability, are important properties that influence further manufacturing processes such as gluing and coating. The aim of this paper was to determine the influence of the surface roughness of thermally and chemically modified teak wood on their SFE, wettability and bonding quality for water-based acrylic and solvent-based alkyd varnishes. In addition, durability against subterranean termites in the field of these modified teak woods was also investigated to give a valuable information for their further application.

The woods tested in this study were fast-growing teak woods that were prepared in untreated and treated with furfuryl alcohol (FA), glycerol maleic anhydride (GMA) and thermal. SFE values were calculated using the Rabel method. The wettability values were measured based on the contact angle between varnish liquids and wood surfaces using the sessile drop method, and the Shi and Gardner model model was used to evaluate the wettability of the varnishes on the wood surface. The bonding quality of the varnishes was measured using a cross-cut test based on ASTM 3359-17 standard. In addition, durability against subterranean termites in the field of these modified teak woods was also investigated according to ASTM D 1758-06.

The results showed that furfurylated and GMA-thermal 220°C improved the durability of teak wood against termites. The furfurylated teak wood had the roughest surface with an arithmetic average roughness (Ra) value of 15.65 µm before aging and 27.11 µm after aging. The GMA-thermal 220°C treated teak wood was the smoothest surface with Ra value of 6.44 µm before aging and 13.75 µm after aging. Untreated teak wood had the highest SFE value of 46.90 and 57.37 mJ/m² before and after aging, respectively. The K values of untreated and treated teak wood increased owing to the aging treatment. The K values for the water-based acrylic varnish were lower than that of the solvent-based alkyd varnish. The untreated teak wood with the highest SFE produced the highest bonding quality (grades 4–5) for both acrylic and alkyd varnishes. The solvent-based alkyd varnish was more wettable and generated better bonding quality than the water-based acrylic varnish.

The originality of this research work is that it provides evaluation values of the durability and SFE. The SFE value can be used to quantitatively determine the wettability of paint liquids on the surface of wood and its varnish bonding quality.

This study aims to identify the determinants of adopting financial technology (FinTech) in Haryana (India). Further, the authors also compare the behavioural intention among male and female respondents to deliver a comprehensive understanding of the adoption of FinTech.

The current study contains two cross-sectional surveys about males and females. Study M is completed with (333) males, and Study F is conducted on (317) female users towards FinTech adoption. This study used “Partial least squares-structural equation modelling (PLS-SEM)” for data analysis.

The outcomes indicate that in both (Studies M and F), perceived usefulness and perceived ease of use substantially impact attitude and behavioural intention. Moreover, the results show that perceived value significantly influences, while perceived risks insignificantly influence behavioural intention. Surprisingly, relative advantage (in Study M) and trialability (in Study F) has insignificant impact on behavioural intention. Further, the outcomes also confirm that in both studies (M and F), attitude and behavioural intention substantially influence the actual use of FinTech.

To the best of the authors’ knowledge, this is the preliminary research on FinTech to inspect the role of gender in the technology adoption process. The adoption difference between males and females and the insightful result that the authors found help shed light on the uniqueness of the context. This study is also one of the initial to test three credible technology determinant theories and then offer a robust model for the actual use of FinTech that is to be used by both practitioners and researchers.

Material extrusion-based additive manufacturing is a prominent manufacturing technique to fabricate complex geometrical three-dimensional (3D) parts. Despite the indisputable advantages of material extrusion-based technique, the poor surface and subsurface integrity hinder the industrial application of this technology. The purpose of this study is introducing the hot air jet treatment (HAJ) technique for surface treatment of additive manufactured parts.

In the presented research, novel theoretical formulation and finite element models are developed to study and model the polishing mechanism of printed parts surface through the HAJ technique. The model correlates reflow material volume, layer width and layer height. The reflow material volume is a function of treatment temperature, treatment velocity and HAJ velocity. The values of reflow material volume are obtained through the finite element modeling model due to the complexity of the interactions between thermal and mechanical phenomena. The theoretical model presumptions are validated through experiments, and the results show that the treatment parameters have a significant impact on the surface characteristics, hardness and dimensional variations of the treated surface.

The results demonstrate that the average value of error between the calculated theoretical results and experimental results is 14.3%. Meanwhile, the 3D plots of Ra and Rq revealed that the maximum values of Ra and Rq reduction percentages at 255°C, 270°C, 285°C and 300°C treatment temperatures are (35.9%, 33.9%), (77.6%,76.4%), (94%, 93.8%) and (85.1%, 84%), respectively. The scanning electron microscope results illustrate three different treatment zones and the treatment-induced and manufacturing-induced entrapped air relief phenomenon. The measured results of hardness variation percentages and dimensional deviation percentages at different regimes are (8.33%, 0.19%), (10.55%, 0.31%) and (−0.27%, 0.34%), respectively.

While some studies have investigated the effect of the HAJ process on the structural integrity of manufactured items, there is a dearth of research on the underlying treatment mechanism, the integrity of the treated surface and the subsurface characteristics of the treated surface.

Many practical control problems require achieving multiple objectives, and these objectives often conflict with each other. The existing multi-objective evolutionary reinforcement learning algorithms cannot achieve good search results when solving such problems. It is necessary to design a new multi-objective evolutionary reinforcement learning algorithm with a stronger searchability.

The multi-objective reinforcement learning algorithm proposed in this paper is based on the evolutionary computation framework. In each generation, this study uses the long-short-term selection method to select parent policies. The long-term selection is based on the improvement of policy along the predefined optimization direction in the previous generation. The short-term selection uses a prediction model to predict the optimization direction that may have the greatest improvement on overall population performance. In the evolutionary stage, the penalty-based nonlinear scalarization method is used to scalarize the multi-dimensional advantage functions, and the nonlinear multi-objective policy gradient is designed to optimize the parent policies along the predefined directions.

The penalty-based nonlinear scalarization method can force policies to improve along the predefined optimization directions. The long-short-term optimization method can alleviate the exploration-exploitation problem, enabling the algorithm to explore unknown regions while ensuring that potential policies are fully optimized. The combination of these designs can effectively improve the performance of the final population.

A multi-objective evolutionary reinforcement learning algorithm with stronger searchability has been proposed. This algorithm can find a Pareto policy set with better convergence, diversity and density.

This study aims to enhance merging of additive manufacturing (AM) techniques with powder injection molding (PIM). In this way, the prototypes could be 3D-printed and mass production implemented using PIM. Thus, the surface properties and mechanical performance of parts produced using powder/polymer binder feedstocks [material extrusion (MEX) and PIM] were investigated and compared with powder manufacturing based on direct metal laser sintering (DMLS).

PIM parts were manufactured from 17-4PH stainless steel PIM-quality powder and powder intended for powder bed fusion compounded with a recently developed environmentally benign binder. Rheological data obtained at the relevant temperatures were used to set up the process parameters of injection molding. The tensile and yield strengths as well as the strain at break were determined for PIM sintered parts and compared to those produced using MEX and DMLS. Surface properties were evaluated through a 3D scanner and analyzed with advanced statistical tools.

Advanced statistical analyses of the surface properties showed the proximity between the surfaces created via PIM and MEX. The tensile and yield strengths, as well as the strain at break, suggested that DMLS provides sintered samples with the highest strength and ductility; however, PIM parts made from environmentally benign feedstock may successfully compete with this manufacturing route.

This study addresses the issues connected to the merging of two environmentally efficient processing routes. The literature survey included has shown that there is so far no study comparing AM and PIM techniques systematically on the fixed part shape and dimensions using advanced statistical tools to derive the proximity of the investigated processing routes.