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The purpose of this paper is to determine the thermo-gravitational convective state of a non-Fourier fluid layer of the single-phase-lagging type, heated from below. Unlike existing methodologies, the spectral modes are not imposed arbitrarily. They are systematically identified by expanding the spectral coefficients in terms of the relative departure in the post-critical Rayleigh number (perturbation parameter). The number and type of modes is determined to each order in the expansion. Non-Fourier effects become important whenever the relaxation time (delay in the response of the heat flux with respect to the temperature gradient) is of the same order of magnitude as process time.

In the spectral method the flow and temperature fields are expanded periodically along the layer and orthonormal shape functions are used in the transverse direction. A perturbation approach is developed to solve the nonlinear spectral system in the post-critical range.

The Nusselt number increases with non-Fourier effect as suggested in experiments in microscale and nanofluid convection.

Unlike existing nonlinear formulations for RB thermal convection, the present combined spectral-perturbation approach provides a systematic method for mode selection.

Rapid tooling (RT) integrated with additive manufacturing technologies have been implemented in various sectors of the RT industry in recent years with various kinds of prototype applications, especially in the development of new products. The purpose of this study is to analyze the current application trends of RT techniques in producing hybrid mold inserts.

The direct and indirect RT techniques discussed in this paper are aimed at developing a hybrid mold insert using metal epoxy composite (MEC) in increasing the speed of tooling development and performance. An extensive review of the suitable development approach of hybrid mold inserts, material preparation and filler effect on physical and mechanical properties has been conducted.

Latest research studies indicate that it is possible to develop a hybrid material through the combination of different shapes/sizes of filler particles and it is expected to improve the compressive strength, thermal conductivity and consequently increasing the hybrid mold performance (cooling time and a number of molding cycles).

The number of research studies on RT for hybrid mold inserts is still lacking as compared to research studies on conventional manufacturing technology. One of the significant limitations is on the ways to improve physical and mechanical properties due to the limited type, size and shape of materials that are currently available.

This review presents the related information and highlights the current gaps related to this field of study. In addition, it appraises the new formulation of MEC materials for the hybrid mold inserts in injection molding application and RT for non-metal products.

This paper reviews the synergy of Industry 4.0 and additive manufacturing (AM) and discusses the integration of data-driven manufacturing systems and product service systems as a key component of the Industry 4.0 revolution. This paper aims to highlight the potential effects of Industry 4.0 on AM via tools such as digitalisation, data transfer, tagging technology, information in Industry 4.0 and intelligent features.

In successive phases of industrialisation, there has been a rise in the use of, and dependence on, data in manufacturing. In this review of Industry 4.0 and AM, the five pillars of success that could see the Internet of Things (IoT), artificial intelligence, robotics and materials science enabling new levels of interactivity and interdependence between suppliers, producers and users are discussed. The unique effects of AM capabilities, in particular mass customisation and light-weighting, combined with the integration of data and IoT in Industry 4.0, are studied for their potential to support higher efficiencies, greater utility and more ecologically friendly production. This research also illustrates how the digitalisation of manufacturing for Industry 4.0, through the use of IoT and AM, enables new business models and production practices.

The discussion illustrates the potential of combining IoT and AM to provide an escape from the constraints and limitations of conventional mass production whilst achieving economic and ecological savings. It should also be noted that this extends to the agile design and fabrication of increasingly complex parts enabled by simulations of complex production processes and operating systems. This paper also discusses the relationship between Industry 4.0 and AM with respect to improving the quality and robustness of product outcomes, based on real-time data/feedback.

This research shows how a combined approach to research into IoT and AM can create a step change in practice that alters the production and supply paradigm, potentially reducing the ecological impact of industrial systems and product life cycle. This paper demonstrates how the integration of Industry 4.0 and AM could reshape the future of manufacturing and discusses the challenges involved.

The purpose of this paper is the development of a reference model contributing to the identification of the retail supply chain activities that can be improved by the implementation of Lean Six Sigma (LSS) initiatives, following DMAIC steps and using proposed tools, based on contemporary technologies and best practices.

The reference model was developed combining the existing literature knowledge about the areas of supply chain that LSS can improve and the data collected by two big retailers in Greece about their supply chain operation. The integration of two sources of information led to the modeling of the retail supply chain processes and the proposal of appropriate LSS initiatives for their improvement.

This paper aimed to detect the pain points of a retail supply chain, which can be achieved by following the steps of DMAIC and applying specific LSS initiatives as described in the reference model, to introduce the trends of the sector in terms of logistics and supply chain.

The reference model of this paper can be used as a comprehensive guide of LSS implementation in the retail supply chain and help the companies of such a significant sector for the economy.

The literature review revealed that there is a lack of publications concerning LSS implementation in retail supply chain and in the retail sector in general. The development of this model contributes to the filling of this gap by providing a complete reference model, which strives to overcome the barriers of LSS adoption in retail supply chain.

The increased health-care costs, improved service quality and sustainability-oriented customer demand have forced the health-care sector to relook their current process. The present work deals with the identification, analysis and prioritization of just in time (JIT) enablers in the health-care sector.

JIT leads to waste reduction, improves productivity and provides high-quality patient care. The practical implementation of JIT depends on vital factors known as enablers. The enablers have been found through the comprehensive literature review and prioritized using responses from different health-care facilities of the national capital region of India. Grey relational analysis (GRA) has been used in the present study to rank enablers and ranks were further validated using the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) and sensitivity analysis.

It has been found that top management support, teamwork and real-time information sharing are the most significant enablers of JIT in health care with grey relational grades 0.956, 0.832 and 0.718, respectively. The corresponding closeness coefficients of the fuzzy TOPSIS for the enablers were found as 0.875, 0.802 and 0.688, respectively. The findings of the present research work will facilitate the health-care organizations to implement a comprehensive JIT approach that further leads to improved patient care at a low cost.

The present study is unique in terms of the exploration of the readiness measures or enablers of JIT using GRA and fuzzy TOPSIS. The findings of the present research work will facilitate the health-care organizations to optimize their resources for better patient care.

This study aims to examine the effect of e-customer relationship management (e-CRM) on customer e-loyalty through e-service quality and e-satisfaction. This study also examines how customers’ e-loyalty affects their willingness to recommend a banking service.

A total of 372 private bank customers from Chattogram, the second largest and only port city of Bangladesh, were chosen using a convenience sampling technique. Structured equation modelling was used to analyse the data.

E-CRM positively impacts e-service quality, customer e-satisfaction and customer e-loyalty. The association between e-CRM and customer e-loyalty is sequentially mediated by e-service quality and e-satisfaction. E-loyalty has a significant influence on willingness to recommend a banking service.

The findings will help Bangladeshi banks boost the number of prospective customers implementing e-CRM. In addition, mediators between e-CRM and e-loyalty provides managers a new insight on willingness to recommend a banking service.

The sequential mediation effect of e-service quality and customer e-satisfaction on the connection between e-CRM and e-loyalty represents the unique contribution and enriches the present e-CRM literature, particularly in the Bangladeshi private banking sector.

The COVID-19 pandemic has caused significant mortality and morbidity worldwide. However, the role of dietary patterns as a potential risk factor for COVID-19 has not been well established, especially in studies with large samples. Therefore, this study aims to identify and evaluate the association between major dietary patterns and COVID-19 among adults from Iran.

In this cross-sectional study, the authors included 9,189 participants aged 20–70 who participated in the Yazd Health Study (YaHS) and Taghzieh Mardom-e-Yazd study (TAMIZ). They used factor analysis to extract dietary patterns based on a food frequency questionnaire (FFQ). Then, they assessed the relationship between these dietary patterns and the odds of COVID-19.

This study identified two major dietary patterns: “high protein and high fiber” and “transitional”. Participants in the highest tertile of the “high protein and high fiber” dietary pattern, which included vegetables, fruits, dairy and various kinds of meats such as red meat, fish and poultry, had a lower odds of COVID-19 compared with those in the lowest tertile. However, the “transitional” dietary pattern did not affect the risk of COVID-19.

In conclusion, a “high protein, high fiber” diet may lower the odds of COVID-19. This study suggests that dietary patterns may influence the severity and spread of future similar pandemics.

The aim of this study is to investigate the printing parameters of fused deposition modeling (FDM), a material extrusion-based method, and to examine the mechanical and thermal properties of their polylactic acid (PLA) components reinforced with copper, bronze, and carbon fiber micro particles.

Tensile test samples were created by extruding composite filament materials using FDM-based 3D printer. Taguchi method was used to design experiments where layer thickness, infill density, and nozzle temperature were the printing variables. Analysis of variance (ANOVA) was applied to determine the effect of these variables on tensile strength.

The results of this study showed that the reinforcement of metal particles in PLA material reduces strength and increases elongation. The highest tensile strength was obtained when the layer thickness, infill density, and nozzle temperature were set to 100 µm, 60%, and 230 °C, respectively. As a result of thermal analysis, cooper-PLA showed the highest thermal resistance among metal-based PLA samples.

It is very important to examine the mechanical and thermal quality of parts fabricated in FDM with metal-PLA composites. In the literature, the mechanical properties of metal-reinforced composite PLA parts have been examined using different factors and levels. However, the fabrication of parts using the FDM method with four different metal-added PLA materials has not been examined before. Another unique aspect of the study is that both mechanical and thermal properties of composite materials will be examined.

This paper aims to enhance the surface finish of the fused deposition modeling (FDM) part using the vapor smoothening (VS) post-processing method and to study the combined effect of FDM and VS process parameters on the quality of the part.

Analysis of variance method is used to understand the significance of the FDM and VS process parameters. Following this, the optimized parameter for multiple criteria response is reported using the technique for order preference by similarity to ideal solution. The process parameters alternatives are build orientation angle, build surface normal and exposure time and the criteria are surface roughness and dimensional error percentage.

The result observed contradicts the result reported on the independent parameter optimization of FDM and VS processes. There is a radical improvement in the surface finish on account of the coating process and an increase in the exposure time results in the decrease of the surface roughness. Minimum surface roughness of 0.11 µm is observed at 1,620 build angle and the least dimensional error of 0.01% is observed at build orientation angle 540. The impact of VS on the up-facing surface is different from the down-facing surface due to the removal of support material burrs and the exposure of the surface to vapor direction.

A study on the multi-criteria decision-making to ascertain the effect of post-processing on FDM component surface normal directed both to downward (build angle 0°–90°) and to upward (build angle 99°–180°) are reported for the first time in this article. The data reported for the post-processed FDM part at the build angle 0°–180° can be used as a guideline for selecting the optimal parameter and for assigning appropriate tolerance in the CAD model.

The objective is to address financing challenges with an innovative technical approach and provide financial support to facilitate the sustainable development of characteristic tourist towns.

In this study, a novel decision model is proposed, which utilizes the CRITIC improved G1 weighting method to analyze financing factors and applies GRA to enhance the TOPSIS model under a Z-Number fuzzy environment. Finally, sensitivity analysis and comparative assessment were conducted to validate the findings and the model.

The findings indicate that equity financing is the optimal mode of financing for characteristic tourist towns, with bond financing serving as a viable alternative. Key factors influencing financing include economic benefit, social benefit and policy risk. Managers should carefully consider these factors when selecting financing methods in order to enhance efficiency and mitigate risks.

As a new business model and value creation method of cultural and tourism integration, the financing decision of a characteristic tourism town has always been the key to the overall promotion and operation. The research constructs a financing index system for characteristic tourism towns based on benefit and risk considerations, using the hot spring town in Anhui Province, China, as a case study to evaluate eight financing models.