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The purpose of this article is to use an extended model of self to understand the consumption of music and similar entertainment products.

In-depth interviews using experts within the music field were used to penetrate the private worlds of musical theatre enthusiasts. Multiple qualitative analytic techniques were used to explore the different aspects of the self underlying music consumption.

Repeated exposure to musical theatre allowed subjects to refine their consumption of specific performances that reflect the preferred aspect of their extended self. It is found higher order consumption needs are an integral part of the extended self, and form an important basis for consumption decisions. Of particular importance is the reflection of the self that assists others in their consumption choices.

Present research widely recognises consumers are seeking more than just “entertainment” when they consume an entertainment product, but struggle to characterise what it is consumers are actually seeking. This research provides this insight through an elaboration of the extended self-model.

The implications of metallic biomaterials involve stress shielding, bone osteoporosis, release of toxic ions, poor wear and corrosion resistance and patient discomfort due to the need of second operation. This study aims to use additive manufacturing (AM) process for fabrication of biodegradable orthopedic small locking bone plates to overcome complications related to metallic biomaterials.

Fused deposition modeling technique has been used for fabrication of bone plates. The effect of varying printing parameters such as infill density, layer height, wall thickness and print speed has been studied on tensile and flexural properties of bone plates using response surface methodology-based design of experiments.

The maximum tensile and flexural strengths are mainly dependent on printing parameters used during the fabrication of bone plates. Tensile and flexural strengths increase with increase in infill density and wall thickness and decrease with increase in layer height and wall thickness.

The present work is focused on bone plates. In addition, different AM techniques can be used for fabrication of other biomedical implants.

Studies on application of AM techniques on distal ulna small locking bone plates have been hardly reported. This work involves optimization of printing parameters for development of distal ulna-based bone plate with high mechanical strength. Characterization of microscopic fractures has also been performed for understanding the fracture behavior of bone plates.

The purpose of this study is to propose the use of a pre-deposition heating system for fused filament fabrication (FFF) as a means to enhance interlayer bonding by elevating the substrate temperature. The effects of the heating on thermal profile at the bonding interface and the mechanical properties of three-dimensional printed parts are investigated.

A 12-W laser head is integrated to a commercial printer as the pre-deposition heating system. The laser beam heats up substate before the deposition of a fresh filament. Effects of laser powers are investigated and the thermal profile is measured with thermocouple, infrared camera and finite element model. The correlation between the temperature at the bonding interface and the bonding quality is investigated by conducting tensile testing and neck width measurement with microscope.

The pre-deposition heating system is proven to be effective in enhancing the inter-layer strength in FFF parts. Tensile testing of specimens along build direction (Z) shows an increase of around 50% in ultimate strength. A linear relationship is observed between the pre-deposition temperature at bond interface and bonding strength. It is evident that elevating the pre-deposition temperature promotes interlayer polymer diffusion as shown by the increased neck width between layers.

Thermocouples that are sandwiched between layers are used to achieve accurate measurement of the interfacial temperature. The temperature profiles under pre-deposition heating are analyzed and correlated to the interlayer bonding strengths.

Three-dimensional (3D) printing is highly dependent on printing process parameters for achieving high mechanical strength. It is a time-consuming and expensive operation to experiment with different printing settings. The current study aims to propose a regression-based machine learning model to predict the mechanical behavior of ulna bone plates.

The bone plates were formed using fused deposition modeling (FDM) technique, with printing attributes being varied. The machine learning models such as linear regression, AdaBoost regression, gradient boosting regression (GBR), random forest, decision trees and k-nearest neighbors were trained for predicting tensile strength and flexural strength. Model performance was assessed using root mean square error (RMSE), coefficient of determination (R²) and mean absolute error (MAE).

Traditional experimentation with various settings is both time-consuming and expensive, emphasizing the need for alternative approaches. Among the models tested, GBR model demonstrated the best performance in predicting both tensile and flexural strength and achieved the lowest RMSE, highest R2 and lowest MAE, which are 1.4778 ± 0.4336 MPa, 0.9213 ± 0.0589 and 1.2555 ± 0.3799 MPa, respectively, and 3.0337 ± 0.3725 MPa, 0.9269 ± 0.0293 and 2.3815 ± 0.2915 MPa, respectively. The findings open up opportunities for doctors and surgeons to use GBR as a reliable tool for fabricating patient-specific bone plates, without the need for extensive trial experiments.

The current study is limited to the usage of a few models. Other machine learning-based models can be used for prediction-based study.

This study uses machine learning to predict the mechanical properties of FDM-based distal ulna bone plate, replacing traditional design of experiments methods with machine learning to streamline the production of orthopedic implants. It helps medical professionals, such as physicians and surgeons, make informed decisions when fabricating customized bone plates for their patients while reducing the need for time-consuming experimentation, thereby addressing a common limitation of 3D printing medical implants.

The purpose of this research is present an experimental and numerical study of the mechanical properties of the acrylonitrile butadiene styrene (ABS) in the additive manufacturing (AM) by fused filament fabrication (FFF). The characterization and mechanical models obtained are used to predict the elastic behavior of a prosthetic foot and the failure of a prosthetic knee manufactured with FFF.

Tension tests were carried out and the elastic modulus, yield stress and tensile strength were evaluated for different material directions. The material elastic constants were determined and the influence of infill density in the mechanical strength was evaluated. Yield surfaces and failure criteria were generated from the tests. Failures over prosthetic elements in tridimensional stresses were analyzed; the cases were evaluated via finite element method.

The experimental results show that the material is transversely isotropic. The elasticity modulus, yield stress and ultimate tensile strength vary linearly with the infill density. The stresses and the failure criteria were computed and compared with the experimental tests with good agreement.

This research can be applied to predict failures and improve reliability in FFF or fused deposition modeling (FDM) products for applications in high-performance industries such as aerospace, automotive and medical.

This research aims to promote its widespread adoption in the industrial and medical sectors by increasing reliability in products manufactured with AM based on the failure criterion.

Most of the models studied apply to plane stress situations and standardized specimens of printed material. However, the models applied in this study can be used for functional parts and three-dimensional stress, with accuracy in the range of that obtained by other researchers. The researchers also proposed a method for the mechanical study of fragile materials fabricated by processes of FFF and FDM.

The purpose of this study is to investigate wear mechanisms of a multi-layered TiAlN/AlCrN-coated carbide tool during the milling of AISI 4340 steel under cryogenic machining.

The wear progression was measured using a toolmaker microscope and an optical microscope. Later, a field emission scanning electron microscope and energy-dispersive X-ray analysis were used to investigate the wear mechanisms in detail.

A comprehensive analysis revealed that the main causes of tool wear mechanisms were abrasion and adhesion wear on the flank face.

The investigations presented in this paper may be used by the machining industry to prolong the tool life at higher cutting speed by the application of liquid nitrogen.

This study investigates the psychological impact of the COVID-19 lockdown on Vietnamese people and examines the factors affecting their psychological well-being during and after the lockdown period.

Based on the survey answers of 701 Vietnamese respondents, this study explores the psychological impact associated with COVID-19 lockdown in Vietnam. Using a newly developed “mvord” package in R that controls the heterogeneity in error structure of the sample units (Hirk et al., 2020), the study runs multivariate ordinal logistic regression models to examine the determinants of the emotional outcomes.

The study discloses negative psychological states among the Vietnamese community during and after the lockdown, including boredom, anxiety, sadness, stress, anger, precautionary measures and post-traumatic stress symptoms. Demographic characteristics (male gender, young age, poor-health condition, high educational level, small family size, officers or professionals, using public transport, quarantine experience before the lockdown, non-extended lockdown period and living in rural areas) and various difficulties during lockdown (insufficient information about COVID-19, income loss, having daily-life difficulties and unhappy experiences during lockdown) are related to higher degrees of different psychological symptoms during and after lockdown in Vietnam.

This study identifies the importance of mitigating the detrimental effects of the COVID-19 lockdown on Vietnamese well-being and prepares the Vietnamese government better to handle the public mental issues during future lockdowns.

Drawing on goal-setting theory and team effectiveness theory, the study aims to examine the impact of agile taskwork and agile teamwork on team performance. In addition, it investigates the mediating effect of project commitment on the impact of agile taskwork and agile teamwork on team performance. Furthermore, the study also tests the moderating role of career level on the impact of agile taskwork and agile teamwork on team performance.

Survey data were collected from 563 employees working in 290 information technology (IT) companies in India using a self-reporting structured questionnaire. Partial least squares path modeling was used to test the hypothesized model, and the Process macro was used to test the moderating effect.

The results show that agile taskwork and agile teamwork positively affect team performance and project commitment, and project commitment positively impacts team performance. Furthermore, project commitment fully mediates the relationship between agile taskwork and team performance and partially mediates the relationship between agile teamwork and team performance. Furthermore, the career level negatively moderates the impact of agile taskwork and agile teamwork on team performance.

The study shows the importance of agile work practices and project commitment to enhance team performance. Thus, the study provides managers with two strategies to improve their team performance.

There is a scarcity of research examining the distinct effects of agile taskwork and agile teamwork on team performance and the mediating role of project commitment in these relationships. Furthermore, as per the empirical evidence, no previous research has empirically examined the moderating role of career level in the agile taskwork-team performance and agile teamwork-team performance relationships.

This study aims to analyze the structure and trend in employment in the Indian economy between 1980-8081 and 2015-2016.

Use of India KLEMS data set. Estimate growth rate of employment and discuss employment prospects using “Point” employment elasticity.

Whilst India’s GDP growth rate has been quite impressive since the reforms of 1991, the rate of employment growth, especially in the recent period of 2003-2015, has been quite slow (1 per cent) with low employment elasticity (0.1). The pattern of employment growth has also been imbalanced with slow rate of employment growth in manufacturing and rapid growth rate in the construction sector. India now also has low labour force participation rate and a large share of informal employment in the economy.

The limitation is the lack of reliable data on employment for the recent period.

With overall low employment elasticity, India would have to explore sectors where more employment opportunities could be created.

India has to create not only more jobs but also “good” jobs.

The India KLEMS data provide a time series for employment, which has been used in this paper to find “Point” elasticity instead of arc elasticity of employment and is an improvement over existing employment elasticity estimates.

As corporate sustainability is systemic, it cannot be achieved without effective involvement of suppliers. This study aims to examine the drivers of supplier companies’ resource allocation to a sustainability issue that affects customer companies and society at large.

Supplier companies’ resource allocation for a sustainability issue is explained from variables at the levels of the institutional, supply chain and internal environments of a supplier company. The framework is tested with a moderated regression model on 102 supplier companies in animal-based supply chains, focussing on their resource allocation for farm animal welfare.

The findings show that supply chain factors have the strongest influence on suppliers’ resource allocation, including a strong effect of investment specificity and a U-shaped effect of chain integration. Also, significant effects from institutional variables, namely, the pressure on consumer companies, and an inverted U-shaped effect of sustainability competition are found. The innovativeness, referring to the internal environment of supplier companies, appears as another important factor for the allocation of resources to animal welfare, as a sustainability issue.

The results have implications for consumer market companies to deal with sustainability issues that require involvement of their suppliers, for supplier companies to increase their competitive positions and strengthen their relationships within the supply chain, and for policymakers seeking solutions for sustainability issues in the market domain.

While existing literature focusses mostly on the corporate sustainability of highly visible and large consumer companies, to the best of the authors’ knowledge, this study is the first to examine the drivers of supplier companies’ resource allocation for a sustainability issue, namely, animal welfare. It provides insights on what drives supplier companies, usually operating outside the spotlight, to become part of a sustainability transition.