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The purpose of this study is to investigate the effect of the sintering temperature on the microstructural, mechanical and physical properties of Cu-SiC composites.

The powder metallurgy route was used to fabricate the samples. Cold compaction of powders was conducted at 250 MPa which was followed by sintering at 850°C–950°C at the interval of 50 °C in the open atmospheric furnace. SiC was used as a reinforcement and the volumetric fraction of the SiC was varied as 10%, 15% and 20%. The processed samples were metallurgically characterized by the scanning electron microscope (SEM). Mechanical characterization was done using tensile and Vickers’ micro-hardness testing to check the hardness and strength of the samples. Archimedes principle and Four-point collinear probe method were used to measure the density and electrical resistivity of the samples.

SEM micrograph reveals the uniform dispersion of the SiC particles in the Cu matrix element. The results revealed that the Hardness and tensile strength were improved due to the addition of SiC and were maximum for the samples sintered at 950 °C. The addition of SiC has also increased the electrical resistivity of the Cu-SiC composite and was lowest for Cu 100% while the relative density has shown the reverse trend. Further, it was found that the maximum hardness of 91.67 Hv and ultimate tensile strength of 312.93 MPa were found for Cu-20% SiC composite and the lowest electrical resistivity of 2.017 µ- Ω-cm was found for pure Cu sample sintered at 950 °C, and this temperature was concluded as the optimum sintering temperature.

The powder metallurgy route for the fabrication of the composites is a challenging task as the trapping of oxygen cannot be controlled during the compaction process as well as during the sintering process. So, a more intensive study is required to overcome these kinds of limitations.

As of the author’s best knowledge, no work has been reported on the effect of sintering temperature on the properties of the Cu-SiC composites which has huge potential in the industries.

Indian families are undergoing a transition due to a combination of factors such as rapid urbanization, economic development, educational advancements and major global connect. The shift from traditional joint families to urban nuclear families, changing pattern of the size of families, transformed gender roles in the domestic sphere, rising educational and career aspirations and increased occupational choices, new technological and economic contributions collectively contribute to a new landscape for Indian families. While these changes bring about new forms and structures of modern Indian families, they also reflect the resilience of the conventional value system of Indian families in adapting to the demands of a rapidly changing world. This chapter outlines the changing dynamics of Indian families in the 21st century. Changes in marriages and their influence on family making have been discussed with a special focus on inter-caste and intra-caste marriages and modern families. In urban India, age at marriage is also discussed in the background of formation of families. This chapter provides a discussion on changed gender roles and hierarchies within families. This chapter also highlights separation and divorce which led to single-parent families and broken families in Indian society.

This study proposes a new model for assessing supply chain sustainability risk integrating subjectivity and objectivity of decision-maker. Research has shown the vacancy of study in dealing with the above issue. To fill this research gap, a new decision support model considering the subjectivity and objectivity of decision-makers in assigning the weight of the supply chain risk reprioritization criteria is presented and demonstrated using a case example.

This study adopts a new decision support model for assessing supply chain sustainability risk based on additional failure mode and effect analysis (FMEA) parameters and its integration with preference selection index (PSI) methodology and the Shannon entropy. A case example of the supply chain small and medium enterprise (SME) producing handy crafts has been used in this study.

The result of the study reveals critical sustainability risk dimensions and their risk elements demanding management attention to support realization to a more sustainable business operation.

The use of a single case study is often associated as a limitation in the research studies, and this study is based on findings from SMEs in the handy craft sector in a developing country. Nonetheless, future studies may focus on replicating this study using more samples. This preliminary study provides academics and practitioners with an exemplar of supply chain sustainability risk assessment from the SME in a developing country.

The result of this study is beneficial for practitioners, particularly owner-managers of SMEs who can use this study as guidance on how to identify and select the critical sustainability risks and plan mitigating strategies accordingly.

Scientific effort on appraising criticality of supply chain sustainability risks considering subjectivity and objectivity of decision-maker simultaneously is missing in earlier studies. To the best of the author’s knowledge, this is the first paper applying the PSI and Shannon entropy method and using it for evaluating the impact of supply chain risk based on five sustainability pillars. The findings and suggestions for future research initiatives will provide new insights for scholars and practitioners in managing SME supply chain sustainability risks.

Adopting the circular economy (CE) notion in the supply chain perspective is necessary for the sustainability viewpoint. However, such practices are deficient, especially in developing countries like India, because of several obstacles. The purpose of this study was to create an approach for circular supply chain management (CSCM) adaption in Indian rubber industries by identifying and evaluating its associated obstacles.

A hybrid approach of analytic hierarchy process (AHP) and the grey-based ELECTRE method had been employed in this research to obtain the mutual rankings of the identified obstacles based on their impressions on the CSCM prosperity criteria through a case study and involving diverse expert's opinions.

Presented study's findings illustrate that “Lack of consumer knowledge and consciousness towards environmental sustainability” was found to be the top-ranked obstacle followed by “Unwillingness towards supply chain re-structuring”.

The obstacles' prioritized rankings could help leaders to create sequential strategies for adapting a resilient CSCM structure by systematically eliminating these obstacles. Moreover, the pinpointed critical obstacles could be investigated further in separate studies and generate future studies' scope.

During the extensive literature survey, it had been found that the CSCM practices are in the fledgling stage in the developing country's context. Moreover, studies related to CSCM adaption in rubber-based manufacturing industries were much lacking. Presented work is peculiar, aiming to accelerate the CSCM adaption in the industrial rubber sector in developing countries like India.

Logistics is the part of the supply chain (SC) that plans, executes and handles forward and reverse movement and storage of products, services and related information, in order to respond to customers' needs effectively and efficiently. The main concern for logistics is to ensure that the correct product is placed at the right time. This paper introduces a linear model of shipping focused on decision-making, which includes configuration of shipping network, choosing of transport means and transfer of individual customer shipments through a particular transport system.

In this study, authors try to address the problem of supply chain network (SCN) where the primary goal is to determine the appropriate order allocation of products from different sources to different destinations. They also seek to minimize total transportation cost and inventory cost by simultaneously determining optimal locations, flows and shipment composition. The formulated problem of getting optimal allocation turns out to be a problem of multi-objective programming, and it is solved by using the max-addition fuzzy goal programming approach, for obtaining optimal order allocation of products. Furthermore, the problem demand and supply parameters have been considered random in nature, and the maximum likelihood estimation approach has been used to assess the unknown probabilistic distribution parameters with a specified probability level (SPL).

A case study has also been applied for examining the effectiveness and applicability of the developed multi-objective model and the proposed solution methods. Results of this study are very relevant for the manufacturing sector in particular, for those facing logistics issues in SCN. It enables researchers and managers to cope with various types of uncertainty and logistics risks associated with SCN.

The principal contribution of the proposed model is the improved modelling of transportation and inventory, which are affected by different characteristics of SCN. To demonstrate computational information of the suggested methods and proposed model, a case illustration of SCN is provided. Also, environmentalism is increasingly becoming a significant global concern. Hence, the concept proposed could be extended to include environmental aspects as an objective function or constraint.

Efficient integration of logistical cost components, such as transportation costs, inventory costs, with mathematical programming models is an important open issue in logistics optimization. This study expands conventional facility location models to incorporate a range of logistic system elements such as transportation cost and different types of inventory cost, in a multi-product, multi-site network. The research is original and is focused on case studies of real life.

Construction is a highly dynamic environment with numerous interacting factors that affect construction processes and decisions. Uncertainty is inherent in most aspects of construction engineering and management, and traditionally, it has been treated as a random phenomenon. However, there are many types of uncertainty that are not naturally modelled by probability theory, such as subjectivity, ambiguity and vagueness. Fuzzy logic provides an approach for handling such uncertainties. However, fuzzy logic alone has some limitations, including its inability to learn from data and its extensive reliance on expert knowledge. To address these limitations, fuzzy logic has been combined with other techniques to create fuzzy hybrid techniques, which have helped solve complex problems in construction. In this chapter, a background on fuzzy logic in the context of construction engineering and management applications is presented. The chapter provides an introduction to uncertainty in construction and illustrates how fuzzy logic can improve construction modelling and decision-making. The role of fuzzy logic in representing uncertainty is contrasted with that of probability theory. Introductory material is presented on key definitions, properties and methods of fuzzy logic, including the definition and representation of fuzzy sets and membership functions, basic operations on fuzzy sets, fuzzy relations and compositions, defuzzification methods, entropy for fuzzy sets, fuzzy numbers, methods for the specification of membership functions and fuzzy rule-based systems. Finally, a discussion on the need for fuzzy hybrid modelling in construction applications is presented, and future research directions are proposed.

The objective of this work is to demonstrate the relationships between the two main processes of research and development (R&D) activities: the knowledge generation phase (KPP) and the knowledge commercialization, or transfer, phase (KCP), in a sector that is intensive in this type of activity, such as the pharmaceutical sector. In addition, within the framework of the general objective of this work, the authors propose two other objectives: (1) make advances in network efficiency measurement models, and (2) determine the factors associated with efficiency in the KPP and in the KCP in companies of the pharmaceutical sector in Spain.

A Network Data Envelopment Analysis (NDEA) model (Färe and Grosskopf, 2000) with categorical variables (Lee et al., 2020; Yeh and Chang, 2020) has been applied, and a sensitivity analysis of the obtained results has been performed through a DEA model of categorical variables, in accordance with the work of Banker and Morey (1986), to corroborate the results of the proposed model. The sample is made up of 77 companies in the pharmaceutical sector in Spain.

The results obtained point to a greater efficiency of pharmaceutical companies in the KPP, rather than in the KCP. Furthermore, the study finds that 1) alliances between companies have been the accelerating factors of efficiency in the KCP (but patents have slowed this down the most); 2) the quality of R&D and the number of R&D personnel are the factors that most affect efficiency in the KPP; and 3) the quality of R&D again, the benefits obtained and the position in the market are the factors that most affect efficiency in the KCP.

The authors have not found studies that show whether the efficiency obtained by R&D-intensive companies in the KPP phase is related to better results in terms of efficiency in the KCP phase. No papers have been found that analyse the role of alliances between R&D-intensive companies and patents, as agents that facilitate efficiency in the KCP phase, covering the gap in the research on both problems. Notwithstanding, this work opens up a research path which is related to the improvement of network efficiency models (since it includes categorical variables) and the assessment of the opinions of those who are responsible for R&D departments; it can be applied to decision-making on the aspects to improve efficiency in R&D-intensive companies.

The real challenges in online crack detection testing based on guided waves are random noise as well as narrow-band coherent noise; and to achieve efficient structural health assessment methodology, magnificent extraction of noise and analysis of the signals are essential. The purpose of this paper is to provide optimal noise filtering technique for Lamb waves in the diagnosis of structural singularities.

Filtration of time-frequency information of guided elastic waves through the noisy signal is investigated in the present analysis using matched filtering technique which “sniffs” the signal buried in noise and most favorable mother wavelet based denoising methods. The optimal wavelet function is selected using Shannon’s entropy criterion and verified by the analysis of root mean square error of the filtered signal.

Wavelet matched filter method, a newly developed filtering technique in this work and which is a combination of the wavelet transform and matched filtering method, significantly improves the accuracy of the filtered signal and identifies relatively small damage, especially in enormously noisy data. A comparative study is also performed using the statistical tool to know acceptability and practicability of filtered signals for guided wave application.

The proposed filtering techniques can be utilized in online monitoring of civil and mechanical structures. The algorithm of the method is easy to implement and found to be successful in accurately detecting damage.

Although many techniques have been developed over the past several years to suppress random noise in Lamb wave signal but filtration of interferences of wave modes and boundary reflection is not in a much matured stage and thus needs further investigation. The present study contains detailed information about various noise filtering methods, newly developed filtration technique and their efficacy in handling the above mentioned issues.