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During the post-globalization period, tariff imposition on manufacturing trade has a possible effect on the economy of developed and developing nations. Along with the volume and balance of trade, the study accounts for both export and import separately in order to observe their dynamisms under the tariff regime and makes comparisons between developing and developed countries. Using the World Development Indicators and World Integrated Trade Solution databases of World Bank (2020) on China (developing nation) and the United States (developed nation) over the period of 1970–2019, the co-integration tests and thereafter vector error correction models indicate that the relationship between tariff and manufacturing trade is positive and statistically significant.

The chapter empirically investigates the effects of tariff imposition on manufacturing trade comparatively in the north and south economies across the globe during the last three decades. Traditionally north and south represent the developed and developing world, respectively. Along with the volume and balance of trade, the study accounts for both export and import separately to observe their dynamisms under the tariff regime and makes comparisons between developing and developed groups of countries. Using World Development Indicators (2019) and World Integrated Trade Solutions (2019) databases on 77 developing and 48 developed nations for 1991–2018, the robust difference panel generalized method of moments estimates imply that impositions of domestic tariffs significantly reduce manufacturing trade in both groups of countries; however, developing countries experience this effect in a greater extent.

The knowledge economy (KE) which provide for an alternative to production-based economy and brick and mortar economy has a tremendous opportunity. KE has emerged due to the advent of skill concentration in nation states. However, the traditional production economy provides individual arbitrage opportunity which acts as a sideline for growth of the economy. In the modern economy, the higher the ability to create an edge for price for the knowledge, the greater will be the ability of the nation state to create and arbitrage process. Any economy which is driven by an innovative education system, appreciates, and adopts knowledge is the one which becomes successful in the knowledge process and a developed KE. Information technology forms a major component of knowledge process but is not the entire gamete of knowledge. Hence, it should not be confused that KEs are information industry driven alone. This paper tries to develop a model to check whether KE has the ability to support arbitrage process. Here the probability rate of growth in GDP is taken as the key element for the purpose of solving the theoretical proposition. The result shows that there are positive probabilities of the KE in providing arbitrage premium for individual which can fire the growth of the economy.

Reverse logistics (RL) is a type of supply chain management that moves goods from the end customer to the original manufacturer for reuse, remanufacturing and disposal purposes. Owing to growing environmental legislations and the development of new technologies in marketing, RL has attracted more significance among experts and academicians. Outsourcing RL practices to third-party reverse logistics provider (3PRLP) has been identified as one of the most important management strategies due to complexity of RL operations and the lack of available resource. Current sustainability trends have made 3PRLP assessment and selection process more complex. In order to select the 3PRLP, the existence of several aspects of sustainability motivates the experts to establish a new multi-criteria decision analysis (MCDA) approach.

With the growing complexity and high uncertainty of decision environments, the preference values of 3PRLPs are not always expressed with real numbers. As the generalized version of fuzzy set, intuitionistic fuzzy set and Fermatean fuzzy set, the theory of q-rung orthopair fuzzy set (q-ROFS) is used to permit decision experts (DEs) to their assessments in a larger space and to better cope with uncertain information. Given that the combined compromise solution (CoCoSo) is an innovative MCDA approach with higher degree of stability and reliability than several existing methods.

To exhibit the potentiality and applicability of the presented framework, a case study of S3PRLPs assessment is taken from q-rung orthopair fuzzy perspective. The assessment process consists of three sustainability aspects namely economic, environment and social dimensions related with a total of 14 criteria. Further, sensitivity and comparative analyses are made to display the solidity and strength of the presented approach. The results of this study approve that the presented methodology is more stable and efficient in comparison with other methods.

Thus, the objective of the study is to develop a hybrid decision-making methodology by combining CoCoSo method and discrimination measure with q-ROFS for selecting an appropriate sustainable 3PRLP (S3PRLP) candidate under uncertain environment. In the proposed method, a novel procedure is proposed to obtain the weights of DEs within q-ROFS context. To calculate the criteria weights, a new formula is presented based on discrimination measure, which provides more realistic weights. In this respect, a new discrimination measure is proposed for q-ROFSs.

The purpose of this paper is to study the effectiveness of multiple signals. Specifically, the paper investigates how the individual strength of a marketplace signal varies as a function of whether consumers are exposed to that signal alone or in combination with another signal.

The research uses experimental designs to empirically address the research questions. Hypotheses are formulated primarily based on signaling theory and these hypotheses are tested with laboratory experiments using real consumers.

The key finding is that a signal's stand‐alone credibility largely determines whether its individual strength would be diluted or augmented by the coexistence of another signal. Further, when signals with different stand‐alone strengths coexist, the individual strength of the weaker signal is higher than when that signal is present alone. These effects are observed in brick‐and‐mortar and online shopping media.

Past research reports mixed findings about whether the individual strength of a signal is diluted (dilution effect) or augmented (augmentation effect) by the presence of another signal. This research attempts to resolve this issue, for the first time, by demonstrating that whether dilution effect or augmentation effect occurs depends on the stand‐alone credibility of the individual signals in a mix.

The purpose of this paper is to design and develop precipitation hardened Al-Mg alloy imparting enhanced strength with acceptable ductility through minor addition of Sc and Cr by using multi-objective genetic algorithm-based searching. In earlier attempts of strengthening aluminum alloys, owing to the formation of Al₃Sc and Al₇Cr phase, addition of Sc and Cr have yielded attractive precipitation hardening, respectively. Both the Al-Sc and Al-Cr system are quench sensitive due to presence of a sloping solvus in their phase diagrams. It is also known that both the Al₃Sc and Al₇Cr phases nucleate directly from the supersaturated solid solution without formation of GP-zones or transient phases prior to the formation of the Al₃Sc and Al₇Cr. Sc also found to have beneficial effect on the corrosion property of such alloys. In view of the above, it is of interest to explore the possibility of enhancing the age hardening effect in Al-Mg alloy by addition of Sc and Cr.

The paper uses an approach where experimental information of two different alloy systems (namely, Al-Mg-Sc and Al-Cr) has been combined to generate a single database involving the potential features of both the systems with the aim to formulate the suitable artificial neural network (ANN) models for strength and ductility. The models are used as the objective functions for the optimization process. The patterns of the optimized Pareto front are analyzed to recognize the optimal property of the alloy system. The hitherto unexplored Al-Mg-Sc-Cr alloy, designed from the Pareto solutions and suitably modified on the basis of prior knowledge of the system, is then synthesized and characterized.

The paper has demonstrated the ANN- and genetic algorithm (GA)-based design of a hitherto unexplored alloy by utilizing the existing information concerning the component alloy systems. The paper also established that analyses of the Pareto solutions generated through multi-objective optimization using GA provide an insight of the variation of the parameters at different combination of strength and ductility. It also revealed that the Al-Mg-Sc-Cr alloy has exhibited a two-stage age hardening effect. The first and second stages are due to the precipitation of Al₃Sc and Al₇Cr phases, respectively.

In the present study the two alloy systems are used in tandem to develop models to describe the properties involving the distinct mechanistic features of phase evolution inherent in both the systems. Though the ANN models having the capability to capture huge non-linearity of a system have been employed to predict the convoluted effects of those characteristics when an alloy containing Mg, Sc and Cr are added simultaneously, but the ANN models predictions can be checked experimentally by the future researchers.

The paper demonstrates the role of scandium and chromium addition on the ageing characteristics of the alloy by analyzing the age hardening behavior of the designed alloy in cast and cold rolled condition clearly.

The approach stated in this paper is a novel one, in the sense that experimental data of two different alloy systems have been clubbed to generate a single database with the aim to formulate the suitable ANN models for strength and ductility.