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Milling is a flexible creation process for the manufacturing of dies and aeronautical parts. While machining thin-walled parts, heat generation during machining essentially affects the accuracy. The workpiece temperature (WT), as well as the responses like material removal rate (MRR) and surface roughness (SR) for input parameters like cutting speed (CS), feed rate (F), depth-of-cut (DOC), step over (SO) and tool diameter (TD), becomes critical for sustaining the accuracy of the thin walls.

Response surface methodology was used to make 46 tests. To convert the multi-character problem into a single-character problem, the weightage was assessed using the entropy approach and the grey relational coefficient (GRC) was determined. To investigate the connection among input parameters and single-objective (GRC), a fuzzy mathematical modelling technique was used. The optimal performance of process parameters was estimated by grey relational entropy grade (GREG)-fuzzy and genetic algorithm (GA) optimization.

SR was found to be a significant process parameter, with CS, feed and DOC, respectively. Similarly, F, DOC and TD were found to be significant process parameters with MRR, respectively, and F, DOC, SO and TD were found to be significant process parameters with WT, respectively. GREG-fuzzy-GA found more suitable for minimizing the WT with the constraint s of SR and MRR and provide maximum desirability of 0.665. The projected and experimental values have a good agreement, with a standard error of 5.85%, and so the responses predicted by the suggested method are better optimized.

The GREG-fuzzy-GA is a new hybrid technique for analysing Inconel625 behaviour during machining in a 2.5D milling process.

The purpose of current study deals with the development and wear testing of jute and cotton fiber reinforced with nano fly ash-based epoxy composites. Performance of waste cotton fabric nano hybrid composites are compared with waste jute fabric nano hybrid composites.

Basic hand layup technique was used to develop composites. To optimize the parameters and design of experiments, Taguchi design was implemented to test wear rate and co-efficient of friction as per ASTM standards. Performance of waste cotton fabric nano hybrid composites is compared with waste jute fabric nano hybrid composites.

Result shows that nano fly ash lowers the wear rate and co-efficient of friction in developed composites. Findings reveals that hybrid composites of waste jute Fabric with 3 Wt.% of nano fly ash performed best amongst all composites developed. Morphology of nano composites worn out surfaces are also analyzed through SEM.

Practically, textile waste, i.e. jute, cotton and nano fly ash (thermal power plant) all wastes, is used to develop composites for multi-function application.

Wastes are reused and recycled to develop epoxy-based composites for sustainable structures in aviation.

To the best of the authors’ knowledge, nano fly ash and jute, cotton combination is used for the first time to develop and test for wear application.

The primary purpose of this study is to examine whether the classification of industries into the tradable and nontradable matters for the Balassa–Samuelson (BS) effect.

The study uses annual data for 38 countries from 1995 to 2014. To examine whether the classification of industries matter, the study proceeds with two approaches, that is, “traditional” and “benchmark”.

First, by applying panel cointegration tests of Pedroni and Westerlund, the results validate the BS hypothesis. However, the coefficients of long-run elasticities show appreciation of real exchange rate (RER) due to increase in productivity in the case of “traditional approach”, whereas depreciation of RER in the case of “benchmark approach”. Second, by applying the Dumitrescu-Hurlin panel Granger causality test, the results reveal the bi-directional causality among RER and productivity for both the approaches. Further, to provide more insights, the study employs a fixed-effects panel threshold model. The results indicate that increase in productivity leads to both appreciation and depreciation of RER depending on threshold regimes.

The study ascertains that the evidence of BS effect depends on the choice of approach considered. However, irrespective of the classification, there exists a BS effect beyond a threshold.

Although the BS effect is well established in the literature; there is no study examining the importance of classification of industries at a disaggregated level. Furthermore, there is no consideration of threshold effects.

Design-thinking (DT) in education has attracted significant interest from practitioners and academics, as it proffers new-age thinking to transform learning processes. This paper synthesises extant literature and identifies the current intellectual frontiers.

First, a systematic-literature-review was undertaken employing a robust process of selecting papers (from 1986 to 2022) by reading titles, abstracts and keywords based on a required criterion, backward–forward chaining and strict quality evaluations. Next, a bibliometric analysis was undertaken using VOSviewer. Finally, text analysis using RStudio was done to trace the implications of past work and future directions.

At first, we identify and explain 12 clusters through bibliometric coupling that include “interdisciplinary-area”, “futuristic-learning”, “design-process” and “design-education”, amongst others. We explain each of these clusters later in the text. Science, Technology, Engineering, Arts and Mathematics (STEAM), management education, design and change, teacher training, entrepreneurship education and technology, digital learning, gifted education and course development) Secondly, through co-word-analysis, we identify and explain four additional clusters that include “business education and pedagogy”, “content and learning environment”, “participants and outcome” and finally, “thinking-processes”. Based on this finding, we believe that the future holds a very positive presence sentiment for design thinking and education (DT&E) in changing the 21st century learning.

For investigating many contemporary challenges related to DT&E, like virtual reality experiential learning, sustainability education, organisational learning and management training, etc. have been outlined.

Academics may come up with new or improved courses for the implementation of DT in educational settings and policymakers may inculcate design labs in the curricula to fortify academic excellence. Managers who would employ DT in their training, development and policy design, amongst others, could end up gaining a competitive advantage in the marketplace.

This study conducted a comprehensive review of the field, which to our limited knowledge, no prior studies have been done so far. Besides, the study also outlines interesting research questions for future research.

The COVID-19 pandemic has contributed to making workers more uncompromising with respect to issues such as quality of workplace relations and work-life balance. Hence, motivation and leadership style assume a key relevance for keeping the workforce engaged. We hypothesize that individuals may exhibit different preferences for motivational drivers and for leadership style, and that these two sets of preferences might be correlated with each other and with employees’ personality traits.

Here, we empirically investigate the relationship between leadership style and motivation, by also hypothesizing the possible contribution of personality traits. An online survey was developed and distributed to 150 employees or interns/trainees to collect measures related to their preference for leadership, their motivational drivers, as well as their personality traits. The data were analyzed by means of mediation and moderation analyses to disentangle the three-level relationship existing between these constructs.

Our results suggest that indeed there exists a relationship between preferences for leadership style and motivational drivers. Furthermore, one of these relationships appears to be critically mediated by specific personality traits.

This work is the first, to our knowledge, empirically testing the existence of a three-level relationship between leadership preferences, motivation and personality traits of employees and to contribute to disentangle their reciprocal influences.