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The purpose of this study to identify and optimize the machining of polyvinyl butyral (PVB) material for industrial uses. The research is based on input machining parameters of rotary ultrasonic machining for better understand the output response surface roughness (SR) property of polyvinyl butyral (PVB) by using the Taguchi approach. The grey relational grade analysis (GRG) is also implemented to resolve the complex interrelationship of SR data for optimization and predicting and validate the results.

In experimental work, the input parameters, namely, concentration, abrasives, power rate, grit size, tool material and hydrofluoric (HF) acid has been selected. The experiment’s design was created using MINITAB Software; the L27 orthogonal array was selected for the experimentation. SR was examined with the GRG technique for process optimization. On the other hand, for single parameter optimization analysis of variance (ANOVA) has been used.

ANOVA optimization technique gives the best result on concentration (40%) of abrasive (Al2O3+SiC+B4C), power rate (40%), grit size (600), HF acid (1.5%) and tool material (D2 alloy) are the optimal parameters to provide the slightest degree of SR. GRG optimization of multi-response parameter setting: 40% concentration, SiC+B4C mixed abrasive slurry, 40% of power rating, 280 grit size, 0.5% HF acid and high-speed tool steel tool material gives better results. The SR of PVB glass material improved by 20% after grey relational analysis.

There are several practical applications in a variety of material processing sectors, including metallurgy, machinery, electronics and transportation. These real-world applications have produced substantial and discernible economic benefits.

The analytical and optimization results will be used in the various material processing sectors, including metallurgy, machinery, electronics and transportation.

The ANOVA and grey theory approaches offer the reader a primary picture of the machining research and process parameter optimization. Combined abrasive slurry of Al₂O3+SiC+B₄C with a high power-rating exhibits lower SR. Similarly, grit size is vital; larger grits produce better SR. Ra – 0. 611 m is the lowest SR value at the hole found in trial 25 after the experimentation.

The purpose of this paper is to assess the bioactive ingredients of whole flours of oat, maize and soyabean, utilise them for developing functional cookies and evaluate their physical and nutritional quality parameters.

Cookies were prepared from blends of whole flours of wheat, oats, maize and full fat soyabean. Whole wheat and oat flours were blend in different proportions (100:0, 80:20, 60:40, 40:60 and 20:80 and 0:100, wheat:oat, w/w) and supplemented with quality protein maize (QPM) and full fat soyabean flours, at 10 per cent (w/w) each, of the total blend. Cookies were studied for their physical attributes, proximate and nutritional compositions. A significant (p=0.05) increase in spread ratio, crude protein, crude fat and crude fibre contents of the cookies was observed with increase in the proportion of oat flour in the blend.

Cookies prepared from blend containing QPM and soyabean flours had higher antioxidant activity than control whole wheat cookies. An increase in extractable β-glucan content and in-vitro protein digestibility was also observed with the increase in the proportion of oat flour. The highest overall acceptability was observed for cookies prepared from blends having a wheat–oat proportion of 60:40. These cookies also had a 17.5 per cent higher genistein content as compared to their raw flour blend. Higher genistein levels were observed in the composite cookies.

The cookies prepared from composite whole flours rich in isoflavones and β-glucan reflect their potential as a new functional food for the prevention and management of diseases such as cancers, diabetes and cardiovascular disorders.

This research work aims to make an effort to investigate the effect of fused deposition modelling (FDM) process parameters on the surface finish of acrylonitrile butadiene styrene (ABS) replicas (as pre-processing stage), followed by chemical vapor smoothing (CVS) process (as a post-processing stage) as a case study.

The Taguchi L18 orthogonal array has been used for optimizing process parameters of FDM and CVS processes.

This study highlights that orientation and part density, and the interaction between these two have a significant effect on the surface finish at the pre-processing stage of FDM. However, after post-processing with CVS, there is hardly any influence of pre-processing FDM parameters.

The study highlights that for improving the productivity of the FDM process, the parametric optimization of process may be made on the basis of production cost and time in place of surface finish of ABS replicas. The results obtained have been verified by performing the confirmation experiments.

The thermoplastic polymers do not decompose easily due to the presence of long-chain stable polymeric structure, and thus, causes serious effects on the environment. Recycling of these polymer wastes becomes the only solution to minimize their adverse effects on the environment. The purpose of this study was to explore the feasibility of using recycled thermoplastic material as filament for fused deposition modeling technique.

In this study, the researchers fabricated fused filaments (in-house) for fused deposition modeling (FDM) technique of additive manufacturing from secondary recycled acrylonitrile butadiene styrene (ABS) by using a twin-screw extruder. After measuring the melt flow index of the secondary recycled ABS, the twin-screw extrusion parameters (rpm/speed of the screw, extrusion temperature and load) were varied to predict their influence on the various properties (rheological/mechanical/thermal) of the fabricated filaments. Experimental work was executed as per Taguchi’s L9 orthogonal array.

Thermal analysis performed to estimate the heat carrying capacity of recycled ABS highlighted that the heat capacity of ABS increases significantly from 0.28 J/g to 3.94 J/g during the heating cycle. The maximum value of peak strength and percentage break elongation for the fused filaments was investigated at 12.5 kg load, 2,250 C extrusion temperature and 70 rpm speed.

The filaments fabricated by recycling the polymeric waste has been successfully used in the FDM machine for the preparation of the three-dimensional printed tensile specimen.

The purpose of this paper is to evaluate the relative impact of dynamic capabilities on various dimensions of strategic flexibility in Indian manufacturing industry.

In this study, 102 manufacturing organizations have been extensively surveyed, to assess the relative impact of different dynamic capabilities on various dimensions of strategic flexibility. The correlations between dynamic capabilities and strategic flexibility have been evaluated and validated by employing various statistical tools.

The research focuses upon the significant contributions of dynamic capabilities such as human resource capabilities, innovative capabilities, technological capabilities, alliance capabilities and research and development capabilities, towards managing flexibility at strategic level in manufacturing organizations.

This study provides the first empirical evidence of such a relationship with a relative choice between dynamic capabilities for managing strategic flexibility in large and medium scale organizations in India.

The study aims to study the extent of research done in luxury marketing in an emerging economy like India by conducting a bibliometric analysis. A period of 21 years has been considered to present a comprehensive picture for results and analysis. Key findings indicate the gaps and scope of further research for academics in India and abroad. The findings indicate a dearth of research by scholars and academicians in luxury, counterfeit and masstige, especially when there is a surge of the upper middle class in India. More specifically, Indian-grown luxury brands also present a massive scope for future research.

Sustainable consumption is an important topic for different industries, including the fashion industry. Despite a favourable attitude of consumers towards sustainable products in the fashion industry, the actual purchase by the consumers is limited. Thus, the present study examines sustainable consumption using the theory of planned behaviour (TPB). The purpose of this paper is to study the mediating impact of strategies of justification of unethical behaviour on the gap-based relationship between a purchase intention and a purchase decision for a consumer in a sustainable clothing context.

For the study, the primary data from 229 graduate-level fashion students enrolled in universities across India has been analyzed with the help of structural equation modelling.

The study results have proven that attitudes and subjective norms can positively affect purchase intentions when it comes to purchase of the environmentally sustainable products. Further, economic rationality (ER) and government dependency (GD) partially mediate the purchase intention–behaviour gap of the justification strategies for unethical behaviour.

The results would be helpful in implementing sustainable clothing consumption among Indian consumers. The study would be beneficial for industry professionals, export houses and scholars to discover possible reasons which can lead to the widening of the intention–behaviour gap when it comes to the purchase of the sustainable clothing consumption for Indian consumers. Critical implications for marketers from the present research assert that ER and GD are important factors that could increase the purchase intention of young consumers towards sustainable clothing.

The results of the study contribute to the existing literature in a novel way by adding justification strategies for unethical behaviour to the TPB model. This study is innovative as it adds new constructs to the TPB model by including the three justification strategies that people use for unethical consumption behaviour (ER, economic development and GD) to gain insight into why a purchase intention–behaviour gap exists for sustainable clothing.

The purpose of this paper is to highlight the health benefits, functional role of alpha linolenic acid (ALA) in combating diet-related non-communicable diseases, absorption, metabolism, toxicity and extraction of ALA.

A wide range of publications were identified through electronic databases (Google Scholar, PubMed, SciELO, ScienceDirect, SpringerLink and ResearchGate) on the basis of different keywords such as dietary sources, functional role, metabolism, beneficial effects, toxicity and extraction.

ALA, an omega-3 fatty acid, is commonly found in dietary sources such as flaxseeds, rapeseed, pumpkin seeds, walnuts and fatty fish, with flaxseed being the richest among all the sources. Several convincing evidences depict the functional role of ALA in ameliorating cardiovascular functions, regulating systolic and diastolic blood pressure and improving neurological disorders, thus possessing anti-arrhythmic as well as anti-inflammatory properties. Its promising effect is also noticed on post-menopausal symptoms like hot flashes, insomnia and headache as well as in balancing the levels of sex hormones in women suffering from PCOS. Furthermore, it was also responsible for reducing the concentrations of C-reactive protein and interleukin-6. Its excess consumption may lead to gastrointestinal disturbances such as flatulence and bloating. Various extraction techniques, namely, medium pressure liquid chromatography, silver silica gel chromatography, silver ion-solid phase extraction and silver ion-high pressure liquid chromatography are used for the extraction of ALA from its sources.

This is a novel review which will provide a brief overview of dietary sources, functional role, absorption and metabolism, health benefits, toxicity and extraction of ALA.

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.