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Soybean has great nutritional potential. Its high protein content makes it an alternative protein source to milk in situations where milk cannot be used due to allergic reactions or intolerance. However, the potential benefits of soybean might be limited by the presence of antinutritional factors, including trypsin inhibitor activity (TIA). The purpose of the study is to evaluate the effect of dehulling and germination on the nutritive value of the soy flour and on the factors that could negatively affect the nutritional potential of the bean.

Soybean seeds were soaked for 24 h and allowed to germinate for one to three days. Soaked soybeans were manually dehulled and the flours obtained were evaluated for nutritional and antinutritional factors.

Dehulling and germination produce significant increase in crude protein and crude fiber and ash content (p = 0.05). Crude fat and starch content decreased, but the reduction was insignificant. Trypsin inhibitor levels were significantly lower after germination and dehulling of the seeds (p = 0.05).

Dehulling and germination are cost-effective processing techniques to improve the nutritional quality of the legume.

The purpose of this paper is to analyze the nutritional composition and the acceptability of value-added products prepared from the dehydrated leaf mixture of underutilized green leafy vegetables (GLVs). GLVs are dense in micronutrients and are of great importance to the nutrition of population in developing countries. Nutritive value of commonly consumed GLVs has been studied extensively, but there is limited information available on nutritive value and acceptability of unconventional leafy vegetables.

The nutritional potential and acceptability of leaf mixtures (LMs) prepared from the less-utilized leaves of beet root (Beta vulgaris), carrot (Daucus carota), cauliflower (Brassica oleracea) and turnip (Brassica rapa) which are usually discarded or are used as animal fodder were analyzed in the present study. The LM was prepared by mixing the powders of above-mentioned greens in a definite ratio (1:2:1:1). The LM was analyzed for the proximate, mineral composition (Ca, P, Fe, Cu, Zn, Mn and Mg) and antinutritional factors (oxalate and phenols). In total, 20 different recipes with different levels (0, 5, 10, 15 and 20 per cent) of LM incorporation were prepared and were assessed for quality on the basis of sensory attributes.

The LM contains appreciable amount of proteins, fat, fiber, carbohydrate and calorific value, mineral elements and generally low levels of antinutrients. Products were well-accepted to the level of 10 per cent. Protein, iron and calcium content was significantly (p < 0.05) higher in the LM-incorporated recipes, and the increase was directly proportional to the level of LM incorporated.

Dehydrated GLVs are concentrate source of micronutrients and can be used in product formulation. Value addition of traditional products with dehydrated GLVs can be advocated as a feasible food-based approach to combat micronutrient deficiencies.

This research article aims to prioritize the risks associated with the implementation of the project-based learning (PBL) concept in engineering institutions and develop possible strategies for risk management.

In this research article, various risks associated with the implementation of the PBL concept in engineering institutions are discovered by taking inputs from academicians and performing a literature survey of peer-reviewed journal articles. Then, identified risks are prioritized by using the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method. Finally, the risk mitigation strategies are developed.

From the literature survey, 11 risks associated with the implementation of the PBL concept are identified. The TOPSIS method resulted in group dynamics risk and faculty training risk as the top two risks in the implementation of the PBL concept, whereas anxiety risk and poor prior learning experience risk are relatively low-ranked risks.

The outcome of the research is based on the responses received through questionnaires. There are other methods also available for risk analysis, which are beyond this study.

The outcome of this research work will help the implementer of the PBL concept to effectively deal with the risks involved in implementing the PBL concept in engineering institutions by adopting strategies.

This research paper gives an idea about risks associated with the PBL implementation in engineering institutions. Also, this paper uses TOPSIS method for ranking of identified risks.

This paper presents the application of Differential Evolution (DE), an evolutionary computation technique for the optimal design of gas transmission network. As a gas transmission system includes source of gas, delivery sites with pipeline segments and compressors, the design of efficient and economical network involves lot of parameters. In addition, there are many equality and inequality constraints to be satisfied making the problem highly non‐linear. Hence an efficient strategy is needed in searching for the global optimum. In this study, DE has been successfully applied for optimal design of gas transmission network. The results obtained are compared with those of nonlinear programming technique and branch and bound algorithm. DE is able to find an optimal solution with a cost that is less than reported in the earlier literature. The proposed strategy takes less computational time to converge when compared to the existing techniques without compromising with the accuracy of the parameter estimates.

The purpose of this study is to investigate the influence of environmental concern, perceived value, personal norms and willingness to pay on generation Z’s purchase intention for recycled clothing.

The data were collected from five Indian universities. A total of 497 usable responses were analyzed. Confirmatory factor analysis was used for examining the validity and reliability of the scales. Further, the structural equation modeling was used to assess the relationship among the constructs.

Findings suggested that willingness to pay, environmental concern, perceived value and personal norms influence generation Z’s purchase intention for recycled clothing. Willingness to pay, environmental concern and perceived value were major predictors of purchase intention for recycled clothing.

This study holds much importance to the marketers of recycled clothing as it provides useful insights for formulating the appropriate promotional strategies. The study also contributes to the consumer behavior literature by addressing the existing research gap.

Most of the studies existing in this area have focused on the manufacturing side only except few which explored the consumption side of recycled clothing. Hence, the current study is an attempt to fill this research gap.

A zero-day vulnerability is a complimentary ticket to the attackers for gaining entry into the network. Thus, there is necessity to device appropriate threat detection systems and establish an innovative and safe solution that prevents unauthorised intrusions for defending various components of cybersecurity. We present a survey of recent Intrusion Detection Systems (IDS) in detecting zero-day vulnerabilities based on the following dimensions: types of cyber-attacks, datasets used and kinds of network detection systems.

Purpose: The study focuses on presenting an exhaustive review on the effectiveness of the recent IDS with respect to zero-day vulnerabilities.

Methodology: Systematic exploration was done at the IEEE, Elsevier, Springer, RAID, ESCORICS, Google Scholar, and other relevant platforms of studies published in English between 2015 and 2021 using keywords and combinations of relevant terms.

Findings: It is possible to train IDS for zero-day attacks. The existing IDS have strengths that make them capable of effective detection against zero-day attacks. However, they display certain limitations that reduce their credibility. Novel strategies like deep learning, machine learning, fuzzing technique, runtime verification technique, and Hidden Markov Models can be used to design IDS to detect malicious traffic.

Implication: This paper explored and highlighted the advantages and limitations of existing IDS enabling the selection of best possible IDS to protect the system. Moreover, the comparison between signature-based and anomaly-based IDS exemplifies that one viable approach to accurately detect the zero-day vulnerabilities would be the integration of hybrid mechanism.

The current study aims to investigate the various consumption motives (hedonic, gain and normative) responsible for strengthening consumers' intentions toward purchase behavior for electric vehicle (EV).

A total of 411 valid survey responses were collected using a structured questionnaire. Data were analyzed using confirmatory factor analysis and structural equation modeling to investigate the empirical fit of the hypothesized framework.

The results of structural equation modeling revealed that all three motives were positively correlated with purchase intentions for EV. Hedonic motives were found to have the strongest influence on purchase intentions. In addition, gain and normative motives were also found to be significant predictors of EV buying behavior. Further analysis revealed a positive correlation between gain, normative and hedonic motives. Moreover, personal moral standards seem to have a significant and positive impact on the positive emotions associated with buying EV.

The results of current research can be useful for marketers while designing promotional strategies for all the high-involvement green products. Marketing professionals and policymakers can use these results to build effective marketing strategies for EVs and reduce greenhouse gas emissions resulting from personal vehicle use.

To the best of the authors' knowledge, this is the first study in the South Asian region that explores consumers' motives for EV purchase behavior. Further, this is among a few studies, which have attempted to investigate the impact of hedonic, gain and normative motives on green purchase behavior in the context of high involvement green products.

The purpose of this paper is to carry out numerical modeling of single-blow transient analysis using FLUENT porous media model for estimation of heat transfer and pressure drop characteristics of offset and wavy fins.

A computational fluid dynamics program FLUENT has been used to predict the design data in terms of j and f factors for plate-fin heat exchanger wavy and offset strip fins, which are widely used in aerospace applications.

The suitable design data in terms of Colburn j and Fanning friction f factors is generated and presented correlations for wavy fins covering the laminar, transition and turbulent flow regimes.

The correlations for the friction factor f and Colburn factor j have been found to be good by comparing with other references. The correlations can be used by the heat exchanger designers and can reduce the number of tests and modification of the prototype to a minimum for similar applications and types of fins.

Introduction: In a world characterised by volatility, uncertainty, complexity, and ambiguity, change is the only constant. Over the years, human resource management (HRM) has evolved from conventional functions of hiring and firing to being a strategic partner in organisations. Similarly, there has been a paradigm shift in the landscape of artificial intelligence (AI) from being a mere searching tool to the design and development of intelligent robots. Over the years, AI has emerged into a collection of powerful technologies re-inventing different functional areas, including HRM. The application of AI in HRM is perceived as an optimistic opportunity since it ought to bring maximum value at minimum cost. AI focuses on building tools that exhibit human-level intelligence and discernment in making decisions.

Purpose: The purpose of this chapter is to draw deeper insights into the relevance of AI in different functional areas of HRM. Integrating AI into HRM functions such as talent acquisition, training and development, performance management, employee engagement, and the like can help leverage efficiency and create an engaging employee experience. In the wake of Industry 4.0, where digitalisation has become imperative, this chapter explores the integration of AI into specific HR functions for a synergistic competitive advantage in companies. The purpose of this chapter is to signify the integration of AI into four vital functions of HRM, namely talent acquisition, training and development, performance management, and employee engagement. The objective is to chart how companies integrate various AI tools in four specific HRM functions to enhance efficiency. Also, the companies willing to implement AI in their HR functions can refer to the case studies used as exemplars in the chapter.

Methodology: This conceptual chapter is based on the secondary sources, which also build upon case studies of different companies that have implemented AI-enabled solutions and integrated them into different HRM functions and processes per needs. This chapter utilises the conceptual framework of both AI and HRM functions to give deeper insight into the challenges and implementation of technology-enabled solutions.

Findings: AI is used in HRM functions to automate repetitive and operational tasks to shift the focus to more strategic aspects. Despite many advantages of AI and machine learning, very few companies are using it, and companies may integrate technology-enabled solutions based on the size and nature of business.

In aerospace applications, due to the severe limitations on the weight and space envelope, it is mandatory to use high performance compact heat exchangers (CHEs) for enhancing the heat transfer rate. The most popularly used ones in CHEs are the plain fins, offset strip fins (OSFs), louvered fins and wavy fins. Amongst these fin types, wavy and offset fins assume a lot of importance due to their enhanced thermo‐hydraulic performance. The purpose of this paper is to investigate the influence of geometrical fin parameters, in addition to Reynolds number, on the thermo‐hydraulic performance of OSFs.

A computational fluid dynamics approach is used to conduct a number of numerical experiments for determination of thermo‐hydraulic performance of OSFs considering the various geometrical parameters, which are generally used in the aerospace industry. These investigations include the study of flow pattern for laminar, transition and turbulent regions. Studies are conducted with different fin geometries and comparisons are made with available data in open literature. Finally, the generalized correlations are developed for OSFs taking all geometrical parameters into account for the entire range of operations of the aerospace industry covering laminar, transition and turbulent regions. In addition, the effects of various geometrical parameters are presented as parametric studies.

Thermo‐hydraulic design of CHEs is strongly dependent upon the predicted/measured dimensionless performance (Colburn factor “j” and Fanning friction “f” vs Reynolds number Re) of heat transfer surfaces. Several types of OSFs used in the compact plate‐fin heat exchangers are analyzed numerically.

The present numerical analysis is carried out for “air” media and hence these results may not be accurate for other fluids with large variations of Prandtl numbers.

In open literature, these fins are generally evaluated as a function of Reynolds number experimentally, which are expensive. However, their performance will also depend to some extent on geometrical parameters such as fin thickness, fin spacing, offset fin length and fin height.

This numerical estimation can reduce the number of tests/experiments to a minimum for similar applications.