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Article
Publication date: 2 November 2020

Saumya Saumya and Tushar Singh

The paper reports the feedback collected from students of the Master of Social Work (MSW) Programme of the School of Social Work (SOSW), Indira Gandhi National Open…

Abstract

Purpose

The paper reports the feedback collected from students of the Master of Social Work (MSW) Programme of the School of Social Work (SOSW), Indira Gandhi National Open University (IGNOU), through open and distance learning (ODL), in India. The paper discusses findings related to learner profile, student support services, assignments, academic counselling, fieldwork, audio/video/teleconferencing facilities, Internet access and challenges faced by the learners. The findings will be useful for researchers and practitioners, will help in improving the overall quality of the programme, in designing the delivery mechanism as per the needs of MSW learners and in preparing them to be trained professionals to work in social development sector in India.

Design/methodology/approach

For data collection, a questionnaire was prepared and sent to all the students of the MSW programme along with assignment, across India. Responses from 290 students were voluntarily received.

Findings

The research findings are that MSW (ODL) students are older, mostly married with the average male learners age being 35 years and that of female learners being 30 years, there are more female learners than male learners, majority of the learners are Hindu from general category, tend to be employed, mostly full-time and some part-time, with work experience. They are from urban, semi-urban, rural and tribal areas with Internet access. Most of the students preferred to read printed self-learning materials than digitally available on eGyanKosh or IGNOUmobile app especially in rural areas though with increasing access to Internet, students are gradually opting for online materials while filling up the admission form. Majority of students found the quality and standard of study materials to be very good. Though maximum respondents gave positive feedback about the student support services and their learning experiences, some of the learners faced challenges like unco-operative staff members, administrative delays, non-allotment of academic counsellor/fieldwork supervisor, irregularity, late reception of study materials, lack of staff members at study centre, far distance of regional centre/study centre from residence, etc.

Research limitations/implications

The findings will help in designing and delivering the MSW programme in a more effective way. Based on the feedback received, the next revision of the programme will take into consideration the concerns of the learner. The limitation of the study is that not all learners responded to all the questions. Not all potential MSW learners filled the questionnaire and submitted it at the school. And those who responded had left some questions unanswered. Those who did not submit response may differ in their responses from what is received.

Originality/value

It is an original work and will be valuable in understanding the distance learner of MSW programme in India, programme delivery and challenges.

Details

Asian Association of Open Universities Journal, vol. 15 no. 3
Type: Research Article
ISSN: 1858-3431

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Article
Publication date: 18 May 2018

Manoj Kumar Mahawar, Kirti Jalgaonkar, Bhushan Bibwe, Tushar Kulkarni, Bharat Bhushan and Vijay Singh Meena

This paper aims to optimize the quantum of aonla pulp that could be mixed with guava pulp to make a nutritional rich fruit bar. The developed fruit bar will not only help…

Abstract

Purpose

This paper aims to optimize the quantum of aonla pulp that could be mixed with guava pulp to make a nutritional rich fruit bar. The developed fruit bar will not only help in the improvement of processing value of both Guava and underused but highly nutritional Aonla but also serve the purpose of improvement in nutritional status of consumers.

Design/methodology/approach

Response surface methodology (RSM) using Box–Behnken design was used with the process variables as aonla and guava pulp ratio, PR (30:70, 40:60, 50:50); pectin concentration, PC (0, 0.15, 0.30%); and drying temperature, DT (50, 60, 70°C) for optimization of process conditions. The prepared mixed fruit leather was evaluated for physico-chemical, textural and sensory properties such as titratable acidity (TA), ascorbic acid content (AA), L value (lightness), cutting force (CF), taste and overall acceptability (OAA).

Findings

Second-order regression models fitted for TA, AA, L value (lightness), CF, taste and OAA were highly significant (P = 0.01) with the coefficient of determination (R2 = 0.85). The TA and AA of mixed fruit bar increased whereas L value, CF, taste and OAA decreased with increasing level of aonla pulp in the blend formulation. The optimum process conditions for mixed aonla-guava bar with desirable characteristics were 40:60 (PR), 0.02% (PC) and 56°C (DT). The corresponding optimum values of TA, AA, L value, CF, taste and OAA were 1.00%, 164 mg/100 g, 50, 5066 g, 7.83 and 7.92, respectively. The design formulation and data analysis using RSM validated the optimum solution.

Originality/value

This paper demonstrates that optimum blending of aonla and guava pulp has improved the overall nutritional characteristics and acceptability of the final product. This will not only help in reducing the associated post-harvest losses but also encourage the cultivators/local processing industries by stabilizing the price during glut sea.

Details

Nutrition & Food Science, vol. 48 no. 4
Type: Research Article
ISSN: 0034-6659

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Article
Publication date: 14 June 2018

Shiv Nath Sinha and Tushar Chaudhari

The purpose of this paper is to examine the impact of CSR initiative of ITC Limited on the stakeholders especially impact on the learning outcome of primary school…

Abstract

Purpose

The purpose of this paper is to examine the impact of CSR initiative of ITC Limited on the stakeholders especially impact on the learning outcome of primary school students. The research further attempts to discover the level of impact of CSR on learning outcomes.

Design/methodology/approach

The data were collected from the rural areas of Pune in the state of Maharashtra and Mysuru in the state of Karnataka in India. The total number of data collected was 227. The data were collected with the help of self-administered questionnaires via personal visits to the schools using systematic random sampling method. Parametric test, t-test is used to test research hypothesis. Multiple linear regression analysis is performed to identify which aspects have better contribution towards overall impact level of the CSR program.

Findings

The study results clearly underscore the impact of firm’s CSR activities on the stakeholders. The study findings suggest a significant impact of CSR on the stakeholder, primarily on the learning outcome of the primary school students.

Practical implications

The study offers a new insight for the CSR heads of companies who are planning and implementing CSR initiatives of companies for widespread impact on the stakeholders. This study addresses the concerns of business managers and CSR heads to prove the potential of CSR initiatives and the measurement of the value generated for the society through CSR interventions.

Originality/value

The previously conducted research works have explored the impact of CSR on financial performance, organizational stability, employee turnover, customer retention, etc. This study advances existing body of knowledge beyond developed western economies by exploring the value of CSR in India and its impact on the stakeholders. This study finds the impact of CSR initiative on learning outcome. The study makes a novel contribution by not only determining the impact of CSR on learning outcome but also by going a step further to unfurl the various underlying factors which contribute towards the overall impact.

Details

Management of Environmental Quality: An International Journal, vol. 29 no. 6
Type: Research Article
ISSN: 1477-7835

Keywords

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Article
Publication date: 9 July 2018

Gunjan Yadav, Dinesh Seth and Tushar N. Desai

This paper aims to guide about prioritisation and ranking of the solutions and overcoming barriers to facilitate the adoption of Lean Six Sigma (LSS) by using a hybrid framework.

Abstract

Purpose

This paper aims to guide about prioritisation and ranking of the solutions and overcoming barriers to facilitate the adoption of Lean Six Sigma (LSS) by using a hybrid framework.

Design/methodology/approach

It identifies LSS barriers and solutions to facilitate LSS adoption through literature review and by involving subject experts. The study makes use of fuzzy set theory and proposes a fuzzy analytical hierarchy process (AHP)-modified TOPSIS (technique for order preference by similarity to ideal solution) framework. It uses sensitivity analysis to establish framework robustness.

Findings

The key findings of this techno-managerial study are identification and prioritisation of 27 LSS barriers and 22 solutions to overcome adoption challenges, proposition and usage of fuzzy AHP-modified TOPSIS framework, guidance regarding where to focus for facilitating LSS adoption and ensuring robustness using sensitivity analysis, which establishes insignificant deviation in rankings when criteria weights are altered.

Research limitations/implications

Some biasness and subjectivity may exist during pairwise comparisons as human judgements are involved.

Practical implications

Handling a hybrid solution like LSS is never easy. It is expected that the study will help industry professionals to plan their LSS adoption attempts effectively. Guidance regarding LSS barriers will assist in observing necessary precautions to avoid failures. It will open up new research fronts for researchers also.

Originality/value

Literature is full of studies regarding LSS barriers and its rankings. It is very rare to witness a study like ours, which discusses the barriers and links with solutions and its prioritisation. Proposed hybrid framework for a hybrid techno-managerial approach such as LSS is unique and acts as the roadmap for smooth implementation.

Details

International Journal of Lean Six Sigma, vol. 9 no. 3
Type: Research Article
ISSN: 2040-4166

Keywords

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Article
Publication date: 25 June 2020

Tushar Agrawal

The purpose of this paper is to examine the interrelation between two important dimensions of gender segregation: education and occupation. It further investigates the…

Abstract

Purpose

The purpose of this paper is to examine the interrelation between two important dimensions of gender segregation: education and occupation. It further investigates the gender wage gap.

Design/methodology/approach

The author uses a three-way additive decomposition of the mutual information index – an index based on the concept of entropy. A non-parametric wage decomposition method that uses matching comparisons is used for measuring the wage gap.

Findings

The results show that the extent of gender segregation in India is higher in urban areas than that in rural areas. Most of the observed segregation in rural labour markets originates from educational outcomes, whereas in urban markets it is due to occupational profile of individuals. The findings of the wage decomposition analysis suggest that education in rural areas also explains a sizeable part of the gender wage differential. Nevertheless, a large share of the wage gap remains unexplained in both rural and urban areas.

Originality/value

While much research has looked at occupational segregation, less attention has been paid to educational segregation. The paper uses a unique approach to understand the joint effect of occupation and education in explaining gender segregation.

Details

International Journal of Manpower, vol. 42 no. 1
Type: Research Article
ISSN: 0143-7720

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Article
Publication date: 24 August 2010

Tushar Jain, Srinivasan Alavandar, Singh Vivekkumar Radhamohan and M.J. Nigam

The purpose of this paper is to propose a novel algorithm which hybridizes the best features of three basic algorithms, i.e. genetic algorithm, bacterial foraging, and…

Abstract

Purpose

The purpose of this paper is to propose a novel algorithm which hybridizes the best features of three basic algorithms, i.e. genetic algorithm, bacterial foraging, and particle swarm optimization (PSO) as genetically bacterial swarm optimization (GBSO). The implementation of GBSO is illustrated by designing the fuzzy pre‐compensated PD (FPPD) control for two‐link rigid‐flexible manipulator.

Design/methodology/approach

The hybridization is carried out in two phases; first, the diversity in searching the optimal solution is increased using selection, crossover, and mutation operators. Second, the search direction vector is optimized using PSO to enhance the convergence rate of the fitness function in achieving the optimality. The FPPD controller design objective was to tune the PD controller constants, normalization, and denormalization factors for both the joints so that integral square error, overshoots, and undershoots are minimized.

Findings

The proposed algorithm is tested on a set of mathematical functions which are then compared with the basic algorithms. The results showed that the GBSO had a convergence rate better than the other algorithms, reaching to the optimal solution. Also, an approach of using fuzzy pre‐compensator in reducing the overshoots and undershoots for loading‐unloading and circular trajectories had been successfully achieved over simple PD controller. The results presented emphasize that a satisfactory tracking precision could be achieved using hybrid FPPD controller with GBSO.

Originality/value

Simulation results were reported and the proposed algorithm indeed has established superiority over the basic algorithms with respect to set of functions considered and it can easily be extended for other global optimization problems. The proposed FPPD controller tuning approach is interesting for the design of controllers for inherently unstable high‐order systems.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 3 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

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Case study
Publication date: 6 February 2018

Sudipendra Nath Roy and Tuhin Sengupta

Operations Management

Abstract

Subject area

Operations Management

Study level/applicability

MBA/Post Graduate

Case overview

This case attempts to highlight a very common resource allocation dilemma in a real-life scenario. The majority of today’s problems are solved by the methodology of trial and error. This case shows how a generic trial-and-error solution, if buttressed by a proper quantitative methodology, can have substantial impact on the bottom-line of an organization. The case concentrates on three disparate focus areas in a didactic fashion, namely, the ability to retrieve raw data and convert it into a utilizable form if a quantitative method is to be applied; the ability to comprehend the resource constraints of a typical real-life situation; and the skill required to develop and solve an optimization problem in Excel Solver, a product which can easily be accessed by any practitioner.

Expected learning outcomes

Expected learning outcomes are as follows: students learn to formulate a Mixed-Integer programming model; to interpret optimal solutions and appreciate the application of “Optimization”; to recommend a resource allocation strategy; and to understand the importance of cost minimization in organizations.

Supplementary materials

Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

Subject code

CSS: 9: Operations and Logistics

Details

Emerald Emerging Markets Case Studies, vol. 8 no. 1
Type: Case Study
ISSN: 2045-0621

Keywords

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Article
Publication date: 30 April 2021

Tushar Jain

The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are…

Abstract

Purpose

The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts.

Design/methodology/approach

Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. Object recognition is a type of pattern recognition. Object recognition is widely used in the manufacturing industry for the purpose of inspection. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. In this work, recognition of objects manufactured in mechanical industry is considered. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such mechanical part. Red, green and blue RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum.

Findings

One important finding is that there is not any considerable change in the network performances after 500 iterations. It has been found that for data smaller network structure, smaller learning rate and momentum are required. The relative sample size also has a considerable effect on the performance of the classifier. Further studies suggest that classification accuracy is achieved with the confusion matrix of the data used. Hence, with these results the proposed system can be used efficiently for more objects. Depending upon the manufacturing product and process used, the dimension verification and surface roughness may be integrated with proposed technique to develop a comprehensive vision system. The proposed technique is also highly suitable for web inspections, which do not require dimension and roughness measurement and where desired accuracy is to be achieved at a given speed. In general, most recognition problems provide identity of object with pose estimation. Therefore, the proposed recognition (pose estimation) approach may be integrated with inspection stage.

Originality/value

This paper considers the problem of recognizing and classifying the objects of such mechanical part. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. ANN is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum.

Details

International Journal of Intelligent Unmanned Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2049-6427

Keywords

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Article
Publication date: 8 October 2018

Tushar Jain, Meenu Gupta and H.K. Sardana

The field of machine vision, or computer vision, has been growing at fast pace. The growth in this field, unlike most established fields, has been both in breadth and…

Abstract

Purpose

The field of machine vision, or computer vision, has been growing at fast pace. The growth in this field, unlike most established fields, has been both in breadth and depth of concepts and techniques. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. The goal of a machine vision system is to create a model of the real world from images. Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. The purpose of this paper is to consider recognition of objects manufactured in mechanical industry. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such parts. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects.

Design/methodology/approach

The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts.

Findings

Classification accuracy is affected by the changing network architecture. ANN is computationally demanding and slow. A total of 20 hidden nodes network structure produced the best results at 500 iterations (90 percent accuracy based on overall accuracy and 87.50 percent based on κ coefficient). So, 20 hidden nodes are selected for further analysis. The learning rate is set to 0.1, and momentum term used is 0.2 that give the best results architectures. The confusion matrix also shows the accuracy of the classifier. Hence, with these results the proposed system can be used efficiently for more objects.

Originality/value

After calculating the variation of overall accuracy with different network architectures, the results of different configuration of the sample size of 50 testing images are taken. Table II shows the results of the confusion matrix obtained on these testing samples of objects.

Details

International Journal of Intelligent Unmanned Systems, vol. 6 no. 4
Type: Research Article
ISSN: 2049-6427

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Article
Publication date: 31 May 2021

Praveen Puram and Anand Gurumurthy

International Journal of Lean Six Sigma (IJLSS) has completed a decade. To celebrate the same, this study aims to review the articles published in IJLSS from its inception…

Abstract

Purpose

International Journal of Lean Six Sigma (IJLSS) has completed a decade. To celebrate the same, this study aims to review the articles published in IJLSS from its inception to the year 2020. The journal’s trends and professional impact over the years are investigated and potential future research directions are proposed.

Design/methodology/approach

A bibliometric analysis comprising of citation, co-citation and keyword co-occurrence methods is used on all the articles published in IJLSS till the year 2020. Content analysis is further done to analyse the type of research, type of industry studied and the articles’ target audience.

Findings

The journal has improved its reputation, productivity and impact over the years. Currently, studies published in IJLSS have been cited more than 5,000 times, with the most prominent themes being Six Sigma, Lean Six Sigma (LSS) and Lean in manufacturing and services. Researchers from India, the USA and the UK have contributed a significant number of publications. Most of the work published is case-based. There is a need for more empirical or survey-based research having high generalizability. Future studies should also focus on integrating LSS with emerging topics such as sustainability, Industry 4.0 and the like.

Research limitations/implications

The study provides evidence of the impact of IJLSS and highlights the trend in the domain of LSS. It can be of use for the editorial board members to identify potential areas to focus on in the future. Researchers can use it to further their research by working on the research gaps identified.

Originality/value

This paper is the first to trace the progress of IJLSS from its inception till the year 2020.

Details

International Journal of Lean Six Sigma, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2040-4166

Keywords

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