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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.

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.

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.

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.

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

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The primary objective of this investigation is to optimize the constricted arc tungsten inert gas (CA-TIG) welding parameters specifically welding current (WC), arc constriction current (ACC), ACC frequency (ACCF) and CA traverse speed to maximize the tensile properties of thin Inconel 718 sheets (2 mm thick) using a statistical technique of response surface methodology and desirability function for gas turbine engine applications.

The four factor – five level central composite design (4 × 5 – CCD) matrix pertaining to the minimum number of experiments was chosen in this investigation for designing the experimental matrix. The techniques of numerical and graphical optimization were used to find the optimal conditions of CA-TIG welding parameters.

The thin sheets of Inconel 718 (2 mm thick) can be welded successfully using CA-TIG welding process without any defects. The joints welded using optimized conditions of CA-TIG welding parameters showed maximum of 99.20%, 94.45% and 73.5% of base metal tensile strength, yield strength and elongation.

The joints made using optimized CA-TIG welding parameters disclosed 99.20% joint efficiency which is comparatively 20%–30% superior than conventional TIG welding process and comparable to costly electron beam welding and laser beam welding processes. The parametric mathematical equations were designed to predict the tensile properties of Inconel 718 joints accurately with a confidence level of 95% and less than 4.5% error. The mathematical relationships were also developed to predict the tensile properties of joints from the grain size (secondary dendritic arm spacing-SDAS) of fusion zone microstructure.

The purpose of this paper is to provide benefits for companies or organizations, which deal with fewer input-outputs and wanted to control their industrial processes remotely with a robust control strategy.

In this paper, an active disturbance rejection control (ADRC) strategy is used for the two tank level process plant and it is remotely monitored with the industrial internet of things technology. The disturbances in a primary and secondary loop of the cascade process, which are affecting the overall settling time (ts) of the process, are eliminated by using the proposed, ADRC-ADRC structure in the cascade loop. The stability of the proposed controller is presented with Hurwitz’s stability criteria for selecting gains of observers. The results of the proposed controller are compared with the existing active disturbance rejection control-proportional (ADRC-P) and proportional-integral derivative-proportional (PID-P)-based controller by experimental validation.

It is observed that the settling time (ts) in the case of the proposed controller is improved by 60% and 55% in comparison to PID-P and ADRC-P, respectively. The level process is interfaced with an industrial controller and real-time data acquired in matrix laboratory (MATLAB), which acted as a remote monitoring platform for the cascade process.

The proposed controller is designed to provide robustness against disturbance and parameter uncertainty. This paper provides an alternate way for researchers who are using MATLAB and ThingSpeak cloud server as a tool for the implementation.

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.

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.

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.

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.

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.

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.

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.

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.