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The purpose of the study was to utilize rohu deboning by-product to develop edible cutlets and to study its effects on quality and acceptability.

Fish cutlets were prepared with varying mince and by-product ratios (100:0, 75:25, 50:50, 25:75 and 0:100 per cent) and the effect of by-product inclusion on composition, cooking characteristics, texture and acceptability were studied.

Cutlets from control group had highest values for protein, fat, diameter reduction, fat retention and height increase (29.9, 14.9, 7.6, 78.3 and 31.9 per cent) compared to other lots. Highest values for cooking yield (p > 0.01), lightness and whiteness were (92.7, 35.4 and 31.2 per cent) found in cutlets from 100 per cent BP lot. Hardness and shear force increased with by-product content (p < 0.01) being maximum in cutlets from 100 per cent BP lot. Though all the cutlets were acceptable, low sensory scores were recorded for 75 BP and 100 per cent BP lots (p > 0.01). By-products can be incorporated up to 50 per cent into fish cutlets without negatively affecting the eating quality.

Amount of by-product to be added was the limitation during the work. More by-product content in the cutlets resulted in low-palatability. Total calcium content in the cutlets need to be checked as calcium content per serving should not be above the regulations.

In general, nutritionally rich fish deboning by-product is used for cattle feeding or discarded into dumping grounds causing pollution, nutritional, as well as economic loss to the farmers. The findings of this study will be helpful to develop nutritionally rich and edible food products from this deboning by-product. Incorporation of by-product into cutlets will allow the farmers to sell their products at lower prices without compromising the profits, which will improve the marketing as well as consumption.

Utilization of fish deboning by-product to develop edible cutlets helps in reducing the operational costs, pollution and efficient utilization of process by-products.

The work is completely original in nature. The results reported are unique and the outcome of the research has social applicability.

This study aims to develop fiber-enriched cutlets from minced meat of rohu and study the effects of fiber inclusion on composition, cooking properties and acceptability of cutlets.

Cutlet mix prepared using rohu mince was divided into four lots, out of which three lots were enriched with fibers from ragi (RFC), jowar (JFC) and oat (OFC) at 10 per cent over and above its quantity. Prepared cutlets were analyzed for nutritional composition, texture, cooking properties and sensorial quality.

Moisture, protein, fat, fiber and ash contents (per cent) of the fiber-enriched cutlets ranged between 40.4-42.0, 24.5-25.5, 13.2-14.7, 1.8-2.0 and 2.4-2.5 per cent, respectively. The addition of fibers increased the cooking yield of cutlets from 84.6 to 87.5 per cent. RFC lot cutlets had the lowest whiteness values and the highest hardness and shear force values. Cutlets from all the lots had an overall acceptability scores more than 7.0, indicating the sensorial acceptability. Although the differences were insignificant, cutlets from the RFC lot were less acceptable, which might be because of the dark color and tough texture. Hence, jowar and oat flour at 10 per cent can be used to enrich rohu cutlets without affecting their sensorial acceptability.

Sources of fiber will be the major limitation in the work, as the fiber inclusion may negatively affect the quality and acceptability of cutlets. Time of cooking will also influence the final product characteristics, which need to be standardized.

Fish is a rich source of protein but lacks fiber, which is essential for body metabolism. Grains are rich source of fibers but lack some essential nutrients required by the body. In India, many people are dependent on fish for protein due to its high nutritious value. So enriching fish meat with fiber sources will address the health-related problems associated with low-fiber diets. The methodology developed in this work can be used to fulfill the demand for balanced and nutritious diet. Because of increasing health awareness, fish products with added fiber will also increase its market potential.

The methodology developed can be used by small-scale entrepreneurs to earn more income by developing functional fish products with low-cost ingredients. The developed products will not only address the issues related to the consumption of low-fiber diets but also create a market for fish products because of their health-benefiting effects.

The work is completely original in nature. The results reported are unique and the outcome of the research has social applicability.

The purpose of this book is to open a conversation on the idea of information experience, which we understand to be a complex, multidimensional engagement with information. In developing the book we invited colleagues to propose a chapter on any aspect of information experience, for example conceptual, methodological or empirical. We invited them to express their interpretation of information experience, to contribute to the development of this concept. The book has thus become a vehicle for interested researchers and practitioners to explore their thinking around information experience, including relationships between information experience, learning experience, user experience and similar constructs. It represents a collective awareness of information experience in contemporary research and practice. Through this sharing of multiple perspectives, our insights into possible ways of interpreting information experience, and its relationship to other concepts in information research and practice, is enhanced. In this chapter, we introduce the idea of information experience. We also outline the book and its chapters, and bring together some emerging alternative views and approaches to this important idea.

This chapter presents insights about prospective students’ information experience when using social media to support their decision-making concerning which university to attend. When choosing a university, prospective students experience different ways of using information, engaging with a variety of sources, which have changed rapidly from traditional print and mass media, exhibitions and road shows, to the Internet and university websites. Increasingly, prospective students use information via diverse social media platforms where they can engage, participate and collaborate as information users on the social web. As a result, their information experience is expanding beyond information seeking to engagement with social media and participation in a dynamic online community.

Drawing on a literature review and my own research, I demonstrate that prospective students’ information experience involves collaboration, engagement and communities via social media. I present findings that contrast prospective students’ dynamic and wider multidimensional information experience of the social web, with static and unidimensional information seeking of traditional sources. In particular, I demonstrate that prospective students can now ‘experience’ the university and seek peer advice by collaborating in online communities. In this way, they gain tangible as well as intangible university information.

Some functional properties of engineering materials, i.e. physical, mechanical and thermal ones, depend directly on the microstructure, which is a result of processes occurring in the material during the forming and thermomechanical processing. The proper microstructure can be obtained in many cases by the phase transformation. This phenomenon is one of the most important processes during hot forming and heat treatment. The purpose of this paper is to develop a new comprehensive hybrid model for modeling diffusion phase transformations. A problem has been divided into several tasks and is carried out on several stages. The purpose of this stage is a development of the structure of a hybrid model, development of an algorithm used in the diffusion module and one-dimensional heat flow and diffusion modeling. Generally, the processes of phase transformations are studied well enough but there are not many tools for their complex simulations. The problems of phase transformation simulation are related to the proper consideration of diffusion, movement of phase boundaries and kinetics of transformation. The proposed new model at the final stage of development will take into account the varying grain growth rate, different shape of growing grains and will allow for proper modeling of heat flow and carbon diffusion during the transformation in many processes, where heating, annealing and cooling can be considered (e.g. homogenizing and normalizing).

One of the most suitable methods for modeling of microstructure evolution during the phase transformation is cellular automata (CA), while lattice Boltzmann method (LBM) suits for modeling of diffusion and heat flow. Then, the proposed new hybrid model is based on CA and LBM methods and uses high performing parallel computations.

The first simulation results obtained for one-dimensional modeling confirm the correctness of interaction between LBM and CA in common numerical solution and the possibility of using these methods for modeling of phase transformations. The advantages of the LBM method can be used for the simulation of heat flow and diffusion during the transformation taking into account the results obtained from the simulations. LBM creates completely new possibilities for modeling of phase transformations in combination with CA.

The studies are focused on diffusion phase transformations in solid state in condition of low cooling rate (e.g. transformation of austenite into ferrite and pearlite) and during the heating and annealing (e.g. transformation of the ferrite-pearlite structure into austenite, the alignment of carbon concentration in austenite and growth of austenite grains) in carbon steels within a wide range of carbon content. The paper presents the comprehensive modeling system, which can operate with the technological processes with phase transformation during heating, annealing or cooling.

A brief review of the modeling of phase transformations and a description of the structure of a new CA and LBM hybrid model and its modules are presented in the paper. In the first stage of model implementation, the one-dimensional LBM model of diffusion and heat flow was developed. The examples of simulation results for several variants of modeling with different boundary conditions are shown.

The purpose of this paper is to analyze and build a deep learning model that can furnish statistics of COVID-19 and is able to forecast pandemic outbreak using Kaggle open research COVID-19 data set. As COVID-19 has an up-to-date data collection from the government, deep learning techniques can be used to predict future outbreak of coronavirus. The existing long short-term memory (LSTM) model is fine-tuned to forecast the outbreak of COVID-19 with better accuracy, and an empirical data exploration with advanced picturing has been made to comprehend the outbreak of coronavirus.

This research work presents a fine-tuned LSTM deep learning model using three hidden layers, 200 LSTM unit cells, one activation function ReLu, Adam optimizer, loss function is mean square error, the number of epochs 200 and finally one dense layer to predict one value each time.

LSTM is found to be more effective in forecasting future predictions. Hence, fine-tuned LSTM model predicts accurate results when applied to COVID-19 data set.

The fine-tuned LSTM model is developed and tested for the first time on COVID-19 data set to forecast outbreak of pandemic according to the authors’ knowledge.