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The purpose of this study is to assess the effect of chicken breeds on quality characteristics of meat nuggets.

The formulation of meat nuggets prepared from meat of Cobb-400, Vanraja, Aseel and Kadaknath separately consisted of 60 per cent lean meat. The emulsion was prepared by standard method and moulded into nuggets. Cooking was performed under pressure (120°C/15 Psi for 30 min).

Emulsion and cooked nuggets both showed no significant differences in pH values among the breeds. Higher moisture and fat content was observed in emulsion and nuggets prepared from Cobb-400, while respective protein and ash was maximum in Kadaknath and Vanraja meat-based emulsions and nuggets. The per cent emulsion stability (87.04 ± 0.45) and cooking yield (85.24 ± 0.06) was reported highest in Cobb-400, which indicates the better water holding capacity and suitability of Cobb-400 meat for the development of nuggets at six weeks of age. The mean sensory scores for colour and appearance (7.12 ± 0.28), as well as flavour (7.00 ± 0.04), were significantly (p < 0.05) higher in Cobb-400 nuggets and lowest in Kadaknath (6.21 ± 0.03 and 6.65 ± 0.06). However, no significant differences were noticed in other sensory attributes among treatments.

The fatty acid and amino acid profile analysis may be helpful to understand the original nutritional difference in prepared nuggets.

The study will be off immense help in optimum utilization of meat of locally available chicken breeds for breed-specific and cost-effective product formulations.

The products will be acceptable to all commodities because it is made up of chicken meat.

The effect of chicken breeds on meat nuggets is relatively new aspect and essential to establish suitability of meat of locally available chicken breeds for product development.

The purpose of present study was to evaluate the quality characteristics of cow?s milk in the holy city Mathura, which is famous for it?s gau dhan and Lord Krishna.

The milk samples were collected from dairy shops, vendors and milk producers and evaluated on the basis of various organoleptic tests, physico-chemical properties, proximate estimation and microbiological studies following the standard procedures.

The milk samples of Township and Chungi areas had more clear appearance and normal texture/consistency than other three areas. No cow milk sample was observed with pure white colour; however, 74 per cent of the samples had normal light yellow colour. No milk sample had rancid/oxidized odour; however, few milk samples contained weedy or absorbed odour. Watery consistency was observed in 50 per cent of the samples, whereas thick, ropy or slimy consistency was observed in 4, 4 and 20 per cent of the samples, respectively. The temperature, pH and specific gravity of milk collected from different regions were lower, but titratable acidity was higher than normal prescribed range (<0.14 per cent). The moisture content of all the samples was higher; however, other proximate parameters showed quite variable values than normal values of cow milk. Out of the total, 28 per cent of the samples of cow milk were positive for formalin. The microbial load was higher than normal prescribed limit.

Food safety and food security are very much at the top of the agenda in India, so it is of utmost importance to screen the quality of milk and milk products in the market for avoidance of skimming practices and/or adulteration of milk with water and human health problems.

This paper aims to conceptualize ahimsa at the workplace as an alternate coping response to negative workplace behaviours. The response strategy aims to impede conflict escalation and transform a hostile situation into a collaborative one.

The conceptualization of the indigenous construct bases upon Bhawuk's methodological suggestion on building psychological models from the scriptures (Bhawuk, 2010, 2017, 2019). The construct ahimsa explicates by synthesizing the micro-world (Bhagawad Gita, BG and Patanjali Yoga Sutras, PYS) and through the lifeworld of Gandhiji.

The conceptual analysis illustrates the efficacy of ahimsa as an alternate response to negative workplace behaviours. The definition delineates its three core characteristics, i.e. conscious non-violent action, self-empowerment and rehumanizing the perpetrator. Besides, it proposes to enhance metacognition, creativity and individual learning at the workplace.

The conceptual paper gives a new direction to management researchers on coping and responding to stress.

This research attempts to present a solid transportation problem (STP) mechanism in uncertain and indeterminate contexts, allowing decision makers to select their acceptance, indeterminacy and untruth levels.

Due to the lack of reliable information, changeable economic circumstances, uncontrolled factors and especially variable conditions of available resources to adapt to the real situations, the authors are faced with a kind of uncertainty and indeterminacy in constraints and the nature of the parameters of STP. Therefore, an approach based on neutrosophic logic is offered to make it more applicable to real-world circumstances. In this study, the triangular neutrosophic numbers (TNNs) have been utilized to represent demand, transportation capacity, accessibility and cost. Then, the neutrosophic STP was converted into an interval programming problem with the help of the variation degree concept. Then, two simple linear programming models were extracted to obtain the lower and upper bounds of the optimal solution.

The results reveal that the new model is not complicated but more flexible and more relevant to real-world issues. In addition, it is evident that the suggested algorithm is effective and allows decision makers to specify their acceptance, indeterminacy and falsehood thresholds.

Under the transportation literature, there are several solutions for TP and STP in crisp, fuzzy set (FS) and intuitionistic fuzzy set (IFS) conditions. However, the STP has never been explored in connection with neutrosophic sets to the best of the authors’ knowledge. So, this work tries to fill this gap by coming up with a new way to solve this model using NSs.

The K-means (KM) clustering algorithm is extremely responsive to the selection of initial centroids since the initial centroid of clusters determines computational effectiveness, efficiency and local optima issues. Numerous initialization strategies are to overcome these problems through the random and deterministic selection of initial centroids. The random initialization strategy suffers from local optimization issues with the worst clustering performance, while the deterministic initialization strategy achieves high computational cost. Big data clustering aims to reduce computation costs and improve cluster efficiency. The objective of this study is to achieve a better initial centroid for big data clustering on business management data without using random and deterministic initialization that avoids local optima and improves clustering efficiency with effectiveness in terms of cluster quality, computation cost, data comparisons and iterations on a single machine.

This study presents the Normal Distribution Probability Density (NDPD) algorithm for big data clustering on a single machine to solve business management-related clustering issues. The NDPDKM algorithm resolves the KM clustering problem by probability density of each data point. The NDPDKM algorithm first identifies the most probable density data points by using the mean and standard deviation of the datasets through normal probability density. Thereafter, the NDPDKM determines K initial centroid by using sorting and linear systematic sampling heuristics.

The performance of the proposed algorithm is compared with KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms through Davies Bouldin score, Silhouette coefficient, SD Validity, S_Dbw Validity, Number of Iterations and CPU time validation indices on eight real business datasets. The experimental evaluation demonstrates that the NDPDKM algorithm reduces iterations, local optima, computing costs, and improves cluster performance, effectiveness, efficiency with stable convergence as compared to other algorithms. The NDPDKM algorithm minimizes the average computing time up to 34.83%, 90.28%, 71.83%, 92.67%, 69.53% and 76.03%, and reduces the average iterations up to 40.32%, 44.06%, 32.02%, 62.78%, 19.07% and 36.74% with reference to KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms.

The KM algorithm is the most widely used partitional clustering approach in data mining techniques that extract hidden knowledge, patterns and trends for decision-making strategies in business data. Business analytics is one of the applications of big data clustering where KM clustering is useful for the various subcategories of business analytics such as customer segmentation analysis, employee salary and performance analysis, document searching, delivery optimization, discount and offer analysis, chaplain management, manufacturing analysis, productivity analysis, specialized employee and investor searching and other decision-making strategies in business.

This paper aims to conduct a fuzzy discrete time cost quality risk in the ambiguous mode CO_{2 tradeoff problem} (FDTCQRP*TP) in a megaproject based on fuzzy ground.

A combinatorial evolutionary algorithm using Fuzzy Invasive Weed Optimization (FIWO) is used in the discrete form of the problem where the parameters are fully fuzzy multi-objective and provide a space incorporating all dimensions of the problem. Also, the fuzzy data and computations are used with the Chanas method selected for the computational analysis. Moreover, uncertainty is defined in FIWO. The presented FIWO simulation, its utility and superiority are tested on sample problems.

The reproduction, rearrangement and maintaining elite invasive weeds in FIWO can lead to a higher level of accuracy, convergence and strength for solving FDTCQRP*TP fuzzy rules and a risk ground in the ambiguous mode with the emphasis on the necessity of CO₂ pollution reduction. The results reveal the effectiveness of the algorithm and its flexibility in the megaproject managers' decision making, convergence and accuracy regarding CO₂ pollution reduction.

This paper offers a multi-objective fully fuzzy tradeoff in the ambiguous mode with the approach of CO₂ pollution reduction.

The purpose of this paper is to review the various pre-processing and post-processing approaches used to ameliorate the surface characteristics of fused deposition modelling (FDM)-based acrylonitrile butadiene styrene (ABS) prototypes. FDM being simple and versatile additive manufacturing technique has a calibre to comply with present need of tailor-made and cost-effective products with low cycle time. But the poor surface finish and dimensional accuracy are the primary hurdles ahead the implementation of FDM for rapid casting and tooling applications.

The consequences and scope of FDM pre-processing and post-processing parameters have been studied independently. The comprehensive study includes dominance, limitations, validity and reach of various techniques embraced to improve surface characteristics of ABS parts. The replicas of hip implant are fabricated by maintaining the optimum pre-processing parameters as reviewed, and a case study has been executed to evaluate the capability of vapour smoothing process to enhance surface finish.

The pre-processing techniques are quite deficient when different geometries are required to be manufactured within limited time and required range of surface finish and accuracy. The post-processing techniques of surface finishing, being effective disturbs the dimensional stability and mechanical strength of parts thus incapacitates them for specific applications. The major challenge for FDM is the development of precise, automatic and controlled mass finishing techniques with low cost and time.

The research assessed the feasibility of vapour smoothing technique for surface finishing which can make consistent castings of customized implants at low cost and shorter lead times.

The extensive research regarding surface finish and dimensional accuracy of FDM parts has been collected, and inferences made by study have been used to fabricate replicas to further examine advanced finishing technique of vapour smoothing.