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The purpose of this paper is to identify and prioritize the key food safety practices among street food handlers that lead to microbial contamination in selected street foods of Hyderabad, India. These key food safety practices will help develop and design tailor-made training material for street food vendors in future.

It is a cross-sectional study conducted in south Indian city of Hyderabad. Stratified random sampling method was employed. A total of 463 samples of street foods were collected from five zones of Hyderabad. They included 163 salad toppings, 150 fresh fruit juices and 150 panipuri samples. Identification and enumeration of foodborne pathogens and indicator organisms (S. aureus, E. coli, Salmonella spp., Shigella spp., Bacillus cereus, Yersinia spp.) were performed as described by USFDA-BAM. Information on food safety knowledge and handling practices from street vendors were collected using a structured questionnaire. Associations between hygiene practices and bacterial pathogens were done using ANOVA. Risk estimation of food safety practices was assessed by calculating odds ratio.

Microbiological analysis indicated that a large number of carrot (98.1 percent) and onion (75.5 percent) samples were contaminated with E. coli. Peeled and cut fruits left uncovered have 13.4 times risk (OR: 2.40-74.8) of E. coli contamination compared to the covered ones. Panipuri samples picked from the vendors who did not have soap at the vending unit had significantly (p<0.001) higher contamination of fecal coliforms than those who had.

This study is the first of its kind in the study area.

The purpose of this paper is to scrutinize the heat and mass transfer attributes of three-dimensional bio convective flow of nanofluid across a slendering surface with slip effects. The analysis is carried out subject to irregular heat sink/source, thermophoresis and Brownian motion of nanoparticles.

At first, proper transmutations are pondered to metamorphose the basic flow equations as ODEs. The solution of these ODEs is procured by the consecutive application of Shooting and Runge-Kutta fourth order numerical procedures.

The usual flow fields along with density of motile microorganisms for sundry physical parameters are divulged via plots and scrutinized. Further, the authors analyzed the impact of same parameters on skin friction, heat and mass transfer coefficients and presented in tables. It is discovered that the variable heat sink/source parameters play a decisive role in nature of the heat and mass transfer rates. The density of motile microorganisms will improve if we add Al-Cu alloy particles in regular fluids instead of Al particles solely. A change in thermophoresis and Brownian motion parameters dominates heat and mass transfer performance.

To the best of the knowledge, no author made an attempt to investigate the flow of nanofluids over a variable thickness surface with bio-convection, Brownian motion and slip effects.

One of the major challenges in the design of thermal equipment is to minimize the entropy production and enhance the thermal dissipation rate for improving energy efficiency of the devices. In several industrial applications, the structure of thermal device is cylindrical shape. In this regard, this paper aims to explore the impact of isothermal cylindrical solid block on nanofluid (Ag – H₂O) convective flow and entropy generation in a cylindrical annular chamber subjected to different thermal conditions. Furthermore, the present study also addresses the structural impact of cylindrical solid block placed at the center of annular domain.

The alternating direction implicit and successive over relaxation techniques are used in the current investigation to solve the coupled partial differential equations. Furthermore, estimation of average Nusselt number and total entropy generation involves integration and is achieved by Simpson and Trapezoidal’s rules, respectively. Mesh independence checks have been carried out to ensure the accuracy of numerical results.

Computations have been performed to analyze the simultaneous multiple influences, such as different thermal conditions, size and aspect ratio of the hot obstacle, Rayleigh number and nanoparticle shape on buoyancy-driven nanoliquid movement, heat dissipation, irreversibility distribution, cup-mixing temperature and performance evaluation criteria in an annular chamber. The computational results reveal that the nanoparticle shape and obstacle size produce conducive situation for increasing system’s thermal efficiency. Furthermore, utilization of nonspherical shaped nanoparticles enhances the heat transfer rate with minimum entropy generation in the enclosure. Also, greater performance evaluation criteria has been noticed for larger obstacle for both uniform and nonuniform heating.

The current numerical investigation can be extended to further explore the thermal performance with different positions of solid obstacle, inclination angles, by applying Lorentz force, internal heat generation and so on numerically or experimentally.

A pioneering numerical investigation on the structural influence of hot solid block on the convective nanofluid flow, energy transport and entropy production in an annular space has been analyzed. The results in the present study are novel, related to various modern industrial applications. These results could be used as a firsthand information for the design engineers to obtain highly efficient thermal systems.

The purpose of this paper is to assess steady, two-dimensional natural convection flow of a viscoelastic, incompressible, electrically conducting and optically thick heat-radiating nanofluid over a linearly stretching sheet in the presence of uniform transverse magnetic field taking Dufour and Soret effects into account.

The governing boundary layer equations are transformed into a set of highly non-linear ordinary differential equations using suitable similarity transforms. Finite element method is used to solve this boundary value problem. Effects of pertinent flow parameters on the velocity, temperature, solutal concentration and nanoparticle concentration are described graphically. Also, effects of pertinent flow parameters on the shear stress, rate of heat transfer, rate of solutal concentration and rate of nanoparticle concentration at the sheet are discussed with the help of numerical values presented in graphical form. All numerical results for mono-diffusive nanofluid are compared with those of double-diffusive nanofluid.

Numerical results obtained in this paper are compared with earlier published results and are found to be in excellent agreement. Viscoelasticity, magnetic field and nanoparticle buoyancy parameter tend to enhance the wall velocity gradient, whereas thermal buoyancy force has a reverse effect on it. Radiation, Brownian and thermophoretic diffusions tend to reduce wall temperature gradient, whereas viscoelasticity has a reverse effect on it. Nanofluid Lewis number tends to enhance wall nanoparticle concentration gradient.

Study of this problem may find applications in engineering and biomedical sciences,e.g. in cooling and process industries and in cancer therapy.

Introduction: Historically, the corporate governance (CG) framework was designed primarily to safeguard the economic interests of shareholders, as a result of political and legal interventions, developing into an effective instrument for stakeholders and society in general.

Purpose: The core objectives of the study include: identifying journals/publications responsible for publishing CG studies in India, key CG issues covered by CG researchers, the amount of high-impact CG literature across different time periods, sectors/industries covered by CG researchers and different research instruments (quantitative or qualitative) used in CG studies in India.

Design/methodology: The chapter used a sample of 130 corporate governance studies that fulfil the selection criteria, drawn from the repository of over 100 reputed journals that are either recognised by the Australian Business Deans Council (ABDC) or indexed by SCOPUS. A systematic literature review has been carried out pertaining to CG issues in India, based on various statistical tools, data, industries, research outlets & citations, etc.

Findings: The results show an overwhelming number of studies have assessed the relationship between CG variables and firm performance, which could be measured through a variety of performance metrics such as ROA and ROI. Apart from empirical analysis, many conceptual studies use repetitive basic statistical tools like descriptive statistics or regression analysis. The chapter offers insights into current achievements and future development.

Originality/value: This bibliometric study is a useful guide for policymakers, corporate leaders, research organisations and management faculty to draw insights from work produced by eminent researchers in GC in India.

The analysis of fluid flow and thermal transport performance inside the cavity has found numerous applications in various engineering fields, such as nuclear reactors and solar collectors. Nowadays, researchers are concentrating on improving heat transfer by using ternary nanofluids. With this motivation, the present study analyzes the natural convective flow and heat transfer efficiency of ternary nanofluids in different types of porous square cavities.

The cavity inclination angle is fixed ω = 0 in case (I) and ω=π4 in case (II). The traditional fluid is water, and Fe3O4+MWCNT+Cu/H2O is treated as a working fluid. Ternary nanofluid's thermophysical properties are considered, according to the Tiwari–Das model. The marker-and-cell numerical scheme is adopted to solve the transformed dimensionless mathematical model with associated initial–boundary conditions.

The average heat transfer rate is computed for four combinations of ternary nanofluids: Fe3O4(25%)+MWCNT(25%)+Cu(50%),Fe3O4(50%)+MWCNT(25%)+Cu(25%),Fe3O4(33.3%)+MWCNT(33.3%)+Cu(33.3%) and Fe3O4(25%)+MWCNT(50%)+Cu(25%) under the influence of various physical factors such as volume fraction of nanoparticles, inclined magnetic field, cavity inclination angle, porous medium, internal heat generation/absorption and thermal radiation. The transport phenomena within the square cavity are graphically displayed via streamlines, isotherms, local and average Nusselt number profiles with adequate physical interpretations.

The purpose of this study is to determine whether the ternary nanofluids may be used to achieve the high thermal transmission in nuclear power systems, generators and electronic device applications.

The current analysis is useful to improve the thermal features of nuclear reactors, solar collectors, energy storage and hybrid fuel cells.

To the best of the authors’ knowledge, no research has been carried out related to the magneto-hydrodynamic natural convective Fe3O4+MWCNT+Cu/H2O ternary nanofluid flow and heat transmission filled in porous square cavities with an inclined cavity angle. The computational outcomes revealed that the average heat transfer depends not only on the nanoparticle’s volume concentration but also on the existence of heat source and sink.

Purpose: This chapter aims to analyse and highlight the current landscape of performance management (PM) systems, and the benefits of integrating modern technology such as smart analytics (SA) and artificial intelligence (AI) into PM systems. The chapter discusses the application of AI in PM tasks which successively simplify many offline PM tasks.

Methodology: To carry out this analysis, a systematic literature review was performed. The review covers literature detailing PM components as well as research concerned with the integration of SA and AI into PM systems.

Findings: This study uncovers the merits of using SA and AI in PM. SA technology provides organisations with a clear direction for improvement, rather than simply state failure in performance. AI can be used to automate redundant tasks while retaining the human element of decision-making. AI also helps reduce the time required to take action on feedback.

Significance: The findings of this research provide insights into the use of SA and AI to make PM tasks fast, scalable, and error-free.

In today’s world, the recommender systems are very valuable systems for the online users, as the World Wide Web is loaded with plenty of available information causing the online users to spend more time and money. The recommender systems suggest some possible and relevant recommendation to the online users by applying the recommendation filtering techniques to the available source of information. The recommendation filtering techniques take the input data denoted as the matrix representation which is generally very sparse and high dimensional data in nature. Hence, the sparse data matrix is completed by filling the unknown or missing entries by using many matrix completion techniques. One of the most popular techniques used is the matrix factorization (MF) which aims to decompose the sparse data matrix into two new and small dimensional data matrix and whose dot product completes the matrix by filling the logical values. However, the MF technique failed to retain the loss of original information when it tried to decompose the matrix, and the error rate is relatively high which clearly shows the loss of such valuable information.

To alleviate the problem of data loss and data sparsity, the new algorithm from formal concept analysis (FCA), a mathematical model, is proposed for matrix completion which aims at filling the unknown or missing entries without loss of valuable information to a greater extent. The proposed matrix completion algorithm uses the clustering technique where the users who have commonly rated the items and have not commonly rated the items are captured into two classes. The matrix completion algorithm fills the mean cluster value of the unknown entries which well completes the matrix without actually decomposing the matrix.

The experiment was conducted on the available public data set, MovieLens, whose result shows the prediction error rate is minimal, and the comparison with the existing algorithms is also studied. Thus, the application of FCA in recommender systems proves minimum or no data loss and improvement in the prediction accuracy of rating score.

The proposed matrix completion algorithm using FCA performs good recommendation which will be more useful for today’s online users in making decision with regard to the online purchasing of products.

This paper presents the new technique of matrix completion adopting the vital properties from FCA which is applied in the recommender systems. Hence, the proposed algorithm performs well when compared to other existing algorithms in terms of prediction accuracy.

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.

The purpose of this paper is to understand the influence of technology change in the banking sector by employing data envelopment analysis (DEA) and also to determine the change in total factor productivity (TFP) and its components, namely technical change and technical efficiency change.

The DEA method has been used to assess the efficiency of the entire banking sector and the bank groups. The purpose has been to investigate TFP change and its components' (obtained using Malmquist index) influence on the growth in the banking sector as well as in the four bank groups. In doing so, for each bank group the levels of technical efficiency, technical efficiency change, efficiency change and TFP change have been estimated. Further investigation has been done to determine if significant differences in these exist between the different bank groups in terms of size, time period and ownership. The determinants of productivity have been assessed.

The TFP growth over the entire period (1995‐2006) was driven by technical change as compared to efficiency change, showing that technology and innovation had a greater impact than efficiency change, or the catch‐up effect. The fixed effects estimates of the determinants of TFP change and its components show that size, ownership and time period exert significant effect on technical change.

The results of the analysis presented in this paper suggest that policies that result in efficiency change are likely to have little impact on the future prospects of the banking sector relative to policies that foster the adoption of the latest technologies. This has exactly been the focus of Reserve Bank of India and though some banks may consider it as an imposition of technology, the result of this requirement appears to be positive as is apparent from this paper's analysis.

The value of this paper comes from the empirical testing that in the Indian banking sector growth in the more recent period came from technology change or frontier shifts as compared to efficiency change. Also, growth is larger due to frontier shifts than due to efficiency change. This endorses Lucas' findings regarding the focus on the positive impacts of deregulation and competition in the Indian banking sector.