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This study aims to propose a new non-isolated Multi-Input Zeta-SEPIC (MIZS) dc–dc converter for renewable energy sources integration with different voltage levels (low-voltage source, high-voltage source). The chosen configuration of the converter is capable of performing bucking as well as boosting operations in various modes of operation.

Parameters of the selected MIZS converter are designed using the time-domain analysis. The selected converter belongs to the sixth-order family with two switches and six energy storage elements. State-space model of the converter is developed for each mode of operation, and using these individual state-space models, an average state-space model of the converter useful to carry out detailed analysis for different operating conditions is developed. Analysis related to operational stability of the converter is also carried out using Participation Factor (PaF)-based Eigen value analysis.

Using the PaF-based Eigen analysis, participation of the various state variables in different Eigen modes and vice versa is carried out. Performance of the converter for different parameter variations in the allowable range is determined and the same has been used to find the operational stability of the converter under different modes of operation. The selected converter has low inductor ripple currents and output voltage ripples when delivering the power to load.

Because operational stability of the converter under various operating conditions is one of the key performance indicators for selecting a particular type of converter, PaF-based Eigen value analysis has been carried out using the average state-space model developed for the selected MIZS converter. Operational stability analysis of the converter is carried out for parameter variations also. In addition, participation of the various states in each Eigen mode and vice versa have been analyzed for designed parameter values and also variation within the specified range of variations.

As COVID-19 outbreak has created a global crisis, treating patients with minimum resources and traditional methods has become a hectic task. In this technological era, the rapid growth of coronavirus has affected the countries in lightspeed manner. Therefore, the present study proposes a model to analyse the resource allocation for the COVID-19 pandemic from a pluralistic perspective.

The present study has combined data analytics with the K-mean clustering and probability queueing theory (PQT) and analysed the evolution of COVID-19 all over the world from the data obtained from public repositories. By using K-mean clustering, partitioning of patients’ records along with their status of hospitalization can be mapped and clustered. After K-mean analysis, cluster functions are trained and modelled along with eigen vectors and eigen functions.

After successful iterative training, the model is programmed using R functions and given as input to Bayesian filter for predictive model analysis. Through the proposed model, disposal rate; PPE (personal protective equipment) utilization and recycle rate for different countries were calculated.

Using probabilistic queueing theory and clustering, the study was able to predict the resource allocation for patients. Also, the study has tried to model the failure quotient ratio upon unsuccessful delivery rate in crisis condition.

The study has gathered epidemiological and clinical data from various government websites and research laboratories. Using these data, the study has identified the COVID-19 impact in various countries. Further, effective decision-making for resource allocation in pluralistic setting has being evaluated for the practitioner's reference.

Further, the proposed model is a two-stage approach for vulnerability mapping in a pandemic situation in a healthcare setting for resource allocation and utilization.

This chapter focusses on the identification of sustainability factors. Out of the 77 variables used in the questionnaire for collection of the information, on sustainability of financial innovation in e-payment system (EPS), the important factors are identified and derived using exploratory factor analysis (EFA). This chapter further presents validation of the identified factors through confirmatory factor analysis (CFA). Based on the identified factors, a model for EPS is proposed. The chapter also presents a scale developed based on identified factors.

Hybrid Reynolds-averaged Navier–Stokes large eddy simulation (RANS-LES) methods have become popular for simulation of massively separated flows at high Reynolds numbers due to their reduced computational cost and good accuracy. The current study aims to examine the performance of LES and hybrid RANS-LES model for a given grid resolution.

For better assessment and contrast of model performance, both mean and instantaneous flow fields have been investigated. For studying instantaneous flow, proper orthogonal decomposition has been used.

Current analysis shows that hybrid RANS-LES is capable of achieving similar accuracy in prediction of both mean and instantaneous flow fields at a very coarse grid as compared to LES.

Focusing mostly on the practical applications of computation, most of the attention has been given to the prediction of one-point flow statistics and little consideration has been put to two-point statistics. Here, two-point statistics has been considered using POD to investigate unsteady turbulent flow.

Presents the fixed‐point paradigm and its aspects from vector algebra to dynamical systems, not forgetting eigenbehaviours. Then considers the problem of subjects (and objects) in interaction, using the author's concepts of observation operator, pragmatical operator and epistemo‐praxiological loop which give rise to the intervention of a fixed point. More precise conclusions are obtained in the recurrent case. A comparison is made with Heinz von Förster's views on subject and object.

Among various efforts pursued to produce fuel efficient vehicles, light weight engineering (i.e. the use of low‐density structurally‐efficient materials, the application of advanced manufacturing and joining technologies and the design of highly‐integrated, multi‐functional components/sub‐assemblies) plays a prominent role. In the present work, a multi‐disciplinary design optimization methodology has been presented and subsequently applied to the development of a light composite vehicle door (more specifically, to an inner door panel). The door design has been optimized with respect to its weight while meeting the requirements /constraints pertaining to the structural and NVH performances, crashworthiness, durability and manufacturability. In the optimization procedure, the number and orientation of the composite plies, the local laminate thickness and the shape of different door panel segments (each characterized by a given composite‐lay‐up architecture and uniform ply thicknesses) are used as design variables. The methodology developed in the present work is subsequently used to carry out weight optimization of the front door on Ford Taurus, model year 2001. The emphasis in the present work is placed on highlighting the scientific and engineering issues accompanying multidisciplinary design optimization and less on the outcome of the optimization analysis and the computational resources/architecture needed to support such activity.

The purpose of this paper is to report a study in which analytic hierarchy process (AHP) has been used for selecting the best concept in lean environment.

There existed a need for the case organization to identify the best concept from the perspective of lean manufacturing. The concept selection problem is a typical multi‐criterion decision‐making problem. AHP judges and selects the elements/concepts which have a greater influence on the pre‐determined objective. In this study, AHP has been used for selecting the best concept.

The validation indicated that AHP is an effective approach for enabling best concept selection, thereby improving the leanness of the organization.

The implementation study has been carried out by substituting the data gathered from only one manufacturing organization. Yet the findings and contribution of this research work would be useful to the leaders of majority of the manufacturing companies situated in the world.

The usage of the approach will indicate that AHP enables the best concept selection in advanced manufacturing environments.

A case study has been reported to indicate the feasibility of selecting the best concept in a lean environment. Hence, the contributions are original.

The purpose of this study is to use the Taguchi Method for parametric design in the early stages of product development. electromagnetic compatibility (EMC) issues can be considered in the early stages of product design to reduce counter-measure components, product cost and labor consumption increases due to a number of design changes in the R&D cycle and to accelerate the R&D process.

The three EMC characteristics, including radiated emission, conducted emission and fast transient impulse immunity of power, are considered response values; control factors are determined with respect to the relevant parameters for printed circuit board and mechanical design of the product and peripheral devices used in conjunction with the product are considered as noise factors. The optimal parameter set is determined by using the principal component gray relational analysis in conjunction with both response surface methodology and artificial neural network.

Market specifications and cost of components are considered to propose an optimal parameter design set with the number of grounded screw holes being 14, the size of the shell heat dissipation holes being 3 mm and the arrangement angle of shell heat dissipation holes being 45 degrees, to dispose of 390 O filters on the noise source.

The optimal parameter set can improve EMC effectively to accommodate the design specifications required by customers and pass test regulations.

The purpose of this paper is to propose a numerical method to solve time dependent Burgers' equation with appropriate initial and boundary conditions.

The presence of the nonlinearity in the problem leads to severe difficulties in the solution approximation. In construction of the numerical scheme, quasilinearization is used to tackle the nonlinearity of the problem which is followed by semi discretization for spatial direction using differential quadrature method (DQM). Semi discretization of the problem leads to a system of first order initial value problems which are followed by fully discretization using RK4 scheme. The method is analyzed for stability and convergence.

The method is illustrated and compared with existing methods via numerical experiments and it is found that the proposed method gives better accuracy and is quite easy to implement.

The new scheme is developed by using some numerical schemes. The scheme is analyzed for stability and convergence. In support of predicted theory some test examples are solved using the presented method.

A large number of roads have been constructed in the rural areas of India to connect habitations with the nearest major roads. With time, the pavements of these roads have deteriorated and they need some kind of maintenance, although they all do not need maintenance at the same time, as they have all not deteriorated to the same level. Hence, they have to be prioritized for maintenance.

In order to present a scientific methodology for prioritizing pavement maintenance, the factors affecting prioritization and the relative importance of each were identified through an expert survey. Analytic Hierarchy Process (AHP) was used to scientifically establish weight (importance) of each factor based on its relative importance over other factors. The proposed methodology was validated through a case study of 203 low volume rural roads in the state of Himachal Pradesh in India. Ranking of these roads in order of their priority for maintenance was presented as the final result.

The results show that pavement distresses, traffic volume, type of connectivity and the socioeconomic facilities located along a road are the four major factors to be considered in determining the priority of a road for maintenance.

The methodology provides a comprehensive, scientific and socially responsible pavement maintenance prioritization method which will automatically select roads for maintenance without any bias.

Timely maintenance of roads will also save budgetary expenditure of restoration/reconstruction, leading to enhancement of road service life. The government will not only save money but also provide timely benefit to the needy population.

Road transportation is the primary mode of inland transportation in rural areas. Timely maintenance of the pavements will be of great help to the socioeconomic development of rural areas.

The proposed methodology lays special emphasis on rural roads which are small in length, but large in number. Instead of random, a scientific method for selection of roads for maintenance will be of great help to the public works department for better management of rural road network.