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The effective, efficient and optimal design of micromixer is the need in the field of biochemical and biomedical diagnostic systems.

In this paper, multi-objective optimization of split and recombine micromixer (SRM) with different geometrical configurations is carried out. The finite element method-based three-dimensional models are prepared and analyzed using COMSOL Multiphysics 5.0 Software. Taguchi’s design of experiment (DoE), main effect plot analysis, ANOVA and grey relational analysis (GRA) method are used to find out optimum condition. The five geometrical parameters with three levels, namely, angle between inlets, pillar size, pillar shape, aspect ratio and constriction height of SRM are considered as design variables. The mixing index (MXI) and pressure drop (∆P) are considered objective functions.

The MXI is significantly influenced by pillar shape and aspect ratio, whereas the pressure drop (∆P) by constriction height. Maximum MXI (0.97) with minimum pressure drop (64,587 Pa) is the optimal conditions and obtained at 180 deg angle between inlets, 50 µm of pillar size, 1.5 of aspect ratio, 100 µm of constriction height and ellipse shape pillar cross-section, respectively.

This optimized SRM can be combined with lab-on-a-chip for biochemical and biomedical analysis.

This work is useful to obtain optimal geometry of SRM for getting efficient performance of micromixer.

In Malaysia, there seems to be no mutual and general agreement among construction industry players and researchers on what the critical success factors (CSFs) of construction projects are since characteristics of construction projects vary across various projects complexity. This chapter thus examines the CSFs for affordable high-rise public housing projects in Malaysia through a thorough review of the literature on the CSFs, which were then contextually customised via a pilot study and presented in the form of Relative Importance Index (RII). Primary data were collected via administration of questionnaire surveys to 170 construction stakeholders of affordable high-rise public housing projects in Malaysia. The findings of this study revealed that the top 10 ranks are good management and supervision at site, good personal behaviour by all participants, good implementation in safety management and practice, high commitment in quality control by management, competent manager/supervisor, good communication and coordination, good and enough material and equipment, experience worker, clear objective and goals and good support by senior employee/management, whereby it is crucial in ensuring the project success of affordable high-rise public housing projects in Malaysia. This chapter provides clear guidelines for the contractors of affordable high-rise public housing projects to be more competitive in the construction industry by illustrating the important factors to be considered while delivering the projects to their respective clients.

This study aims to improve the performance of hydrodynamic journal bearings through partial grooving on the bearing surface.

Bearing performance analysis is numerically carried out using the thin film flow physics of COMSOL Multiphysics 5.0 software. Initially, the static performance analysis is carried out for hydrodynamic journal bearing system with smooth surface, and the results of the same are validated with results from the literature. In the later part of the paper, the partial rectangular shape micro-textures are modeled on bearing surface. The effects of partial groove pattern on the bearing performance parameters, namely, fluid film pressure, load carrying capacity, frictional power loss and frictional torque, are studied in detail.

The numerical results show that the values of maximum fluid film pressure, load carrying capacity, frictional power loss and frictional torque are considerably improved due to deterministic micro-textures. Bearing surface with partial groove along 90°-180° region results in 81.9 per cent improvement in maximum fluid film pressure and 75.9 per cent improvement in load carrying capacity as compared with smooth surface of journal bearing, with no increase in frictional power loss and frictional torque. Maximum decrease in frictional power loss and frictional torque is observed for partially grooving along 90°-360° region. The simulations are supported by proof-of-concept experimentation.

This study is useful in the appropriate selection of groove parameters on bearing surface to the bearing performance characteristics.

This paper aims to explore the ways in which religious tourism in India fosters religious tolerance.

The paper uses a conceptual apparatus derived from the basic structure of religious tourism comprising motivation, journey and destination, to understand various aspects of tolerance. Tolerance, with the implicit meaning of diversity and pluralism, is examined at two levels – intra-religion and inter-religion – using field investigations from three Hindu pilgrimage sites, namely, Vrindavan, Tuljapur, Shegaon and review of one Muslim site called Ajmer Sharif. These sites exhibit a range of combinations, sectarian traditions within Hindu and their interactions with others, including Muslims and foreigners.

Each of the sites provides different sets of opportunities for the “others” to get exposed to religious and cultural aspects. It is found that tolerance within the Hindu sects and with non-Hindus from other religious faiths is a function of their engagement with cultural performances and participation in the religious tourism economy in a pilgrimage site.

On a broader level, this paper argues that conceptualising tolerance within a social and cultural sphere helps in a better understanding of tolerance and identifying areas within religious tourism where it can be promoted. A conscious effort to promote tolerance through religious tourism will add value to religious tourism and help it thrive.

Aluminium-doped zinc oxide thin films exhibit interesting optoelectronic properties, which make them suitable for fabrication of photovoltaic cell, flat panel display electrode, etc. It has been shown that aluminium dopant concentration and annealing treatment in reduced atmosphere are the major factors affecting the electrical and optical properties of aluminium doped zinc oxide (AZO) film. Here, the authors report the structural, optical and electrical properties of aluminium-doped zinc oxide thin films fabricated by dip coating technique and annealed in air atmosphere, thereby avoiding hazardous environments such as hydrogen. The aim of this paper was to systematically investigate the effect of annealing temperature on the electrical properties of dip-coated film.

Aluminium-doped ZnO thin films were prepared on corning substrates by dip coating method. Aluminium concentration in the film varied from 0.8 to 1.4 mol per cent. Films have been characterized by X-ray diffraction, scanning electron microscopy, atomic force microscopy, UV-visible spectroscopy and Hall measurements. The deposited films were heat treated at 450-600°C, in steps of 50°C for 1 h in air to study the improvement in electrical properties. Films were also prepared by annealing at 600°C in air for durations of 1, 2, 4 and 6 h. Envelope method was used to calculate the variation of the refractive index and extinction coefficient with wavelength.

The electrical resistivity is found to decrease considerably when the annealing time is increased from 1 to 4 h. The films exhibited high transmittance (>90 per cent) in the visible range, and the optical band gaps were found to change as per the Moss–Burstien effect, and this was consistent with the observed changes in the carrier concentration.

The study shows the effect of annealing in air, avoiding hazardous reduced environment, such as hydrogen, to study the improvement in electrical and optical properties of aluminum-doped zinc oxide films. Envelope method was used to calculate the variation of optical constants with wavelength.

This study aims to understand how climatic drivers of change will affect rural communities living in the hot semiarid region of Bhokardan Taluka of Jalna district in the Indian state of Maharashtra. In the context of the economic and social change they are experiencing, the concern is to evolve ways that enable them to cope with, adapt to and benefit from these challenges.

The focus of most of the climate change studies is on the short- to long-term trends of weather parameters such as rainfall, temperature and extreme weather events. The impact of climate variability and changing patterns on the local communities, the local economy, livelihoods and social life in specific geographies is less explored.

As the impacts of climatic and nonclimatic drivers of change are cross-sectoral, diverse, multidimensional, interlinked and dynamic, this study has adopted a transdisciplinary “research-in-use” approach involving multidisciplinary teams covering the aspects such as changes in land use and land cover, surface and groundwater status, edaphic conditions, crops and livestock, climate analysis including projected changes, socioeconomic analysis, people’s experience of climate variability and their current coping strategies and resilience (vulnerability) analysis of communities and various livelihood groups.

The study was based on the peoples’ perspective and recommendation based on the local communities ability to cope up with climate change. However, a statistical analysis perspective is missing in the present study.

Based on these findings, a set of implementation-focused recommendations are made that are aimed at conserving and enhancing the resilience of the foundations that uphold and sustain the social and economic well-being of the rural communities in Bhokardan taluka, namely, land, water, agriculture, livestock, food and nutrition security, livelihoods, market access and social capital.

Since December 2019, global attention has been drawn to the rapid spread of COVID-19. Corona was discovered in India on 30 January 2020. To date, in India, 178,014 disease cases were reported with 14,011 deaths by the Indian Government. In the meantime, with an increasing spread speed, the COVID-19 epidemic occurred in other countries. The survival rate for COVID-19 patients who suffer from a critical illness is efficiently and precisely predicted as more fatal cases can be affected in advanced cases. However, over 400 laboratories and clinically relevant survival rates of all present critically ill COVID-19 patients are estimated manually. The manual diagnosis inevitably results in high misdiagnosis and missed diagnosis owing to a lack of experience and prior knowledge. The chapter presents an option for developing a machine-based prognostic model that exactly predicts the survival of individual severe patients with clinical data from different sources such as Kaggle data.gov and World Health Organization with greater than 95% accuracy. The data set and attributes are shown in detail. The reasonableness of such a mere three elements may depend, respectively, on their representativeness in the indices of tissue injury, immunity and inflammation. The purpose of this paper is to provide detailed study from the diagnostic aspect of COVID-19, the work updates the cost-effective and prompt criticality classification and prediction of survival before the targeted intervention and diagnosis, in particular the triage of the vast COVID-19 explosive epidemic.

Automated machine learning (ML) provides resources and platforms to render ML available to non-ML experts, to boost efficiency in ML and to accelerate research in machine learning. H2O AutoML is used to generate the results (Dulhare et al., 2020). ML has achieved major milestones in recent years, and it is on which an increasing range of disciplines depend. But this performance is crucially dependent on specialists in human ML to perform the following tasks: preprocess the info and clean it; choose and create the appropriate apps; choose a family that fits the pattern; optimize hyperparameters for layout; and models of computer learning post processes. Review of the findings collected is important.

These days, the concept of automated ML techniques is being used in every field and domain, for example, in the stock market, education institutions, medical field, etc. ML tools play an important role in harnessing the massive amount of data. In this paper, the data set relatively holds a huge amount of data, and appropriate analysis and prediction are necessary to track as the numbers of COVID cases are increasing day by day. This prediction of COVID-19 will be able to track the cases particularly in India and might help researchers in the future to develop vaccines. Researchers across the world are testing different medications to cure COVID; however, it is still being tested in various labs. This paper highlights and deploys the concept of AutoML to analyze the data and to find the best algorithm to predict the disease. Appropriate tables, figures and explanations are provided.

As the difficulty of such activities frequently goes beyond non-ML-experts, the exponential growth of ML implementations has generated a market for off-the-shelf ML solutions that can be used quickly and without experience. We name the resulting work field which is oriented toward the radical automation of AutoML machine learning. The third class is that of the individuals who have illnesses such as diabetes, high BP, asthma, malignant growth, cardiovascular sickness and so forth. As their safe frameworks have been undermined effectively because of a common ailment, these individuals become obvious objectives. Diseases experienced by the third classification of individuals can be lethal (Shinde et al., 2020). Examining information is fundamental in having the option to comprehend the spread and treatment adequacy. The world needs a lot more individuals investigating the information. The understanding from worldwide data on the spread of the infection and its conduct will be key in limiting the harm. The main contributions of this study are as follows: predicting COVID-19 pandemic in India using AutoML; analyzing the data set predicting the patterns of the virus; and comparative analysis of predictive algorithms. The organization of the paper is as follows, Sections I and II describe the introduction and the related work in the field of analyzing the COVID pandemic. Section III describes the workflow/framework for AutoML using the components with respect to the data set used to analyze the patterns of COVID-19 patients.

This review paper aims to provide an overview of applications of direct rapid manufacturing assisted mold with conformal cooling channels (CCCs) and shows the potential of this technique in different manufacturing processes.

Key publications from the past two decades have been reviewed.

This study concludes that direct rapid manufacturing technique plays a dominant role in the manufacturing of mold with complicated CCC structure which helps to improve the quality of final part and productivity. The outcome based on literature review and case study strongly suggested that in the near future direct rapid manufacturing method might become standard procedure in various manufacturing processes for fabrication of complex CCCs in the mold.

Advanced techniques such as computer-aided design, computer-aided engineering simulation and direct rapid manufacturing made it possible to easily fabricate the effective CCC in the mold in various manufacturing processes.

This paper is beneficial to study the direct rapid manufacturing technique for development of the mold with CCC and its applications in different manufacturing processes.