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Corona Virus Disease 2019 (COVID-19) is a deadly virus named after severe acute respiratory syndrome coronavirus 2; it affects the respiratory system of the human and sometimes leads to death. The COVID-19 mainly attacks the person with previous lung diseases; the major cause of lung diseases is the exposure to nitrogen dioxide (NO2) for a longer duration. NO2 is a gaseous air pollutant caused as an outcome of the vehicles, industrial smoke and other combustion processes. Exposure of NO2 for long-term leads to the risk of respiratory and cardiovascular diseases and sometimes leads to fatality. This paper aims to analyze the NO2 level impact in India during pre- and post-COVID-19 lockdown. The study also examines the relationship between the fatality rate of humans because of exposure to NO2 and COVID-19.

Spatial analysis has been conducted in India based on the mortality rate caused by the COVID-19 using the data obtained through Internet of Medical things. Meanwhile, the mortality rate because of the exposure of NO2 has been conducted in India to analyze the relationship. Further, NO2 level assessment is carried out using Copernicus Sentinel-5P satellite data. Moreover, aerosol optical depth analysis has been carried out based on NASA’s Earth Observing System data.

The results indicate that NO2 level has dropped 20-year low because of the COVID-19 lockdown. The results also determine that the mortality rate because of long-time exposure to NO2 is higher than COVID-19 and the mortality rate because of COVID-19 may be a circumlocutory effect owing to the inhalation of NO2.

Using the proposed approach, the COVID-19 spread can be identified by knowing the air pollution in major cities. The research also identifies that COVID-19 may have an effect because of the inhalation of NO2, which can severe the COVID-19 in the human body.

Rapid tooling (RT) technique using rapid prototyping (RP) process has been looked upon as an approach which reduces time and cost of production. This study aims to produce electrode for electrical discharge machining (EDM) from acrylonitrile butadiene styrene (ABS) material using the fused deposition modeling (FDM) process of RP. The electrode is coated with copper to a depth of 1 mm by using electroplating to make it conductive. This electrode is termed as RP electrode. The performance of RP electrode having square shape is compared with that of solid electrode of copper having identical size.

In this study, the work piece material is chosen to be titanium Grade-V alloy (Ti-Al6-V4). The input parameters on the EDM machine such as discharge current, pulse on time and voltage are studied, and experiments are designed using the Taguchi method.

The results pertaining to the material removal rate (MRR), electrode wear rate (EWR) and surface roughness (Ra) are reported. It is found that the performance of a coated RP electrode is equally satisfactory when compared with that of a solid electrode.

This paper reports the machining performance of a square-shaped ABS electrode coated with copper. This technique, particularly when the electrode is of intricate shape, saves on cost and time of production of electrode to be used for EDM.

The purpose of this paper is to study numerically the influence of a magnetic field on the transient free convective boundary layer flow of a nanofluid over a moving semi-infinite vertical cylinder with heat transfer

The problem is governed by the coupled non-linear partial differential equations with appropriate boundary conditions. The fluid is a water-based nanofluid containing nanoparticles of copper. The Brinkman model for dynamic viscosity and Maxwell–Garnett model for thermal conductivity are used. The governing boundary layer equations are written according to The Tiwari–Das nanofluid model. A robust, well-tested, implicit finite difference method of Crank–Nicolson type, which is unconditionally stable and convergent, is used to find the numerical solutions of the problem. The velocity and temperature profiles are studied for significant physical parameters such as the magnetic parameter, nanoparticles volume fraction and the thermal Grashof number Gr. The local skin-friction coefficient and the Nusselt number are also analysed and presented graphically.

The present computations have shown that an increase in the values of either magnetic parameter M or nanoparticle volume fraction decreases the local skin-friction coefficient, whereas the opposite effect is observed for thermal Grashof number Gr. The local Nusselt number increases with a rise in Gr and ϕ values. But an increase in M reduces the local Nusselt number.

This paper is relatively original and presents numerical investigation of transient two-dimensional laminar boundary layer free convective flow of a nanofluid over a moving semi-infinite vertical cylinder in the presence of an applied magnetic field. The present study is of immediate application to all those processes which are highly affected by heat enhancement concept and a magnetic field. Further the present study is relevant to nanofluid materials processing, chemical engineering coating operations exploiting nanomaterials and others.

Over the years, the fruit and vegetable supply chain has encountered several challenges. From the harvesting stage until it reaches the consumer, a significant portion of fruits and vegetables gets wasted in the supply chain. As a result, the present study attempts to identify and analyze the growth barriers in the fresh produce supply chain (FPSC) in the Indian context.

An integrated grey theory and DEMATEL based approach is used to analyze growth barriers in the FPSC. The growth barriers were analyzed and sorted based on their influence and importance relations.

The results emphasize that the most critical growth barriers in the FPSC that should be addressed to ensure food waste reduction are as follows: Lack of cold chain facilities (B2), lack of transportation or logistic facilities (B1), lack of collaboration and information sharing between supply chain partners (B3), lack of proper quality and safety protocols (B15), a lack of processing and packaging facilities (B14), and poor productivity and efficiency (B13). Results are also verified by conducting a sensitivity analysis.

The results are highly useful for policymakers to exploit growth barriers within the FPSC that require more attention. The obtained results show that the managers and policymakers need to utilize more funds to develop the cold chain facilities and logistics facilities to develop the FPSC. By improving the cold chain facilities, it is possible to improve the quality of food, make the food safe for human consumption, reduce waste, and increase the efficiency and productivity of the supply chain. Also, this study may encourage policymakers and industrial managers to adopt the most influential SCM practices for food waste reduction.

Many researchers have attempted to analyze the causes of food waste and growth barriers in the FPSC using various decision-making methods. Still, no attempts are made to explore the causal relations among various growth barriers in FPSC through the integrated Grey-DEMATEL technique. Also, we devise policy implications in the light of the new farm bills or the Indian agricultural acts of 2020. Lack of cold chain facilities (B2) was found to be the critical driving barrier in the FPSC, as it influences multiple barriers. Also, there is a dire need for cold chain facilities and transportation systems to enhance productivity and efficiency.

The most difficult tasks in the design and development of products for diverse engineering applications were the selection of suitable materials. Choice of inappropriate process variables leads to poor performance, which increases the cost of the product. The selection of the best option of available alternatives is important to improve the performance and productivity of the manufacturing enterprises.

The paper aims to develop Hybrid Multi-Criteria Decision Making (HMCDM) by integrating two potential optimization techniques Elimination Et Choix Traduisant la REalité and multi-objective optimization on the basis of ratio analysis. The weight of the criteria was calculated using the critic weight method.

The efficiency and flexibility of the proposed HMCDM technique were illustrated and validated by two examples. In the first case, the best electrode material among the five available alternatives was selected for the electrical discharge machining of AZ91/B4Cp magnesium composites. In the second case, the optimum weight percentage of composites providing the best tribological properties was chosen.

It was noted that the HMCDM methodology was quite simple to comprehend, easy to apply and provided reliable rankings of the material alternatives. The proposed hybrid algorithm is suitable for product optimization as well as design optimization.

Chemical reaction effects are added to the governing equation. This paper aims to get the solution by converting the partial differential equation into an ordinary differential equation and solve using a perturbation scheme and applying the boundary conditions.

In this paper, the authors discussed the chemical reaction effects of heat and mass transfer on megnato hydro dynamics free convective rotating flow of a visco-elastic incompressible electrically conducting fluid past a vertical porous plate through a porous medium with suction and heat source. The authors analyze the effect of time dependent fluctuating suction on a visco-elastic fluid flow.

Using variable parameters of the fluid, the velocity, temperature and concentration of the fluid are analyzed through graphs.

The velocity profile reduces by increasing the values of thermal Grashof number (G_r), mass Grashof number (G_c) and the magnetic parameter (M). On the other hand, the velocity profile gets increased by increasing the permeability parameter (K). The temperature profile decreases by raising the value of Prandtl number (P_r) and frequency of oscillation parameter (ω). However, the source parameter (S) has the opposite effect on the temperature profile. The concentration profile reduces in all points by raising the chemical reaction parameter K_l, Schmidt number S_c, frequency of oscillation ω and the time t.

The purpose of this study is to identify sustainability practices and to propose a framework for evaluating the sustainability index of logistics service providers (LSPs).

Sustainable practices followed by LSPs are identified through literature review and analysis of a case study. Thirteen such sustainable practices are identified. Thereafter, with expert inputs, nine sustainable practices are shortlisted and considered for the evaluation of the sustainability index in the proposed framework. Graph Theory Matrix Approach has been applied to evaluate the sustainability index of an LSP.

Major practices identified for evaluating sustainability index include the use of recyclable packaging, use of renewable energy sources, green procurement, reduction in carbon emissions, use of CNG/electric vehicles, rainwater harvesting and so on. The sustainability index of an LSP is evaluated by using the proposed framework.

LSPs can benchmark their sustainability index with respect to the best in the industry. Based on it, LSPs can also identify potential areas for improvement.

Novelty of the study lies in the proposed framework for evaluation of sustainability index which can be used to develop strategies for green logistics. LSPs can also improve their performance in terms of sustainability measures by adopting green logistics.

This paper aims to present a numerical analysis and comparison of two types of conductive fillers of polymeric composites subjected to lightning strikes.

Two types of conductive fillers were considered in the developed numerical models of electrically conductive composites: carbon nanotubes and polyaniline. For these fillers, the representative volume elements were developed to consider distribution of the particles that ensures percolation and homogenization of the materials within the Eshelby-based semi-analytical mean-field homogenization approach. The performed numerical analyses allowed determination of effective volume fractions of conducting particles, resistivity and conductivity tensors, and finally the current density for the simulated materials subjected to lightning strike.

The obtained results allowed for comparison of electrical conductivity of two simulated materials. It was observed that besides fair results obtained in the previous studies for intrinsically conducting polymers as fillers of composites dedicated for lightning strike protection, the composites filled with carbon nanotubes reveal much better conductivity.

The presented simulation results can be considered as initial information for further experimental tests on electrical conductivity of such materials.

The originality of the paper lies in the proposed design and simulation procedures of conductive composites as well as the comparison of selected composites dedicated for lightning strike protection as the most intensively developed materials for this purpose.

In recent years, deep learning and extended reality (XR) technologies have gained popularity in the built environment, especially in construction engineering and management. A significant amount of research efforts has been thus dedicated to the automation of construction-related activities and visualization of the construction process. The purpose of this study is to investigate potential research opportunities in the integration of deep learning and XR technologies in construction engineering and management.

This study presents a literature review of 164 research articles published in Scopus from 2006 to 2021, based on strict data acquisition criteria. A mixed review method, consisting of a scientometric analysis and systematic review, is conducted in this study to identify research gaps and propose future research directions.

The proposed research directions can be categorized into four areas, including realism of training simulations; integration of visual and audio-based classification; automated hazard detection in head-mounted displays (HMDs); and context awareness in HMDs.

This study contributes to the body of knowledge by identifying the necessity of integrating deep learning and XR technologies in facilitating the construction engineering and management process.

The increasing number of banks in the Ghanaian banking industry has brought about intense competition in the industry. The purpose of this paper is, therefore, to examine the factors that influence retail banking customers’ loyalty intentions.

In order to validate the proposed research model, the study adopts a survey design. Data were collected from 565 customers of the top performing banks in terms of customer deposits. Data analysis employed the partial least squares structural equation modeling (PLS–SEM) using SmartPLS version 3.

Results from the PLS–SEM analysis indicated that satisfaction, service quality and trust had significant effect on loyalty, with satisfaction having the most significant effect. Interestingly corporate image was found to have a significant effect on both satisfaction and trust but not on loyalty. In all, the proposed model accounted for 63.3 percent of the variation in loyalty.

The current study samples customers from only the top performing banks in Ghana. The use of cross-sectional data makes it impossible to study how customers’ perceptions change over time. Results from this study could, however, help managers of banks in designing strategies aimed at improving customer loyalty in order to consolidate their market share.

This paper adds to existing works that focus on loyalty in the retail banking sector, especially from the context of a developing economy. The study draws attention to the interrelationship among service quality, perceived value, satisfaction, image, trust and loyalty.