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The purpose of this study was to identify and exhibit the interrelationships among COVID-19’s impacts on supply chain activities. Based on a literature review and the manager’s input, twenty COVID-19 impacts were collected. An integrated approach of exploratory factor analysis (EFA) and grey-decision-making trial and evaluation laboratory (G-DEMATEL) was used to reveal the causal interrelationships among the COVID-19 impacts. Initially, a questionnaire survey was administered among 220 respondents for EFA. Based on the outcome of EFA, the twenty COVID-19 impacts were categorized into seven critical areas. Then, based on the experts’ inputs, G-DEMATEL was utilized to reveal the causal interrelationships among various COVID-19 impacts. The results indicate that disruption management, relationship management, and production management are the top three critical areas that need to be addressed in the COVID-19 crisis. Disruption in supply, ripple effect on supply chain operations, and obsolescence of machines were found to be the most influential impacts while disproportionateness between supply and demand, difficulty in demand forecasting, and reduced cash inflow were found to be the most influenced impacts. This study’s outcomes will help policymakers and supply chain managers develop strategies to restructure supply chain networks. This study is an original contribution to the analysis of COVID-19 impacts in the supply chain activities in India due to the use of EFA and G-DEMATEL. This study considers India only, and hence, the outcomes lack generalizability. A study considering multiple developing countries could generalize these findings.

The purpose of this study was to identify and exhibit the interrelationships among COVID-19’s impacts on supply chain activities. Based on a literature review and the manager’s input, twenty COVID-19 impacts were collected. An integrated approach of exploratory factor analysis (EFA) and grey-decision-making trial and evaluation laboratory (G-DEMATEL) was used to reveal the causal interrelationships among the COVID-19 impacts. Initially, a questionnaire survey was administered among 220 respondents for EFA. Based on the outcome of EFA, the twenty COVID-19 impacts were categorized into seven critical areas. Then, based on the experts’ inputs, G-DEMATEL was utilized to reveal the causal interrelationships among various COVID-19 impacts. The results indicate that disruption management, relationship management, and production management are the top three critical areas that need to be addressed in the COVID-19 crisis. Disruption in supply, ripple effect on supply chain operations, and obsolescence of machines were found to be the most influential impacts while disproportionateness between supply and demand, difficulty in demand forecasting, and reduced cash inflow were found to be the most influenced impacts. This study’s outcomes will help policymakers and supply chain managers develop strategies to restructure supply chain networks. This study is an original contribution to the analysis of COVID-19 impacts in the supply chain activities in India due to the use of EFA and G-DEMATEL. This study considers India only, and hence, the outcomes lack generalizability. A study considering multiple developing countries could generalize these findings.

Using the UCI-HAR dataset, this paper examines human activity recognition (HAR) from the perspectives of data science and artificial intelligence. The primary objective is to present and evaluate the effectiveness of a multi-layer perceptron (MLP) model, concentrating on six different activity categories. We train and assess the MLP model using the UCI-HAR dataset, contrasting its results with those of convolutional neural networks (CNN). The MLP model shows competitive results, attaining an amazing 97% validation and testing accuracy, highlighting its efficiency for smaller datasets. An extensive study is carried out to assess the model's adaptation to a larger Motion Sense dataset using confusion matrices and cross-entropy, the model shows robustness with an accuracy of 89%. The MLP model performs admirably, demonstrating its capacity to pick up complex patterns. Results from comparative analysis with CNN are competitive, especially when dealing with smaller datasets. The suggested MLP model shows up as a practical and efficient way to advance HAR techniques. Its remarkable performance and versatility not only show its usefulness in real-world scenarios but also point to interesting directions for further study in the area of HAR.

Using the UCI-HAR dataset, this paper examines human activity recognition (HAR) from the perspectives of data science and artificial intelligence. The primary objective is to present and evaluate the effectiveness of a multi-layer perceptron (MLP) model, concentrating on six different activity categories. We train and assess the MLP model using the UCI-HAR dataset, contrasting its results with those of convolutional neural networks (CNN). The MLP model shows competitive results, attaining an amazing 97% validation and testing accuracy, highlighting its efficiency for smaller datasets. An extensive study is carried out to assess the model's adaptation to a larger Motion Sense dataset using confusion matrices and cross-entropy, the model shows robustness with an accuracy of 89%. The MLP model performs admirably, demonstrating its capacity to pick up complex patterns. Results from comparative analysis with CNN are competitive, especially when dealing with smaller datasets. The suggested MLP model shows up as a practical and efficient way to advance HAR techniques. Its remarkable performance and versatility not only show its usefulness in real-world scenarios but also point to interesting directions for further study in the area of HAR.

Prior works have suggested various strategies to increase construction waste recycling (CWR) rates. However, choosing the strategies is challenging without a lateral comparison. Therefore, this study aims to compare the usability of various strategies that target the enhancement of CWR implementation. To achieve this purpose, thirteen CWR enhancement strategies were identified from a systematic literature review. Then, questionnaire survey data were collected from 106 construction project managers. The collected data were analyzed via mean score ranking, normalization, overlap analysis, agreement analysis, and factor analysis. Additionally, the data were analyzed using a proposed formula for computing usability indexes using the cost, easiness, and effectiveness values. The results show that three strategies have high usability indexes: organize temporary bins in each construction zone, identify construction activities that produce recyclable materials, and enhance company policies related to CWR. These strategies with high usability indexes are consistent with the overlapping cheap, effective, and easy strategies. This study provides researchers and practitioners with optimal strategies for enhancing CWR implementation. Effective CWR enhancement strategies can improve CWR rates in construction projects. Future researchers can also adopt this study’s approach in computing usability indexes through questionnaire surveys.

Prior works have suggested various strategies to increase construction waste recycling (CWR) rates. However, choosing the strategies is challenging without a lateral comparison. Therefore, this study aims to compare the usability of various strategies that target the enhancement of CWR implementation. To achieve this purpose, thirteen CWR enhancement strategies were identified from a systematic literature review. Then, questionnaire survey data were collected from 106 construction project managers. The collected data were analyzed via mean score ranking, normalization, overlap analysis, agreement analysis, and factor analysis. Additionally, the data were analyzed using a proposed formula for computing usability indexes using the cost, easiness, and effectiveness values. The results show that three strategies have high usability indexes: organize temporary bins in each construction zone, identify construction activities that produce recyclable materials, and enhance company policies related to CWR. These strategies with high usability indexes are consistent with the overlapping cheap, effective, and easy strategies. This study provides researchers and practitioners with optimal strategies for enhancing CWR implementation. Effective CWR enhancement strategies can improve CWR rates in construction projects. Future researchers can also adopt this study’s approach in computing usability indexes through questionnaire surveys.

Reactions of ortho-, meta- and para-xylenes with benzyl chloride in the presence of anhydrous aluminium chloride as catalyst have been studied. The effects of variation of temperature, molar ratio of xylene to benzyl chloride, amount of catalyst and reaction time on the reactions have been investigated. Bangladesh J. Sci. Ind. Res. 41(3-4), 251-256, 2006

Reactions of ortho-, meta- and para-xylenes with benzyl chloride in the presence of anhydrous aluminium chloride as catalyst have been studied. The effects of variation of temperature, molar ratio of xylene to benzyl chloride, amount of catalyst and reaction time on the reactions have been investigated. Bangladesh J. Sci. Ind. Res. 41(3-4), 251-256, 2006