6 research outputs found
The Impact of Recent Currency Devaluation on the Export and Import Performance of Bangladesh: An Empirical Study
No full text<p>This study examines the effect of the devaluation of local currency BDT against the US Dollar on the import-export performance of Bangladesh. Time series monthly basis data on export, import & exchange rate of Bangladesh from January-2021 to September- 2022 is analyzed in this study to explore the impact. Data from secondary sources like Bangladesh Bank, Export Promotion Bureau, etc. is used in this paper. The regression model is applied here to estimate the outcomes. Major findings of the paper are: the regression model estimates the devaluation of BDT against USD has a significant impact on imports of Bangladesh where it shows insignificant impact on exports of the country. In addition, the finding of this paper is against the basic economic theory in the aspect of imports of Bangladesh since despite the devaluation of local currency, the volume of imports is increasing continuously where it needs to decrease according to economics. Moreover, both the exports and imports of Bangladesh are in increasing trend over the last couple of years.</p><p>Keywords:- Currency Devaluation, Exchange Rate Volatility, Export, Import, BDT, USD, Regression Model, Bangladesh Bank.</p>
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Data Envelopment Analysis (DEA) for Improving Ergonomics and Workplace Performance
No full textAddressing the Needs of F-2 Wives in the United States
No full textThe author analyzes the impact of current university regulations and policies on the everyday lives of wives of international students. The research process involved interviews with twenty-six women, located at two educational institutions, who came to the US on an F-2 visa (student dependent visa). It also included analysis of documents related to immigration policies and university regulations that had a direct impact on the experiences of wives of international students. The findings show that F-2 wives' adjustment experiences are strongly influenced by the level of institutional support provided by the university. The chapter concludes with recommendations for federal and university policies that create a welcoming environment for international students and their families.</jats:p
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Ergonomic Risk and Performance Assessment using Data Envelopment Analysis (DEA)
No full textDriver Fatigue Detection using CWT-Extracted Features and a Deep Learning Approach (CNNLSTM)
No full textDriver fatigue is a leading reason behind traffic accidents globally, resulting in significant threats to public safety and substantial economic costs. Electroencephalography (EEG) has shown to be a critical tool for identifying driver fatigue as it can record brain activity associated with sleepiness, which gives it an advantage over other physiological modalities. Although raw EEG data can provide insightful information, accurate fatigue identification requires strong feature extraction techniques because of the inherent complexity of the data. This emphasizes how urgent it is to investigate revolutionary deep-learning architectures that can successfully extract discriminative features from raw EEG data. This study provides an innovative framework for driver fatigue identification from EEG that combines continuous wavelet transform (CWT) with convolutional neural networks (CNN) and long-short term memory (LSTM). In order to gain the advantage over the drawbacks of manual feature extraction, this study generates time-frequency spectrum representations of EEG data using the CWT. Following extracting information, each channel’s time-frequency images are concatenated and fed into a CNN-LSTM architecture. This combination models the temporal and spatial characteristics of the EEG data and automatically learns discriminative features for identifying drivers’ normal and fatigued states. A publicly available EEG dataset with recordings from twelve subjects is used to evaluate the proposed CWT-CNN-LSTM architecture. The result shows a prominent classification accuracy of 98.34% for both the average of each subject and combined subjects. These findings show how well the CNN-LSTM framework captures EEG patterns associated with fatigue, which may result in more reliable driver fatigue detection systems and improved traffic safety
