Biodiversity in realism is a measure of the adherents of species that characterize a biological community and thought to be one of the extremely imperative aspects of community establishment and structure. The study regarding the fish biodiversity is very much needed as it is directly related to the fisheries resource structure and also contributes significantly towards resource richness. Therefore the present study was performed to evaluate the concurrent fish species composition, abundance and some major fish biodiversity indices of the River Dhaleshwari of Bangladesh. The study area was about 564.20 ha and 20 kilometers long along the main stream of the river Dhaleshwari. The starting point was the Tulshikhali bridge, Keranigonj and the end point was Balur char, Munshigonj. The geographical locations of the sampling stations were between 90̊ 17ʹ E to 90̊ 25ʹ E and 23º 40´ N to 23º 37´ N. The study was conducted between August’2015 and October’2016.The fish species diversity showed spatial variation among the sampling stations. The biodiversity appraisal validates Shannon index (0.122-0.634) with highest value in Balur char and lowest in Pathor ghata; Simpson’s index (0.325-0.893) with utmost valuation for the Pathor ghata and lowermost for Balur char; Pielou’s evenness index (0.117-0.588) with maximum value for the Balur char and least for Pathor ghata; Margaleff index (4.793-7.438) with uppermost value for the Balur char and minimum in Tulshikhali; topmost abundance of fish was recorded from Tulshikhali and least for Balur char and maximum number of unique species was recorded for Tulshikhali and minimum from Balur char. Moreover, the current study correspondingly has ascertained the pragmatism and efficacy of biodiversity assessment to scrutinize and epitomize fisheries resources for better management of the river Dhaleshwari. Effective management approach should be applied for precisely maintaining the fish habitat health and ecological condition intact before it’s too late.
Carrot is a fast-growing and nutritious vegetable cultivated throughout the world for its edible roots. The farmers are still learning the scientific methods of carrot production worldwide. For the production of good quality carrots, modern technology is not being used to its fullest to detect carrot vegetable diseases in the farms. As a result, the farmers face difficulties now and then in continuous monitoring and detecting defects in carrot crops. Hence, this paper proposes an efficient carrot disease identification and classification method using a deep learning approach, especially Convolutional Neural Network (CNN). In this research, five different carrot diseases including healthy carrots have been examined and experimented with four different pretrained models of CNN architecture, i.e., VGG16, VGG19, MobileNet, and Inception v3. Among the four models, the Inception v3 model is selected as an efficient pretrained CNN architecture to build an effective and robust system. The Inception v3 based system proposed here takes carrot images as input and examines whether they are healthy or infected, and provides output accordingly. To train and evaluate the system, a robust dataset is used, which consists of original and synthetic data. In the Fully Connected Neural Network (FCNN), dropout is used to solve the problem of overfitting as well as to improve the accuracy of the system. The accuracy achieved from the method which uses Inception v3 is 97.4%, which is undoubtedly helpful for the farmers to identify carrot disease and maximize their benefits to establish sustainable agriculture.
Since its independence, Bangladesh has supplied millions of its skilled and unskilled manpower to hundreds of countries, which, in turn, has positively influenced the domestic labour market and GDP growth. Remittances coming from migrant workers have immensely contributed to the micro and macro economy of Bangladesh. Studies show that the rising flow of overseas employment due to labour shortage in numerous developed and developing countries after the pandemic has generated both opportunities and challenges for Bangladeshi manpower. This study found that there are inflated demands for skilled and professional workers all over the world. However, Bangladesh is still unable to supply expected quality workers foreign individuals, companies and countries have demanded. The reasons behind this, our study found, are insufficient government initiatives, recruitment agencies failure, not having enough opportunity for training and education and lack of awareness among workers as well as syndicate issues and illegal migration. This study suggests that addressing the above-mentioned issues could help the respective authorities perpetuate the rising flow of foreign employment of Bangladeshi manpower.
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