Globalizing Standard of Patent Protection in WTO Law and Policy Options for the LDCs: The Context of Bangladesh

This Article analyzes the globalizing standard of patent protection as adopted under the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS Agreement) of the World Trade Organization (WTO) and possible options for the Least Developed Countries (LDCs) such as Bangladesh against the experiences of Brazil, India, and South Africa with special reference to pharmaceutical patent issues

The Experiences of TRIPS-Compliant Patent Law Reforms in Brazil, India, and South Africa and Lessons for Bangladesh

This study analyzes the policy options used by Brazil, India, and South Africa in their transitions to a TRIPS-compliant patent law and their introduction of pharmaceutical patents. This comparative review can be used to explore possible policy options that can also be utilized by LDCs, including Bangladesh

Globalizing Standard of Patent Protection in WTO Law and Policy Options for the LDCs: The Context of Bangladesh

This Article analyzes the globalizing standard of patent protection as adopted under the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS Agreement) of the World Trade Organization (WTO) and possible options for the Least Developed Countries (LDCs) such as Bangladesh against the experiences of Brazil, India, and South Africa with special reference to pharmaceutical patent issues

A comprehensive review on sustainable coastal zone management in Bangladesh: Present status and the way forward

Bangladesh, a coastal developing nation with a diverse sustainable biodiversity of natural resources is currently focused upon by international communities as a result of its high potential of the coastal zone (CZ) with natural gas. Sustainable Coastal Zone Management (SCZM) is key to its national development. SCZM refers to the management of coastal resources in order to provide secure and alternative livelihoods, as well as to manage all types of coastal hazards and social and cultural well-being in order to ensure long-term productivity and minimize environmental impact. This paper aims to delineate the current initiatives and status of coastal management in Bangladesh, highlighting key issues such as climate changes, sea level rise, tropical cyclones, coastal and marine pollution, coastal erosions, saltwater intrusions, and mangrove degradations as well as the future trend in Bangladesh which will facilitate sustainable development by emphasizing the social, ecological, and economic pillars of sustainability. Unsustainable coastal development practices in Bangladesh are going to damage the coastal ecosystems, particularly mangrove forests and coral reefs, which provide protection against tropical cyclones caused by global climate change and coastal erosions. The paper concludes by outlining a roadmap toward achieving SCZM in Bangladesh. The road to achieving SCZM requires collaboration, integration of scientific research, policy frameworks, community engagement, capacity building, and long-term commitment from all stakeholders involved. So, it is required to address all kinds of coastal issues and reframes all existing coastal management practices to ensure a healthy productive ecosystem to achieve SCZM as well as the sustainable development of the country

Integrating convolutional neural networks for microscopic image analysis in acute lymphoblastic leukemia classification: A deep learning approach for enhanced diagnostic precision

Leukemia is a type of cancer characterized by the exponential growth of abnormal blood cells, which damages white blood cells and disrupts the function of the human body’s bone marrow. It is very challenging to classify because blood smear images are complicated, and there is a lot of variation between each class. Acute Lymphoblastic Leukemia (B-ALL) is one of the subtypes of leukemia. It is a rapidly progressing cancer that originates in B lymphocytes, characterized by the overproduction of immature B lymphoblasts. The purpose of this work is to classify different types of B-ALL subtypes such as Benign, Malignant Early Pre-B, Malignant Pre-B, and Malignant Pro-B from the peripheral blood smear images effectively. To accomplish this task, a novel deep-learning technique based on a fine-tuned ResNet-50 model has been developed. Our fine-tuned ResNet-50 model integrates several additional customized fully connected layers, including dense and dropout layers. Various data augmentation techniques such as flipping, rotation, and zooming have been applied to mitigate the risk of overfitting. In addition, a five-fold cross-validation technique has been employed to enhance the model’s generalization. The performance of our proposed technique is compared with several other methods, including VGG-16, DenseNet-121, and EfficientNetB0, as well as existing baselines, using different performance metrics. Experimental results demonstrate the superiority of the fine-tuned ResNet-50 model, achieving the highest accuracy and an F1-score of 99.38%. It also outperforms existing state-of-the-art approaches by a significant margin. The proposed fine-tuned ReNet-50 model achieves such performance without the need for microscopic image segmentation which indicates its potential utility in healthcare sectors in enhancing precise leukemia diagnosis