Technological prospects of Belt and Road Initiative in Bangladesh.

This study provides an alternative agenda to better explain the Belt and Road Initiative's (BRI's) technological connotations in Bangladesh using the Game Theory and Demand Curve approaches. BRI can proceed as a means to technology development for Bangladesh based on foreign direct investment (FDI) spillover effects that ranked China as the top FDI source, with 1159.42 million USD invested in 2018-2019. The findings suggest that motivated by mutual interests of economic transformation (China) and technological requirements (Bangladesh), BRI offers a bargaining game of cooperation. Thus, while economic transformation may force China to relocate its garment factories, Bangladesh's low wages and geopolitical location give it a superior position regarding relocation. The technological effects of such relocation will be two-fold: exchanges of tacit knowledge (conventional) and techno-based infrastructural support (component) that align with the proposed technology development framework on a macro level. More conventional technological projects and additional sector-based technology transfer are required to amplify BRI's technological forecasts. Moreover, to encourage more abundant FDI, bank loan interest must be decreased, and political stability has to be ensured. Both survey-based fieldwork and projects-based qualitative research need to be conducted to discover BRI's tangible technological implications.

Boosting Breast Cancer Detection Using Convolutional Neural Network.

Breast cancer forms in breast cells and is considered as a very common type of cancer in women. Breast cancer is also a very life-threatening disease of women after lung cancer. A convolutional neural network (CNN) method is proposed in this study to boost the automatic identification of breast cancer by analyzing hostile ductal carcinoma tissue zones in whole-slide images (WSIs). The paper investigates the proposed system that uses various convolutional neural network (CNN) architectures to automatically detect breast cancer, comparing the results with those from machine learning (ML) algorithms. All architectures were guided by a big dataset of about 275,000, 50 × 50-pixel RGB image patches. Validation tests were done for quantitative results using the performance measures for every methodology. The proposed system is found to be successful, achieving results with 87% accuracy, which could reduce human mistakes in the diagnosis process. Moreover, our proposed system achieves accuracy higher than the 78% accuracy of machine learning (ML) algorithms. The proposed system therefore improves accuracy by 9% above results from machine learning (ML) algorithms.