Urbanization is a continuous process for a city’s economic development. Though rapid urbanization provides a huge employment opportunity for people, urban threats also increase proportionately due to natural and man-made hazards. Understanding urban resilience and sustainability is an urgent matter to face hazards in the rapidly urbanized world. Therefore, this study aims to clarify the concept and develop key indications of urban resilience and sustainability from the existing literature. A systematic literature review guided by PRISMA has been conducted using literature from 1 January 2001 to 30 November 2021. It argues that sustainability and resilience are interrelated paradigms that emphasize a system’s capacity to move toward desirable development paths. Resilience and sustainability are fundamentally concerned with preserving societal health and well-being within the context of a broader framework of environmental change. There are significant differences in their emphasis and time scales, particularly in the context of urbanization. This study has identified key indicators of urban resilience under three major components like adaptive capacity (education, health, food, and water), absorptive capacity (community support, urban green space, protective infrastructure, access to transport), and transformative capacity (communication technology, collaboration of multi-stakeholders, emergency services of government, community-oriented urban planning). This study also identified several indicators under major dimensions (social, economic, and environmental) of urban sustainability. The findings will be fruitful in understanding the dynamics of urban vulnerability and resilience and its measurement and management strategy from developed indicators.

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.

Bangladesh is one of the most disaster-prone countries in the world. In particular, its riverine-island (char) dwellers face continuous riverbank erosion, frequent flooding, and other adverse effects of climate change that increase their vulnerability. This paper aims to assess the livelihood vulnerability of riverine communities by applying the Intergovernmental Panel on Climate Change (IPCC) vulnerability framework and the livelihood vulnerability index (LVI). Results indicate substantial variation in the vulnerability of char dwellers based on mainland proximity. The main drivers of livelihood vulnerability are char-dweller adaptation strategies and access to food and health services. The study further reveals that riverbank erosion, frequent flood inundation, and lack of employment and access to basic public services are the major social and natural drivers of livelihood vulnerability. Char-based policy focusing on short- and long-term strategy is required to reduce livelihood vulnerability and enhance char-dweller resilience.