Fisheries and the aquaculture sector can play a significant role in the achievement of several of the goals of the 2030 Sustainable Development agenda. However, the current COVID-19 situation can negatively impact the fisheries sector, impeding the pace of the achievement of development goals. Therefore, this paper highlighted the performance and challenges of the fisheries sector in Bangladesh, emphasising the impact of COVID-19 and the significance of this sector for achieving the Sustainable Development Goals (SDGs), through primary fieldwork and secondary data. The total fish production in the country has increased more than six times over the last three decades (7.54 to 43.84 lakh MT) with improved culture techniques and extension services. Inland closed water contributions have increased to 16%, while inland open water has declined to 10%, and marine fisheries have dropped to 6% over the past 18 financial years (2000–2001 to 2018–2019). COVID-19, a significant health crisis, has also affected various issues associated with aquatic resources and communities. Transportation obstacles and complexity in the food supply, difficulty in starting production, labour crisis, sudden illness, insufficient consumer demand, commodity price hikes, creditor’s pressure, and reduced income were identified as COVID-19 drivers affecting the fisheries sector. The combined effect of these drivers poses a significant threat to a number of the SDGs, such as income (SDG1), nutrition (SDG2), and food security (SDG3 and SDG12), which require immediate and comprehensive action. Several recommendations were discussed, the implementation of which are important to the achievement of the SDGs and the improved management of the aquatic sector (SDG14—life below, and SDG16—life above water).

The electric power grid, as a critical national infrastructure, is under constant threat from cyber-attacks. State estimation (SE) is at the foundation of a series of critical control processes in a power transmission system. A false data injection (FDI) attack against SE can disrupt these control processes, crippling a power system and wreaking havoc in a region. With knowledge of the system topology, a cyber-attacker can formulate and execute stealthy FDI attacks that are very difficult to detect. Statistical and, more recently, machine learning approaches have been undertaken to detect FDI attacks on SE of the power grid. In this paper, we propose a Deep Learning (DL) based method to accurately detect stealthy FDI attacks on the SE of power grid. We compare the performance of the DL method with three popular machine learning algorithms, which are: gradient boosting machines (GBM), generalized linear modelings (GLM) and distributed random forests (DRF). All four algorithms analyze a dataset simulating the IEEE 14-bus system. The results demonstrate that these algorithms perform well in accurately and precisely detecting stealthy FDI attacks on the smart grid, with the DL-based approach showing best results.