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Syzygium cumini (L.) skeels (Family: Myrtaceae) is a renowned medicinal plant traditionally used in various diseases and available in Bangladesh. This investigation was aimed to explore whether this plant has any potential antioxidant, antibacterial, and analgesic effects of methanolic extracts of S. cumini (leaves and bark). Quantitative determination of methanolic extracts of leaves and bark found the total phenolic contents as 199.11 mg GAE/g DW and 204.03 mg GAE/g DW, respectively. The leaves extract possessed mild antibacterial activity (leaves: 7.00 mm at 1 mg/disc, 9.15 mm at 5 mg/disc) against two different bacteria at different concentration whereas the bark extract showed no effect. A significant writhing (leaves: 23.17 ± 1.80, p≤0.001; bark: 24.33 ± 1.15, p≤0.001) and licking effects (leaves: 8.17 ± 1.49 sec, p≤0.05; bark: 9.08 ± 1.96 sec, p≤0.05) were found at 400 mg/kg of leaves and bark extracts of this plant which is very close to standard drugs mentioned in the tables. Results of this study demonstrated that methanolic extracts of S. cumini leaf and bark possessed significant antioxidant, analgesic, and mild antibacterial like activities which tend to suggest medicinal aspects. Dhaka Univ. J. Pharm. Sci. 21(1): 15-24, 2022 (June)

The offshore wind energy is increasingly becoming an attractive source of energy due to having lower environmental impact. Effective operation and maintenance that ensures the maximum availability of the energy generation process using offshore facilities and minimal production cost are two key factors to improve the competitiveness of this energy source over other traditional sources of energy. Condition monitoring systems are widely used for health management of offshore wind farms to have improved operation and maintenance. Reliability of the wind farms are increasingly being evaluated to aid in the maintenance process and thereby to improve the availability of the farms. However, much of the reliability analysis is performed offline based on statistical data. In this article, we propose a drone-assisted monitoring based method for online reliability evaluation of wind turbines. A blade system of a wind turbine is used as an illustrative example to demonstrate the proposed approach., A modified version of this work has been published in the 2022 International Conference on Computing, Electronics & Communications Engineering (iCCECE). This work is a draft author version

Safety and reliability are rigorously assessed during the design of dependable systems. Probabilistic risk assessment (PRA) processes are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). In conventional PRA, failure data about components is required for the purposes of quantitative analysis. In practice, it is not always possible to fully obtain this data due to unavailability of primary observations and consequent scarcity of statistical data about the failure of components. To handle such situations, fuzzy set theory has been successfully used in novel PRA approaches for safety and reliability evaluation under conditions of uncertainty. This paper presents a review of fuzzy set theory based methodologies applied to safety and reliability engineering, which include fuzzy FTA, fuzzy FMEA, fuzzy ETA, fuzzy Bayesian networks, fuzzy Markov chains, and fuzzy Petri nets. Firstly, we describe relevant fundamentals of fuzzy set theory and then we review applications of fuzzy set theory to system safety and reliability analysis. The review shows the context in which each technique may be more appropriate and highlights the overall potential usefulness of fuzzy set theory in addressing uncertainty in safety and reliability engineering.