Pharmacological Activities of Banana

Plants have been in use in traditional medicine since antiquity, and many active metabolic products with biological significance are obtained from them. Recently, pharmaceutical industries have developed great interest in utilizing these products as an alternative to the chemically synthesized drugs. This is due to the discovery of important new medicines from the plants, because of studies on how people of different background use plants as cure and treatment for many diseases, and side effects of the synthesized drugs. Banana, an eatable fruit produced by some herbaceous flowering plants of the genus Musa, is one of the valuable fruits with proven pharmacological potentials. Bananas are spread almost all over the world. Different chemical constituents like apigenin glycosides, myricetin-3-O-rutinoside, kaempferol-3-O-rutinoside, dopamine, and serotonin have been reported in different parts and varieties of banana. The presence of carbohydrate, proteins as well as flavonoids, makes bananas useful in both nutrition and therapeutics. Pharmacologically, bananas have been shown to possess antiulcer, antimicrobial, and antioxidant activities. This chapter discusses the essential information on banana, including its varieties, distribution, pharmacological actions, and its relevance in pharmaceutical industries. This will be beneficial for researchers to further harness the robustness of this fruit in controlling many diseases and modification of drugs

Biochemical and kinetic properties of crude phospholipase A2 from Naja nigricollis venom

Snake envenomation has been a source of concern worldwide due to potential mortality risks associated with it. This study was carried out to investigate the biochemical and kinetic properties of crude phospholipase A2 (PLA2) from Naja nigricollis venom. Biochemical and kinetic properties of the venom was determined using standard procedure. Though the results showed that the enzyme PLA2 is stable at pH 4 and 6, the optimum pH of activity of the PLA2 is 3, while the optimum temperature of activity of the enzyme is 70 oC. Of all the activators used (CoCl2, CaCl2, MnCl2 and MgCl2), MgCl2 was the highest activator of the enzyme activity at 5 mM. The suggestive amino acids in the active site of the PLA2 were aspartate and glutamate. The kinetic parameters, Km and Vmax of the PLA2 were found to be 0.6 mg/ml and 3.3 μmol/min respectively. These findings could lend support to more understanding of biochemical and kinetic properties of crude Naja nigricollis PLA2

Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems

The demand for cyber-physical systems (CPSs) has recently increased in various domains, such as smart grids, intelligent transportation, and critical infrastructure. The massive data networks and communication layers generated make CPSs vulnerable to threats and cyberattacks. To mitigate these threats, artificial intelligence (AI) approaches are employed. However, AI models struggle to keep up with the constantly changing attack landscape. This study investigates the application of extreme gradient boosting (XGBoost) and long-short-term memory (LSTM) AI models for cyberattack detection in a CPS. Accuracy, precision, recall, and the F1-score validate the approach as evaluation metrics. The methods were tested on a gas pipeline industrial control system dataset and other benchmark datasets, such as NetML-2020 and IoT-23, which contain various cyberattacks. The performance of the two methods was found to be better than other models such as support vector machine (SVM) and artificial neural networks (ANN) on several evaluation metrics. Finally, we present recommendations for future research

Development and Integration of Metocean Data Interoperability for Intelligent Operations and Automation Using Machine Learning: A Review

The current oil industry is moving towards digitalization, which is a good opportunity that will bring value to all its stakeholders. The digitalization of oil and gas discovery, which are production-based industries, is driven by enabling technologies which include machine learning (ML) and big data analytics. However, the existing Metocean system generates data manually using sensors such as the wave buoy, anemometer, and acoustic doppler current profiler (ADCP). Additionally, these data which appear in ASCII format to the Metocean system are also manual and silos. This slows down provisioning, while the monitoring element of the Metocean data path is partial. In this paper, we demonstrate the capabilities of ML for the development of Metocean data integration interoperability based on intelligent operations and automation. A comprehensive review of several research studies, which explore the needs of ML in oil and gas industries by investigating the in-depth integration of Metocean data interoperability for intelligent operations and automation using an ML-based approach, is presented. A new model integrated with the existing Metocean data system using ML algorithms to monitor and interoperate with maximum performance is proposed. The study reveals that ML is one of the crucial and key enabling tools that the oil and gas industries are now focused on for implementing digital transformation, which allows the industry to automate, enhance production, and have less human capacity. Lastly, user recommendations for potential future investigations are offered