Plants have had historical significance in medicine since the beginning of civilization. The oldest medical pharmacopeias of the African, Arabian, and Asian countries solely utilize plants and herbs to treat pain, oral diseases, skin diseases, microbial infections, multiple types of cancers, reproductive disorders among a myriad of other ailments. The World Health Organization (WHO) estimates that over 65% of the world population solely utilize botanical preparations as medicine. Due to the abundance of plants, plant-derived medicines are more readily accessible, affordable, convenient, and have safer side-effect profiles than synthetic drugs. Plant-based decoctions have been a significant part of Jamaican traditional folklore medicine. Jamaica is of particular interest because it has approximately 52% of the established medicinal plants that exist on earth. This makes the island particularly welcoming for rigorous scientific research on the medicinal value of plants and the development of phytomedicine thereof. Viral infections caused by the human immunodeficiency virus types 1 and 2 (HIV-1 and HIV-2), hepatitis virus B and C, influenza A virus, and the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) present a significant global burden. This is a review of some important Jamaican medicinal plants, with particular reference to their antiviral activity.

The use of technology in higher education science classrooms rose significantly in the advent of the COVID-19 pandemic. In many universities, academic programs including introductory physics classes were taken online. Some institutions adopted online learning but also maintained face-to-face (F2F) laboratories when COVID-19 restrictions began to ease. Here, the effectiveness of the online learning approach in comparison with F2F learning is explored. The percentage difference in performance for students who took the online introductory physics course, Physics for Scientists and Engineers, versus that of students simultaneously taking the same course F2F is reported. This is done both across different sections taught by different instructors, and for the same course taught online versus F2F by the same professor. Furthermore, a short survey was conducted to assess the student learning experience and opinion about online and F2F learning. The results show equal or better overall performance for online learning with 4.2% higher performance when comparing results across different sections taught by different instructors. A similar 6.1% performance improvement was seen when comparing results across different sections taught by the same instructor. In contrast with the performance outcomes, the survey results indicate that about 72% of students prefer F2F compared to online learning. The findings provide a useful reference as many institutions and programs transition back to more standard F2F or hybrid learning modes. The benefits and drawbacks of each mode are discussed in the specific context of student preferences and challenges faced in online learning during COVID-19.

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Neural computing is an emerging research topic today due to its massive increase in demand and applications for machine learning. In this virtual simulation research work, using a free software, a program has been trained a neural network model and translate its functionality into the hardware. In the context of analog neural network, this research seeks to verify a shift sigmoid function that can approximate the transfer function of CMOS inverter. By showing this approximation accurately and reducing the number of components, it would help to implement the neural network based integrated chips. A conciliation is selected for the distance matric of the proposed function. This distance metric between the given CMOS transfer function and the shifted sigmoid function is minimized using the gradient descent. However, this approximate transfer function of CMOS inverter is chosen to verify in a three-layer perceptron networks. The network topology randomly generates weights to provide a diverse set of truth tables. We report two networks whose weights are chosen randomly using a back propagation algorithm due to volatile nature of the network topology and the activation function. The results of this research conclude that the transfer function of CMOS inverter is able to approximate the CMOS transfer function adequately for the purposes of these perceptron networks.