In recent years scientists worldwide have realized that the effective life span of any antimicrobial agent is limited, due to increasing development of resistance by microorganisms. Consequently, numerous studies have been conducted to find new alternative sources of antimicrobial agents, especially from plants. The aims of this project were to examine the antimicrobial properties of essential oils distilled from Australian-grown Ocimum tenuiflorum (Tulsi), to quantify the volatile components present in flower spikes, leaves and the essential oil, and to investigate the compounds responsible for any activity. Broth micro-dilution was used to determine the minimum inhibitory concentration (MIC) of Tulsi essential oil against selected microbial pathogens. The oils, at concentrations of 4.5 and 2.25% completely inhibited the growth of Staphylococcus aureus (including MRSA) and Escherichia coli, while the same concentrations only partly inhibited the growth of Pseudomonas aeruginosa. Of 54 compounds identified in Tulsi leaves, flower spikes, or essential oil, three are proposed to be responsible for this activity; camphor, eucalyptol and eugenol. Since S. aureus (including MRSA), P. aeruginosa and E. coli are major pathogens causing skin and soft tissue infections, Tulsi essential oil could be a valuable topical antimicrobial agent for management of skin infections caused by these organisms.

The current paper aims to introduce a proposed framework for gamification in the e-learning system at Saudi Arabia universities, by reviewing and analyzing the concept of gamification and its components, and types of players, as well as conditions for designing successful gamification, and the technical capabilities of Saudi Arabia universities, and also by reviewing and analyzing many designing models for gamification and e-learning systems that were introduced by the other researchers. Then incorporate all of these previous considerations into the general stages of instructional design, which is represented in analysis, design, development, implementation, and evaluation. The paper also deals with a review of the difference between gamification, games, and game-based learning, in addition to the benefits for students from employing gamification in the e-learning system.

BackgroundThe foraging choices of honey bees are influenced by many factors, such as floral aroma. The composition of volatile compounds influences the bioactivity of the aromatic plants and honey produced from them. In this study, Agastache rugosa was evaluated as part of a project to select the most promising medicinal plant species for production of bioactive honey.MethodsHeadspace solid-phase microextraction HS-SPME /GC-MS was optimized to identify the volatile bioactive compounds in the leaves, flower spikes, and for the first time, the flower nectar of Australian grown A. rugosa.ResultsMethyl chavicol (= estragole) was the predominant headspace volatile compound in the flowers with nectar, flower spikes, and leaves, with a total of 97.16%, 96.74% and 94.35%, respectively. Current results indicate that HS–SPME/GC–MS could be a useful tool for screening estragole concentration in herbal products.ConclusionRecently, estragole was suspected to be carcinogenic and genotoxic, according to the European Union Committee on Herbal Medicinal Products. Further studies are needed on safe daily intake of Agastache as herbal tea or honey, as well as for topical uses.

One of the challenges in e-learning is the customization of the learning environment to avoid learners’ failures. This paper proposes a Stacked Generalization for Failure Prediction (SGFP) model to improve students’ results. The SGFP model mixes three ensemble learning classifiers, namely, Light Gradient Boosting Machine (LGBM), eXtreme Gradient Boosting machine (XGB), and Random Forest (RF), using a Multilayer Perceptron (MLP). In fact, the model relies on high-quality training and testing datasets that are collected automatically from the analytic reports of the Blackboard Learning Management System (i.e., analytic for learn (A4L) and full grade center (FGC) modules. The SGFP algorithm was validated using heterogeneous data reflecting students’ interactivity degrees, educational performance, and skills. The main output of SGFP is a classification of students into three performance-based classes (class A: above average, class B: average, class C: below average). To avoid failures, the SGFP model uses the Blackboard Adaptive Release tool to design three learning paths where students have to follow automatically according to the class they belong to. The SGFP model was compared to base classifiers (LGBM, XGB, and RF). The results show that the mean and median accuracies of SGFP are higher. Moreover, it correctly identified students’ classifications with a sensitivity average of 97.3% and a precision average of 97.2%. Furthermore, SGFP had the highest F1-score of 97.1%. In addition, the used meta-classifier MLP has more accuracy than other Artificial Neural Network (ANN) algorithms, with an average of 97.3%. Once learned, tested, and validated, SGFP was applied to students before the end of the first semester of the 2020-2021 academic year at the College of Computer Sciences at Umm al-Qura University. The findings showed a significant increase in student success rates (98.86%). The drop rate declines from 12% to 1.14% for students in class C, for whom more customized assessment steps and materials are provided. SGFP outcomes may be beneficial for higher educational institutions within fully online or blended learning schemas to reduce the failure rate and improve the performance of program curriculum outcomes, especially in pandemic situations.