Flavor is an expression of olfactory and gustatory sensations experienced through a multitude of chemical processes triggered by molecules. Beyond their key role in defining taste and smell, flavor molecules also regulate metabolic processes with consequences to health. Such molecules present in natural sources have been an integral part of human history with limited success in attempts to create synthetic alternatives. Given their utility in various spheres of life such as food and fragrances, it is valuable to have a repository of flavor molecules, their natural sources, physicochemical properties, and sensory responses. FlavorDB (http://cosylab.iiitd.edu.in/flavordb) comprises of 25,595 flavor molecules representing an array of tastes and odors. Among these 2254 molecules are associated with 936 natural ingredients belonging to 34 categories. The dynamic, user-friendly interface of the resource facilitates exploration of flavor molecules for divergent applications: finding molecules matching a desired flavor or structure; exploring molecules of an ingredient; discovering novel food pairings; finding the molecular essence of food ingredients; associating chemical features with a flavor and more. Data-driven studies based on FlavorDB can pave the way for an improved understanding of flavor mechanisms.

Recently, there has been a substantial amount of research on smart classrooms, encompassing a number of areas, including Information and Communication Technology, Machine Learning, Sensor Networks, Cloud Computing, and Hardware. Smart classroom research has been quickly implemented to enhance education systems, resulting in higher engagement and empowerment of students, educators, and administrators. Despite decades of using emerging technology to improve teaching practices, critics often point out that methods miss adequate theoretical and technical foundations. As a result, there have been a number of conflicting reviews on different perspectives of smart classrooms. For a realistic smart classroom approach, a piecemeal implementation is insufficient. This survey contributes to the current literature by presenting a comprehensive analysis of various disciplines using a standard terminology and taxonomy. This multi-field study reveals new research possibilities and problems that must be tackled in order to integrate interdisciplinary works in a synergic manner. Our analysis shows that smart classroom is a rapidly developing research area that complements a number of emerging technologies. Moreover, this paper also describes the co-occurrence network of technological keywords using VOSviewer for an in-depth analysis.

The term "blended learning" has gained considerable interest in recent years as a description of particular forms of teaching combined with technology. This project reports in some detail the experiences of a small group of undergraduate learners as they progress through their Bachelor course at University of Wollongong in Dubai (UOWD) in the United Arab Emirates. In particular, this study looks at discussion forum approach as a blended learning initiative and what that entails to the learners in terms of making the subject more interactive and enhances students analytical and research skills. From the findings, conclusion has been drawn regarding the role of Blackboard tool in learning by helping students to obtain a deep sense of understanding of how to operate in a virtual team despite the challenges.

Smart building incorporates the ubiquitous sensing ability of Internet of things (IoT) technology with the industrial infrastructure for automating decision‐making. Inspired by this, a novel technique of stochastic game net (SGN) has been proposed to minimize the tangible and intangible infrastructural losses due to disasters. Specifically, IoT technology is used for disaster management and control in a smart building based on SGN. In the proposed technique, every IoT sensor act as an individual player with prefixed action sets and strategies. Complete SGN of the IoT network in the smart building is formalized by collaborating SGN of an individual sensor. The model deployed in IoT building with different sensors detects disasters beforehand and generates early warning alerts to management units based on game‐theoretic decision‐making. Experimental evaluation on four challenging datasets, namely, disaster/accident sources, gas sensor drift, red cross smoke alarm, and oil spills show the effectiveness of a proposed framework in the smart building. The results are compared with several state‐of‐the‐art decision‐making techniques to measure the overall effectiveness of the proposed system. Based on simulations performed on NetLogo 5.3.1, enhanced results of performance parameters including temporal delay, statistical efficiency, reliability, and stability were registered. Moreover, mathematical analysis has been carried out to determine the overall performance of the presented technique.