Electrospun technology becomes a valuable means of fabricating functional polymeric nanofibers with distinctive morphological properties for drug delivery applications. Nanofibers are prepared from the polymer solution, which allows the direct incorporation of therapeutics such as small drug molecules, genes, and proteins by merely mixing them into the polymeric solution. Due to their biocompatibility, adhesiveness, sterility, and efficiency in delivering diverse cargoes, electrospun nanofibers have gained much attention. This review discusses the capabilities of the electrospun nanofibers in delivering different therapeutics like small molecules, genes, and proteins to their desired target site for treating various ailments. The potential of nanofibers in administering through multiple administration routes and the associated challenges has also been expounded along with a cross‐talk about the commercial products of nanofibers for biomedical applications.
This paper introduces a real-time neighbour scoring system, using data collected from various web-based APIs, to facilitate "15-minute city" designs. The system extends on the current state of the art in three ways; first, it incorporates a multi-source urban API, to automate the extraction of location-based information from online sources; second, it provides a quantitative method to calculate and index "15-minute city" performance; and third, it provides a web-based application, to allow real-time feedback of neighbourhood design performance complementing the design refinements at a building and tenancy level. In addition to discussing its theoretical basis, and technical implementation, this paper provides a case study to demonstrate how the neighbourhood scoring system is incorporated into the design of a hypothetical mixed-use urban development.
Synthetic Machine Learning" offers a revolutionary leap in real-time environmental analysis for conceptual architectural design. By integrating automatic synthetic data generation, artificial neural network (ANN) training and online deployment, Synthetic Machine Learning offers two main advantages over conventional simulation; First, it reduces the analysis time for a reference simulation from minutes to seconds; Second, it is possible to deploy ANN as a web service in an online design environment, which therein increases accessibility, significantly reducing simulation costs and setup time. The application of Synthetic Machine Learning to perform Daylight Autonomy (DA) and Spatial Daylight Autonomy (sDA) studies to maximise building daylighting for a given use, window to wall ratio, and floorplan arrangement is showcased through a preliminary demonstration work. Comparatively the use of algorithmically generated synthetic data versus real-world data is becoming ubiquitous in other disciplines, the advantages of this approach to the building design process are further discussed.
Market Basket Analysis is a technique to identify items likely to be purchased together. A predictive market basket analysis is used to identify sets of products/services purchased or events that occur generally in sequence. The basic approach is to find the associated pairs of items in a store when there are transaction data sets. Hence, our proposed system performing "Market Basket Analysis" will help the retailers to make better decisions throughout the entire company which will help in increasing the profits and effectiveness of the organization. Also, by controlling the order of products and marketing visits or the transactions of the customers could be increased. The system will take the large transactional data sets from the retailers and find the associations between different items from the item sets. These associations of the items purchased frequently and the items that are purchased together will be presented in graphical formats such as tables, pie-charts, bar graphs etc. There are different functionalities or patterns providing for performing analysis such as weekend-weekday sales analysis, month-end sales analysis analysis on different customer profiles etc. The system will be built in "Apache SPARK" framework using Scala and processed on Amazon AWS and the data will be stored at its HDFS on the cluster.
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