Smart Search System of Autonomous Flight UAVs for Disaster Rescue.

UAVs (Unmanned Aerial Vehicles) have been developed and adopted for various fields including military, IT, agriculture, construction, and so on. In particular, UAVs are being heavily used in the field of disaster relief thanks to the fact that UAVs are becoming smaller and more intelligent. Search for a person in a disaster site can be difficult if the mobile communication network is not available, and if the person is in the GPS shadow area. Recently, the search for survivors using unmanned aerial vehicles has been studied, but there are several problems as the search is mainly using images taken with cameras (including thermal imaging cameras). For example, it is difficult to distinguish a distressed person from a long distance especially in the presence of cover. Considering these challenges, we proposed an autonomous UAV smart search system that can complete their missions without interference in search and tracking of castaways even in disaster areas where communication with base stations is likely to be lost. To achieve this goal, we first make UAVs perform autonomous flight with locating and approaching the distressed people without the help of the ground control server (GCS). Second, to locate a survivor accurately, we developed a genetic-based localization algorithm by detecting changes in the signal strength between distress and drones inside the search system. Specifically, we modeled our target platform with a genetic algorithm and we re-defined the genetic algorithm customized to the disaster site's environment for tracking accuracy. Finally, we verified the proposed search system in several real-world sites and found that it successfully located targets with autonomous flight.

Improving the accuracy of publicly available search engines in recognizing and classifying dental visual assets using convolutional neural networks.

AIM: To assess the accuracy of DigiBrain4, Inc (DB4) Dental Classifier and DB4 Smart Search Engine* in recognizing, categorizing, and classifying dental visual assets as compared with Google Search Engine, one of the largest publicly available search engines and the largest data repository. MATERIALS AND METHODS: Dental visual assets were collected and labeled according to type, category, class, and modifiers. These dental visual assets contained radiographs and clinical images of patients' teeth and occlusion from different angles of view. A modified SqueezeNet architecture was implemented using the TensorFlow r1.10 framework. The model was trained using two NVIDIA Volta graphics processing units (GPUs). A program was built to search Google Images, using Chrome driver (Google web driver) and submit the returned images to the DB4 Dental Classifier and DB4 Smart Search Engine. The categorical accuracy of the DB4 Dental Classifier and DB4 Smart Search Engine in recognizing, categorizing, and classifying dental visual assets was then compared with that of Google Search Engine. RESULTS: The categorical accuracy achieved using the DB4 Smart Search Engine for searching dental visual assets was 0.93, whereas that achieved using Google Search Engine was 0.32. CONCLUSION: The current DB4 Dental Classifier and DB4 Smart Search Engine application and add-on have proved to be accurate in recognizing, categorizing, and classifying dental visual assets. The search engine was able to label images and reject non-relevant results.

Constructing a bilingual website with validated database for Herb and Western medicine interactions using Ginseng, Ginkgo and Dong Quai as examples.

BACKGROUND: Concerns have been raised regarding the efficacy and safety resulting from the potential interactions of herbs with Western medications due to the use of both herbs and Western medicine by the general public. Information obtained from the web must be critically evaluated prior to its use in making decisions. DESCRIPTION: This study aimed to construct an herb-drug interaction (HDI) website (https://drug-herb-interaction.netlify.com) with a critically reviewed database. Node.js was used to store the database by running JavaScript. Vue.js is a front-end framework used for web interface development. A total of 135 sets of information related to the interactions of ginseng, ginkgo and dong quai with Western medicine from the literature identified in Medline were collected, followed by critical reviews to prepare nineteen items of information for each HDI monograph. A total of 80 sets of validated HDIs met all criteria and were further assessed at the individual reliability level (likely, possible, and unevaluable) and labeled with the "interaction" item. This query system of the website can be operated in both the Chinese and English languages to obtain all monographs on HDIs in the database, including bilingual interaction data. The database of HDI monographs can be updated by simply uploading a new version of the information Excel file. The designed "smart search" module, in addition to the "single search", is convenient for requesting multiple searches. Among the "likely" interactions (n = 26), 50% show negative HDIs. Ten of these can increase the effect of the Western drug, and the others (n = 3) imply that the HDI can be beneficial. CONCLUSIONS: The current study provides a website platform and 80 sets of validated bilingual HDIs involving ginseng, ginkgo and dong quai in an online database. A search of HDI monographs related to these three herbs can be performed with this bilingual, easy-to-use query website, which is feasible for professionals and the general public. The identified reliability level for each HDI may assist readers' decisions regarding whether taking Western medications concomitant with one of three herbal medicinal foods is safe or whether caution is required due to potentially serious outcomes.