Bridge inspection is a crucial part of maintaining key infrastructures and ensuring public safety. However, the bridge safety inspections require substantial manpower and a variety of equipment, causing issues of high cost, accessibility limitations, and safety risks. To solve these issues, some states have begun to explore the use of rapidly growing drone technology to replace the traditional inspection method. The project teams at Marshall University created a prototype of Interactive Bridge Inspection Research using Drone (iBIRD) as proof of the concept to support the process of bridge inspection using drones. iBIRD consists of (1) a data collection module with a mobile application gathering inspection data using drones and (2) a web framework module of a database-driven Bridge Inspection Management tool. The mobile application in the data collection module is developed using React Native and the web framework of the bridge inspection management system is developed using PHP and MySQL. This paper describes details of the iBIRD system and the drone-assisted bridge inspection process. The results of this study will be of value to federal, state, and local transportation agencies and industry practitioners conducting bridge inspections. Further research will be conducted on improving data security of iBird and an automatic bridge inspection using image processing techniques with machine learning.

Periodontitis is a chronic inflammatory disease of the tissue around the teeth. The early detection of periodontitis before it manifests undesirable irreversible destruction of periodontal tissues has been an important issue in public dental health. The clinical examination is a traditional way of the diagnosis of periodontal diseases but is often insufficient and does not provide information on the current activity of periodontitis or its progression. After intensive clinical studies in the Department of Preventive and Social Dentistry at Seoul National University (SNU) in South Korea, the salivary matrix-metalloproteinase (MMP)-9 was identified as one of the major enzymes responsible for the initiation of periodontitis. SNU developed a point-of-care (POC) kit for a lateral flow test (LFT) using MMP-9 and created a diagnostic model based on a patient's personal information for screening periodontitis. After the successful clinical studies, the Rapid Periodontitis screening Tool (RPT), a database-driven web application, was developed to measure the risk of periodontitis online with the collaboration between the School of Dentistry at SNU and the Computer Science Program at Marshall University in the United States. The web interface in RPT allows anyone to enter their personal data and the value of LFT test results to receive the screening result immediately. The RPT also provides member pages to track down the test results in the long run. Once commercialized, the RPT will help early detection of periodontitis to enhance public health. This paper describes details of the tool and future research direction.

Diabetes is a long-lasting health condition associated with improper regulation of glucose levels in the body. This chronic disease occurs when blood glucose is too high, causing a variety of complications as well as being the leading cause of death. As the prevalence of diabetes increases, it is critical to plan a new approach for the prevention and management of the disease. However, current disease control methods have limitations in that user convenience and accessibility are low. Recent studies have shown that the development of a disease control system using machine learning is a suitable prediction of diabetes incidences with high accuracy in adults. In this paper, we present a diabetes incidence prediction system using a multilayer perceptron neural network that allows users to enter simple data and increases the convenience and accessibility of the prediction capabilities. The proposed system was evaluated on Pima Indians Diabetes and showed a good performance in predicting diabetes incidences.