Narongrit Waraporn

A global model of malaria climate sensitivity: comparing malaria response to historic climate data based on simulation and officially reported malaria incidence.

BackgroundThe role of the Anopheles vector in malaria transmission and the effect of climate on Anopheles populations are well established. Models of the impact of climate change on the global malaria burden now have access to high-resolution climate data, but malaria surveillance data tends to be less precise, making model calibration problematic. Measurement of malaria response to fluctuations in climate variables offers a way to address these difficulties. Given the demonstrated sensitivity of malaria transmission to vector capacity, this work tests response functions to fluctuations in land surface temperature and precipitation.MethodsThis study of regional sensitivity of malaria incidence to year-to-year climate variations used an extended Macdonald Ross compartmental disease model (to compute malaria incidence) built on top of a global Anopheles vector capacity model (based on 10 years of satellite climate data). The predicted incidence was compared with estimates from the World Health Organization and the Malaria Atlas. The models and denominator data used are freely available through the Eclipse Foundation’s Spatiotemporal Epidemiological Modeller (STEM).ResultsAlthough the absolute scale factor relating reported malaria to absolute incidence is uncertain, there is a positive correlation between predicted and reported year-to-year variation in malaria burden with an averaged root mean square (RMS) error of 25% comparing normalized incidence across 86 countries. Based on this, the proposed measure of sensitivity of malaria to variations in climate variables indicates locations where malaria is most likely to increase or decrease in response to specific climate factors. Bootstrapping measures the increased uncertainty in predicting malaria sensitivity when reporting is restricted to national level and an annual basis. Results indicate a potential 20x improvement in accuracy if data were available at the level ISO 3166–2 national subdivisions and with monthly time sampling.ConclusionsThe high spatial resolution possible with state-of-the-art numerical models can identify regions most likely to require intervention due to climate changes. Higher-resolution surveillance data can provide a better understanding of how climate fluctuations affect malaria incidence and improve predictions. An open-source modelling framework, such as STEM, can be a valuable tool for the scientific community and provide a collaborative platform for developing such models.

Intelligent Medical Search Engine by Knowledge Machine

Many modern search engines for information retrieval system retrieve a large number of documents from a word-search query. Yet a simple question in natural language requests only an answer that is simple, accurate, and likely only one. A whole document of search result causes the inquirers to read through or find the answer in the document. Also too many documents in return distract the inquirers from their concern. We propose an idea of using knowledge machine to retrieve one finest answer for one-question-one-answer query. A search engine for natural-language application on medical field is introduced and its possible impact is discussed

Virtual Credit Cards on Mobile for M-Commerce Payment

The number of credit companies has been increasing due to the directed and undirected extra incomes from credit card holders. It effects to rapid growing rate of the number of credit card holders. Each individual is holding many credit cards that are harder to manage than before. At the same time, the usage of gadget devices such as mobiles is rising to the top where everyone carries at least one mobile, especially, every credit card holder. To manage the usage of several credit cards, we proposed the prototype of Virtual Credit Cards (VCC) on mobile phone running on Android platform in our system called Mobile Credit-card with Analytical Transaction (MCAT), an alternative of the existing Point-of-Sale (POS).

Results of Real Time Simulation of Medical Knowledge Technology (MKT) System for Medical Diagnosis on Distributed Knowledge Nodes

The Internet has been widely used for various purposes while the telemedicine has been researched to provide better care to people in the remote area. The combination of two concepts can be used with multiple health-care agencies containing medical knowledge in distributed databases. We are currently developing an Internet-based medical diagnosis system, called Medical Knowledge Technology (MKT) system to support both medical experts and patients in the way that multiple medical experts coordinately diagnose the illnesses for a patient with confidence levels. The goal of MKT system is to support world wide, especially in the rural areas that lack of medical teams but have an Internet access

Water level prediction model using back propagation neural network: Case study: The lower of chao phraya basin

Global warming is the cause of climate change effected to the severe flood disaster. Improvements of water level prediction model are needed. The accuracy of prediction model can reduce flood damage. This research aims to extend the water level prediction model with back propagation neural network. The proposed model tested the important factors in order to predict the water levels. The input of the model composes of water level, the capacity of water discharge, average rainfall runoff, height of basin at gauging station, and the maximum capacity of water discharge at gauging station. Mean Square Error and Relative Absolute Error were used for measure the accuracy of the prediction model between the actual water level and the predicted water level. The result of the prediction model has high accuracy when comparing with the actual values.

A Push for Software Process Improvement in Thailand

Capability Maturity Model Integration (CMMI) is considered by far the most comprehensive and well-known model for Software Process Improvement (SPI). The cost of having CMMI appraisal, including effort, time, and money is almost certainly out of reach for most very-small-sized, small-sized, and medium-sized software companies in Thailand. Thus most of them resort to the less expensive and locally invented standard in hope that they could improve their software processes and still stay competitive financially. However, to compete successfully in a long run, they need to formally implement and institutionalize SPI, using the more well-known and mature model such as CMMI. As more and more multinational companies outsource to Asia Pacific region, it even becomes imperative for the software companies to deliver high quality products in time and within budget to demanding customers. Due to the aforementioned needs, Software Industry Promotion Agency (SIPA), one of the leading government’s arms that promote, support, and steer a software industry in Thailand, funded us in 2008 to thoroughly and formally investigate a status of the Thai software industry with respect to SPI, specifically the impact of adopting CMMI on the Thai software industry. The obtained results would then be used to form both near-term and long-term macro strategies, which would help lay down directions and push burgeoning Thai software companies to become competent players in a global software market.

Metal Ship and Robotic Car: A Hands-on Activity to Develop Scientific and Engineering Skills for High School Students

Metal Ship and Robotic Car is one of the hands-on activities in the course, the Fundamental of Engineering that can be divided into three parts. The first part, the metal ships, was made by using engineering drawings, physics and mathematics knowledge. The second part is where the students learned how to construct a robotic car and control it using computer programming. In the last part, the students had to combine the workings of these two objects in the final testing. This aim of study was to investigate the effectiveness of hands-on activity by integrating Science, Technology, Engineering and Mathematics (STEM) concepts to develop scientific and engineering skills. The results showed that the majority of students felt this hands-on activity lead to an increased confidence level in the integration of STEM. Moreover, 48% of all students engaged well with the STEM concepts. Students could obtain the knowledge of STEM through hands-on activities with the topics science and mathematics, engineering drawing, engineering workshop and computer programming; most students agree and strongly agree with this learning process. This indicated that the hands-on activity: ?Metal Ship and Robotic Car? is a useful tool to integrate each aspect of STEM. Furthermore, hands-on activities positively influence a student?s interest which leads to increased learning achievement and also in developing scientific and engineering skills.

Time Lagged Back Propagation Neural Network with rainfall for flood forecasting

Flooding is a major problem in many countries while flood models have been proposed by various research groups on hydrological model in the past. Recently, Artificial Neural Network has been applied to the flood model. However, the learning factors of Neural Network for flood models need to include water level and rainfall which are two major impacts to the flooding. Therefore, this paper proposed Time-Lagged Back Propagation Neural Network Model with water flow and rainfall for flood forecasting. We ran our model with water flow and rainfall at the rivers around the lower part of northern region of Thailand. We compared the results between Back Propagation Neural Network Model and Time Lagged Back Propagation Neural Network Model with Gamma Memory. The study shows that Time Lagged Back Propagation Neural Network Model with Gamma Memory has better performance.

Design of a contactless sensor system for woven-bag manufacture monitoring

An affordable and robust contactless sensor system was designed for woven-bag manufacture production monitoring. Based on user requirements and the need to function in the presence of moisture, dust, and other harsh environmental conditions in a factory such as constant vibrations, a closed-loop Hall-effect magnetic field sensor was selected. The system was constructed and then lab-tested to be rugged, reliable and maintenance-free. In addition, a production management system using the sensor was developed to improve the effectiveness and efficiency in controlling the overall manufacturing process. This system is capable of detecting defects related to machine operations in a woven-bag manufacturing factory in order to improve production rates.