Sirindhorn International Institute of Technology

About: Sirindhorn International Institute of Technology is a based out in . It is known for research contribution in the topics: Supply chain & Combustion. The organization has 1048 authors who have published 1678 publications receiving 30067 citations.

Preparation and characterizations of naproxen-loaded magnetic nanoparticles coated with PLA- g -chitosan copolymer

Abstract: Naproxen (NPX) drug-loaded magnetic nanoparticles (MNPs) have been prepared in a one-step process utilizing a biocompatible polylactide-grafted-chitosan copolymer. The copolymer serves both as a NPX drug carrier as well as a polymeric surfactant for the synthesis of MNPs without the use of any additional surfactant. Highly stable MNPs with high magnetization in the form of maghemite (γ-Fe2O3) are prepared in aqueous media. Effects of preparation conditions on structures and properties of the copolymer-coated and drug-loaded MNPs are investigated by employing particle size and zeta potential measurements, transmission electron microscopy, vibrating sample magnetometer, X-ray diffraction, Fourier-transform infrared, nuclear magnetic resonance, and confocal Raman spectroscopy. The results show that average particle size (150–300 nm), coating efficiency, and coating structures of the resulting MNPs materials are strongly dependent on MNP/copolymer and MNP/NPX ratios in feed. It is also observed that NPX acts as co-surfactant in the drug-loading process, resulting in different encapsulating structures with the variation in the MNP/copolymer and MNP/NPX ratios. Properties of the MNPs materials can be further optimized for use in specific biomedical applications.

Automatic Extraction of the Structure of the Retinal Blood Vessel Network of Premature Infants

Abstract: Objective: Automatically detect the structure of blood vessels in ROP infants to allow ophthalmologist to analyze and detect the symptom early. Material and Method: This study presents a set of methods for detection of the skeletonized structure of premature infant’s low-contrast retinal blood vessel network. Steps has been optimized for this study, namely statistically optimized LOG edge detection filter, Otsu thresholding, Medial Axis transform skeletonization, pruning, and edge thinning. Results: A set of 100 test images are grouped together into five testing groups based on their similar characteristics and clinicians suggestions. The authors applied the series of methods proposed on all the 100 images. The result from the algorithm was compared with ophthalmologists’ hand-drawn ground truth and it can detect the blood vessel with a high specificity of 0.9879 and sensitivity of 0.8935. Conclusion: The authors’ algorithm can detect blood vessels effectively even though the image quality may not be good, have high noise, and low contrast. The algorithm can also detect the blood vessel at important locations such as the edge of the retina.

Flood evacuation decision modeling for high risk urban area in the Philippines

Abstract: Analysis of influential factors to evacuation decision, a key input to evacuation planning, is important for better management in future evacuations. Evacuation decision indicates the choice of households to fully, partially evacuate or stay from the area at risk of impending hazard. This study aims to develop a model of flood evacuation decision using data collected from households in Bagong Silangan, one of the biggest sub-districts in terms of land area and population as well as one of the most depressed communities in Quezon City, Metro Manila, Philippines. A post flood event face to face interview was conducted drawing information including broad range of socio-demographic and household characteristics, their capacities and hazard-related ones. The data was eventually processed and analyzed using discrete choice model under the utility maximizing framework. Findings indicate that factors having strong influence to evacuation decision include age of the household head, income, house ownership status, number of house floor levels, and flood level. In addition, gender, education and type of work of the head of the household, number of household members, and distance from the source of flood show some level of influence to the decision. An internal validation using bootstrap technique shows consistent results. This study provides useful insights for understanding household flood evacuation decision.

EEGANet: Removal of Ocular Artifact from the EEG Signal Using Generative Adversarial Networks.

Abstract: The elimination of ocular artifacts is critical in analyzing electroencephalography (EEG) data for various brain-computer interface (BCI) applications. Despite numerous promising solutions, electrooculography (EOG) recording or an eye-blink detection algorithm is required for the majority of artifact removal algorithms. This reliance can hinder the model's implementation in real-world applications. This paper proposes EEGANet, a framework based on generative adversarial networks (GANs), to address this issue as a data-driven assistive tool for ocular artifacts removal (source code is available at https://github.com/IoBT-VISTEC/EEGANet). \textcolor{red}{After the model was trained, the removal of ocular artifacts could be applied calibration-free without relying on the EOG channels or the eye blink detection algorithms.} First, we tested EEGANet's ability to generate multi-channel EEG signals, artifacts removal performance, and robustness using the EEG eye artifact dataset, which contains a significant degree of data fluctuation. According to the results, EEGANet is comparable to state-of-the-art approaches that utilize EOG channels for artifact removal. Moreover, we demonstrated the effectiveness of EEGANet in BCI applications utilizing two distinct datasets under inter-day and subject-independent schemes. Despite the absence of EOG signals, the classification performance of the signals processed by EEGANet is equivalent to that of traditional baseline methods. This study demonstrates the potential for further use of GANs as a data-driven artifact removal technique for any multivariate time-series bio-signal, which might be a valuable step towards building next-generation healthcare technology.