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PublicationCOVID-19 mRNA vaccine degradation rate prediction using artificial intelligence techniques: a narrative review(IASE, 2024-06)As diseases become more common, the use of mRNA (messenger ribonucleic acid) vaccines is becoming more important. These vaccines can be developed quickly and have a low risk of side effects. However, they are sensitive to environmental conditions, which means they need careful storage and transport, creating challenges in distributing them. Testing the stability of an mRNA vaccine requires a lot of work and time, as it needs many lab tests. Artificial Intelligence (AI) offers a new solution by using the genetic information in RNA sequences to predict how quickly these vaccines might break down. This approach helps address potential shortages of vaccines by avoiding some of the challenges with vaccine distribution. The COVID-19 pandemic has greatly sped up the use of AI in this area. This change is significant because using AI to predict and improve the stability of mRNA vaccines was not well explored before the pandemic. This paper reviews recent studies that use AI to study mRNA vaccines during the COVID-19 pandemic. It points out that the main issue with these vaccines is how long they can be stored before they are no longer effective due to their sensitivity to environmental conditions. By looking at these studies, the paper not only shows how AI and vaccine research are coming together but also points out opportunities for more research. The goal of this review is to outline effective methods to improve the use of mRNA vaccines and encourage more scientific research and development in this field. This is an important step in improving how we deal with pandemics.
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PublicationWide bandgap SiC-based oxide thickness optimization by computation and simulation using enhanced electron mobility with regulated gate voltage technique for high-power 4H-SiC MOSFET(Institut Teknologi Bandung, 2024-06-20)This work analyzed the interactions between gate oxide thickness (Tox), voltage dependence, and electron mobility (E-mobility) in the inversion layer, which controls the electron movement properties of 4H-SiC/SiO2. This paper also presents a calculation of gate oxide thickness in correlation with gate voltage mainly for high-voltage applications. The results of this work revealed that at low resistance, E-mobility increases with gate voltage and oxide thickness, which saturates at the point of value. Coulomb scattering and surface phonons at the inversion region of SiC MOSFETs are regarded as the two primary factors that limit E-mobility in these devices. In addition, the high interface trap density (Dit) causes a decrease in E-mobility. The findings from this study confirmed that the computed values of oxide thickness and simulation-based oxide thickness with regulated gate voltages have the least variation below 1%, asserting experimental and theoretical outcomes about the role of oxide thickness and electron movement at the 4H-SiC/SiO2 interfaces. These results indicate that understanding the E-mobility effect on oxide thickness in the SiC MOSFET inversion layer according to gate voltage is important, particularly in achieving an optimal 4H-SiC/SiO2 interface for high-power applications.
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PublicationDirect-written silver electrodes for all-solution-processed low-voltage organic thin film transistors towards flexible electronics applications(Akademia Baru Publishing (M) Sdn Bhd, 2024)Recent progress inprinted electronics has offered the possibility of fabricatingvarious organic-based electronic devices,such as organic light-emitting diodes (OLEDs) and organic thin film transistors (OTFTs).As one of the important deposition methods inprinted electronics, an inkjet printing technique offers the deposition of solution-processable materials onto a variety of substrates using simpler fabrication steps at lower processing temperatures, which is suitable for flexible electronic applications. Despite being the leading choice in OTFT fabrication,the clogging issues that frequently occurred at the printhead nozzle havenot only limitedthe material selection but also restrained the efforts to bring organic electronic devices to the market. Apart from that, although remarkable progress has been made to enhance the performance of the OTFT, the high operating voltage resulting from the low gate capacitance density of the inorganic oxide-based dielectric layer remains a critical limitation that hinders the practical application of the OTFT. Hence, in this paper, we propose a simple solution-based method to develop a low-voltageOTFT. This work utilized a direct-write printing technique to print silver source/drain and gate electrodes incorporated into a bottomgate bottom contact OTFT structure, a spin-coating deposition method to deposit both small molecule TIPS-pentacene organic semiconducting layer and high-kPVA dielectric layer. Notably, the proposed OTFT achieved a micrometre channel length with a saturation mobility of 4.49 × 10-1cm2/Vs, a threshold voltage of -1.5V, an on/off current ratio of 108, and a subthreshold swing of 66.8mV/dec whilethe overall fabrication temperature and operating voltage arekept below 150 °Cand -15 V, respectively.The direct ink writing technology incorporated into the high-kdielectric layer provides a new strategy to fabricate organic-related components,particularly the OFTFs at lower manufacturing cost and temperature towardsflexible and low-operating voltage electronic devices.
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PublicationOptimizing hybrid neural networks for precise COVID-19 mRNA vaccine degradation prediction(Institute of Advanced Science Extension (IASE), 2024-07)Conventional hybrid models often miss an essential factor that can lead to less effective performance: intrinsic sequence dependence when combining various neural network (NN) architectures. This study addresses this issue by highlighting the importance of sequence hybridization in NN architecture integration, aiming to improve model effectiveness. It combines NN layers—dense, long short-term memory (LSTM), and gated recurrent unit (GRU)—using the Keras Sequential API for defining the architecture. To provide better context, bidirectional LSTM (BiLSTM) and bidirectional GRU (BiGRU) replace their unidirectional counterparts, enhancing the models through bidirectional structures. Out of 25 NN models tested, 18 four-layer hybrid NN models consist of one-quarter dense layer and the rest BiLSTM and BiGRU layers. These hybrid NN models undergo supervised learning regression analysis, with mean column-wise root mean square error (MCRMSE) as the performance metric. The results show that each hybrid NN model produces unique outcomes based on its specific hybrid sequence. The Hybrid_LGSS model performs better than existing three-layer BiLSTM networks in predictive accuracy and shows lower overfitting (MCRMSEs of 0.0749 and 0.0767 for training and validation, respectively). This indicates that the optimal hybridization sequence is crucial for achieving a balance between performance and simplicity. In summary, this research could help vaccinologists develop better mRNA vaccines and provide data analysts with new insights for improvement.
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PublicationEnhancing retina images by lowpass filtering using binomial filter(MDPI, 2024-08-05)This study presents a method to enhance the contrast and luminosity of fundus images with boundary reflection. In this work, 100 retina images taken from online databases are utilized to test the performance of the proposed method. First, the red, green and blue channels are read and stored in separate arrays. Then, the area of the eye also called the region of interest (ROI) is located by thresholding. Next, the ratios of R to G and B to G at every pixel in the ROI are calculated and stored along with copies of the R, G and B channels. Then, the RGB channels are subjected to average filtering using a 3 × 3 mask to smoothen the RGB values of pixels, especially along the border of the ROI. In the background brightness estimation stage, the ROI of the three channels is filtered by binomial filters (BFs). This step creates a background brightness (BB) surface of the eye region by levelling the foreground objects like blood vessels, fundi, optic discs and blood spots, thus allowing the estimation of the background illumination. In the next stage, using the BB, the luminosity of the ROI is equalized so that all pixels will have the same background brightness. This is followed by a contrast adjustment of the ROI using CLAHE. Afterward, details of the adjusted green channel are enhanced using information from the adjusted red and blue channels. In the color correction stage, the intensities of pixels in the red and blue channels are adjusted according to their original ratios to the green channel before the three channels are reunited. The resulting color image resembles the original one in color distribution and tone but shows marked improvement in luminosity and contrast. The effectiveness of the approach is tested on the test images and enhancement is noticeable visually and quantitatively in greyscale and color. On average, this method manages to increase the contrast and luminosity of the images. The proposed method was implemented using MATLAB R2021b on an AMD 5900HS processor and the average execution time was less than 10 s. The performance of the filter is compared to those of two other filters and it shows better results. This technique can be a useful tool for ophthalmologists who perform diagnoses on the eyes of diabetic patients.