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Fulltext Seroprevalence and incidence of hepatitis A in Southeast Asia: A systematic review

Hernandez-Suarez G, Saha D, Lodroño K, Boonmahittisut P, Taniwijaya S, Saha A, et al.

PLoS One, 2021;16(12):e0258659.
PMID: 34851983 DOI: 10.1371/journal.pone.0258659

BACKGROUND: A previous review on hepatitis A virus (HAV) seroprevalence in 2005 categorized Southeast Asia as a low HAV endemicity region. In 2010, the World Health Organization modified this from low to low/medium endemicity, pointing out that these estimates were based on limited evidence. Since then, there has been no attempt to review HAV epidemiology from this region. We conducted a systematic review of literature to collect information on HAV incidence and seroprevalence in select countries in the Southeast Asian region, specifically, The Association of Southeast Asian Nations over the last 20 years.
METHODOLOGY: This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. From the relevant articles, we extracted data and conducted a risk of bias assessment of individual studies.
RESULTS: The search yielded 22 and 13 publications on HAV seroprevalence and incidence, respectively. Overall, our findings point to a very low HAV endemicity profile in Thailand and Singapore and evidence of a shift towards low HAV endemicity in Indonesia, Lao People's Democratic Republic, Malaysia, the Philippines, and Vietnam. Only Singapore, Thailand, Malaysia, and the Philippines have existing HAV disease surveillance and reported incidence rates below 1 per 100,000. Several outbreaks with varying magnitude documented in the region provide insights into the evolving epidemiology of HAV in the region. Risk of bias assessment of studies revealed that the individual studies were of low to medium risk.
CONCLUSIONS/SIGNIFICANCE: The available HAV endemicity profiles in Southeast Asian countries, aside from Thailand, are limited and outdated, but suggest an endemicity shift in the region that is not fully documented yet. These findings highlight the need to update information on HAV epidemiology through strengthening of disease surveillance mechanisms to confirm the shift in HAV endemicity in the region.
Fulltext Machine learning-based technique for gain and resonance prediction of mid band 5G Yagi antenna

Haque MA, Rahman MA, Al-Bawri SS, Yusoff Z, Sharker AH, Abdulkawi WM, et al.

Sci Rep, 2023 Aug 03;13(1):12590.
PMID: 37537201 DOI: 10.1038/s41598-023-39730-1

In this study, we present our findings from investigating the use of a machine learning (ML) technique to improve the performance of Quasi-Yagi-Uda antennas operating in the n78 band for 5G applications. This research study investigates several techniques, such as simulation, measurement, and an RLC equivalent circuit model, to evaluate the performance of an antenna. In this investigation, the CST modelling tools are used to develop a high-gain, low-return-loss Yagi-Uda antenna for the 5G communication system. When considering the antenna's operating frequency, its dimensions are [Formula: see text]. The antenna has an operating frequency of 3.5 GHz, a return loss of [Formula: see text] dB, a bandwidth of 520 MHz, a maximum gain of 6.57 dB, and an efficiency of almost 97%. The impedance analysis tools in CST Studio's simulation and circuit design tools in Agilent ADS software are used to derive the antenna's equivalent circuit (RLC). We use supervised regression ML method to create an accurate prediction of the frequency and gain of the antenna. Machine learning models can be evaluated using a variety of measures, including variance score, R square, mean square error, mean absolute error, root mean square error, and mean squared logarithmic error. Among the nine ML models, the prediction result of Linear Regression is superior to other ML models for resonant frequency prediction, and Gaussian Process Regression shows an extraordinary performance for gain prediction. R-square and var score represents the accuracy of the prediction, which is close to 99% for both frequency and gain prediction. Considering these factors, the antenna can be deemed an excellent choice for the n78 band of a 5G communication system.
Fulltext Machine learning-based technique for resonance and directivity prediction of UMTS LTE band quasi Yagi antenna

Haque MA, Saha D, Al-Bawri SS, Paul LC, Rahman MA, Alshanketi F, et al.

Heliyon, 2023 Sep;9(9):e19548.
PMID: 37809766 DOI: 10.1016/j.heliyon.2023.e19548

In this study, we have presented our findings on the deployment of a machine learning (ML) technique to enhance the performance of LTE applications employing quasi-Yagi-Uda antennas at 2100 MHz UMTS band. A number of techniques, including simulation, measurement, and a model of an RLC-equivalent circuit, are discussed in this article as ways to assess an antenna's suitability for the intended applications. The CST simulation gives the suggested antenna a reflection coefficient of -38.40 dB at 2.1 GHz and a bandwidth of 357 MHz (1.95 GHz-2.31 GHz) at a -10 dB level. With a dimension of 0.535λ0×0.714λ0, it is not only compact but also features a maximum gain of 6.9 dB, a maximum directivity of 7.67, VSWR of 1.001 at center frequency and a maximum efficiency of 89.9%. The antenna is made of a low-cost substrate, FR4. The RLC circuit, sometimes referred to as the lumped element model, exhibits characteristics that are sufficiently similar to those of the proposed Yagi antenna. We use yet another supervised regression machine learning (ML) technique to create an exact forecast of the antenna's frequency and directivity. The performance of machine learning (ML) models can be evaluated using a variety of metrics, including the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and mean squared logarithmic error (MSLE). Out of the seven ML models, the linear regression (LR) model has the lowest error and maximum accuracy when predicting directivity, whereas the ridge regression (RR) model performs the best when predicting frequency. The proposed antenna is a strong candidate for the intended UMTS LTE applications, as shown by the modeling results from CST and ADS, as well as the measured and forecasted outcomes from machine learning techniques.

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