To model the monthly number of dengue fever cases in northeastern Thailand using time series analysis. Autoregressive Integrated Moving Average (ARIMA) models have been developed on the monthly data collected from January 1981 to December 2006 and validated using the data from January 2007 to April 2010. The ARIMA (3,1,4) model has been found as the most suitable model with the least Akaike Information Criterion (AIC) of 14.060 and Mean Absolute Percent Error (MAPE) of 7.000. The model was further validated by the Portmanteau test with no significant autocorrelation between residuals at different lag times. Early warning based on the data in the previous months could assist in improving vector control, community intervention, and personal protection.

This study investigated land use and land cover (LULC) patterns and changes in the Bandon Bay area of Thailand from 1991 to 2021 using satellite imagery, the first comprehensive effort to assess historical LULC trends over the past 30 years and forecast future LULC scenarios using the CA-Markov model for 2031, 2041, and 2051. Results showed the predominant LULC during 1991-2001 was the abandoned paddy fields, and during 2006-2021 was the oil palm plantations. During 1991-2001, the abandoned paddy fields changed significantly, with a net gain of 59.28 km2. From 2001-2011 and 2011-2021, the oil palm plantations experienced the most crucial change, with a net gain of 292.94 km2 and 70.06 km2. In 2031, 2041, and 2051, the LULC was predicted to be oil palms, shrimp farms, mangroves, and urban and built-up lands. The LULC changes were consistent with the government policies implemented and indicated government policy as a driving force in LULC dynamics on Bandon Bay area forestry, aquaculture, and agriculture, particularly on oil palm cultivation. Government management and regulation on land use is crucial for reducing the expansion of agricultural areas, especially oil palm plantations and aquaculture areas, to mitigate negative impacts on the Bandon Bay ecosystem. Doi: 10.28991/ESJ-2024-08-05-06 Full Text: PDF

The objective of this study was to investigate the differences in climatic factors and fruit quality between on- and off-season production periods. Climate, soil, and mangosteen measurements were all studied during on- and off-season production. We chose 40 mangosteen trees and observed flowering and fruit set rates over two production periods. The results showed that the number of flowers per branch, the number of fruits per branch, the circumference of fruits, and the fruit weight were higher during the on-season mangosteen production period than during the off-season mangosteen production period. However, the number of edible pulp segments, peel thickness, percentage of translucent flesh, and fruit gumminess were lower in the on-season mangosteen production period than in the off-season mangosteen production period. The percentage of fruit scars did not differ between the on- and off-season mangosteen production periods. When compared to the on-season mangosteen production period, there was lower relative humidity, soil moisture at 120 cm depth, and leaf wetness at 15 cm above ground during the off-season mangosteen production period; however, there was higher air temperature, soil moisture, and soil temperature at all four depth levels. Doi: 10.28991/ESJ-2023-07-02-020 Full Text: PDF

Affordable IoT PM2.5 sensors, enabled by the Internet of Things, offer new ways to monitor air quality. However, concerns exist about their data accuracy. This study aimed (1) to investigate the low-cost PM sensor's performance under various outdoor ambient circumstances and (2) to evaluate seven calibration methods, which include decision trees, gradient-boosted trees, linear regression, nearest neighbors, neural networks, random forests, and the Gaussian Process. The Davis AirLink was used as a reference to compare the Plantower PMS3003 sensor's performance. The data from the Plantower PMS3003 sensor were then compared to the Davis AirLink values using calibration curves created by machine learning algorithms. Calibration curves were generated using machine learning algorithms trained on sensor measurements collected in two Thai cities (Nakhon Si Thammarat and Phuket). Our results show that all machine learning methods outperformed traditional linear regression, with decision trees and neural networks demonstrating the most significant improvement. This research highlights the need for sensor calibration and the limitations of current calibration methods and paves the way for advancements in cloud-based calibration and machine learning for improved data accuracy in IoT PM2.5sensor technology. Doi: 10.28991/ESJ-2024-08-06-08 Full Text: PDF

"EFFECTS OF SIZE AND RESIDENCY ON FIGHTING OUTCOMES IN THE FIDDLER CRAB, UCA VOCANS HESPERIAE (DECAPODA, BRACHYURA, OCYPODIDAE)" published on 01 Jan 2002 by Brill.