Showing papers by "Sirindhorn International Institute of Technology published in 2019"

Deep learning based banana plant detection and counting using high-resolution red-green-blue (RGB) images collected from unmanned aerial vehicle (UAV).

Abstract: The production of banana—one of the highly consumed fruits—is highly affected due to loss of certain number of banana plants in an early phase of vegetation. This affects the ability of farmers to forecast and estimate the production of banana. In this paper, we propose a deep learning (DL) based method to precisely detect and count banana plants on a farm exclusive of other plants, using high resolution RGB aerial images collected from Unmanned Aerial Vehicle (UAV). An attempt to detect the plants on the normal RGB images resulted less than 78.8% recall for our sample images of a commercial banana farm in Thailand. To improve this result, we use three image processing methods—Linear Contrast Stretch, Synthetic Color Transform and Triangular Greenness Index—to enhance the vegetative properties of orthomosaic, generating multiple variants of orthomosaic. Then we separately train a parameter-optimized Convolutional Neural Network (CNN) on manually interpreted banana plant samples seen on each image variants, to produce multiple results of detection on our region of interest. 96.4%, 85.1% and 75.8% of plants were correctly detected on three of our dataset collected from multiple altitude of 40, 50 and 60 meters, of same farm. Further discussion on results obtained from combination of multiple altitude variants are also discussed later in the research, in an attempt to find better altitude combination for data collection from UAV for the detection of banana plants. The results showed that merging the detection results of 40 and 50 meter dataset could detect the plants missed by each other, increasing recall upto 99%.

Highly selective and sensitive CH4 gas sensors based on flame-spray-made Cr-doped SnO2 particulate films

Abstract: In this research, flame-spray-made 0–2 wt% Cr-doped SnO2 nanoparticles were synthesized and methodically studied for specific detection of methane (CH4) for the first time. From characterizations using X-ray diffraction, X-ray photoelectron spectroscopy, scanning/transmission electron microscopy and nitrogen adsorption, Cr was found to form substitutional solid solution in 5–20 nm nanoparticles with tetragonal SnO2 structure. In addition, grain and particle sizes decreased while surface area increases considerably with increasing Cr content. The sensing films prepared by spin coating technique were evaluated towards various flammable and toxic gases in dry air at 200–400 °C. Gas–sensing data evidently showed that the SnO2layer with the optimum Cr-doping level of 0.5 wt% offered a remarkable response of ˜1268 with a decent response time of ˜3.9 s to 1 vol% CH4at the optimum working temperature of 350 °C. In addition, the optimal Cr-doped SnO2sensor gave high CH4 selectivity against H2, C2H2, NO2, NO, N2O, CO, NH3, SO2, C2H5OH, C3H6O and H2O. Therefore, the flame-spray-made Cr-doped SnO2material is an attractive choice for selective and sensitive detection of CH4.

Magnetic and pH-responsive magnetic nanocarriers

Abstract: Smart hybrid magnetic colloidal particles are one of the most important and attractive classes of colloidal materials due to their interest in life science (in vitro diagnostic and in vivo application). These hybrid dispersed materials bearing or not intrinsic physical properties can be used in vivo and in vitro biomedical applications. In this direction, various stimuli-responsive magnetic particles have prepared and nowadays commonly used as solid support in numerous biomedical applications such as immunoassays, for specific nucleic acids concentration, cell labeling, and separation and in numerous biotechnological applications. For in vivo applications, various kinds of pH and temperature-sensitive hybrid magnetic nanoparticles, nanospheres, and nanocapsules have been developed and numerous applications have been investigated such as in vivo diagnosis, drug delivery, and more recently theranostic applications.

Internal capabilities, external cooperation and proactive CSR on financial performance

Abstract: The impact of proactive corporate social responsibility on a firm’s financial performance has received considerable attention. Grounded in the knowledge-based view (KBV), we aimed to examine the im...

Axial strength and deformability of concrete confined with natural fibre-reinforced polymers

Abstract: This paper presents the results of an experimental study on the compressive behaviour of concrete confined by natural fibre-reinforced polymer (NFRP) jackets which are formed by embedding sisal fib...

The association between dengue incidences and provincial-level weather variables in Thailand from 2001 to 2014

Abstract: Dengue and dengue hemorrhagic pose significant burdens in many tropical countries. Dengue incidences have perpetually increased, leading to an annual (uncertain) peak. Dengue cases cause an enormous public health problem in Thailand because there is no anti-viral drug against the dengue virus. Searching for means to reduce the dengue incidences is a challenging and appropriate strategy for primary prevention in a dengue outbreak. This study constructs the best predictive model from past statistical dengue incidences at the provincial level and studies the relationships among dengue incidences and weather variables. We conducted experiments for 65 provinces (out of 77 provinces) in Thailand since there is no dengue information for the remaining provinces. Predictive models were constructed using weekly data during 2001-2014. The training set are data during 2001-2013, and the test set is the data from 2014. Collected data were separated into two parts: current dengue cases as the dependent variable, and weather variables and previous dengue cases as the independent variables. Eight weather variables are used in our models: average pressure, maximum temperature, minimum temperature, average humidity, precipitation, vaporization, wind direction, wind power. Each weather variable includes the current week and one to three weeks of lag time. A total of 32 independent weather variables are used for each province. The previous one to three weeks of dengue cases are also used as independent variables. There is a total of 35 independent variables. Predictive models were constructed using five methods: Poisson regression, negative binomial regression, quasi-likelihood regression, ARIMA(3,1,4) and SARIMA(2,0,1)(0,2,0). The best model is determined by combinations of 1-12 variables, which are 232,989,800 models for each province. We construct a total of 15,144,337,000 models. The best model is selected by the average from high to low of the coefficient of determination (R2) and the lowest root mean square error (RMSE). From our results, the one-week lag previous case variable is the most frequent in 55 provinces out of a total of 65 provinces (coefficient of determinations with a minimum of 0.257 and a maximum of 0.954, average of 0.6383, 95% CI: 0.57313 to 0.70355). The most influential weather variable is precipitation, which is used in most of the provinces, followed by wind direction, wind power, and barometric pressure. The results confirm the common knowledge that dengue incidences occur most often during the rainy season. It also shows that wind direction, wind power, and barometric pressure also have influences on the number of dengue cases. These three weather variables may help adult mosquitos to survive longer and spread dengue. In conclusion, The most influential factor for further cases is the number of dengue cases. However, weather variables are also needed to obtain better results. Predictions of the number of dengue cases should be done locally, not at the national level. The best models of different provinces use different sets of weather variables. Our model has an accuracy that is sufficient for the real prediction of future dengue incidences, to prepare for and protect against severe dengue outbreaks.

Electron Population and Water Splitting Activity Controlledby Strontium Cations Doped in KTaO 3 Photocatalysts

Abstract: KTaO3 was doped with Sr cations, and its photocatalytic activity for the overall water splitting was investigated. By doping with Sr, a Sr-rich shell was formed over a Sr-poor core. Extended X-ray ...

Multiobjective Optimization Model for Sustainable Waste Management Network Design

Abstract: Inefficient or poorly planned waste management systems are a burden to society and economy. For example, excessively long waste transportation routes can have a negative impact on a large share of the population. This is exacerbated by the rapid urbanization happening worldwide and in developing countries. Sustainability issues should be accounted for at every stage of decision making, from strategic to daily operations. In this paper, we propose a multiobjective optimization model to design a cost-effective waste management supply chain, while considering sustainability issues such as land-use and public health impacts. The model is applied to a case study in Pathum Thani (Thailand) to provide managerial insights.

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.

The impact of different GHG reduction scenarios on the economy and social welfare of Thailand using a computable general equilibrium (CGE) model

Abstract: The Nationally Determined Contribution (NDC) of Thailand intends to reduce greenhouse gas (GHG) emissions by 20 to 25% from the projected business as usual level by 2030 with the deployment of renewable energy technologies and energy efficiency improvement measures in both the supply and demand sectors. However, in order to contribute towards meeting the long-term goal of the Paris Agreement to stay well below 2 °C, ambitious mitigation efforts beyond 2030 are needed. As such, it is necessary to assess the effects of imposing more stringent long-term GHG reduction targets in Thailand beyond the NDC commitment. This paper analyses the macroeconomic effects of limiting the GHG emissions by using a computable general equilibrium (CGE) model on Thailand’s economy during 2010 to 2050. Besides the business as usual (BAU) scenario, this study assesses the macroeconomic effects of ten low to medium GHG mitigation scenarios under varying GHG reduction targets of 20 to 50%. In addition, this study also assesses three different peak emission scenarios, each targeting a GHG reduction of up to 90% by 2050, to analyze the feasibility of zero GHG emissions in Thailand to pursue efforts to hold the global temperature rise to 1.5 °C above pre-industrial levels, as considered in the Paris Agreement. According to the BAU scenario, the GHG emissions from the electricity, industry, and transport sectors would remain the most prominent throughout the planning period. The modeling results indicate that the medium to peak emission reduction scenarios could result in a serious GDP loss compared to the BAU scenario, and therefore, the attainment of such mitigation targets could be very challenging for Thailand. Results suggest that the development and deployment of energy-efficient and renewable energy-based technologies would play a significant role not only in minimizing the GHG emissions but also for overcoming the macroeconomic loss and lowering the price of GHG emissions. The results reveal that without a transformative change in the economic structure and energy system of Thailand, the country would have to face enormous cost in reducing its GHG emissions.

GHG mitigation in Agriculture, Forestry and Other Land Use (AFOLU) sector in Thailand

Abstract: The Agriculture, Forestry and Other Land Use (AFOLU) sector is responsible for almost a quarter of the global Greenhouse gases (GHG) emissions. The emissions associated with AFOLU activities are projected to increase in the future. The agriculture sector in Thailand accounted for 21.9% of the country’s net GHG emissions in 2013. This study aims to estimate the GHG emissions in the AFOLU sector and mitigation potential at various carbon prices during 2015–2050. This study uses an AFOLU bottom-up (AFOLUB) model to estimate GHG emissions in a business-as-usual (BAU) scenario, and then identifies no-regret options, i.e. countermeasures that are cost-effective without any additional costs. In addition, the study also identifies countermeasure options and mitigation potential at various carbon prices. Results show that emissions from the agriculture sector in the BAU will increase from 45.3 MtCO2eq in 2015 to 63.6 MtCO2eq in 2050, whereas net emission from the AFOLU will be 8.3 MtCO2eq in 2015 and 24.6 MtCO2eq in 2050. No-regret options would reduce emissions by 6.1 and 6.8 MtCO2eq in 2030 and 2050, respectively. The carbon price above $10 per tCO2eq will not be effective to achieve significant additional mitigation/sequestration. In 2050, no-regret options could reduce total AFOLU emissions by 27.5%. Increasing carbon price above $10/tCO2eq does not increase the mitigation potential significantly. Net sequestration (i.e., higher carbon sequestration than GHG emissions) in AFOLU sector would be possible with the carbon price. In 2050, net sequestration would be 1.2 MtCO2eq at carbon price of $5 per tCO2eq, 21.4 at $10 per tCO2eq and 26.8MtCO2eq at $500 per tCO2eq.

Assessment of Thailand’s Energy Policy on CO2 Emissions: Implication of National Energy Plans to Achieve NDC Target

Abstract: The industrial sector is one of the main energy-intensive sectors, and it accounted for 35.2% of total energy consumption in 2017. In terms of electricity consumption, the building sector was the largest electricity consuming sector, and it accounted for 57.4% of total electricity consumption in 2017. The objective of this paper is to assess the long-term energy policy in the building and the industrial sectors during 2005-2050 through a perspective of energy saving potentials and greenhouse gas (GHG) mitigation by using the Long-range Energy Alternative Planning system (LEAP). Results indicate that energy labeling and monetary incentive in the energy efficiency plan (EEP2015) and renewable energy plan (AEDP2015) are the most effective measures in the building and industrial sectors. This study discloses that plans are effective policies to reduce not only energy demand but also GHG emissions. Therefore, such reduction potentials can meet Thailand’s Nationally Determined Contributions (NDC) target. In 2050, the deployment of biogas will significantly reduce GHG emissions in the residential sector. The GHG emission reduction from the non-metallic, papers and pulps, and chemical industries will be diminished by the carbon capture and storage (CCS) technology in 2030 onwards. This study also considers energy security by focusing on economic and environmental aspects.

Analysis of neck and shoulder postures, and muscle activities relative to perceived pain during laptop computer use at a low-height table, sofa and bed.

Abstract: BACKGROUND Laptop computers are used in various places and situations. The number of laptop users experiencing musculoskeletal disorders (MSDs) has increased drastically due to, in part, inappropriate workstations. OBJECTIVE To investigate the neck and shoulder postures, and muscle activity relative to perceived pain when using the laptop at a low-height table, sofa, and bed. METHODS Twenty male participants aged 18-25 years were randomly assigned to perform laptop computer operation at 3 workstations for 10 minutes during which neck and shoulder angles, muscle activity, and pain were recorded by using an Electrogoniometer, Electromyography (EMG), and visual analog scale (VAS), respectively. RESULTS Neck flexions when working at the sofa (18.6°±12.2°) and bed (17.2°±10.5°) were significantly (p < 0.05) greater than that at the low-height table (7.8°±6.5°). However, shoulder flexion when working at the low-height table (28.2°±13.0°) was significantly (p < 0.05) greater than that at the sofa (13.8°±8.6°) and bed (10.91°±7.8°). Working at the low-height table caused the shoulder flexor muscle activity to be significantly (p < 0.05) higher than working at the sofa and bed. Neck pain was reported during laptop computer use at the sofa and bed, and upper back pain when working at the low-height table. CONCLUSIONS High neck flexion and pain were found while working at sofa and bed, whereas high muscle activity at shoulder and upper back pain were found while working at the low-height table.

Impact of homeowner's behaviours on residential energy consumption in Bangkok, Thailand

Abstract: This study investigates homeowners’ energy-saving behaviours and their attitudes, which influence household energy conservation in Bangkok. Field surveys are carried out for 400 households in Bangkok to observe socio-economic factors, residents’ priority of actual saving behaviours, and usage patterns of electric devices. Then, the saving behaviours and usage patterns from the field survey and assigned levels of energy efficient devices are used to randomly generate 200 sets of energy-saving scenarios using the Latin Hypercube method. The household energy consumptions from those energy-saving scenarios are calculated. It is found that the household energy consumption depends strongly on residents’ attitudes and energy-saving behaviours. Gender and level of knowledge in energy efficiency are found to have a significant impact on energy consumption. Residents in Bangkok actually save energy within a range of 7–15% (484.2–1037.6 kWh/year/household). Female residents potentially save more energy than males do via reducing the use of air conditioners in their bedrooms. The results provide a better understanding of residents’ attitudes towards energy efficiency in Bangkok. The results will help policy-makers in determining effective techniques that could promote the role of residents in energy efficient homes in Bangkok and other hot and humid countries.