A novel control method to maximize the energy-harvesting capability of an adjustable slope angle wave energy converter

This paper introduces a novel control approach to maximizing the output energy of an adjustable slope angle wave energy converter (ASAWEC) with oil-hydraulic power take-off. Different from typical floating-buoy WECs, the ASAWEC is capable of capturing wave energy from both heave and surge modes of wave motions. For different waves, online determination of the titling angle plays a significant role in optimizing the overall efficiency of the ASAWEC. To enhance this task, the proposed method was developed based on a learning vector quantitative neural network (LVQNN) algorithm. First, the LVQNN-based supervisor controller detects wave conditions and directly produces the optimal titling angles. Second, a so-called efficiency optimization mechanism (EOM) with a secondary controller was designed to regulate automatically the ASAWEC slope angle to the desired value sent from the supervisor controller. A prototype of the ASAWEC was fabricated and a series of simulations and experiments was performed to train the supervisor controller and validate the effectiveness of the proposed control approach with regular waves. The results indicated that the system could reach the optimal angle within 2s and subsequently, the output energy could be maximized. Compared to the performance of a system with a vertically fixed slope angle, an increase of 5% in the overall efficiency was achieved. In addition, simulations of the controlled system were performed with irregular waves to confirm the applicability of the proposed approach in practice

Proposition and experiment of a sliding angle self-tuning wave energy converter

The hydraulic power-take-off mechanism (HPTO) is one of the most popular methods in wave energy converters (WECs). However, the conventional HPTO with a fixed direction motion has some drawbacks which limit its power capture capability. This paper proposes a sliding angle self-tuning wave energy converter (SASTWEC) to find the optimal sliding angle automatically, with the purpose of increasing the power capture capability and energy efficiency. Furthermore, a small scale WEC test rig was fabricated and a wave making source has been employed to verify the sliding angle performance and efficiency of the proposed system throughout experiments

GRADIENT KIẾN TẠO HIỆN ĐẠI KHU VỰC NINH THUẬN VÀ LÂN CẬN

The estimation of the present day tectonic movement and tectonic gradient (strain rate) has an important practical signification in the assessment of active fault and seismic hazards for the selection of Ninh Thuan nuclear power plant. Based on the three campaigns of GPS measurement between 2012 - 2013, we used BERNESE 5.0 software to determine present day slip rates of 13 stations in ITRF08 frame. The GPS stations move eastwards at the slip rates of 22 - 25 mm/yr, southwards at the velocities of 5 - 10 mm/yr. The standard errors in latitudinal and longitudinal directions are 1.2 mm/yr and 0.9 mm/yr, respectively. Combined with GPS data from the project of the study on actual geodynamics in Tay Nguyen TN3/06, we determined the strain rate ranging from 50 to 100 nanostrains with the standard error of 50 nanostrains. The direction of  maximum compressive strain rate is from northwest - southeast to east - west.Đánh giá vận tốc chuyển động kiến tạo hiện đại và gradient kiến tạo hiện đại có ý nghĩa thực tiễn quan trọng trong việc đánh giá đứt gãy hoạt động nguy hiểm động đất phục vụ xây dụng nhà máy điện hạt nhân Ninh Thuận. Trên cơ sở đo 3 chu kỳ GPS vào các năm 2012 - 2013, sử dụng phần mềm BERNESE 5.0, chúng tôi đã xác lập được vận tốc chuyển động kiến tạo hiện đại tại 13 điểm đo GPS trong khu vực lân cận bao gồm kéo dài từ Nha Trang tới đảo Phú Quý. Vận tốc chuyển dịch kiến tạo hiện đại về phía đông thay đổi từ 22 - 25 mm/năm và chuyển dịch về phía nam với vận tốc giao động từ 5 - 10 mm/năm trên hệ tọa độ toàn cầu ITRF08. Sai số vận tốc chuyển dịch kiến tạo về phía đông giao động trong khoảng 1,2 - 1,5 mm/năm và về phía nam giao động trong khoảng 0,9 - 1,2 mm/năm. Liên kết với giá trị đo GPS từ đề tài nghiên cứu địa động lực hiện đại khu vực Tây Nguyên mã số TN3/T06, chúng tôi đã xác định được giá trị vận tốc biến dạng giao động từ 50 nano tới 100 nano biến dạng với sai số giao động trong khoảng 50 nano biến dạng. Trục biến dạng nén cực đại giao động theo phương thay đổi từ bắc nam sang đông bắc - tây nam. Trục biến dạng căng cực đại có phương thay đổi từ tây bắc - đông nam sang phương đông - tây

Microscopic Observation Drug Susceptibility Assay (MODS) for Early Diagnosis of Tuberculosis in Children

MODS is a novel liquid culture based technique that has been shown to be effective and rapid for early diagnosis of tuberculosis (TB). We evaluated the MODS assay for diagnosis of TB in children in Viet Nam. 217 consecutive samples including sputum (n = 132), gastric fluid (n = 50), CSF (n = 32) and pleural fluid (n = 3) collected from 96 children with suspected TB, were tested by smear, MODS and MGIT. When test results were aggregated by patient, the sensitivity and specificity of smear, MGIT and MODS against “clinical diagnosis” (confirmed and probable groups) as the gold standard were 28.2% and 100%, 42.3% and 100%, 39.7% and 94.4%, respectively. The sensitivity of MGIT and MODS was not significantly different in this analysis (P = 0.5), but MGIT was more sensitive than MODS when analysed on the sample level using a marginal model (P = 0.03). The median time to detection of MODS and MGIT were 8 days and 13 days, respectively, and the time to detection was significantly shorter for MODS in samples where both tests were positive (P<0.001). An analysis of time-dependent sensitivity showed that the detection rates were significantly higher for MODS than for MGIT by day 7 or day 14 (P<0.001 and P = 0.04), respectively. MODS is a rapid and sensitive alternative method for the isolation of M.tuberculosis from children

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Modelling of hydrological extremes and rice irrigation optimization for decision support in Central Vietnam

Central Vietnam is characterized by a complex climatology, which in combination with the sparse hydrometeorological observation network, creates a challenge in the quantification of projected hydrological extremes under a changing climate. In the region, farmers report increasing damages on agriculture caused by extreme floods and drought conditions. Particularly during the summer-autumn rice season, water is often insufficient to irrigate the entire rice production areas, and thus significantly affecting rice productivity. Therefore, scientifically sound information on the expected future hydrological extremes as well as water-efficient agricultural strategies are urgently required for sustainable water resources management. In this thesis a complex hydrometeorological modelling chain is employed to investigate the impact of climate change on future hydrological extremes in the Vu Gia - Thu Bon (VGTB) river basin, Central Vietnam. The modelling chain consists of six Global Circulation Models (GCMs) (CCAM, CCSM, ECHAM3, ECHAM5, HadCM3Qs, and MRI), six Regional Climate Models (RCMs) (CCAM, MM5, RegCM, REMO, HadRM3P and MRI), six bias correction (BC) approaches (linear scaling, local intensity scaling, power law transform (monthly), empirical and gamma quantile mapping, and power law transform), the fully distributed hydrological Water Flow and Balance Simulation Model (WaSiM) which was calibrated for the VGTB basin using two different calibration approaches, and extreme values analysis. The nonlinear parameter estimation tool PEST, which is based on the Gauss-Marquardt-Levenberg method, was combined with the distributed hydrological model WaSiM. Confidence bounds for all estimated parameters of the WaSiM model were developed based on a covariance analysis. A reasonable quality of fit between modelled and observed runoffs was achieved showing the reasonable performance of the WaSiM model in this region. Both bias corrected and raw RCM data are used as input for the WaSiM to simulate flows for the VGTB basin. To derive high ow and low ow frequency curves for the control (baseline) period (1980-1999) and the future periods 2011-2030, 2031-2050, and 2080-2099, the generalized extreme value (GEV) distribution is fitted to the annual maxima/minima of the simulated continuous discharge series. Permutation tests are developed and applied to the observed discharge series (1980-1999) to quantify the uncertainties related to the relatively small size to estimate the GEV distribution. Results show that the GEV fits based on sample size of n = 20 can partially be considered as robust. Due to limitations in the performance of the BC methods, the delta change approach was applied to facilitate extreme ow analysis as required for hydrological decision support. The results exhibit a remarkable variation among the different climate scenarios. As indicated by the majority of the discharge projections, a tendency towards increased high flows and decreased low flows is concluded. The results highlight challenges in using current GCM/RCMs in combination with state-of-the-art BC methods for local impact studies on both high and low flows. A second central objective of this PhD dissertation was the development and application of an integrated hydrological-irrigation modelling system to optimize irrigation strategies for a typical rice irrigation system in Central Vietnam. The modelling system comprises WaSiM to simulate the inflow to a reservoir and an irrigation model, which optimizes the rice irrigation technology, i.e. Alternate Wetting and Drying (AWD) or Continuous Flooding (CF), the rice irrigation area and the irrigation scheduling under given water constraints. Irrigation strategies are derived based on different initial water levels in the reservoir at the beginning of the cropping season as well as different maximum water releases. The simulation results show that the initial level of water in the reservoir is crucial: water levels of less than 90% do not provide sufficient water to irrigate the entire cropping area, whereas a level of 70% restricts the cropping area to 75% under current design maximum outflow of 0.3 m3/s. AWD is able to reduce the water irrigation input, ranging from 4% to 10% and reduce the number of irrigation events compared to CF. The adoption of AWD, which has been not popular in Central Vietnam therefore, has the potential to save more water and may help to increase the profit of the farmers. However, the benefits of AWD can only be achieved after significant investment in the canal system and the reservoir outlet. The impact of the different computing environments on the solutions of the integrated model is estimated, since the robustness of the optimization results(performance variability) is crucial for decision support. Only limited performance variability due to the computing environment is finally found, giving confidence in the robustness of the model for decision support. Prior to the application and the transfer of the model to similar irrigation schemes in other regions, the model must be further validated by field experiments under various conditions