Improvements and Applications of Satellite-Derived Soil Moisture Data for Flood Forecasting

Accurate knowledge of the spatiotemporal behavior of soil moisture can greatly improve hydrological forecasting capability. While ground-based soil moisture measurements are ideal, they tend to be sparse in space and only available for limited periods. To overcome this, a viable alternative is space-borne microwave remote sensing because of the observational capability it offers for retrieving soil moisture in near real-time at the global scale. However, its direct applications have been limited due to the uncertainty associated, and the coarse spatial resolution these are available at. Therefore, this thesis aims to use satellite soil moisture products for assessing flood risk by redressing their drawbacks in terms of accuracy and spatial resolution. The research consists of three inter-dependent focal areas; evaluation, improvement and application of the soil moisture products. For the first objective, this thesis compared two alternate soil moisture products using spatiotemporally identical passive microwave observations but different retrieval algorithms. Complementarity in the performance of the products was identified and accordingly provided the basis for the improvement in soil moisture. For the second objective, based on the identified complementarity, different formulations of weighted linear combination were proposed as a means of reducing the structural uncertainty associated with each retrieval algorithm. To address the limitation of resulting retrievals existing over coarse grid resolutions, an approach was presented to spatially disaggregate coarse soil moisture by only using a remotely sensed vegetation index product. The method provides a continuous timeseries of disaggregated soil moisture with a persistence structure closer to what is observed. Lastly, for the third objective, a fully remote sensing based flood warning method using readily available soil moisture and rainfall data, open-access topographic and soil data, was developed. This method was applied over a number of anthropogenically unaffected river basins and was shown to have promise for flood warning in ungauged watersheds. Ongoing and future research will form an integrated pathway for producing an improved soil moisture product available at finer spatial resolution, which can be used for various regional applications, along with using this to provide real-time flood warnings using freely available information to rural and remote communities worldwide

Loss of aquaporin-1 expression is associated with worse clinical outcomes in clear cell renal cell carcinoma: an immunohistochemical study

Background Aquaporin (AQP) expression has been investigated in various malignant neoplasms, and the overexpression of AQP is related to poor prognosis in some malignancies. However, the expression of AQP protein in clear cell renal cell carcinoma (ccRCC) has not been extensively investigated by immunohistochemistry with large sample size. Methods We evaluated the AQP expression in 827 ccRCC with immunohistochemical staining in tissue microarray blocks and classified the cases into two categories, high and low expression. Results High expression of aquaporin-1 (AQP1) was found in 320 cases (38.7%), but aquaporin-3 was not expressed in ccRCC. High AQP1 expression was significantly related to younger age, low TNM stage, low World Health Organization/International Society of Urologic Pathology nuclear grade, and absence of distant metastasis. Furthermore, high AQP1 expression was also significantly associated with longer overall survival (OS; p<.001) and progression-specific survival (PFS; p<.001) and was an independent predictor of OS and PFS in ccRCC. Conclusions Our study revealed the prognostic significance of AQP1 protein expression in ccRCC. These findings could be applied to predict the prognosis of ccRCC

Dissipation patterns of acrinathrin and metaflumizone in Aster scaber

Abstract The establishment of preharvest residue limits (PHRLs) is important to minimize damage to producer and consumers caused by agricultural products which pesticide residue exceeds maximum residue limits (MRLs). Dissipation patterns of acrinathrin and metaflumizone in Aster scaber in greenhouse were studied during 10days in order to determine a pre-harvest interval after application. Acrinathrin and metaflumizone were applied in two different greenhouse, located in Taean-gun (field 1) and Gwangyang-si (field 2). Samples were collected at 0, 1, 2, 3, 5, 7, and 10days after insecticides application. The recoveries of two insecticides analyzed by LC–MS/MS and HPLC–DAD were ranged from 77.1 to 111.3%. The half-lives of acrinathrin and metaflumizone residues respectively were 3.8 and 5.9days in field 1 and 9.2 and 4.5days in field 2. The PHRLs 10days before harvesting A. scaber were 0.610mg/kg (field 1), 0.946mg/kg (field 2) for acrinathrin, and 5.930mg/kg (field 1), 5.147mg/kg (field 2) for metaflumizone. This results can be used as basic data for the establishment of PHRL in A. scaber

Design approaches for improved performance of asymmetric Doherty power amplifier

1

MINA: Multi-Input Network Augmentation for Enhancing Tiny Deep Learning

Network Augmentation (NetAug) is a recent method used to improve the performance of tiny neural networks on large-scale datasets. This method provides additional supervision to tiny models from larger augmented models, mitigating the issue of underfitting. However, the capacity of the augmented models is not fully utilized, resulting in underutilization of resources. In order to fully utilize the capacity of a larger augmented model without exacerbating the underfitting of a tiny model, we propose a new method called Multi-Input Network Augmentation (MINA). MINA converts tiny neural networks into a multi-input configuration, allowing only the augmented model to receive more diverse inputs during training. Additionally, tiny neural network can be converted back into their original single-input configuration after training. Our extensive experiments on large-scale datasets demonstrate that MINA is effective in improving the performance of tiny neural networks. We also demonstrate that MINA is consistently effective in downstream tasks, such as fine-grained image classification tasks and object detection tasks

How does increasing temperature affect the sub-annual distribution of monthly rainfall?

This paper investigates the relationship between temperature and sub-annual rainfall patterns using long-term monthly rainfall and temperature data from 1920 to 2018 in Australia. A parameter ( τ ) is used to measure the evenness of temporal rainfall distribution within each year, with τ = 0 indicating a uniform pattern. The study examines the relationship between τ and temperature for each year, considering whether it was warmer or cooler than average across five climate zones (CZs) in Australia, including tropical, arid, and three temperate climate classes. This study discovered a considerable association between annual maximum temperature and the distribution of monthly rainfall, with high temperatures resulting in greater variation (as represented by larger τ values) in the sub-annual distribution of monthly rainfall throughout all CZs, particularly in arid regions with τ values ranging from 0.27 to 0.52. In contrast, regions with temperate climates without dry seasons had a lower and narrower range of τ , from 0.15 to 0.26. This variability in rainfall distribution makes managing water resources more challenging in arid regions in Australia

Highly Efficient Saturated Amplifier with Harmonic Tuned Load

1

Operation of Highly Efficient Saturated Amplifier with Harmonic Tuning

1

Highly Linear 2-stage Doherty Power Amplifier Using GaN MMIC

국내22kc

Optimized Doherty Power Amplifier With a New Offset Line

1