Determination of Regional Scale Evapotranspiration of Texas from NOAA - AVHRR Satellite

Evapotranspiration (ET) is defined as the combined loss of water by evaporation from soil and transpiration from plants. Depending on the geographic location, 60-80% of total annual precipitation is lost in the form of evapotranspiration. Since ET accounts for a major portion of water lost to the atmosphere, accurate estimation is essential for the success of hydrologic modeling studies. ET is estimated using climatic data like net radiation, air temperature, wind velocity, vapor pressure deficit and relative humidity obtained from the nearest weather stations. However, interpolating ET using data obtained from a point data source to derive regional ET could introduce errors of large magnitude. During the last two decades, GIS and Remote Sensing have evolved as an indispensable tool for monitoring natural resources. Due to the availability of spatially distributed data from satellites, and adopting GIS principles, accurate determination of ET is possible. The present study aims at deriving spatially distributed ET using NOAA-AVHRR satellite data

Building a Winning Team: Selecting Source Model Ensembles using a Submodular Transferability Estimation Approach

Estimating the transferability of publicly available pretrained models to a target task has assumed an important place for transfer learning tasks in recent years. Existing efforts propose metrics that allow a user to choose one model from a pool of pre-trained models without having to fine-tune each model individually and identify one explicitly. With the growth in the number of available pre-trained models and the popularity of model ensembles, it also becomes essential to study the transferability of multiple-source models for a given target task. The few existing efforts study transferability in such multi-source ensemble settings using just the outputs of the classification layer and neglect possible domain or task mismatch. Moreover, they overlook the most important factor while selecting the source models, viz., the cohesiveness factor between them, which can impact the performance and confidence in the prediction of the ensemble. To address these gaps, we propose a novel Optimal tranSport-based suBmOdular tRaNsferability metric (OSBORN) to estimate the transferability of an ensemble of models to a downstream task. OSBORN collectively accounts for image domain difference, task difference, and cohesiveness of models in the ensemble to provide reliable estimates of transferability. We gauge the performance of OSBORN on both image classification and semantic segmentation tasks. Our setup includes 28 source datasets, 11 target datasets, 5 model architectures, and 2 pre-training methods. We benchmark our method against current state-of-the-art metrics MS-LEEP and E-LEEP, and outperform them consistently using the proposed approach.Comment: To appear at ICCV 202

Modeling Water-Quality Loads to the Reservoirs of the Upper Trinity River Basin, Texas, USA

The Upper Trinity River Basin (TRB) is the most populated river basin and one of the largest water suppliers in Texas. However, sediment and nutrient loads are reducing the capacity of reservoirs and degrading water quality. The objectives of this study are to calibrate and validate the Soil and Water Assessment Tool (SWAT) model for ten study watersheds within the Upper TRB in order to assess nutrient loads into major reservoirs in the basin and to predict the effects of point source elimination and urbanization on nutrient loads through scenario analyses. SWAT performed reasonably well for the current condition except for two out of five tributaries in the Eagle Mountain watershed and total phosphorous OPEN ACCESS Water 2015, 7 5690 in Richland-Chambers. The impacts of simulated scenarios varied within watersheds. Point-source elimination achieved reductions ranging from 0.3% to 24% in total phosphorus and 1% to 56% in total nitrogen received by the reservoirs. Population and development projections were used to examine the impacts of urbanization on each watershed. Projected urbanization in 2030 had large effects on simulated total phosphorus loads in some watersheds, ranging from a reduction of 1% to an increase of 111%. Projected urbanization also affected simulated total nitrogen loads, from a reduction of 3% to an increase of 24%. One limitation of this study is the lack of long-term, up-to-date water quality data due to discontinued water-quality monitoring stations. Although careful considerations were given to the adjustment of parameter values reflecting various aspects of the nutrient processes, further data collection will enhance modeling study for assessment of these watersheds’ water resources and environmental problem

of Struma Ovarii: A Rare Ovarian Tumor

Abstract Struma ovarii or monodermal teratoma is a specialized ovarian neoplasm which mainly constitutes mature thyroid tissue. It is a rare tumor which comprises 1% of all ovarian tumors and 2.7% of all dermoid tumors. Thyroid tissue can be observed in 5-15% of dermoid tumors but to designate the tumor as struma ovarii, it must comprise more than 50% of the ovarian tissue. This study was conducted in the Department of Pathology, Manipal Teaching Hospital in Pokhara, Nepal over a period of 10 years (Jan 2006 to Sep 2015. Age, clinical findings, preoperative imaging diagnosis, size and side of the tumor, gross and microscopic findings along with type of surgery performed are included in the study. During this 10 years period, there were 7 cases of struma ovarii with age ranging from 26 to 56 years. 2 cases had tumor on the right and 4 cases had tumor on the left side while 1 case had bilateral struma ovarii. Initial presenting symptom was palpable mass, abdominal pain and vaginal bleeding. The size of the tumor ranged from 4 to 15 cm. The capsule was smooth and cut surface shows multiloculated cyst filled with greenish to pale brown gelatinous thick fluid. Microscopic examination revealed well encapsulated tumor composed entirely of thyroid follicles. Diagnoses of struma ovarii were made in all cases. The preoperative imaging may not exactly give the diagnosis. Clinically, lesser age group was more affected and left side is more commonly involved in our series, in contrary to other literature. Out of 7 cases, bilateral struma ovarii was seen in 1 patient. No malignant features were seen in any of these cases

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