NREL Pyrheliometer Comparison: September 16 to 27, 2013 (NPC-2013)

Accurate measurements of direct normal (beam) solar irradiance from pyrheliometers are important for the development and deployment of solar energy conversion systems, improving our understanding of the Earth's energy budget for climate change studies, and for other science and technology applications involving solar flux. Providing these measurements places many demands on the quality system used by the operator of commercially available radiometers. Maintaining accurate radiometer calibrations traceable to an international standard is the first step in producing research-quality solar irradiance measurements. As with all measurement systems, absolute cavity radiometers and other types of pyrheliometers are subject to performance changes over time. NREL has developed and maintained a group of absolute cavity radiometers with direct calibration traceability to the World Radiometric Reference (WRR). These reference instruments are used by NREL to calibrate pyrheliometers and pyranometers using the ISO 17025 accredited Broadband Outdoor Radiometer Calibration (BORCAL) process (Reda et al. 2008). NPCs are held annually at the Solar Radiation Research Laboratory (SRRL) in Golden, Colorado. Open to all pyrheliometer owners/operators, e.g. NREL, NASA, NIST, NOAA, USA industry and academia, USA-DOE and other national laboratories, and national and international organizations. Each NPC provides an opportunity to determine the unique World Radiometric Reference (WRR) transfer factor (WRR-TF) for each participating pyrheliometer. By adjusting all subsequent pyrheliometer measurements by the appropriate WRR-TF, the solar irradiance data are traceable to the International System of Units through WRR

Commercial wind turbines modeling using single and composite cumulative probability density functions

As wind turbines more widely used with newer manufactured types and larger electrical power scales, a brief mathematical modelling for these wind turbines operating power curves is needed for optimal site matching selections. In this paper, 24 commercial wind turbines with different ratings and different manufactures are modelled using single cumulative probability density functions modelling equations. A new mean of a composite cumulative probability density function is used for better modelling accuracy. Invasive weed optimization algorithm is used to estimate different models designing parameters. The best cumulative density function model for each wind turbine is reached through comparing the RMSE of each model. Results showed that Weibull-Gamma composite is the best modelling technique for 37.5% of the reached results

Utility of diffusion weighted imaging (DWI) in assessment of liver fibrosis

Objectives: hepatic fibrosis occurs due to chronic liver injury. Early fibrosis can be reversed by treatment with specific antifibrotic therapy in addition to removal of the cause if possible, that is why, identification of the early liver fibrosis is important. MRI DWI is a non-invasive non-contrast imaging technique which help in diagnosis of different stages of hepatic fibrosis.Aim of the work: was to study the predictive value of diffusion weighted MRI for assessing liver fibrosis in comparison to liver biopsy in chronic hepatitis C virus patients.Methods: all the studied cases were subjected to the followings: (1) History and laboratory examination (PCR for HCV and liver function tests). (2) MRI DWI and post processing ADC map. (3) Percutaneous liver biopsy in cases with HCV for histopathological examination to assess the stage of fibrosis.Results: this study was carried out on 75 subjects, divided into two group, 50 cases and 25 controls, the mean age in the two studied groups was 36.5 ± 9.32 and 35.8 ± 6.75 respectively in patients and control. ADC of both liver and spleen showed a highly significant increase in the control than in the cases with mean liver ADC in the control group = 2.3 ± 0.25. There was a significant negative correlation between the mean ADC of the liver, spleen and the stage of liver fibrosis

Community knowledge, attitude and practice on rabies, incidence in humans and animals and risk factors to rabies in selected districts of Tigray Region, Ethiopia

Community awareness and assessing the trend of suspected rabies cases play a significant role in preventing its fatality. Therefore, a cross-sectional study design was employed (October 2016 - April 2017) to assess community knowledge, attitude and practice (KAP), and Incidence and risk factors to rabies (human and animal) in the study area. A semi-structured questionnaire was employed to collect required information from 1440 study participants. Retrospective data of five-year (2012-2016) from hospitals and health centers (human cases), and veterinary clinics (animal cases) was used. Majority of the study participants (64.3%) were rural residents, 95.2% have heard about rabies and 50.1% were found dog owners. Among the study participants, 72.2%, 66.0%, and 62.4% have a good level of knowledge, attitude, and practices about rabies, respectively. A strong association between knowledge, attitude and practice with sex; educational level; occupation, dog ownership and rural/urban dwellers (p<0.05) was recorded. Furthermore, a total dog bite cases of 398 domestic animals and 4617 humans were found registered on casebooks of both veterinary and human health service centers of the study districts during the five years study period among which the highest percentage (36.4%) was recorded from canines. The highest anti-rabies vaccine coverage recorded was 36.0% in the year 2016, and higher human dog bite cases recorded was 50.1% on individuals aged between 5-15 years (both male and female). Hence, the current findings suggest that there is a need for coordinated and integrated effort of government, professionals (medical and veterinarians), community and other stake holders towards rabies control and prevention.Keywords: Animal, Human, Tigray, Rabies, Statu

DEEP LEARNING FOR OBJECT DETECTION USING RADAR DATA

Recently, Deep learning algorithms are becoming increasingly instrumental in autonomous driving by identifying and acknowledging road entities to ensure secure navigation and decision-making. Autonomous car datasets play a vital role in developing and evaluating perception systems. Nevertheless, the majority of current datasets are acquired using Light Detection and Ranging (LiDAR) and camera sensors. Utilizing deep neural networks yields remarkable outcomes in object recognition, especially when applied to analyze data from cameras and LiDAR sensors which perform poorly under adverse weather conditions such as rain, fog, and snow due to the sensor wavelengths. This paper aims to evaluate the ability to use RADAR dataset for detecting objects in adverse weather conditions, when LiDAR and Cameras may fail to be effective. This paper presents two experiments for object detection using Faster-RCNN architecture with Resnet-50 backbone and COCO evaluation metrics. Experiment 1 is object detection over only one class, while Experiment 2 is object detection over eight classes. The results show that as expected the average precision (AP) of detecting one class is (47.2) which is better than the results from detecting eight classes (27.4). Comparing my results from experiment 1 to the literature results which achieved an overall AP (45.77), my result was slightly better in accuracy than the literature mainly due to hyper-parameters optimization. The outcomes of object detection and recognition based on RADAR indicate the potential effectiveness of RADAR data in automotive applications particularly in adverse weather conditions, where vision and LiDAR may encounter limitations