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Snapshot reports use data from DOE's LED Lighting Facts product list to compare the LED performance to standard technologies, and are designed to help lighting retailers, distributors, designers, utilities, energy efficiency program sponsors, and other stakeholders understand the current state of the LED market and its trajectory.
Snapshot reports use data from DOE's LED Lighting Facts product list to compare the LED performance to standard technologies, and are designed to help lighting retailers, distributors, designers, utilities, energy efficiency program sponsors, and other stakeholders understand the current state of the LED market and its trajectory.
Snapshot reports use data from DOE's LED Lighting Facts product list to compare the LED performance to standard technologies, and are designed to help lighting retailers, distributors, designers, utilities, energy efficiency program sponsors, and other stakeholders understand the current state of the LED market and its trajectory.
Snapshot reports use data from DOE's LED Lighting Facts product list to compare the LED performance to standard technologies, and are designed to help lighting retailers, distributors, designers, utilities, energy efficiency program sponsors, and other stakeholders understand the current state of the LED market and its trajectory.
Snapshot reports use data from DOE's LED Lighting Facts product list to compare the LED performance to standard technologies, and are designed to help lighting retailers, distributors, designers, utilities, energy efficiency program sponsors, and other stakeholders understand the current state of the LED market and its trajectory.
Snapshot reports use data from DOE's LED Lighting Facts product list to compare the LED performance to standard technologies, and are designed to help lighting retailers, distributors, designers, utilities, energy efficiency program sponsors, and other stakeholders understand the current state of the LED market and its trajectory.
Snapshot reports use data from DOE's LED Lighting Facts product list to compare the LED performance to standard technologies, and are designed to help lighting retailers, distributors, designers, utilities, energy efficiency program sponsors, and other stakeholders understand the current state of the LED market and its trajectory.
Snapshot reports use data from DOE's LED Lighting Facts product list to compare the LED performance to standard technologies, and are designed to help lighting retailers, distributors, designers, utilities, energy efficiency program sponsors, and other stakeholders understand the current state of the LED market and its trajectory.
The OES CO₂ Pipeline project will move captured carbon dioxide from two ethanol facilities near Gibson City, Illinois, roughly 7.8 miles southeast to three injection wells outside Anchor, where it will be permanently stored underground. The system is designed to handle up to 4.5 million metric tonnes per year of dense-phase CO₂ at pressures up to 2,500 psig, using 16-inch mainline pipe and 10.750-inch laterals made from API 5L X-60 and X-65 steel. Wall thicknesses vary depending on location, with thinner pipe in open country, heavier wall at road crossings, and the heaviest where the pipe passes under highways or railroads via horizontal directional drill. The pipe gets a fusion-bonded epoxy coating, with an added abrasion-resistant layer wherever it's bored or drilled. Major water crossings will use HDD rather than open trenching. The pipeline will be cathodically protected, equipped with SCADA-compatible pressure and temperature instrumentation, and monitored for leaks using a computational pipeline monitoring system per API RP 1130. Hydrostatic testing will be performed at 1.25 times design pressure, and an ILI caliper run will follow to catch any construction defects. Several items, including fracture toughness requirements, specific NDE methods, and ILI tool selection, are left for the detailed design phase. The whole system falls under 49 CFR Part 195 and ASME B31.4, and Gulf Interstate Engineering prepared this document as the FEED-level design basis under the CarbonSAFE Phase III program.
Understanding parallel algorithms is crucial for accelerating scientific simulations on complex, distributed memory, high-performance computers. Modern algorithm classification approaches learn semantics directly from source code to differentiate between algorithms, however, accessing source code is not always possible. We can learn about parallel algorithms from observing their performance, as programs running the same algorithms and using the same hardware should exhibit similar performance characteristics. We present an approach to learn algorithm classes from parallel performance data directly in order to classify algorithms without access to the source code. We extend previous work to enable classifying parallel sorting algorithms using automatic instrumentation instead of requiring manual region annotations in the source code. In this work, we design and demonstrate a study for classification of parallel sorting algorithms using parallel performance data collected from automatic instrumentation, and evaluate the performance of our new methodology on classification. We leverage Caliper to collect the performance data, Thicket for our exploratory data analysis (EDA), and PyTorch and Scikit-learn to evaluate the effectiveness of random forests, support vector machines (SVMs), decision trees, neural networks, and logistic regressions on parallel performance data. Additionally, we study noise in parallel performance data, whether the removal of noise and pre-processing of the data is necessary to accurately classify parallel sorting algorithms, and determine the effectiveness of features created from performance data. In conclusion, we demonstrate classification accuracy for these five different models of up to 97.7% across four different parallel algorithm classes.
The HEU Jemima plates have been utilized in numerous International Criticality Safety Benchmark Evaluation Project (ICSBEP) evaluations. ICSBEP evaluations are a vital com ponent of nuclear data validation for applications across the nuclear community. In the past, different combinations of mass, caliper measurements, and drawing dimensions have been used to define the HEU Jemima plates in ICSBEP evaluations. Some of these combinations result in large density ranges (17 g/cm 3 to 19 g/cm 3 ) or unrealistic densities (greater than 19 g/cm 3 ). Additionally, oxidation of the plates has led to questioning the flatness of each plate, which can add additional gaps in experiment configurations. This work proposes a standardized method of defining the commonly used HEU Jemima plates across different experiments.
Purpose: The difference between three-dimensional (3D) and four-dimensional (4D) dose is affected by factors such as tumor size and motion. To quantitatively analyze the effects of these factors, a phantom that can independently control for each factor is required. The purpose of this study is to develop a deformable lung phantom with the above attributes and evaluate characteristics. Methods: A phantom was designed to simulate diaphragm motion with amplitude in the range 1 to 7 cm and various periods of regular breathing. To simulate different size tumors, tumors were produced by pouring liquid silicone into custom molds created by a 3D printer. The accuracy of phantom diaphragm motion was assessed using calipers and protractor. To control tumor motion, tumor trajectories were evaluated using 4D computed tomography (CT), and diaphragm-tumor correlation curve was calculated by curve fitting method. Three-dimensional dose and 4D dose were calculated and compared according to tumor motion. Results: The accuracy of phantom diaphragm motion was less than 1 mm. Maximum tumor motion amplitudes in the left-right and anterior-posterior directions were 0.08 and 0.12 cm, respectively, in a 10 cm{sup 3} tumor, and 0.06 and 0.27 cm, respectively, in a 90 cm{sup 3} tumor. The diaphragm-tumor correlation curve showed that tumor motion in the superior-inferior direction was increased with increasing diaphragm motion. In the 10 cm{sup 3} tumor, the tumor motion was larger than the 90 cm{sup 3} tumor. According to tumor motion, variation of dose difference between 3D and 4D was identified. Conclusion: The developed phantom can independently control factors such as tumor size and motion. In potentially, this phantom can be used to quantitatively analyze the dosimetric impact of respiratory motion according to the factors that influence the difference between 3D and 4D dose. This research was supported by the Mid-career Researcher Program through NRF funded by the Ministry of Science, ICT & Future Planning of Korea (NRF-2014R1A2A1A10050270) and by the Radiation Technology R&D program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (No. 2013M2A2A7038291)