Designing a System for Upgrading of Heavy Crude Oils Through Electron Beam Treatment

Low-quality crude oil reserves require prohibitively high energy costs to extract and transport. The extreme viscosity and impurities of these oils prevents them from being transported via pipeline, requiring the use of more expensive trucks or trains. Light crude oil has a viscosity ranging up to 100 cP at 40°C. In contrast, Crude Oil #1 under investigation measures 33,855 cP, and Crude Oil #2 is 4,570,000 cP at the same temperature as measured in the laboratory. Sulfur content of both exceeds 5% by mass. Effects of the exposure of these oils to an electron beam discharge are being researched to reduce viscosity with higher conversion factors, using less energy at low temperatures. To facilitate this investigation, a flow loop was created with controls to adjust oil initial temperature with line heaters, radiation dose rate with height adjustment, flow shear rate through flow channel angle, and flow residence time through a gear pump.The flow loop uses stainless steel lines with a gear pump built to handle viscous oil at 230°C, and makes extensive use of aluminum versus steel in a modular frame to prevent overheating from the e-beam. To support the flow test cart, a remote control station cart was created, along with a fire safety cart and mobile test cell for safe sample extraction and shakedown testing. In designing the system and writing safety documentation, the test vehicles were further refined as new concerns were addressed and potential hazards mitigated. Preliminary testing of the various system components yielded a successful design. The end result is a set of systems that allows for ease of variability in operating parameters such as dose rate, gas environment, and added hydrogenation

Water. The Geopolitics of Water

In the great geo-strategic game known as the Middle East, peace politics get much of the press and the attention of policy leaders. On occasion, oil takes center stage but often for the wrong reason (see, e.g., The End of the Oil Era and the Price of Oil ). What is important to understand is that water is in fact the key strategic resource in the region and if you follow the flow of water, you\u27ll follow the politics and policy machinations that lie at the heart of the Israeli-Syrian dialogue. IASPS Fellow in Strategy Paul Michael Wihbey co-authored an analysis of the water question that appeared in the Washington Times, Wednesday, March 29, 200

Designing a System for Upgrading of Heavy Crude Oils Through Electron Beam Treatment

Low-quality crude oil reserves require prohibitively high energy costs to extract and transport. The extreme viscosity and impurities of these oils prevents them from being transported via pipeline, requiring the use of more expensive trucks or trains. Light crude oil has a viscosity ranging up to 100 cP at 40°C. In contrast, Crude Oil #1 under investigation measures 33,855 cP, and Crude Oil #2 is 4,570,000 cP at the same temperature as measured in the laboratory. Sulfur content of both exceeds 5% by mass. Effects of the exposure of these oils to an electron beam discharge are being researched to reduce viscosity with higher conversion factors, using less energy at low temperatures. To facilitate this investigation, a flow loop was created with controls to adjust oil initial temperature with line heaters, radiation dose rate with height adjustment, flow shear rate through flow channel angle, and flow residence time through a gear pump.The flow loop uses stainless steel lines with a gear pump built to handle viscous oil at 230°C, and makes extensive use of aluminum versus steel in a modular frame to prevent overheating from the e-beam. To support the flow test cart, a remote control station cart was created, along with a fire safety cart and mobile test cell for safe sample extraction and shakedown testing. In designing the system and writing safety documentation, the test vehicles were further refined as new concerns were addressed and potential hazards mitigated. Preliminary testing of the various system components yielded a successful design. The end result is a set of systems that allows for ease of variability in operating parameters such as dose rate, gas environment, and added hydrogenation

Hardness Preserving Reductions via Cuckoo Hashing

The focus of this work is hardness-preserving transformations of somewhat limited pseudorandom functions families (PRFs) into ones with more versatile characteristics. Consider the problem of domain extension of pseudorandom functions: given a PRF that takes as input elements of some domain $\mathcal{U}$, we would like to come up with a PRF over a larger domain. Can we do it with little work and without significantly impacting the security of the system? One approach is to first hash the larger domain into the smaller one and then apply the original PRF. Such a reduction, however, is vulnerable to a birthday attack : after $\sqrt{|\mathcal{U}|}$ queries to the resulting PRF, a collision (i.e., two distinct inputs having the same hash value) is very likely to occur. As a consequence, the resulting PRF is insecure against an attacker making this number of queries. In this work we show how to go beyond the aforementioned birthday attack barrier by replacing the above simple hashing approach with a variant of cuckoo hashing, a hashing paradigm that resolves collisions in a table by using two hash functions and two tables, cleverly assigning each element to one of the two tables. We use this approach to obtain: (i) a domain extension method that requires just two calls to the original PRF, can withstand as many queries as the original domain size, and has a distinguishing probability that is exponentially small in the amount of non-cryptographic work; and (ii) a security-preserving reduction from non-adaptive to adaptive PRFs

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.

OBJECTIVES:To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND:Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS:Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS:The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS:An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level

Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P<0.001; statistical model, 0.81 [0.75-0.87], P=0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP

a retrospective case–control study from the PARADIGM registry

Funding Information: Dr. Jonathon A. Leipsic serves as a consultant and has stock options in HeartFlow and Circle Cardiovascular Imaging; he also receives grant support from GE Healthcare and speaking fees from Philips. Dr. Habib Samady has an equity interest in Covanos. Dr. Daniel Berman receives software royalties from Cedars-Sinai Medical Center. Dr. James K. Min receives funding from the Dalio Foundation, National Institutes of Health, and GE Healthcare. Dr. Min serves on the scientific advisory board of Arineta and GE Healthcare and has an equity interest in Cleerly. All other authors declare that they have no competing interests. Funding Information: This work was supported by the Korea Medical Device Development Fund grant funded by the Korean government (Ministry of Science and ICT; Ministry of Trade, Industry and Energy; Ministry of Health & Welfare, Republic of Korea; and Ministry of Food and Drug Safety; Project Number: 202016B02). Publisher Copyright: © 2022, The Author(s).Background: The baseline coronary plaque burden is the most important factor for rapid plaque progression (RPP) in the coronary artery. However, data on the independent predictors of RPP in the absence of a baseline coronary plaque burden are limited. Thus, this study aimed to investigate the predictors for RPP in patients without coronary plaques on baseline coronary computed tomography angiography (CCTA) images. Methods: A total of 402 patients (mean age: 57.6 ± 10.0 years, 49.3% men) without coronary plaques at baseline who underwent serial coronary CCTA were identified from the Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging (PARADIGM) registry and included in this retrospective study. RPP was defined as an annual change of ≥ 1.0%/year in the percentage atheroma volume (PAV). Results: During a median inter-scan period of 3.6 years (interquartile range: 2.7–5.0 years), newly developed coronary plaques and RPP were observed in 35.6% and 4.2% of the patients, respectively. The baseline traditional risk factors, i.e., advanced age (≥ 60 years), male sex, hypertension, diabetes mellitus, hyperlipidemia, obesity, and current smoking status, were not significantly associated with the risk of RPP. Multivariate linear regression analysis showed that the serum hemoglobin A1c level (per 1% increase) measured at follow-up CCTA was independently associated with the annual change in the PAV (β: 0.098, 95% confidence interval [CI]: 0.048–0.149; P < 0.001). The multiple logistic regression models showed that the serum hemoglobin A1c level had an independent and positive association with the risk of RPP. The optimal predictive cut-off value of the hemoglobin A1c level for RPP was 7.05% (sensitivity: 80.0%, specificity: 86.7%; area under curve: 0.816 [95% CI: 0.574–0.999]; P = 0.017). Conclusion: In this retrospective case–control study, the glycemic control status was strongly associated with the risk of RPP in patients without a baseline coronary plaque burden. This suggests that regular monitoring of the glycemic control status might be helpful for preventing the rapid progression of coronary atherosclerosis irrespective of the baseline risk factors. Further randomized investigations are necessary to confirm the results of our study. Trial registration: ClinicalTrials.gov NCT02803411.publishersversionpublishe