a comparative analysis of CDM in South Africa and China

Both South Africa and China are emergent economies heavily dependent on fossilfuel based energy sources, and the potential to leverage the Clean Development Mechanism (CDM) is significant in both countries. However, experience to date with CDM indicates South Africa has significantly lagged behind China in the uptake of the CDM, accounting for only 0.9% of the worldwide registered annual Certified Emission Reductions (CERs) while China has dominated the market, generating over 54% of the annual worldwide CERs. Thus, an opportunity exists to redefine the role of CDM in South Africa to better incentivise a lower carbon development trajectory. This paper provides a comparative analysis of the CDM experience in China and South Africa in order to identify the underlying drivers and obstacles to CDM in both countries. It is the authors’ objective to analyse the lessons learnt from marketleading China and laggard South Africa to better understand the structures and policies necessary within host CDM countries to unlock the potential of CDM in a post 2012 regime

A negative mass theorem for surfaces of positive genus

We define the "sum of squares of the wavelengths" of a Riemannian surface (M,g) to be the regularized trace of the inverse of the Laplacian. We normalize by scaling and adding a constant, to obtain a "mass", which is scale invariant and vanishes at the round sphere. This is an anlaog for closed surfaces of the ADM mass from general relativity. We show that if M has positive genus then on each conformal class, the mass attains a negative minimum. For the minimizing metric, there is a sharp logarithmic Hardy-Littlewood-Sobolev inequality and a Moser-Trudinger-Onofri type inequality.Comment: 8 page

Stratified Trajectories: Charting Equity Gaps in Program Pathways Among Community College Students

A primary focus among colleges implementing student success reforms has been to increase overall rates of credential completion and to reduce racial and socioeconomic equity gaps in completion rates. The focus on general completion may overlook inequities in the type of program students complete, which is particularly significant given the wide variety of credentials offered at community colleges—from short-term certificates to transfer-oriented associate degrees that may lead to bachelor’s and graduate degree programs—and the resulting variation in labor market returns among completers. This study examines racial/ethnic stratification among community college students as they enter and progress through different programs leading to higher- and lower-paying jobs. The authors develop a discrete-time survival analysis using longitudinal enrollment and transcript data on first-time-in-college, credential-seeking community college students from a state with more than 20 community colleges. They track student enrollment, completion, and transfer for up to nine years and examine when equity gaps in completion emerge. They also measure the student achievement of academic milestones (such as levels of credit accrual) along educational pathways that are associated with higher rates of credential completion and transfer over the long term. Results suggest that a significant gap in the likelihood of bachelor’s degree completion between Black and White students emerges more episodically, while the gap between Hispanic and White students develops earlier and remains more consistent over time. Results also suggest that while all students generally benefit from the attainment of academic milestones, such as gaining credit momentum or completing pre-transfer associate degrees, doing so disproportionately benefits Black and Hispanic students

Long-term prognosis for individuals with hypertension undergoing coronary artery calcium scoring

To examine the performance of coronary artery calcification (CAC) for stratifying long-term risk of death in asymptomatic hypertensive patients

Induction of Heme Oxygenase-1 Expression Inhibits Platelet-Dependent Thrombosis

Heme oxygenase-1 (HO-1) plays a key role in protecting tissue from oxidative stress. Although some studies implicate HO-1 in modulating thrombosis after vascular injury, the impact of HO-1 on the rate of clot formation in vivo is poorly defined. This study examined the potential function of HO-1 in regulating platelet-dependent arterial thrombosis. Platelet-rich thrombi were induced in C57BL/6J mice by applying 10% ferric chloride to the exposed carotid artery. Mean occlusion time of wild-type mice (n = 10) was 14.6 ± 1.0 min versus 12.9 ± 0.6 min for HO-1-/- mice (n = 11, p = 0.17). However, after challenge with hemin, mean occlusion time was significantly longer in wild-type mice (16.3 ± 1.2 min, n = 15) than HO-1-/- mice (12.0 ± 1.0 min, n = 9; p = 0.021). Hemin administration induced an approximately twofold increase in oxidative stress, measured as plasma thiobarbituric acid reactive substances. Immunohistochemical analysis revealed that hemin induced a robust increase in HO-1 expression within the carotid arterial wall. Ex vivo blood clotting within a collagen-coated perfusion chamber was studied to determine whether the accelerated thrombosis observed in HO-1-/- mice was contributed to by effects on the blood itself. Under basal conditions, mean clot formation during perfusion of blood over collagen did not differ between wild-type mice and HO-1-/- mice. However, after hemin challenge, mean clot formation was significantly increased in HO-1-/- mice compared with wild-type controls. These results suggest that, under basal conditions, HO-1 does not exert a significant effect on platelet-dependent clot formation in vivo. However, under conditions that stimulate HO-1 production, platelet-dependent thrombus formation is inhibited by HO-1. Enhanced HO-1 expression in response to oxidative stress may represent an adaptive response mechanism to down-regulate platelet activation under prothrombotic conditions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63386/1/1523086041361677.pd

Chemical Sharpening, Shortening, and Unzipping of Boron Nitride Nanotubes

Boron nitride nanotubes (BNNTs), the one-dimensional member of the boron nitride nanostructure family, are generally accepted to be highly inert to oxidative treatments and can only be covalently modifi ed by highly reactive species. Conversely, it is discovered that the BNNTs can be chemically dispersed and their morphology modifi ed by a relatively mild method: simply sonicating the nanotubes in aqueous ammonia solution. The dispersed nanotubes are significantly corroded, with end-caps removed, tips sharpened, and walls thinned. The sonication treatment in aqueous ammonia solution also removes amorphous BN impurities and shortened BNNTs, resembling various oxidative treatments of carbon nanotubes. Importantly, the majority of BNNTs are at least partially longitudinally cut, or "unzipped". Entangled and freestanding BN nanoribbons (BNNRs), resulting from the unzipping, are found to be approximately 5-20 nm in width and up to a few hundred nanometers in length. This is the fi rst chemical method to obtain BNNRs from BNNT unzipping. This method is not derived from known carbon nanotube unzipping strategies, but is unique to BNNTs because the use of aqueous ammonia solutions specifi cally targets the B-N bond network. This study may pave the way for convenient processing of BNNTs, previously thought to be highly inert, toward controlling their dispersion, purity, lengths, and electronic properties

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