69 research outputs found
An Automated Analyzer for Financial Security of Ethereum Smart Contracts
At present, millions of Ethereum smart contracts are created per year and
attract financially motivated attackers. However, existing analyzers do not
meet the need to precisely analyze the financial security of large numbers of
contracts. In this paper, we propose and implement FASVERIF, an automated
analyzer for fine-grained analysis of smart contracts' financial security. On
the one hand, FASVERIF automatically generates models to be verified against
security properties of smart contracts. On the other hand, our analyzer
automatically generates the security properties, which is different from
existing formal verifiers for smart contracts. As a result, FASVERIF can
automatically process source code of smart contracts, and uses formal methods
whenever possible to simultaneously maximize its accuracy.
We evaluate FASVERIF on a vulnerabilities dataset by comparing it with other
automatic tools. Our evaluation shows that FASVERIF greatly outperforms the
representative tools using different technologies, with respect to accuracy and
coverage of types of vulnerabilities
The impact of electronic health records (EHR) data continuity on prediction model fairness and racial-ethnic disparities
Electronic health records (EHR) data have considerable variability in data
completeness across sites and patients. Lack of "EHR data-continuity" or "EHR
data-discontinuity", defined as "having medical information recorded outside
the reach of an EHR system" can lead to a substantial amount of information
bias. The objective of this study was to comprehensively evaluate (1) how EHR
data-discontinuity introduces data bias, (2) case finding algorithms affect
downstream prediction models, and (3) how algorithmic fairness is associated
with racial-ethnic disparities. We leveraged our EHRs linked with Medicaid and
Medicare claims data in the OneFlorida+ network and used a validated measure
(i.e., Mean Proportions of Encounters Captured [MPEC]) to estimate patients'
EHR data continuity. We developed a machine learning model for predicting type
2 diabetes (T2D) diagnosis as the use case for this work. We found that using
cohorts selected by different levels of EHR data-continuity affects utilities
in disease prediction tasks. The prediction models trained on high continuity
data will have a worse fit on low continuity data. We also found variations in
racial and ethnic disparities in model performances and model fairness in
models developed using different degrees of data continuity. Our results
suggest that careful evaluation of data continuity is critical to improving the
validity of real-world evidence generated by EHR data and health equity
Associations between plasma metal mixture exposure and risk of hypertension: A cross-sectional study among adults in Shenzhen, China
BackgroundMetal exposure affects human health. Current studies mainly focus on the individual health effect of metal exposure on hypertension (HTN), and the results remain controversial. Moreover, the studies assessing overall effect of metal mixtures on hypertension risk are limited.MethodsA cross-sectional study was conducted by recruiting 1,546 Chinese adults who attended routine medical check-ups at the Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen. The plasma levels of 13 metals were measured using inductively coupled plasma mass spectrometry. Multivariate logistic regression model, restricted cubic spline (RCS) model and the Bayesian Kernel Machine Regression (BKMR) model were applied to explore the single and combined effect of metals on the risk of HTN.ResultsA total of 642 (41.5%) participants were diagnosed with HTN. In the logistic regression model, the adjusted odds ratios (ORs) were 0.71 (0.52, 0.97) for cobalt, 1.40 (1.04, 1.89) for calcium, 0.66 (0.48, 0.90), and 0.60 (0.43, 0.83) for aluminum in the second and third quartile, respectively. The RCS analysis showed a V-shaped or an inverse V-shaped dose-response relationship between metals (aluminum or calcium, respectively) and the risk of HTN (P for non-linearity was 0.017 or 0.009, respectively). However, no combined effect was found between metal mixture and the risk of hypertension.ConclusionsPlasma levels of cobalt, aluminum and calcium were found to be associated with the risk of HTN. Further studies are needed to confirm our findings and their potential mechanisms with prospective studies and experimental study designs
Sleep deprivation, sleep fragmentation, and social jet lag increase temperature preference in Drosophila
Despite the fact that sleep deprivation substantially affects the way animals regulate their body temperature, the specific mechanisms behind this phenomenon are not well understood. In both mammals and flies, neural circuits regulating sleep and thermoregulation overlap, suggesting an interdependence that may be relevant for sleep function. To investigate this relationship further, we exposed flies to 12 h of sleep deprivation, or 48 h of sleep fragmentation and evaluated temperature preference in a thermal gradient. Flies exposed to 12 h of sleep deprivation chose warmer temperatures after sleep deprivation. Importantly, sleep fragmentation, which prevents flies from entering deeper stages of sleep, but does not activate sleep homeostatic mechanisms nor induce impairments in short-term memory also resulted in flies choosing warmer temperatures. To identify the underlying neuronal circuits, we used RNAi to knock down the receptor fo
Volatile organic compound mixing ratios above Beijing in November and December 2016
Volatile organic compounds (VOCs) are emitted into the atmosphere from vegetation and anthropogenic
sources such as fossil fuel combustion, biomass burning and the evaporation of petroleum products. These
compounds play an important role in the chemistry of the lower atmosphere through secondary organic aerosol
(SOA) formation and facilitating the formation of tropospheric ozone. As well as their indirect impact on human
health via the formation of ozone and SOA, some VOCs, including benzene, directly affect human health adversely.
Here we report VOC mixing ratios measured in Beijing during a 5 week intensive field campaign from the
7th November to the 10th December 2016. This work was carried out as part of the Sources and Emissions
of Air Pollutants in Beijing (AIRPOLL-Beijing) work project within the Air Pollution and Human Health in
a Developing Megacity (APHH-Beijing) research programme. APHH is a large multi-institutional study which
aims to record the concentrations and identify the sources of urban air pollutants in Beijing, determine exposure,
understand their effects on human health, and to identify solutions.
VOC mixing ratios were recorded using a Proton Transfer Reaction-Time of Flight-Mass Spectrometer
(PTR-ToF-MS, Ionicon Analytik) and a Selected Ion Flow Tube-Mass Spectrometer (SIFT-MS, SYFT Technologies).
During the measurement period Beijing was subject to multiple pollution events that alternated with periods
of relatively good air quality, allowing the VOCs within the polluted air masses to be identified and quantified.
VOCs were sampled at 102 m with additional gradient measurements made at 3, 15, 32 and 64 m providing a
vertical profile of VOC mixing ratios. Mixing ratios of methanol, acetonitrile, acetaldehyde, acetone, isoprene and
aromatics species will be reported together with a discussion of potential sources. Comparisons will then be drawn
with other large cities
Surface-atmosphere fluxes of volatile organic compounds in Beijing
Mixing ratios of volatile organic compounds (VOCs) were recorded in two field campaigns in central Beijing as part of the Air Pollution and Human Health in a Chinese Megacity (APHH) project. These data were used to calculate, for the first time in Beijing, the surface-atmosphere fluxes of VOCs using eddy covariance, giving a top-down estimation of VOC emissions from a central area of the city. The results were then used to evaluate the accuracy of the Multi-resolution Emission Inventory for China (MEIC). The APHH winter and summer campaigns took place in November and December 2016 and May and June 2017, respectively. The largest VOC fluxes observed were of small oxygenated compounds such as methanol, ethanol + formic acid and acetaldehyde, with average emission rates of 8.31±8.5, 3.97±3.9 and 1.83±2.0nmolm-2s-1, respectively, in the summer. A large flux of isoprene was observed in the summer, with an average emission rate of 5.31±7.7nmolm-2s-1. While oxygenated VOCs made up 60% of the molar VOC flux measured, when fluxes were scaled by ozone formation potential and peroxyacyl nitrate (PAN) formation potential the high reactivity of isoprene and monoterpenes meant that these species represented 30% and 28% of the flux contribution to ozone and PAN formation potential, respectively. Comparison of measured fluxes with the emission inventory showed that the inventory failed to capture the magnitude of VOC emissions at the local scale
Trafficâaware rate control for mixâflow in datacenter
Abstract Datacenter applications generate diverse flows, including deadline flows and nonâdeadline flows. The deadline flows require to complete within strict deadline, while nonâdeadline flows seek a shorter flow completion time. The stateâofâtheâart deadlineâaware methods either transmit deadline flows with bestâeffort at high priority, resulting in the starvation of nonâdeadline flows, or blindly restrict the sending rates of deadline flows, leading to a high deadline missing ratio. To meet the different requirements of mixâflows, a novel trafficâaware rate control (TRC) method is proposed. TRC dynamically adjusts the sending rates of deadline flows according to their deadlines and the predicted future traffic patterns. If the intense competition is predicted among deadline flows, TRC will adopt a more aggressive manner to transmit the current deadline flows to avoid bandwidth contention in the future, reducing the deadline missing ratio. Otherwise, TRC will conservatively transmit deadline flows and complete these flows near their respective deadlines, relinquishing the excess bandwidth to nonâdeadline flows. Meanwhile, TRC schedules nonâdeadline flows in accordance with their sizes, minimizing the average FCT. The performance of TRC in largeâscale scenarios is evaluated through NS2 simulations. The test results show that TRC reduces the deadline missing ratio of deadline flows and the FCT of nonâdeadline flows by up to 69.5% and 78.7% compared to the stateâofâtheâart deadlineâaware schemes, respectively
Preparation and Mechanical Properties of Flexible Prepreg Resin with High Strength and Low Creep
In this study, aiming at the problem of low strength and high creep caused by mediumâlow modulus flexible resin based on the formulation design idea of high-molecular-weight epoxy resin (E12)-reinforced flexible epoxy-terminated urethane resin (EUR), a flexible epoxy prepreg resin with high strength and low bending creep was prepared to be suitable for hot melt processing technology. Flexible EUR was synthesized by grafting flexible polyurethane segments onto the epoxy side chain by urethane bonding. By adjusting the ratio of E12 and EUR, the effects of different ratios of the two components on the mechanical properties and viscoelasticity of the resin were systematically studied with dicyandiamide as the latent curing system. Research has found that when the E12 content is between 20%wt and 40%wt, the resin system has the best coating viscosity at 65 °C to 85 °C. The molecular weight and the content of aromatic heterocyclic groups of the resin determine the strength and creep behavior of the resin. When the content of E12 in the system is less than 50%wt, modulus and strength increase linearly, but after more than 50%wt E12 content, the modulus is almost unchanged and the strength begins to decrease. By increasing the content of E12 in the resin, the creep behavior of the resin is greatly reduced. When the content of E12 increases to 50%wt, the bending creep is the lowest
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