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

Traffic‐aware rate control for mix‐flow in datacenter

Abstract Datacenter applications generate diverse flows, including deadline flows and non‐deadline flows. The deadline flows require to complete within strict deadline, while non‐deadline flows seek a shorter flow completion time. The state‐of‐the‐art deadline‐aware methods either transmit deadline flows with best‐effort at high priority, resulting in the starvation of non‐deadline flows, or blindly restrict the sending rates of deadline flows, leading to a high deadline missing ratio. To meet the different requirements of mix‐flows, a novel traffic‐aware 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 non‐deadline flows. Meanwhile, TRC schedules non‐deadline flows in accordance with their sizes, minimizing the average FCT. The performance of TRC in large‐scale scenarios is evaluated through NS2 simulations. The test results show that TRC reduces the deadline missing ratio of deadline flows and the FCT of non‐deadline flows by up to 69.5% and 78.7% compared to the state‐of‐the‐art deadline‐aware 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 medium–low 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