Quantum molecular dynamics simulations of the thermophysical properties of shocked liquid ammonia for pressures up to 1.3 TPa

We investigate via quantum molecular-dynamics simulations the thermophysical properties of shocked liquid ammonia up to the pressure 1.3 TPa and temperature 120000 K. The principal Hugoniot is predicted from wide-range equation of state, which agrees well with available experimental measurements up to 64 GPa. Our systematic study of the structural properties demonstrates that liquid ammonia undergoes a gradual phase transition along the Hugoniot. At about 4800 K, the system transforms into a metallic, complex mixture state consisting of $\textnormal{N}\textnormal{H}_{3}$, $\textnormal{N}_{2}$, $\textnormal{H}_{2}$, N, and H. Furthermore, we discuss the implications for the interiors of Uranus and Neptune.Comment: 16 pages, 8 figures. arXiv admin note: text overlap with arXiv:1012.488

Quantum molecular dynamics simulations for the nonmetal-metal transition in shocked methane

We have performed quantum molecular-dynamics simulations for methane under shock compressions up to 80 GPa. We obtain good agreement with available experimental data for the principal Hugoniot, derived from the equation of state. A systematic study of the optical conductivity spectra, one-particle density of states, and the distributions of the electronic charge over supercell at Hugoniot points shows that the transition of shocked methane to a metallic state takes place close to the density at which methane dissociates significantly into molecular hydrogen and some long alkane chains. Through analyzing the pair correlation function, we predict the chemical picture of the shocked methane. In contrast to usual assumptions used for high pressure modeling of methane, we find that no diamond-like configurations occurs for the whole density-temperature range studied.Comment: Some revisions have been given in response to referees' sugestion

Cooperative Routing in Multi-Radio Multi-Hop Wireless Network

There are many recent interests on cooperative communication (CC) in wireless networks. Despite the large capacity gain of CC in small wireless networks, CC can result in severe interference in large networks and even degraded throughput. The aim of this chapter is to concurrently exploit multi-radio and multi-channel (MRMC) and CC technique to combat co-channel interference and improve the performance of multi-hop wireless network. Our proposed solution concurrently considers cooperative routing, channel assignment, and relay selection and takes advantage of both MRMC technique and spatial diversity to improve the throughput. We propose two important metrics, contention-aware channel utilization routing metric (CACU) to capture the interference cost from both direct and cooperative transmission, and traffic aware channel condition metric (TACC) to evaluate the channel load condition. Based on these metrics, we propose three algorithms for interference-aware cooperative routing, local channel adjustment, and local path and relay adaptation, respectively, to ensure high-performance communications in dynamic wireless networks. Our algorithms are fully distributed and can effectively mitigate co-channel interference and achieve cooperative diversity gain. To our best knowledge, this is the first distributed solution that supports CC in MRMC networks. Our performance studies demonstrate that our algorithms can significantly increase the aggregate throughput

Accurate Counting Bloom Filters for Large-Scale Data Processing

Bloom filters are space-efficient randomized data structures for fast membership queries, allowing false positives. Counting Bloom Filters (CBFs) perform the same operations on dynamic sets that can be updated via insertions and deletions. CBFs have been extensively used in MapReduce to accelerate large-scale data processing on large clusters by reducing the volume of datasets. The false positive probability of CBF should be made as low as possible for filtering out more redundant datasets. In this paper, we propose a multilevel optimization approach to building an Accurate Counting Bloom Filter (ACBF) for reducing the false positive probability. ACBF is constructed by partitioning the counter vector into multiple levels. We propose an optimized ACBF by maximizing the first level size, in order to minimize the false positive probability while maintaining the same functionality as CBF. Simulation results show that the optimized ACBF reduces the false positive probability by up to 98.4% at the same memory consumption compared to CBF. We also implement ACBFs in MapReduce to speed up the reduce-side join. Experiments on realistic datasets show that ACBF reduces the false positive probability by 72.3% as well as the map outputs by 33.9% and improves the join execution times by 20% compared to CBF

Mlifdect: Android Malware Detection Based on Parallel Machine Learning and Information Fusion

In recent years, Android malware has continued to grow at an alarming rate. More recent malicious apps’ employing highly sophisticated detection avoidance techniques makes the traditional machine learning based malware detection methods far less effective. More specifically, they cannot cope with various types of Android malware and have limitation in detection by utilizing a single classification algorithm. To address this limitation, we propose a novel approach in this paper that leverages parallel machine learning and information fusion techniques for better Android malware detection, which is named Mlifdect. To implement this approach, we first extract eight types of features from static analysis on Android apps and build two kinds of feature sets after feature selection. Then, a parallel machine learning detection model is developed for speeding up the process of classification. Finally, we investigate the probability analysis based and Dempster-Shafer theory based information fusion approaches which can effectively obtain the detection results. To validate our method, other state-of-the-art detection works are selected for comparison with real-world Android apps. The experimental results demonstrate that Mlifdect is capable of achieving higher detection accuracy as well as a remarkable run-time efficiency compared to the existing malware detection solutions

Accurate Counting Bloom Filters for Large-Scale Data Processing

Bloom filters are space-efficient randomized data structures for fast membership queries, allowing false positives. Counting Bloom Filters (CBFs) perform the same operations on dynamic sets that can be updated via insertions and deletions. CBFs have been extensively used in MapReduce to accelerate large-scale data processing on large clusters by reducing the volume of datasets. The false positive probability of CBF should be made as low as possible for filtering out more redundant datasets. In this paper, we propose a multilevel optimization approach to building an Accurate Counting Bloom Filter (ACBF) for reducing the false positive probability. ACBF is constructed by partitioning the counter vector into multiple levels. We propose an optimized ACBF by maximizing the first level size, in order to minimize the false positive probability while maintaining the same functionality as CBF. Simulation results show that the optimized ACBF reduces the false positive probability by up to 98.4% at the same memory consumption compared to CBF. We also implement ACBFs in MapReduce to speed up the reduce-side join. Experiments on realistic datasets show that ACBF reduces the false positive probability by 72.3% as well as the map outputs by 33.9% and improves the join execution times by 20% compared to CBF

Association of Combined Maternal-Fetal TNF-α Gene G308A Genotypes with Preterm Delivery: A Gene-Gene Interaction Study

Preterm delivery (PTD) is a complicated perinatal adverse event. We were interested in association of G308A polymorphism in tumor necrosis factor-α (TNF-α) gene with PTD; so we conducted a genetic epidemiology study in Anqing City, Anhui Province, China. Case families and control families were all collected between July 1999 and June 2002. To control potential population stratification as we could, all eligible subjects were ethnic Han Chinese. 250 case families and 247 control families were included in data analysis. A hybrid design which combines case-parent triads and control parents was employed, to test maternal-fetal genotype (MFG) incompatibility. The method is based on a log-linear modeling approach. In summary, we found that when the mother's or child's genotype was G/A, there was a reduced risk of PTD; however when the mother's or child's genotype was genotype A/A, there was a relatively higher risk of PTD. Combined maternal-fetal genotype GA/GA showed the most reduced risk of PTD. Comparison of the LRTs showed that the model with maternal-fetal genotype effects fits significantly better than the model with only maternal and fetal genotype main effects (log-likelihood = −719.4, P = .023, significant at 0.05 level). That means that the combined maternal-fetal genotype incompatibility was significantly associated with PTD. The model with maternal-fetal genotype effects can be considered a gene-gene interaction model. We claim that both maternal effects and fetal effects should be considered together while investigating genetic factors of certain perinatal diseases

Modeling and Analysis of Online Delay of Nonperiodic CAN Message

In order to analyze the online communication delay of nonperiodic CAN message, the mathematical model of average on-line delay is established based on M/G/1 queuing theory and an experimental platform is designed to analyze the delay of CAN communication, with which the on-line delays of messages with a different ID are tested at different load ratios. The results show that the model is very close to the actual situation indicating the high accuracy of the model. In the results, for the same message, the average and maximum online delays both increase with the raise of load ratio. At the same load ratio, the maximum on-line delay increases with the decrease of the message priority, and the average on-line delay remains almost unchanged