Atomic origins of the high catalytic activity of nanoporous gold

Distinct from inert bulk gold, nanoparticulate gold has been found to possess remarkable catalytic activity towards oxidation reactions. The catalytic performance of nanoparticulate gold strongly depends on size and support, and catalytic activity usually cannot be observed at characteristic sizes larger than 5 nm. Interestingly, significant catalytic activity can be retained in dealloyed nanoporous gold (NPG) even when its feature lengths are larger than 30 nm. Here we report atomic insights of the NPG catalysis, characterized by spherical-aberration-corrected transmission electron microscopy (TEM) and environmental TEM. A high density of atomic steps and kinks is observed on the curved surfaces of NPG, comparable to 3-5 nm nanoparticles, which are stabilized by hyperboloid-like gold ligaments. In situ TEM observations provide compelling evidence that the surface defects are active sites for the catalytic oxidation of CO and residual Ag stabilizes the atomic steps by suppressing {111} faceting kinetics.     

Soft computing based imputation and hybrid data and text mining: The case of predicting the severity of phishing alerts

In this paper, we employ a novel two-stage soft computing approach for data imputation to assess the severity of phishing attacks. The imputation method involves K-means algorithm and multilayer perceptron (MLP) working in tandem. The hybrid is applied to replace the missing values of financial data which is used for predicting the severity of phishing attacks in financial firms. After imputing the missing values, we mine the financial data related to the firms along with the structured form of the textual data using multilayer perceptron (MLP), probabilistic neural network (PNN) and decision trees (DT) separately. Of particular significance is the overall classification accuracy of 81.80%, 82.58%, and 82.19% obtained using MLP, PNN, and DT respectively. It is observed that the present results outperform those of prior research. The overall classification accuracies for the three risk levels of phishing attacks using the classifiers MLP, PNN, and DT are also superior.     

Packer identification using Byte plot and Markov plot

Malware is one of the major concerns in computer security. The availability of easy to use malware toolkits and internet popularity has led to the increase in number of malware attacks. Currently signature based malware detection techniques are widely used. However, malware authors use packing techniques to create new variants of existing malwares which defeat signature based malware detection. So, it is very important to identify packed malware and unpack it before analysis. Dynamic unpacking runs the packed executable and provides an unpacked version based on the system. This technique requires dedicated hardware and is computationally expensive. As each individual packer uses its own unpacking algorithm it is important to have a prior knowledge about the packer used, in order to assist in reverse engineering. In this paper, we propose an efficient framework for packer identification problem using Byte plot and Markov plot. First packed malware is converted to Byte plot and Markov plot. Later Gabor and wavelet based features are extracted from Byte plot and Markov plot. We used SVMs (Support Vector Machine) in our analysis. We performed our experiments on nine different packers and we obtained about 95 % accuracy for nine of the packers. Our results show features extracted from Markov plot outperformed features extracted from Byte plot by about 3 %. We compare the performance of Markov plot with PEID (Signature based PE identification tool). Our results show Markov plot produced better accuracy when compared to PEID. We also performed multi class classification using Random Forest and achieved 81 % accuracy using Markov plot based features.     

Application of a Depletion-Based Stock Reduction Analysis (DB-SRA) to lake sturgeon in Lake Erie

Lake Erie supported the greatest yield of lake sturgeon within the Laurentian Great Lakes near the end of the 19th century with >2000 metric tons caught at the peak of the fishery. The fishery collapsed by the 1920s when <1% of the previous peak catch was removed. Despite closures of the fishery, lake sturgeon remain rare in Lake Erie. We applied a depletion-based stock reduction analysis (DB-SRA) to the catch of lake sturgeon from 1879 to 1929 to gain estimates of sustainable fishery reference points and the historic carrying capacity of Lake Erie for lake sturgeon. We also simulated population growth of lake sturgeon from 1929 to the present with varying assumptions of the current carrying capacity of the lake. The estimated historic carrying capacity of lake sturgeon was 22,652 metric tons. During the height of the fishery, exploitation was as high as 37% which was more than an order of magnitude greater than that required for maximum sustainable yield. Projections of the population from 1929 to 2016 suggest sufficient time has passed since the collapse of the fishery that the population should have recovered to levels that would support a fishery at maximum sustainable yield. However, lake sturgeon remain rare in Lake Erie indicating that other factors such as habitat availability may be limiting their recovery. Our estimates of carrying capacity will be informative when setting recovery targets which consider the amount of habitat loss.     

热流道系统的设计和喷嘴的选择

介绍塑料注射模热流道系统的设计原则和过程,分析热流道喷嘴的选择原理和方法。     

Pressure-driven diffusive gas flows in micro-channels: from the Knudsen to the continuum regimes

Despite the enormous scientific and technological importance of micro-channel gas flows, the understanding of these flows, by classical fluid mechanics, remains incomplete including the prediction of flow rates. In this paper, we revisit the problem of micro-channel compressible gas flows and show that the axial diffusion of mass engendered by the density (pressure) gradient becomes increasingly significant with increased Knudsen number compared to the pressure driven convection. The present theoretical treatment is based on a recently proposed modification (Durst et al. in Proceeding of the international symposium on turbulence, heat and mass transfer, Dubrovnik, 3–18 September, pp 25–29, 2006) of the Navier–Stokes equations that include the diffusion of mass caused by the density and temperature gradients. The theoretical predictions using the modified Navier–Stokes equations are found to be in good agreement with the available experimental data spanning the continuum, transition and free-molecular (Knudsen) flow regimes, without invoking the concept of Maxwellian wall-slip boundary condition. The simple theory also results in excellent agreement with the results of linearized Boltzmann equations and Direct Simulation Monte Carlo (DSMC) method. Finally, the theory explains the Knudsen minimum and suggests the design of future micro-channel flow experiments and their employment to complete the present days understanding of micro-channel flows.     

以性质为基础的同时设计过程和分子的方法

In this work, property clustering techniques and group contribution methods are combined to enable simultaneous consideration of process performance requirements and molecular property constraints. Using this methodology, the process design problem is solved to identify the property targets corresponding to the desired process performance. A significant advantage of the developed methodology is that for problems that can be satisfactorily described by only three properties, the process and molecular design problems can be simultaneously solved visually on a ternary diagram, irrespective of how many molecular fragments are included in the search space. On the ternary cluster diagram, the target properties are represented as individual points if given as discrete values or as a region if given as intervals. The structure and identity of candidate components is then identified by combining or ＂mixing＂ molecular fragments until the resulting properties match the targets.     

The behavior of reactor power and flux resulting from changes in core-coolant temperature for a miniature neutron source reactor

In this work, measurements were performed to verify the theoretical predictions of reactor power and flux parameters that result from changes in core inlet temperature (T_in) and the temperature difference between the coolant inlet and outlet (ΔT) in the Nigeria Research Reactor-1 (NIRR-1), which is a Miniature Neutron Source Reactor (MNSR). The measured data shows that there is a strong dependence of the reactor power on coolant temperature in agreement with the design of MNSR. The experimental parameters were found to be in good agreement with data obtained using a semi-empirical relationship between the reactor power, flux parameters, core inlet temperature, and the coolant temperature rise. The relationship was therefore used to predict the power level of NIRR-1 from its neutron flux parameters to which it has been found to be proportional. The variation of T_in and ΔT with the reactor power and flux was also investigated and the results obtained are hereby discussed.     

Cutting single-walled carbon nanotubes

A two-step process is utilized for cutting single-walled carbon nanotubes (SWNTs). The first step requires the breakage of carbon-carbon bonds in the lattice while the second step is aimed at etching at these damage sites to create short, cut nanotubes. To achieve monodisperse lengths from any cutting strategy requires control of both steps. Room-temperature piranha and ammonium persulfate solutions have shown the ability to exploit the damage sites and etch SWNTs in a controlled manner. Despite the aggressive nature of these oxidizing solutions, the etch rate for SWNTs is relatively slow and almost no new sidewall damage is introduced. Carbon-carbon bond breakage can be introduced through fluorination to ～C(2)F, and subsequent etching using piranha solutions has been shown to be very effective in cutting nanotubes. The final average length of the nanotubes is approximately?100?nm with carbon yields as high as 70-80%.