The Influence of Geometrical Shape Changes on Wave Overtopping: a Laboratory and SPH Numerical Study

This paper presents laboratory investigations of four “retrofit” suggestions for attenuating the overtopping from vertical seawall. Two-dimensional physical model experiments were performed on a vertical seawall with a 1:20 sloping foreshore. Additionally, a Lagrangian, particle based SPH methodology was employed to simulate the wave hydrodynamics and overtopping for the recurve configuration. The experimental and numerical results confirm satisfactory performance. For the tested configurations in the laboratory, the mean overtopping discharges decreased over 60% and maximum individual discharge decreased 40% on recurve wall under both impulsive and non-impulsive conditions. A significant reduction was also observed in mitigating overtopping discharge by using model vegetation and reef breakwater, while diffraction pillar was not found satisfactory

Molecular Dynamics Insights into the Structural and Water Transport Properties of a Forward Osmosis Polyamide Thin-Film Nanocomposite Membrane Modified with Graphene Quantum Dots

An approach combining molecular dynamics (MD) simulations and laboratory experiments was applied to provide new theoretical insights into the chemical structure of polyamide (PA) thin-film composite (TFC) membranes modified with graphene quantum dots (GQDs). Interaction energies, fractional free volumes, mean-square displacements, densities, and water diffusion coefficients were computed for PA and four likely chemical structures of the GQD-embedded PA membranes. These theoretical results aided with experimentally measured water fluxes allowed for determining the most likely structure of the GQD-PA membrane. The compatibility of the GQDs and PA chains was found to be due to the formation of hydrogen and covalent bonds to m-phenylenediamine units. The modified membrane has a higher water diffusivity but a lower overall free volume, compared to the pristine PA membrane. MD simulations in concert with laboratory experiments were found to provide a good understanding of the relationship between the microscopic characteristics and macroscopic transport properties of TFC membranes

Phenotypic and genotypic characteristics of tetracycline resistant Acinetobacter baumannii isolates from nosocomial infections at Tehran hospitals

Objective(s): To date, the most important genes responsible for tetracycline resistance among Acinetobacter baumannii isolates have been identified as tet A and tet B. This study was carried out to determine the rate of resistance to tetracycline and related antibiotics, and mechanisms of resistance. Materials and Methods: During the years 2010 and 2011, a total of 100 A. baumannii isolates were recovered from patients in different hospitals of Tehran, Iran. Antimicrobial susceptibility to tetracycline, minocycline, doxicycline and tigecycline was evaluated by E-test. Polymerase chain reaction (PCR) of the tet A and tet B genes was performed using specific primers, after which the isolates were subjected to Repetitive Extragenic Palindromic-PCR (PCR) to identify the major genotypes. Results: Of all isolates, 89 were resistant to tetracycline (MIC50 = 32 mu g/ml, MIC90 = 512 mu g/ml). Minocycline with the resistant rate of 35 (MIC50 = 16 mu g/ml, MIC90 = 32 mu g/ml) and doxicycline with the resistant rate of 25 (MIC50 = 16 mu g/ml, MIC90= 32 mu g/ml) have a good activity against A. baumannii isolates. All isolates were sensitive to tigecycline. Frequencies of tet B and tet A genes and coexistence of tet A and tet B among the isolates resistant to tetracycline, were 87.6, 2.2 and 1.1, respectively. Distribution of REP-types among A. baumannii isolates was types A (40), B (30), C (10), D (5) and E (5). Conclusion: It seems that tet A and tet B genes play an important role in the induction of resistance towards tetracyclines used in this study. It is suggested that further studies focus on other antimicrobial drugs and combinations in order to achieve a successful therapy against multi drug resistance (MDR) A. baumannii strains in Iran

Discrimination of Overt, Mouthed, and Imagined Speech Activity using Stereotactic EEG

Recent studies have demonstrated that it is possible to decode and synthesize acoustic speech directly from intracranial measurements of brain activity. A current major challenge is to extend the efficacy of this decoding to imagined speech processes toward the development of a practical speech neuroprosthesis for the disabled. The present study used intracranial brain recordings from participants that performed a speaking task consisting of overt, mouthed, and imagined speech trials. In order to better elucidate the unique neural features that contribute to the discrepancies between overt and imagined model performance, rather than directly comparing the performance of speech decoding models trained on respective speaking modes, this study developed and trained models that use neural data to discriminate between pairs of speaking modes. The results further support that, while there exists a common neural substrate across speech modes, there are also unique neural processes that differentiate speech modes

Using impression data to improve models of online social influence

Influence, the ability to change the beliefs and behaviors of others, is the main currency on social media. Extant studies of influence on social media, however, are limited by publicly available data that record expressions (active engagement of users with content, such as likes and comments), but neglect impressions (exposure to content, such as views) and lack “ground truth” measures of influence. To overcome these limitations, we implemented a social media simulation using an original, web-based micro-blogging platform. We propose three influence models, leveraging expressions and impressions to create a more complete picture of social influence. We demonstrate that impressions are much more important drivers of influence than expressions, and our models accurately identify the most influential accounts in our simulation. Impressions data also allow us to better understand important social media dynamics, including the emergence of small numbers of influential accounts and the formation of opinion echo chambers

Performance Analysis of DNN Inference/Training with Convolution and non-Convolution Operations

Today's performance analysis frameworks for deep learning accelerators suffer from two significant limitations. First, although modern convolutional neural network (CNNs) consist of many types of layers other than convolution, especially during training, these frameworks largely focus on convolution layers only. Second, these frameworks are generally targeted towards inference, and lack support for training operations. This work proposes a novel performance analysis framework, SimDIT, for general ASIC-based systolic hardware accelerator platforms. The modeling effort of SimDIT comprehensively covers convolution and non-convolution operations of both CNN inference and training on a highly parameterizable hardware substrate. SimDIT is integrated with a backend silicon implementation flow and provides detailed end-to-end performance statistics (i.e., data access cost, cycle counts, energy, and power) for executing CNN inference and training workloads. SimDIT-enabled performance analysis reveals that on a 64X64 processing array, non-convolution operations constitute 59.5% of total runtime for ResNet-50 training workload. In addition, by optimally distributing available off-chip DRAM bandwidth and on-chip SRAM resources, SimDIT achieves 18X performance improvement over a generic static resource allocation for ResNet-50 inference

BRST Quantization of Noncommutative Gauge Theories

In this paper, the BRST symmetry transformation is presented for the noncommutative U(N) gauge theory. The nilpotency of the charge associated to this symmetry is then proved. As a consequence for the space-like non-commutativity parameter, the Hilbert space of physical states is determined by the cohomology space of the BRST operator as in the commutative case. Further, the unitarity of the S-matrix elements projected onto the subspace of physical states is deduced.Comment: 20 pages, LaTeX, no figures, one reference added, to appear in Phys. Rev.