Prediction of temperature dependent wave dispersion and interaction properties in composite structures

Composite structures are widely used for aerospace and automotive applications. These operate within a broad temperature range varying between -100_C to 200_C for launch vehicles and -60_C to +50_C for aircraft and automotive vehicles. Hereby, the sensitivity of the wave propagation and interaction properties of a composite structure to the ambient flight temperature is investigated. A wave finite element (WFE) and finite element (FE) based computational method is presented by which the temperature dependent wave dispersion characteristics and interaction phenomenon in a composite structures can be predicted. Initially, the temperature dependent mechanical properties of the panel in the range of -100_C to 150_C are measured experimentally using the Thermal Mechanical Analysis (TMA). Temperature dependent wave dispersion characteristics of each waveguide of the structural system, which is discretised as a system of a number of waveguides joined by a coupling element, is calculated using the WFE approach. The wave scattering properties, as a function of temperature, is determined by coupling the WFE wave characteristics models of the waveguides with the full FE modelling of the coupling element on which defect is included. Numerical case studies are exhibited for two waveguides coupled through a coupling element

Evaluation of IR Spectral Analysis and Dyeing Parameters for Plasma and /or Nano-Silver Treatments of Polyester and Nylon Fabrics

In our work of this paper, we study the effect of surface modification of polyester and nylon fabrics induced by DC plasma discharge and/ or nano-siliver treatments .DC plasma discharge was employed at first, as a function of plasma device parameters including different time, different current and different hydrostatic pressure using chemically inert working gas: argon or nitrogen. Optimization of the performance of the applied DC plasma discharge with various applied conditions were performed using Fourier Transform Infra-Red (FTIR) Spectroscopy spectral analysis, by following up the changes in the peak intensity values of the characteristic functional groups that characterize polyester fabric. Then the dyeing properties of different pretreated fabrics with plasma by the best conditions are subjected to nano-silver treatment by concentration 50 ppm under the effect of different dye concentrations, different dyeing temperature and different dyeing time. Finally, the fastness properties to light and washing for the treated samples were studied. The results obtained showed that both of the dyeing parameters and fastness properties were highly improved by the treatment of fabrics by either individual plasma treatment or combined DC cold plasma and nano-silver treatments

The impact of temperature on wave interaction with damage in composite structures

The increased use of composite materials in modern aerospace and automotive structures, and the broad range of launch vehicles’ operating temperature imply a great temperature range for which the structures has to be frequently and thoroughly inspected. A thermal mechanical analysis is used to experimentally measure the temperature-dependent mechanical properties of a composite layered panel in the range of −100 ℃ to 150 ℃. A hybrid wave finite element/finite element computational scheme is developed to calculate the temperature-dependent wave propagation and interaction properties of a system of two structural waveguides connected through a coupling joint. Calculations are made using the measured thermomechanical properties. Temperature-dependent wave propagation constants of each structural waveguide are obtained by the wave finite element approach and then coupled to the fully finite element described coupling joint, on which damage is modelled, in order to calculate the scattering magnitudes of the waves interaction with damage across the coupling joint. The significance of the panel’s glass transition range on the measured and calculated properties is emphasised. Numerical results are presented as illustration of the work

A RESTful API for Supporting Automated BioBrick Model Assembly

Constructing simulatable models for BioBricks by hand is a complex and time-consuming task. The time taken could be reduced by using Computer Aided Design (CAD) tools to aid in designing models, but these tools need to be augmented with domain-specific knowledge. Here we propose a standard for a RESTful (Richardson, 2007) API which facilitates the discovery and publication of models of functional biological units. This API is designed to produce parts models which can be automatically combined into complete, simulatable models of entire systems

Copy number variations residing outside the SHOX enhancer region are involved in Short Stature and Léri-Weill dyschondrosteosis

Background: SHOX enhancer CNVs, affecting one or more of the seven recognized evolutionary conserved non-coding elements (CNEs) represent one of the most frequent cause of SHOX-haploinsufficiency. During the diagnostic workflow deletions/duplications have been identified downstream SHOX not including any of the these CNEs. Methods: Fine tiling aCGH and breakpoint PCR were used to characterize the critical interval and to search for novel alterations in a cohort of selected patients. Results: Screening of 252 controls provided evidence that duplications in this area represent likely benign variants whereas none of the deletions were detected. These findings suggested that other alterations relevant for SHOX-haploinsufficiency might be missed by the standard diagnostic methods. To identify such undisclosed elements, the aCGH was used to reanalyze 52 unresolved cases with clinical features strongly suggestive of SHOX-haploinsufficiency. This analysis followed by the screening of 210 patients detected two partially overlapping small deletions of ~12 and ~8 kb in four unrelated individuals, approximately 15 kb downstream SHOX, that were absent in 720 normal stature individuals. Conclusion: Our results strengthen the hypothesis that alterations of yet unidentified cis-regulatory elements residing outside those investigated through conventional methods, might explain the phenotype in ISS/LWD patients thus enlarging the spectrum of variants contributing to SHOX-haploinsufficiency

Recognising facial expressions in video sequences

We introduce a system that processes a sequence of images of a front-facing human face and recognises a set of facial expressions. We use an efficient appearance-based face tracker to locate the face in the image sequence and estimate the deformation of its non-rigid components. The tracker works in real-time. It is robust to strong illumination changes and factors out changes in appearance caused by illumination from changes due to face deformation. We adopt a model-based approach for facial expression recognition. In our model, an image of a face is represented by a point in a deformation space. The variability of the classes of images associated to facial expressions are represented by a set of samples which model a low-dimensional manifold in the space of deformations. We introduce a probabilistic procedure based on a nearest-neighbour approach to combine the information provided by the incoming image sequence with the prior information stored in the expression manifold in order to compute a posterior probability associated to a facial expression. In the experiments conducted we show that this system is able to work in an unconstrained environment with strong changes in illumination and face location. It achieves an 89\% recognition rate in a set of 333 sequences from the Cohn-Kanade data base

Video face replacement

We present a method for replacing facial performances in video. Our approach accounts for differences in identity, visual appearance, speech, and timing between source and target videos. Unlike prior work, it does not require substantial manual operation or complex acquisition hardware, only single-camera video. We use a 3D multilinear model to track the facial performance in both videos. Using the corresponding 3D geometry, we warp the source to the target face and retime the source to match the target performance. We then compute an optimal seam through the video volume that maintains temporal consistency in the final composite. We showcase the use of our method on a variety of examples and present the result of a user study that suggests our results are difficult to distinguish from real video footage.National Science Foundation (U.S.) (Grant PHY-0835713)National Science Foundation (U.S.) (Grant DMS-0739255

Identification of Small-Molecule Inhibitors of Neutral Ceramidase (nCDase) via Target-Based High-Throughput Screening

There is interest in developing inhibitors of human neutral ceramidase (nCDase) because this enzyme plays a critical role in colon cancer. There are currently no potent or clinically effective inhibitors for nCDase reported to date, so we adapted a fluorescence-based enzyme activity method to a high-throughput screening format. We opted to use an assay whereby nCDase hydrolyzes the substrate RBM 14-16, and the addition of NaIO4 acts as an oxidant that releases umbelliferone, resulting in a fluorescent signal. As designed, test compounds that act as ceramidase inhibitors will prevent the hydrolysis of RBM 14-16, thereby decreasing fluorescence. This assay uses a 1536-well plate format with excitation in the blue spectrum of light energy, which could be a liability, so we incorporated a counterscreen that allows for rapid selection against fluorescence artifacts to minimize false-positive hits. The high-throughput screen of >650,000 small molecules found several lead series of hits. Multiple rounds of chemical optimization ensued with improved potency in terms of IC50 and selectivity over counterscreen assays. This study describes the first large-scale high-throughput optical screening assay for nCDase inhibitors that has resulted in leads that are now being pursued in crystal docking studies and in vitro drug metabolism and pharmacokinetics (DMPK).National Cancer Institute https://doi.org/10.13039/100000054Stony Brook Cancer CenterPeer Reviewe

Biodegradation of Some Polycyclic Aromatic Hydrocarbons by a Bacterial Consortium Isolated from the Red Sea of Jeddah

Polycyclic aromatic hydrocarbons (PAHs) are considered organic pollutants, which are stable, highly toxic, and carcinogenic. Therefore, it was necessary to find an environmentally friendly way to degrade these compounds and remove them from polluted environments. Water samples polluted with petroleum hydrocarbons were collected from the coast of Jeddah on the Red Sea - Saudi Arabia. The results of the current study showed the ability of bacterial consortium that was enriched from the coast of Jeddah, Saudi Arabia to degrade petroleum hydrocarbons wastewater, which proved its ability to degrade PAHs in saline conditions. The bacterial union degraded BENZ compound by more than 95% at the concentration of 100 and 250 mg/L, while the degradation of this compound at the concentration of 1000 mg/L was recorded about 83% while the ANT degradation rate was recorded at more than 90% at different concentrations. In addition, this study revealed the ability of bacterial consortium to treat petroleum hydrocarbons wastewater in bioreactor (CSTR) with 92.7% of COD removed under saline conditions. Hence, this study recommends the investment of bacterial consortium in the treatment of petroleum hydrocarbons wastewater in marine environments and to remove pollutants from them

A supervised hierarchical segmentation of remote-sensing images using a committee of multi-scale convolutional neural networks

This paper presents a supervised, hierarchical remote-sensing image segmentation technique using a committee of multi-scale convolutional neural networks. With existing techniques, segmentation is achieved through fine-tuning a set of predefined feature detectors. However, such a solution is not robust since the introduction of new sensors or applications would require novel features and techniques to be developed. Conversely, the proposed method achieves segmentation through a set of learnt feature detectors. In order to learn feature detectors, the proposed method exploits a committee of convolutional neural networks that perform multi-scale analysis on each band in order to derive individual confidence maps on region boundaries. Confidence maps are then inter-fused in order to produce a fused confidence map. Furthermore, the fused map is intra-fused using a morphological scheme into a hierarchical segmentation map. The proposed method is quantitatively compared to baseline techniques on a publicly available data set. The results presented in this paper highlight the improved accuracy of the proposed method