Release of chylomicrons by isolated cells of rat intestinal mucosa

Fat-laden mucosal cells were isolated by flotation from fed male rats after digesting scrapings of washed jejunum with collagenase in bicarbonate buffer. About 50–60 million cells were obtained per preparation, which were 95–100% viable as assessed by Trypan Blue. The isolated cells were capable of effective incorporation of labeled fatty acids and glucose into triglycerides and phospholipids, and of labeled leucine and glucosamine into the protein envelope of the released chylomicrons. The secretion of the labeled protein paralleled the release of the labeled fat, both of which were linear with the concentration of the albumin in the incubation mixture. About 80% of the total fat of the cell was released as chylomicrons within 30 min when incubated in the presence of albumin-bicarbonate buffer. Injection of puromycin 24 hr prior to harvesting of cells led to a complete inhibition of chylomicron release. Addition of puromycin to the incubation medium gave 50–80% inhibition of release. No inhibition of release of chylomicrons resulted from a treatment with ethionine. The released chylomicrons were separated from the cells by Millipore filtration. Presented in part at the AOCS Meeting, Chicago, September 1970.     

Controlling structural failure modes during an impact in the presence of uncertainties

A methodology to enforce a given structural dynamic behavior during an impact while accounting for uncertainty is presented. The approach is based on locating structural fuses that weaken the structure locally and help enforce a deformation mode. The problem of enforcing the crushing of a tube impacting a rigid wall is chosen. In order to find the positions of the fuses, the method identifies distinct structural dynamic behaviors using designs of experiments and clustering techniques. The changes in behavior are studied with respect to variations of the fuse positions and random parameters, such as the thickness. Based on the probabilistic distributions, a measure of the likelihood of occurrence of global buckling is defined. The positions of the fuses are defined using an optimization problem in terms of the likelihood of global buckling and the amount of absorbed energy in the tube. A first formulation of the problem considers variability in the tube’s thickness only. A second formulation also accounts for uncertainties in the positions of the fuses.     

A kernel trick for sequences applied to text-independent speaker verification systems

This paper presents a principled SVM based speaker verification system. We propose a new framework and a new sequence kernel that can make use of any Mercer kernel at the frame level. An extension of the sequence kernel based on the Max operator is also proposed. The new system is compared to state-of-the-art GMM and other SVM based systems found in the literature on the Banca and Polyvar databases. The new system outperforms, most of the time, the other systems, statistically significantly. Finally, the new proposed framework clarifies previous SVM based systems and suggests interesting future research directions.     

Seismic Response of Interior RC Beam-Column Joints Upgraded with FRP Sheets. II: Analysis and Parametric Study

In this paper a procedure for analytical prediction of joint shear strength of interior beam-column joints, strengthened with externally bonded fiber-reinforced polymer (FRP) sheets, has been presented. The procedure is based on the formulation available in the literature. To implement the available formulation for shear capacity prediction a computer program has been developed. Using this program shear capacity of the joint and joint shear stress variation at various stages of loading have been predicted and compared with experimental observations; presented in Part I of this study. Predictions show good agreement with experimental test results. The formulation is further extended to predict diagonal tensile stresses in the joint. The effectiveness of FRP quantity on joint shear strength and on various strains has been studied on parametric basis. It is observed that even a low quantity of FRP can enhance shear capacity of the joint significantly and its effectiveness can be further increased if debonding is suppressed (e.g., through mechanical anchorages). Effect of column axial load on shear strength of the joint has also been studied. It is observed that axial load increases the confinement of the joint core, which in turn increases the shear capacity of the joint.     

Inverse optimality in the class of Hopfield neural networks with input nonlinearity

This paper presents the chaos suppression problem in the class of Hopfield neural networks (HNNs) with input nonlinearity using inverse optimality approach. Using the inverse optimality technique and based on Lyapunov stability theory, a stabilizing control law, which is optimal with respect to meaningful cost functional, is determined to achieve global asymptotically stability in the closed-loop system. Numerical simulation is performed on a four-dimensional hyper-chaotic HNN to demonstrate the effectiveness of the proposed method.     

Application of terrain analysis to the mapping and spatial pattern analysis of subsurface geological fractures of Kuala Lumpur limestone bedrock,Malaysia

Subsurface geological fractures in karst terrain are often associated with unpredictable environmental and geotechnical engineering problems. This requires precise mapping and an understanding of the distribution of geological fractures on multi-scales. To extract and investigate surface and subsurface geological fractures on such scales, multi-scales, this study presents two approaches. The first involves geological prediction and visual interpretation of terrain parameters using a digital elevation model (DEM). The second is an automatic detection method using a topographical fabric algorithm that uses a DEM to create a map of ridges, which represent the footwalls of geological fractures, and valleys (channels), which reflect geological fracture zones. Unlike wavelet analysis and the Fourier transform, which use optical remote-sensing images, the integration of visual interpretation and a topographical fabric algorithm is capable of the extraction and spatial correlation of subsurface geological fractures. This method was applied to Kuala Lumpur limestone bedrock in Malaysia, by focusing on the adjacent mountainous areas and the geometries of ex-opencast mining ponds. The spatial correlation of the extracted surface geological fractures was clarified by rose diagrams and semivariogram models. Spatial correlation shows that the Malaysian peninsula, surface and subsurface geological fractures and the geometry of ex-opencast mining ponds share similar trends. The results obtained using this methodology is compared to those of subsurface geological fractures reported by means of geophysical surveying and field investigation. This proposed method may be useful for mapping geological fractures in areas of high soil moisture, where geophysical surveying is difficult and/or not available, and is also highly applicable in other parts of Malaysia or Southeast Asia, permitting a better understanding of the geotectonics and geotechnical engineering setting of the study area.     

Performance of glass fiber reinforced polymer bars under elevated temperatures

This paper investigates the residual tensile properties of newly developed glass fiber reinforced polymer (GFRP) bars after being subjected to elevated temperatures for different periods. A total of 120 GFRP specimens were tested in this study. Half of the samples were covered with concrete while the other half were bare bars. The specimens were subjected to three different controlled temperatures (100, 200 and 300 °C) for three different periods (1, 2, and 3 h). Test results showed that almost no losses were observed in the tensile modulus after all exposure periods and temperatures. Losses in the tensile strength, proportional to the level of temperature and exposure period, were recorded. The bars with concrete cover showed higher residual tensile strength compared to their counterparts without coating. The concrete cover was more effective at the lowest temperature level (100 °C) and at the shortest time period (1 h). Scanning Electronic Microscopy (SEM) technique was also used to investigate the effect of elevated temperature on the degradation mechanism of the GFRP bars. The results showed that increasing the temperature level affected the resin matrix surrounding the glass fibers and consequently affected the bond between the fibers and the matrix.     

Recognizing arabic handwritten characters using deep learning and genetic algorithms

Automated techniques for Arabic content recognition are at a beginning period contrasted with their partners for the Latin and Chinese contents recognition. There is a bulk of handwritten Arabic archives available in libraries, data centers, historical centers, and workplaces. Digitization of these documents facilitates (1) to preserve and transfer the country’s history electronically, (2) to save the physical storage space, (3) to proper handling of the documents, and (4) to enhance the retrieval of information through the Internet and other mediums. Arabic handwritten character recognition (AHCR) systems face several challenges including the unlimited variations in human handwriting and the leakage of large and public databases. In the current study, the segmentation and recognition phases are addressed. The text segmentation challenges and a set of solutions for each challenge are presented. The convolutional neural network (CNN), deep learning approach, is used in the recognition phase. The usage of CNN leads to significant improvements across different machine learning classification algorithms. It facilitates the automatic feature extraction of images. 14 different native CNN architectures are proposed after a set of try-and-error trials. They are trained and tested on the HMBD database that contains 54,115 of the handwritten Arabic characters. Experiments are performed on the native CNN architectures and the best-reported testing accuracy is 91.96%. A transfer learning (TF) and genetic algorithm (GA) approach named “HMB-AHCR-DLGA” is suggested to optimize the training parameters and hyperparameters in the recognition phase. The pre-trained CNN models (VGG16, VGG19, and MobileNetV2) are used in the later approach. Five optimization experiments are performed and the best combinations are reported. The highest reported testing accuracy is 92.88%.

     

Large‐scale fading behavior for a cellular network with uniform spatial distribution

Large‐scale fading (LSF) between interacting nodes is a fundamental element in radio communications, responsible for weakening the propagation, and thus worsening the service quality. Given the importance of channel‐losses in general, and the inevitability of random spatial geometry in real‐life wireless networks, it was then natural to merge these two paradigms together in order to obtain an improved stochastical model for the LSF indicator. Therefore, in exact closed‐form notation, we generically derived the LSF distribution between a prepositioned reference base‐station and an arbitrary node for a multi‐cellular random network model. In fact, we provided an explicit and definitive formulation that considered at once: the lattice profile, the users' random geometry, the effect of the far‐field phenomenon, the path‐loss behavior, and the stochastic impact of channel scatters. The veracity and accuracy of the theoretical analysis were also confirmed through Monte Carlo simulations.