Hemoptysis caused by pulmonary arterial aneurysm, disclosing Beh?et disease. Apropos of 2 cases]

MCF7 and ZR75-1 breast cancer cells grow as adherent monolayers in tissue culture. Treatment with the serum serine protease plasmin causes them to detach and to grow as floating multicellular spheroids. Two plasmin activators, urokinase plasminogen activator and streptokinase, induce the same growth pattern changes in the presence of plasminogen. Serum contains also plasminogen activator inhibitors. Aged serum, deficient in plasminogen activator inhibitors, converts spontaneously monolayer breast cancer cells into multicellular spheroids which readily revert to monolayer growth after addition of fresh serum. Urokinase blocks the reversion. The formation of multicellular spheroids does not affect the proliferative rate of breast tumor cells but endows tumor cells with increased resistance to the chemotherapeutic drugs, doxorubicin and paclitaxel.     

Analytic computation of the photocurrent density in a n-6H-SiC/p-Si/n-Si/p-Si0.8Ge0.2 multilayer solar cell

In this work we conceived a model of a multilayer solar cell composed by four layers of opposite conductivities: an n-type 6H-SiC used as a frontal layer to absorb high energy photons (energy gap equals 2.9 eV), a p-type Si layer, an n-type Si layer and a p-type SiGe back layer to absorb low energy photons (Si_0.8Ge_0.2 with an energy gap equal to 0.8 eV). The impurity concentration in every layer of the model is taken equal to 10¹⁷ cm⁻³ to ensure abrupt junctions inside the cell. The optical properties of the separate layers have been fitted and tabulated to be used for thin films devices numerical simulation. We developed the equations giving the minority carrier concentration and the photocurrent density in each abscissa of the model. We used Matlab software to simulate and optimize the layers thicknesses to achieve the maximum photocurrent generated under AM0 solar spectrum. The results of simulation showed that the optimized structure could deliver, assuming 10⁵ cm/s surface recombination velocity, a photocurrent density of more than 53 mA/cm², which represents 88.3% of the ideal photocurrent (59.99 mA/cm²) that can be generated under AM0 solar spectrum.     

The Impact of Metastable Intermolrecular Nanocomposite Particles on Kinetic Decomposition of Heterocyclic Nitramines Using Advanced Solid‐Phase Decomposition Models

Journal of Inorganic and Organometallic Polymers and Materials - Surface oxygen of oxide catalyst has low coordination number; they are negatively charged. Surface oxygen can act active site for...     

A frame-based approach for capturing semantics from Arabic text for text-to-sign language MT

This paper describes the design and implementation of a computational model for Arabic natural language semantics, a semantic parser for capturing the deep semantic representation of Arabic text. The parser represents a major part of an Interlingua-based machine translation system for translating Arabic text into Sign Language. The parser follows a frame-based analysis to capture the overall meaning of Arabic text into a formal representation suitable for NLP applications that need for deep semantics representation, such as language generation and machine translation. We will show the representational power of this theory for the semantic analysis of texts in Arabic, a language which differs substantially from English in several ways. We will also show that the integration of WordNet and FrameNet in a single unified knowledge resource can improve disambiguation accuracy. Furthermore, we will propose a rule based algorithm to generate an equivalent Arabic FrameNet, using a lexical resource alignment of FrameNet1.3 LUs and WordNet3.0 synsets for English Language. A pilot study of motion and location verbs was carried out in order to test our system. Our corpus is made up of more than 2000 Arabic sentences in the domain of motion events collected from Algerian first level educational Arabic books and other relevant Arabic corpora.     

A large vocabulary system for Arabic online handwriting recognition

The success of using Hidden Markov Models (HMMs) for speech recognition application has motivated the adoption of these models for handwriting recognition especially the online handwriting that has large similarity with the speech signal as a sequential process. Some languages such as Arabic, Farsi and Urdo include large number of delayed strokes that are written above or below most letters and usually written delayed in time. These delayed strokes represent a modeling challenge for the conventional left-right HMM that is commonly used for Automatic Speech Recognition (ASR) systems. In this paper, we introduce a new approach for handling delayed strokes in Arabic online handwriting recognition using HMMs. We also show that several modeling approaches such as context based tri-grapheme models, speaker adaptive training and discriminative training that are currently used in most state-of-the-art ASR systems can provide similar performance improvement for Hand Writing Recognition (HWR) systems. Finally, we show that using a multi-pass decoder that use the computationally less expensive models in the early passes can provide an Arabic large vocabulary HWR system with practical decoding time. We evaluated the performance of our proposed Arabic HWR system using two databases of small and large lexicons. For the small lexicon data set, our system achieved competing results compared to the best reported state-of-the-art Arabic HWR systems. For the large lexicon, our system achieved promising results (accuracy and time) for a vocabulary size of 64k words with the possibility of adapting the models for specific writers to get even better results.     

Adaptive type-2 fuzzy sliding mode controller for SISO nonlinear systems subject to actuator faults

In this paper, an adaptive type-2 fuzzy sliding mode control to tolerate actuator faults of unknown nonlinear systems with external disturbances is presented. Based on a redundant actuation structure, a novel type-2 adaptive fuzzy fault tolerant control scheme is proposed using sliding mode control. Two adaptive type-2 fuzzy logic systems are used to approximate the unknown functions, whose adaptation laws are deduced from the stability analysis. The proposed approach allows to ensure good tracking performance despite the presence of actuator failures and external disturbances, as illustrated through a simulation example.     

Adaptive fuzzy sliding mode power system stabilizer using Nussbaum gain

Power system stability is enhanced through a novel stabilizer developed around an adaptive fuzzy sliding mode approach which applies the Nussbaum gain to a nonlinear model of a single-machine infinite-bus （SMIB） and multi-machine power system stabilizer subjected to a three phase fault. The Nussbaum gain is used to avoid the positive sign constraint and the problem of controllability of the system. A comparative simulation study is presented to evaluate the achieved performance.     

Optimal Packet Loss Protection of Progressively Compressed 3-D Meshes

We consider a state-of-the-art system that uses layered source coding and forward error correction with Reed-Solomon codes to efficiently transmit 3-D meshes over lossy packet networks. Given a transmission bit budget, the performance of this system can be optimized by determining how many layers should be sent, how each layer should be packetized, and how many parity bits should be allocated to each layer such that the expected distortion at the receiver is minimum. The previous solution for this optimization problem uses exhaustive search, which is not feasible when the transmission bit budget is large. We propose instead an exact algorithm that solves this optimization problem in linear time and space. We illustrate the advantages of our approach by providing experimental results for the compressed progressive meshes (CPM) mesh compression technique.     

Blind spatial unmixing of multispectral images: New methods combining sparse component analysis,clustering and non-negativity constraints

Remote sensing has become an unavoidable tool for better managing our environment, generally by realizing maps of land cover using classification techniques. Traditional classification techniques assign only one class (e.g., water, soil, grass) to each pixel of remote sensing images. However, the area covered by one pixel contains more than one surface component and results in the mixture of these surface components. In such situations, classical classification is not acceptable for many major applications, such as environmental monitoring, agriculture, mineral exploration and mining, etc. Most methods proposed for treating this problem have been developed for hyperspectral images. On the contrary, there are very few automatic techniques suited to multispectral images. In this paper, we propose new unsupervised spatial methods (called 2D-Corr-NLS and 2D-Corr-NMF) in order to unmix each pixel of a multispectral image for better recognizing the surface components constituting the observed scene. These methods are related to the blind source separation (BSS) problem, and are based on sparse component analysis (SCA), clustering and non-negativity constraints. Our approach consists in first identifying the mixing matrix involved in this BSS problem, by using the first stage of a spatial correlation-based SCA method with very limited source sparsity constraints, combined with clustering. Non-negative least squares (NLS) or non-negative matrix factorization (NMF) methods are then used to extract spatial sources. An important advantage of our proposed methods is their applicability to the possibly globally underdetermined, but locally (over)determined BSS model in multispectral remote sensing images. Experiments based on realistic synthetic mixtures and real multispectral images collected by the Landsat ETM+ and the Formosat-2 sensors are performed to evaluate the performance of the proposed approach. We also show that our methods significantly outperform the sequential maximum angle convex cone (SMACC) method.     

State observer based robust adaptive fuzzy controller for nonlinear uncertain and perturbed systems

A robust adaptive fuzzy controller, based on a state observer, for a nonlinear uncertain and perturbed system is presented. The state observer is introduced to resolve the problem of the unavailability of the state variables. Two control signals are added to a basic state feedback control law, deduced from a nominal model, to guarantee the tracking performance in the presence of structural uncertainties and external disturbances. The first control signal is computed from an adaptive fuzzy system and eliminates the effect of structural uncertainties and estimation errors. Updating the adjustable parameters is ensured by a PID law to obtain a fast convergence. Robustness of the closed-loop system is guaranteed by an H infinity supervisor computed from a Riccati type equation. Simulation example is presented to show the efficiency of the proposed method.