File size models for shared content over the BitTorrent Peer-to-Peer network

Peer-to-Peer (P2P) traffic has increased rapidly over the past few years, with file sharing providing the main drive behind such traffic. In this work we perform a measurement study of the content shared over the popular BitTorrent P2P file sharing network. We mathematically model the file size distributions of shared files after categorizing them into Audio, Video, Archive and CD image classes. For each of these categories we look into the most popular shared file formats and investigate their file size statistics. This provides an important milestone to building a realistic simulation framework for P2P systems, and for future analytical modeling of P2P networks.     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.     

Visual tracking using convolutional features with sparse coding

Artificial Intelligence Review - Visual object tracking has become one of the most active research topics in computer vision, and it has been applied in several commercial...     

Priority-based joint EDF–RM scheduling algorithm for individual real-time task on distributed systems

The Journal of Supercomputing - Multiple tasks arrive in the distributed systems that can be executed in either parallel or sequential manner. Before the execution, tasks are scheduled prioritywise...     

Structural health monitoring of railway tracks using IoT-based multi-robot system

Neural Computing and Applications - A multi-robot-based fault detection system for railway tracks is proposed to eliminate manual human visual inspection. A hardware prototype is designed to...     

Deep neural network architectures for dysarthric speech analysis and recognition

Neural Computing and Applications - This paper investigates the ability of deep neural networks (DNNs) to improve the automatic recognition of dysarthric speech through the use of convolutional...     

Robust H ∞ static output feedback stabilization of T-S fuzzy systems subject to actuator saturation

This paper proposes a method for designing robust H_?? static output feedback stabilization of Takagi-Sugeno (T-S) fuzzy systems under actuator saturation. In this paper, the input saturation is represented by a polytopic model and the modeling error is assumed a norm-bounded uncertainty. A set invariance condition for robust H_?? static output feedback system under actuator saturation is first established. Then, the estimation of the largest domain of attraction for the system is formulated and solved as a Linear Matrix Inequality (LMI) optimization problem. Two examples are used to demonstrate the effectiveness of the proposed design method.     

Characterization of Urine Stem Cell-Derived Extracellular Vesicles Reveals B Cell Stimulating Cargo

Elucidation of the biological functions of extracellular vesicles (EVs) and their potential roles in physiological and pathological processes is an expanding field of research. In this study, we characterized USC–derived EVs and studied their capacity to modulate the human immune response in vitro. We found that the USC–derived EVs are a heterogeneous population, ranging in size from that of micro–vesicles (150 nm–1 μm) down to that of exosomes (60–150 nm). Regarding their immunomodulatory functions, we found that upon isolation, the EVs (60–150 nm) induced B cell proliferation and IgM antibody secretion. Analysis of the EV contents unexpectedly revealed the presence of BAFF, APRIL, IL–6, and CD40L, all known to play a central role in B cell stimulation, differentiation, and humoral immunity. In regard to their effect on T cell functions, they resembled the function of mesenchymal stem cell (MSC)–derived EVs previously described, suppressing T cell response to activation. The finding that USC–derived EVs transport a potent bioactive cargo opens the door to a novel therapeutic avenue for boosting B cell responses in immunodeficiency or cancer.     

Screening of Inhibitors Targeting Heat Shock Protein 47 Involved in the Development of Idiopathic Pulmonary Fibrosis

Heat shock protein 47 (HSP47), a collagen-specific molecular chaperone, is causally related to fibrotic diseases, including idiopathic pulmonary fibrosis. The identification of Compounds that interfere with the HSP47-collagen interaction is essential for the development of relevant therapeutics. Herein, we prepared human HSP47 as a soluble fusion protein expressed in E. coli and established an assay system for HSP47 inhibitor screening. We screened a natural and synthetic Compound library established at Nagasaki University. Among 1023 Compounds, 13 exhibited inhibitory activity against human HSP47, of which three inhibited its function in a dose-dependent manner. Epigallocatechin-3-O-gallate, one of these three Compounds, is a typical polyphenol Compound derived from tea leaves. Structurally related Compounds were synthesized and examined for their activity, revealing a hydroxyl group at A-ring position 5 as important for its activity. The present findings provide valuable insight for the development of natural product-derived therapeutics for fibrotic diseases, including idiopathic pulmonary fibrosis.     

Synthesis and optimization of high surface area mesoporous date palm fiber-based nanostructured powder activated carbon for aluminum removal

Date palm fiber (DPF) derived from agrowaste was utilized as a new precursor for the optimized synthesis of a cost-effective, nanostructured, powder-activated carbon (nPAC) for aluminum (Al3+) removal from aqueous solutions using carbonization, KOH activation, response surface methodology (RSM) and central composite design (CCD). The optimum synthesis condition, activation temperature, time and impregna-tion ratio were found to be 650 ℃, 1.09 hour and 1:1, respectively. Furthermore, the optimum conditions for removal were 99.5％and 9.958 mg·g-1 in regard to uptake capacity. The optimum conditions of nPAC was analyzed and characterized using XRD, FTIR, FESEM, BET, TGA and Zeta potential. Moreover, the adsorption of the Al3+ conditions was optimized with an integrated RSM-CCD experimental design. Regression results revealed that the adsorption kinetics data was well fitted by the pseudo-second order model, whereas the adsorption isotherm data was best represented by the Freundlich isotherm model. Optimum activated carbon indicated that DPF can serve as a cost-effective precursor adsorbent for Al3+removal.