Autonomous topological modeling of a home environment and topological localization using a sonar grid map

This paper presents a method of autonomous topological modeling and localization in a home environment using only low-cost sonar sensors. The topological model is extracted from a grid map using cell decomposition and normalized graph cut. The autonomous topological modeling involves the incremental extraction of a subregion without predefining the number of subregions. A method of topological localization based on this topological model is proposed wherein a current local grid map is compared with the original grid map. The localization is accomplished by obtaining a node probability from a relative motion model and rotational invariant grid-map matching. The proposed method extracts a well-structured topological model of the environment, and the localization provides reliable node probability even when presented with sparse and uncertain sonar data. Experimental results demonstrate the performance of the proposed topological modeling and localization in a real home environment.     

BSPNN: boosted subspace probabilistic neural network for email security

In the modern age of Internet connectivity, advanced information systems have accumulated huge volumes of data. Such fast growing, tremendous amount of data, collected and stored in large databases has far exceeded our human ability to comprehend without proper tools. There has been a great deal of research conducted to explore the potential applications of Machine Learning technologies in Security Informatics. This article studies the Network Security Detection problems in which predictive models are constructed to detect network security breaches such as spamming. Due to overwhelming volume of data, complexity and dynamics of computer networks and evolving cyber threats, current security systems suffer limited performance with low detection accuracy and high number of false alarms. To address such performance issues, a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), has been proposed which combines a Radial Basis Function Neural Network with an innovative diversity-based ensemble learning framework. Extensive empirical analyses suggested that BSPNN achieved high detection accuracy with relatively small computational complexity compared with other conventional detection methods.     

Localizing slab identification numbers in factory scene images

This paper deals with algorithms for text localization and character segmentation in images for process automation in the steel-making industry. Each character which comprises slab identification numbers may be corrupted severely before it is captured by network cameras. Therefore, proper processing is required to localize the target texts successfully. In this paper, we propose (1) a method to evaluate the closeness of an edge patch to the form of a closed contour, (2) an edge inspection method to determine character colors and estimate font thickness, and (3) three reasonable binarization methods to increase the performance of the algorithm for the detection of the left and right boundaries of the text rectangle. The experimental results show that the proposed algorithms are reliable.     

Dynamic deformation behavior of ultrafine-grained low-carbon steels fabricated by equal-channel angular pressing

The dynamic deformation behavior of ultrafine-grained low-carbon steels fabricated by equal-channel angular pressing (ECAP) was investigated in this study. Dynamic torsional tests, using a torsional Kolsky bar, were conducted on four steel specimens, two of which were annealed at 480 °C after ECAP, and then the test data were compared in terms of microstructures, tensile properties, and adiabatic shear-band formation. The equal-channel angular pressed specimen consisted of very fine, equiaxed grains of 0.2 to 0.3 μm in size, which were slightly coarsened after annealing. The dynamic torsional test results indicated that maximum shear stress decreased with increasing annealing time, whereas fracture shear strain increased. Some adiabatic shear bands were observed at the gage center of the dynamically deformed torsional specimen. Their width was smaller in the equal-channel angular pressed specimen than in the 1-hour-annealed specimen, but they were not found in the 24-hour-annealed specimen. Ultrafine, equiaxed grains of 0.05 to 0.2 μm in size were formed inside the adiabatic shear band, and their boundaries had characteristics of high-angle grain boundaries. These phenomena were explained by dynamic recrystallization due to a highly localized plastic strain and temperature rise during dynamic deformation.     

Neuronal ApoE Regulates the Cell-to-Cell Transmission of α-Synuclein

The presence of protein inclusions, called Lewy bodies (LBs) and Lewy neurites (LNs), in the brain is the main feature of Parkinson’s disease (PD). Recent evidence that the prion-like propagation of α-synuclein (α-syn), as a major component of LBs and LNs, plays an important role in the progression of PD has gained much attention, although the molecular mechanism remains unclear. In this study, we evaluated whether neuronal ApoE regulates the cell-to-cell transmission of α-syn and explored its molecular mechanism using in vitro and in vivo model systems. We demonstrate that neuronal ApoE deficiency attenuates both α-syn uptake and release by downregulating LRP-1 and LDLR expression and enhancing chaperone-mediated autophagy activity, respectively, thereby contributing to α-syn propagation. In addition, we observed that α-syn propagation was attenuated in ApoE knockout mice injected with pre-formed mouse α-syn fibrils. This study will help our understanding of the molecular mechanisms underlying α-syn propagation.     

HDAC8-Selective Inhibition by PCI-34051 Enhances the Anticancer Effects of ACY-241 in Ovarian Cancer Cells

HDAC6 is overexpressed in ovarian cancer and is known to be correlated with tumorigenesis. Accordingly, ACY-241, a selective HDAC6 inhibitor, is currently under clinical trial and has been tested in combination with various drugs. HDAC8, another member of the HDAC family, has recently gained attention as a novel target for cancer therapy. Here, we evaluated the synergistic anticancer effects of PCI-34051 and ACY-241 in ovarian cancer. Among various ovarian cancer cells, PCI-34051 effectively suppresses cell proliferation in wild-type p53 ovarian cancer cells compared with mutant p53 ovarian cancer cells. In ovarian cancer cells harboring wild-type p53, PCI-34051 in combination with ACY-241 synergistically represses cell proliferation, enhances apoptosis, and suppresses cell migration. The expression of pro-apoptotic proteins is synergistically upregulated, whereas the expressions of anti-apoptotic proteins and metastasis-associated proteins are significantly downregulated in combination treatment. Furthermore, the level of acetyl-p53 at K381 is synergistically upregulated upon combination treatment. Overall, co-inhibition of HDAC6 and HDAC8 through selective inhibitors synergistically suppresses cancer cell proliferation and metastasis in p53 wild-type ovarian cancer cells. These results suggest a novel approach to treating ovarian cancer patients and the therapeutic potential in developing HDAC6/8 dual inhibitors.     

酚醛树脂弱凝胶驱注入速度的研究

注入速度是弱凝胶驱开发过程中重要的设计参数,选取成胶时间3d的酚醛树脂弱凝胶体系,考察了注入速度对弱凝胶驱油效果的影响.结果表明,随着注入速度增加,对低渗层易造成伤害,注入速度由1 mL/min提高至12 mL/min时,高渗层的分流率由33.7％增加至54.9％,高渗层封堵效果变差,提高采收率下降了7.74％.提出弱凝胶体系注入速度不宜过大.