A Note on Near-factor-critical Graphs

A near-factor of a finite simple graph $G$ is a matching that saturates all vertices except one. A graph $G$ is said to be near-factor-critical if the deletion of any vertex from $G$ results in a subgraph that has a near-factor. We prove that a connected graph $G$ is near-factor-critical if and only if it has a perfect matching. We also characterize disconnected near-factor-critical graphs.Comment: 4 page

Digit Recognition Using Composite Features With Decision Tree Strategy

At present, check transactions are one of the most common forms of money transfer in the market. The information for check exchange is printed using magnetic ink character recognition (MICR), widely used in the banking industry, primarily for processing check transactions. However, the magnetic ink card reader is specialized and expensive, resulting in general accounting departments or bookkeepers using manual data registration instead. An organization that deals with parts or corporate services might have to process 300 to 400 checks each day, which would require a considerable amount of labor to perform the registration process. The cost of a single-sided scanner is only 1/10 of the MICR; hence, using image recognition technology is an economical solution. In this study, we aim to use multiple features for character recognition of E13B, comprising ten numbers and four symbols. For the numeric part, we used statistical features such as image density features, geometric features, and simple decision trees for classification. The symbols of E13B are composed of three distinct rectangles, classified according to their size and relative position. Using the same sample set, MLP, LetNet-5, Alexnet, and hybrid CNN-SVM were used to train the numerical part of the artificial intelligence network as the experimental control group to verify the accuracy and speed of the proposed method. The results of this study were used to verify the performance and usability of the proposed method. Our proposed method obtained all test samples correctly, with a recognition rate close to 100%. A prediction time of less than one millisecond per character, with an average value of 0.03 ms, was achieved, over 50 times faster than state-of-the-art methods. The accuracy rate is also better than all comparative state-of-the-art methods. The proposed method was also applied to an embedded device to ensure the CPU would be used for verification instead of a high-end GPU

Developing an EEG-based on-line closed-loop lapse detection and mitigation system

© 2014 Wang, Huang, Wei, Huang, Ko, Lin, Cheng and Jung. In America, 60% of adults reported that they have driven a motor vehicle while feeling drowsy, and at least 15-20% of fatal car accidents are fatigue-related. This study translates previous laboratory-oriented neurophysiological research to design, develop, and test an On-line Closed-loop Lapse Detection and Mitigation (OCLDM) System featuring a mobile wireless dry-sensor EEG headgear and a cell-phone based real-time EEG processing platform. Eleven subjects participated in an event-related lane-keeping task, in which they were instructed to manipulate a randomly deviated, fixed-speed cruising car on a 4-lane highway. This was simulated in a 1st person view with an 8-screen and 8-projector immersive virtual-reality environment. When the subjects experienced lapses or failed to respond to events during the experiment, auditory warning was delivered to rectify the performance decrements. However, the arousing auditory signals were not always effective. The EEG spectra exhibited statistically significant differences between effective and ineffective arousing signals, suggesting that EEG spectra could be used as a countermeasure of the efficacy of arousing signals. In this on-line pilot study, the proposed OCLDM System was able to continuously detect EEG signatures of fatigue, deliver arousing warning to subjects suffering momentary cognitive lapses, and assess the efficacy of the warning in near real-time to rectify cognitive lapses. The on-line testing results of the OCLDM System validated the efficacy of the arousing signals in improving subjects' response times to the subsequent lane-departure events. This study may lead to a practical on-line lapse detection and mitigation system in real-world environments

Hirsutella sinensis mycelium attenuates bleomycin-induced pulmonary inflammation and fibrosis in vivo

Hirsutella sinensis mycelium (HSM), the anamorph of Cordyceps sinensis, is a traditional Chinese medicine that has been shown to possess various pharmacological properties. We previously reported that this fungus suppresses interleukin-1β and IL-18 secretion by inhibiting both canonical and non-canonical inflammasomes in human macrophages. However, whether HSM may be used to prevent lung fibrosis and the mechanism underlying this activity remain unclear. Our results show that pretreatment with HSM inhibits TGF-β1–induced expression of fibronectin and α-SMA in lung fibroblasts. HSM also restores superoxide dismutase expression in TGF-β1–treated lung fibroblasts and inhibits reactive oxygen species production in lung epithelial cells. Furthermore, HSM pretreatment markedly reduces bleomycin–induced lung injury and fibrosis in mice. Accordingly, HSM reduces inflammatory cell accumulation in bronchoalveolar lavage fluid and proinflammatory cytokines levels in lung tissues. The HSM extract also significantly reduces TGF-β1 in lung tissues, and this effect is accompanied by decreased collagen 3α1 and α-SMA levels. Moreover, HSM reduces expression of the NLRP3 inflammasome and P2X7R in lung tissues, whereas it enhances expression of superoxide dismutase. These findings suggest that HSM may be used for the treatment of pulmonary inflammation and fibrosis