ACADEMIC HOT-SPOT ANALYSIS ON INFORMATION SYSTEM BASED ON THE CO-TERM NETWORK

The amount of research literature is increasing so fast that the scholars are hard to clearly know the state of art about a certain research field. For IS scholars, understanding research hot-spots among numerous academic papers on IS field is always a significant and key task. In this paper, taking Information System field as example, an academic hot-spot analysis method is proposed to automatically find the research hot topic. Firstly, based on the key words of literatures, a co-term network is build, then fast greedy clustering method is used to find research topic, and hot degree of research topics are computed. After downloading the literature information about IS field from WEB OF SCIENCE, research hot topic during latest five years is identified. The result show that three general topics, respectively as GIS, Health-Care IS, Management & Internet, are important research direction. Then, the hot-spots analysis method, which decomposes the Management & Internet topic into 10 topic communities, generates and discussed the IS trends on top 5 academic topics of each year from 2009 to 2013 and the heat map of the IS hot-spots in 2013

Online Big-Data Monitoring and Assessment Framework for Internal Combustion Engine with Various Biofuels

As the primary power source for automobiles, the internal combustion (IC) engines have been widely used and served millions of people worldwide. With increasingly stringent environmental regulations, biofuels have been obtained more attentions and are being used as alternative fuel to power IC engines. However, there are currently no standard solutions or well-established monitoring and assessment methods that can effectively evaluate the IC engine’s performance with biofuels. The expectation for biofuels is to keep the engine’s lifetime as long as the conventional fuels, or even longer. Otherwise, their usage would be unnecessary because they would reduce the lifecycle of the engine and also cause more waste and pollution. To address this challenge, we initially designed two biofuels: waste cooking oil biofuel (WCOB) and lamb fat biofuel (LFB). Then we proposed an online big-data monitoring and assessment framework for IC engines operating with various types of fuel. We conducted comprehensive experiments and comparisons based on the proposed framework. The results indicate that LFB performs best under all the performance indicators

Diffusion basis spectrum imaging as an adjunct to conventional MRI leads to earlier diagnosis of high-grade glioma tumor progression versus treatment effect

BACKGROUND: Following chemoradiotherapy for high-grade glioma (HGG), it is often challenging to distinguish treatment changes from true tumor progression using conventional MRI. The diffusion basis spectrum imaging (DBSI) hindered fraction is associated with tissue edema or necrosis, which are common treatment-related changes. We hypothesized that DBSI hindered fraction may augment conventional imaging for earlier diagnosis of progression versus treatment effect. METHODS: Adult patients were prospectively recruited if they had a known histologic diagnosis of HGG and completed standard-of-care chemoradiotherapy. DBSI and conventional MRI data were acquired longitudinally beginning 4 weeks post-radiation. Conventional MRI and DBSI metrics were compared with respect to their ability to diagnose progression versus treatment effect. RESULTS: Twelve HGG patients were enrolled between August 2019 and February 2020, and 9 were ultimately analyzed (5 progression, 4 treatment effect). Within new or enlarging contrast-enhancing regions, DBSI hindered fraction was significantly higher in the treatment effect group compared to progression group ( CONCLUSIONS: In the first longitudinal prospective study of DBSI in adult HGG patients, we found that in new or enlarging contrast-enhancing regions following therapy, DBSI hindered fraction is elevated in cases of treatment effect compared to those with progression. Hindered fraction map may be a valuable adjunct to conventional MRI to distinguish tumor progression from treatment effect

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Combustion performance of a novel hybrid rocket fuel grain with a nested helical structure

The combustion performance of a novel fuel grain having a nested helical structure was experimentally investigated using a laboratory-scale hybrid rocket engine. This grain comprised a paraffin-based fuel embedded in an acrylonitrile-butadiene-styrene (ABS) substrate that provided a helical structural framework. The helical structure of the grain was maintained throughout the combustion process due to the much lower regression rate of ABS compared with that of the paraffin-based fuel. Using oxygen as the oxidizer at mass flow rates of 7-30 g/s, firing tests were conducted to assess combustion performance parameters of the novel fuel grain, including ignition characteristics, pressure oscillations, regression rate, and combustion efficiency. Pure paraffin-based fuel grains were also tested as a baseline fuel and compared. The novel fuel grain exhibited rapid, reliable ignition with stable combustion pressures. Analysis of pressure fluctuations by fast Fourier transform showed peaks at approximately 62, 130 and 320 Hz, which are consistent with the characteristics of a pure paraffin-based fuel grain. It is highly likely that the nested helical structure did not introduce additional combustion oscillation mechanisms into the hybrid rocket engine. Significant improvements in regression rate were obtained using this novel grain. The regression rate for the novel fuel grain is approximately 20% higher than that of the paraffin-based fuel at an oxidizer mass flow rate of 30 g/s, and the rate of the regression rate rise was higher than that of the pure paraffin-based fuel as the oxidizer mass flow rate increases. Moreover, the nested helical structure was also found to improve combustion efficiency. A tentative explanation of all improvements was proposed, resorting to the exacerbated turbulence and the strengthened heat transfer. (C) 2019 Elsevier Masson SAS. All rights reserved

Pooling region learning of visual word for image classification using bag-of-visual-words model.

In the problem where there is not enough data to use Deep Learning, Bag-of-Visual-Words (BoVW) is still a good alternative for image classification. In BoVW model, many pooling methods are proposed to incorporate the spatial information of local feature into the image representation vector, but none of the methods devote to making each visual word have its own pooling regions. The practice of designing the same pooling regions for all the words restrains the discriminability of image representation, since the spatial distributions of the local features indexed by different visual words are not same. In this paper, we propose to make each visual word have its own pooling regions, and raise a simple yet effective method for learning pooling region. Concretely, a kind of small window named observation window is used to obtain its responses to each word over the whole image region. The pooling regions of each word are organized by a kind of tree structure, in which each node indicates a pooling region. For each word, its pooling regions are learned by constructing a tree with its labelled coordinate data. The labelled coordinate data consist of the coordinates of responses and image class labels. The effectiveness of our method is validated by observing if there is an obvious classification accuracy improvement after applying our method. Our experimental results on four small datasets (i.e., Scene-15, Caltech-101, Caltech-256 and Corel-10) show that, the classification accuracy is improved by about 1% to 2.5%. We experimentally demonstrate that the practice of making each word have its own pooling regions is beneficial to image classification task, which is the significance of our work

Ultrathin Carbon-Coated Porous TiNb₂O₇ Nanosheets as Anode Materials for Enhanced Lithium Storage

TiNb2O7 has been considered as a promising anode material for next-generation high power lithium ion batteries for its relatively high theoretical capacity, excellent safety and long cycle life. However, the unsatisfactory electrochemical kinetics resulting from the intrinsic sluggish electron transport and lithium ion diffusion of TiNb2O7 limit its wide application. Morphology controlling and carbon coating are two effective methods for improving the electrochemical performance of electrode materials. Herein, an ultrathin carbon-coated porous TiNb2O7 nanosheet (TNO@C) is successfully fabricated by a simple and effective approach. The distinctive sheet-like porous structure can shorten the transport path of ions/electrons and provide more active sites for electrochemical reaction. The introduction of nanolayer carbon can improve electronic conductivity and increase the specific surface area of the porous TiNb2O7 nanosheets. Based on the above synergistic effect, TiNb2O7@C delivers an initial discharge capacity of 250.6 mAh g−1 under current density of 5C and can be maintained at 206.9 mAh g−1 after 1000 cycles with a capacity retention of 82.6%, both of which are superior to that of pure TiNb2O7. These results well demonstrate that TiNb2O7@C is a promising anode material for lithium ion batteries