Efficient classification using parallel and scalable compressed model and Its application on intrusion detection

In order to achieve high efficiency of classification in intrusion detection, a compressed model is proposed in this paper which combines horizontal compression with vertical compression. OneR is utilized as horizontal com-pression for attribute reduction, and affinity propagation is employed as vertical compression to select small representative exemplars from large training data. As to be able to computationally compress the larger volume of training data with scalability, MapReduce based parallelization approach is then implemented and evaluated for each step of the model compression process abovementioned, on which common but efficient classification methods can be directly used. Experimental application study on two publicly available datasets of intrusion detection, KDD99 and CMDC2012, demonstrates that the classification using the compressed model proposed can effectively speed up the detection procedure at up to 184 times, most importantly at the cost of a minimal accuracy difference with less than 1% on average

Mining Linguistic Clues From Social Network: Impact of CEO Personality on Business Performance

Researchers in strategic management and organizational theory have demonstrated that executives explain a non-trivial proportion of organizational performance variance. Upper echelons theory further informs us what and why top managers’ characteristics affect organizational performance. As the leader of executives, CEO often has a disproportionate, sometimes dominating, influence on his or her firm. However, limited research has studied the impact of CEOs’ comprehensive personality on business performance. We capture linguistic clues CEOs leaving on social network to recognize their personality by a text mining approach. Meanwhile, we adopt a broader conceptualization of the construct space of business performance and measure it by both financial and operational indicators. The impact of each aspect of CEOs’ personality on business performance is then estimated. Interesting results are found and conceivable explanations are proposed

Internet Celebrity Endorsement: How Internet Celebrities Bring Referral Traffic to E-commerce Sites?

Endorsement marketing has been widely used to generate consumer attention, interest, and purchase behaviors among targeted audience of celebrities. Internet celebrities who become famous by means of the Internet are more dependent on strategy intimacy to appeal to their followers. Limited studies have addressed the new business models in Internet celebrities economy: content advertising and online retailing. Our study aims to examine how Internet celebrity endorsement influencing the consumers’ clickon behaviors and purchase behaviors in the context of e-commerce business. Results suggest that content marketing using Internet celebrity endorsement exhibit a significant role in bringing referral traffic to e-commerce sites but less helpful to boost sales. The impact of Internet celebrity endorsement on consumers’ click-on decisions is U-shaped, but the role of Internet celebrities as online retailers will “shape-flip” such relationship to a negative linear relation. Therefore, Internet celebrity endorsement provides effective ways to bring referral traffic to e-commerce sites

Spillover Effect of Content Marketing in E-commerce Platform under the Fan Economy Era

As the proliferation of social media and live streaming, online celebrity endorsement is a common practice of content marketing in e-commerce platform. Despite the prevalent use of social media and online community, empirical research investigating the economic values of user-generated-content (UGC) and marketer-generated-content (MGC) still lags. This study seeks to contribute theoretically and practically to an understanding of how online celebrity endorsement and fans interaction behaviors affect e-commerce sales. We adopt cross-sectional regression to assess the economic value of online celebrity endorsement, and we employ panel vector autoregressive model to explain the dynamic relationship between marketers’ and consumers’ content marketing behaviors and e-commerce product sales. Empirical results highlight that the interaction within fans community has spillover effect on content marketing under “Fan Economy” era

Comparison of multi-field coupling numerical simulation in hot dry rock thermal exploitation of enhanced geothermal systems

 In order to alleviate the environmental crisis and improve energy structure, countries from all over the world have focused on the hot dry rock geothermal resources with great potential and with little pollution. The geothermal heat production from Enhanced Geothermal System (EGS) comes with complex multi-field coupling process, and it is of great significance to study the temporal and spatial evolution of geothermal reservoir. In this work, a practical numerical model is established to simulate the heat production process in EGS, and the comparison of thermal-hydraulic (TH), thermal-hydraulic-mechanical (THM) and thermal-hydraulic-mechanical-chemical (THMC) coupling in geothermal reservoir is analyzed. The results show that the stable production stage of the three cases is approximately 5 years; however, compared with TH and THMC coupling, the service-life for THM coupling decreased by 1140 days and 332 days, respectively. The mechanical enhanced effects are offset by the chemical precipitation, and the precipitation from SiO2 is much larger than the dissolution of calcite.Cited as: Chen, S., Ding, B., Gong, L., Huang, Z., Yu, B., Sun, S. Comparison of multi-field coupling numerical simulation in hot dry rock thermal exploitation of enhanced geothermal systems. Advances in Geo-Energy Research, 2019, 3(4): 396-409, doi: 10.26804/ager.2019.04.0

A Real-World Disproportionality Analysis of Everolimus: Data Mining of the Public Version of FDA Adverse Event Reporting System

Background: Everolimus is an inhibitor of the mammalian target of rapamycin and is used to treat various tumors. The presented study aimed to evaluate the Everolimus-associated adverse events (AEs) through data mining of the US Food and Drug Administration Adverse Event Reporting System (FAERS). Methods: The AE records were selected by searching the FDA Adverse Event Reporting System database from the first quarter of 2009 to the first quarter of 2022. Potential adverse event signals were mined using the disproportionality analysis, including reporting odds ratio the proportional reporting ratio the Bayesian confidence propagation neural network and the empirical Bayes geometric mean and MedDRA was used to systematically classify the results. Results: A total of 24,575 AE reports of Everolimus were obtained using data from the FAERS database, and Everolimus-induced AEs occurrence targeted 24 system organ classes after conforming to the four algorithms simultaneously. The common significant SOCs were identified, included benign, malignant and unspecified neoplasms, reproductive system and breast disorders, etc. The significant AEs were then mapped to preferred terms such as stomatitis, pneumonitis and impaired insulin secretion, which have emerged in the study usually reported in patients with Everolimus. Of note, unexpected significant AEs, including biliary ischaemia, angiofibroma, and tuberous sclerosis complex were uncovered in the label. Conclusion: This study provided novel insights into the monitoring, surveillance, and management of adverse drug reaction associated with Everolimus. The outcome of serious adverse events and the corresponding detection signals, as well as the unexpected significant adverse events signals are worthy of attention in order to improving clinical medication safety during treatment of Everolimus

Superfast Liquid Transfer Strategy Through Sliding on a Liquid Membrane Inspired from Scorpion Setae

Although diversified biological structures have evolved fog collection abilities, the typical speeds of the condensed water droplets on these surfaces are too slow to have practical utility. The main challenge focuses on the elimination of the interfacial hydrodynamic resistance without external energy support. Here, an unusual strategy for superfast self‐support transfer condensed droplets is supported by sliding on seta of desert scorpion. It can be rapidly wetted by the fog droplets owing to its conical shape with linear gradient channels. A loss of interfacial resistance by this hydrodynamically lubricating water membrane could significantly accelerate the movement of the droplets, thus making its velocity increasing by one order of magnitude, or even more. Inspired by this novel strategy, the novel bioinspired materials are fabricated with the similar gradient channel structures and droplet transportation mode, which can make the condensed droplets spontaneously slide on the low‐friction liquid membrane. The fundamental understanding of superfast fog capture and the sliding dynamics of condensed droplets in this system could inspire to develop novel materials or various systems to transfer liquid fast and efficiently without external energy support.An unusual strategy for superfast transferring condensed droplets by sliding on liquid membrane of desert scorpion seta is reported. A loss of interfacial resistance could significantly accelerate the droplets by this hydrodynamically lubricating liquid membrane. Then, the bioinspired materials with similar droplet transportation mode are fabricated, which will inspire to develop novel materials to transport liquid without external energy.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146306/1/admi201800802-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146306/2/admi201800802.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146306/3/admi201800802_am.pd