Influence of Media Richness of E-WOM on Consumers\u27 Mental Imagery and Attitudes

E-WOM has become a hot research topic. Based on the theory of media richness, this paper divides online reviews into three types according to the degree of e-WOM richness from low to high - text , text + image and text + image + video . From the perspective of mental imagery, this paper explores the consumer attitudes of these three kinds of reviews with different richness. The Experimental data analysis shows that compared with text reviews, text + image reviews have more significant impact on consumers\u27 metal imagery and attitudes; as a form of high richness e-WOM, text + image + video has the most significant impact on consumers\u27 metal imagery and attitudes. This discovery points out several meaningful directions for the further study from the perspective of media richness

Water quality prediction based on multi-task learning

Water pollution seriously endangers people’s lives and restricts the sustainable development of the economy. Water quality prediction is essential for early warning and prevention of water pollution. However, the nonlinear characteristics of water quality data make it challenging to accurately predicted by traditional methods. Recently, the methods based on deep learning can better deal with nonlinear characteristics, which improves the prediction performance. Still, they rarely consider the relationship between multiple prediction indicators of water quality. The relationship between multiple indicators is crucial for the prediction because they can provide more associated auxiliary information

LARSEN-ELM: Selective Ensemble of Extreme Learning Machines using LARS for Blended Data

Extreme learning machine (ELM) as a neural network algorithm has shown its good performance, such as fast speed, simple structure etc, but also, weak robustness is an unavoidable defect in original ELM for blended data. We present a new machine learning framework called LARSEN-ELM for overcoming this problem. In our paper, we would like to show two key steps in LARSEN-ELM. In the first step, preprocessing, we select the input variables highly related to the output using least angle regression (LARS). In the second step, training, we employ Genetic Algorithm (GA) based selective ensemble and original ELM. In the experiments, we apply a sum of two sines and four datasets from UCI repository to verify the robustness of our approach. The experimental results show that compared with original ELM and other methods such as OP-ELM, GASEN-ELM and LSBoost, LARSEN-ELM significantly improve robustness performance while keeping a relatively high speed.Comment: Accepted for publication in Neurocomputing, 01/19/201

Mach cone induced by γ\gamma-triggered jets in high-energy heavy-ion collisions

MMedium excitation by jet shower propagation inside a quark-gluon plasma is studied within a linear Boltzmann transport and a multiphase transport model. Contrary to the naive expectation, it is the deflection of both the jet shower and the Mach-cone-like excitation in an expanding medium that is found to gives rise to a double-peak azimuthal particle distribution with respect to the initial jet direction. Such deflection is the strongest for hadron-triggered jets which are often produced close to the surface of dense medium due to trigger-bias and travel against or tangential to the radial flow. Without such trigger bias, the effect of deflection on $\gamma$-jet showers and their medium excitation is weaker. Comparative study of hadron and $\gamma$-triggered particle correlations can therefore reveal the dynamics of jet-induced medium excitation in high-energy heavy-ion collisions.Comment: 4 pages in RevTeX with 5 figures, finally version in PR

A Pathogen Secreted Protein as a Detection Marker for Citrus Huanglongbing.

The citrus industry is facing an unprecedented crisis due to Huanglongbing (HLB, aka citrus greening disease), a bacterial disease associated with the pathogen Candidatus Liberibacter asiaticus (CLas) that affects all commercial varieties. Transmitted by the Asian citrus psyllid (ACP), CLas colonizes citrus phloem, leading to reduced yield and fruit quality, and eventually tree decline and death. Since adequate curative measures are not available, a key step in HLB management is to restrict the spread of the disease by identifying infected trees and removing them in a timely manner. However, uneven distribution of CLas cells in infected trees and the long latency for disease symptom development makes sampling of trees for CLas detection challenging. Here, we report that a CLas secreted protein can be used as a biomarker for detecting HLB infected citrus. Proteins secreted from CLas cells can presumably move along the phloem, beyond the site of ACP inoculation and CLas colonized plant cells, thereby increasing the chance of detecting infected trees. We generated a polyclonal antibody that effectively binds to the secreted protein and developed serological assays that can successfully detect CLas infection. This work demonstrates that antibody-based diagnosis using a CLas secreted protein as the detection marker for infected trees offers a high-throughput and economic approach that complements the approved quantitative polymerase chain reaction-based methods to enhance HLB management programs

Transient response of near-wellbore supercharging during filter cake growth

Supercharging in the vicinity of a borehole is an important factor that affects formation damage and drilling safety, and the filter cake growth process has a significant impact on supercharging in the vicinity of the borehole. However, existing models that predict pore pressure distribution overlook dynamic filter cake growth. Thus, an analytical supercharging model was developed that considers time-dependent filter cake effects, and this model was verified using a two-dimensional numerical model. The influences of filter cake, formation, and filtrate properties on supercharging were investigated systematically. The results indicate that time-dependent filter cake effects have significant influence on supercharging. Supercharging increases in the early stage but decreases over time because of the dynamic growth of filter cake, and the supercharging magnitude decreases along the radial direction. Because of filter cake growth, the magnitude of supercharging falls quickly across the filter cake, and the decreased magnitude of pore pressure caused by the filter cake increases. Supercharging in low-permeability formations is more obvious and the faster rate of filter cake growth, a lower filtrate viscosity and faster reduction rate of filter cake permeability can help to weaken supercharging. The order of importance of influencing factors on supercharging is overbalance pressure > formation permeability > formation porosity ≈ filtrate viscosity > filter cake permeability attenuation coefficient > initial filter cake permeability control ratio > filter cake growth coefficient > filter cake porosity. To alleviate supercharging in the vicinity of the borehole, adopting drilling fluids that allow a filter cake to form quickly, optimizing drilling fluid with a lower filtrate viscosity, keeping a smaller overbalance pressure, and precise operation at the rig site are suggested for low-permeability formations during drilling