Top quark, heavy fermions and the composite Higgs boson

We study the properties of heavy fermions in the vector-like representation of the electro-weak gauge group $SU(2)_W\times U(1)_Y$ with Yukawa couplings to the standard model Higgs boson. Applying the renormalization group analysis, we discuss the effects of heavy fermions to the vacuum stability bound and the triviality bound on the mass of the Higgs boson. We also discuss the interesting possibility that the Higgs particle is composed of the top quark and heavy fermions. The bound on the composite Higgs mass is estimated using the method of Bardeen, Hill and Lindner, 150GeV$\leq m_H\leq$ 450GeV.Comment: 10 pages and 6 figures. This paper replaces and is an extended version of hep-ph/9602340 (published in Phys. Lett. B370(1996)201;Erratum B382(1996)448

Prediction Of Scour Depth Around Bridge Piers Using Adaptive Neuro-Fuzzy Inference Systems (ANFIS)

Earth\u27s surface is continuously shaped due to the action of geophysical flows. Erosion due to the flow of water in river systems has been identified as a key problem in preserving ecological health but also a threat to our built environment and critical infrastructure, worldwide. As an example, it has been estimated that a major reason for bridge failure is due to scour. Even though the flow past bridge piers has been investigated both experimentally and numerically, and the mechanisms of scouring are relatively understood, there still lacks a tool that can offer fast and reliable predictions. Most of the existing formulas for prediction of bridge pier scour depth are empirical in nature, based on a limited range of data or for piers of specific shape. In this work, the application of a Machine Learning model that has been successfully employed in Water Engineering, namely an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to estimate the scour depth around bridge piers. In particular, various complexity architectures are sequentially built, in order to identify the optimal for scour depth predictions, using appropriate training and validation subsets obtained from the USGS database (and pre-processed to remove incomplete records). The model has five variables, namely the effective pier width (b), the approach velocity (v), the approach depth (y), the mean grain diameter (D50) and the skew to flow. Simulations are conducted with data groups (bed material type, pier type and shape) and different number of input variables, to produce reduced complexity and easily interpretable models. Analysis and comparison of the results indicate that the developed ANFIS model has high accuracy and outstanding generalization ability for prediction of scour parameters. The effective pier width (as opposed to skew to flow) is amongst the most relevant input parameters for the estimation

Population Density-based Hospital Recommendation with Mobile LBS Big Data

The difficulty of getting medical treatment is one of major livelihood issues in China. Since patients lack prior knowledge about the spatial distribution and the capacity of hospitals, some hospitals have abnormally high or sporadic population densities. This paper presents a new model for estimating the spatiotemporal population density in each hospital based on location-based service (LBS) big data, which would be beneficial to guiding and dispersing outpatients. To improve the estimation accuracy, several approaches are proposed to denoise the LBS data and classify people by detecting their various behaviors. In addition, a long short-term memory (LSTM) based deep learning is presented to predict the trend of population density. By using Baidu large-scale LBS logs database, we apply the proposed model to 113 hospitals in Beijing, P. R. China, and constructed an online hospital recommendation system which can provide users with a hospital rank list basing the real-time population density information and the hospitals' basic information such as hospitals' levels and their distances. We also mine several interesting patterns from these LBS logs by using our proposed system