Constraining the Two-Higgs-Doublet-Model parameter space

We confront the Two-Higgs-Doublet Model with a variety of experimental constraints as well as theoretical consistency conditions. The most constraining data are the \bar B\to X_s\gamma decay rate (at low values of M_{H^\pm}), and \Delta\rho (at both low and high M_{H^\pm}). We also take into account the B\bar B oscillation rate and R_b, or the width \Gamma(Z\to b\bar b) (both of which restrict the model at low values of \tan\beta), and the B^-\to\tau\nu_\tau decay rate, which restricts the model at high \tan\beta and low M_{H^\pm}. Furthermore, the LEP2 non-discovery of a light, neutral Higgs boson is considered, as well as the muon anomalous magnetic moment. Since perturbative unitarity excludes high values of \tan\beta, the model turns out to be very constrained. We outline the remaining allowed regions in the \tan\beta-M_{H^\pm} plane for different values of the masses of the two lightest neutral Higgs bosons, and describe some of their properties.Comment: 17 pages, 17 figure

A review on data stream classification

At this present time, the significance of data streams cannot be denied as many researchers have placed their focus on the research areas of databases, statistics, and computer science. In fact, data streams refer to some data points sequences that are found in order with the potential to be non-binding, which is generated from the process of generating information in a manner that is not stationary. As such the typical tasks of searching data have been linked to streams of data that are inclusive of clustering, classification, and repeated mining of pattern. This paper presents several data stream clustering approaches, which are based on density, besides attempting to comprehend the function of the related algorithms; both semi-supervised and active learning, along with reviews of a number of recent studies

Theoretical investigations of a highly mismatched interface: the case of SiC/Si(001)

Using first principles, classical potentials, and elasticity theory, we investigated the structure of a semiconductor/semiconductor interface with a high lattice mismatch, SiC/Si(001). Among several tested possible configurations, a heterostructure with (i) a misfit dislocation network pinned at the interface and (ii) reconstructed dislocation cores with a carbon substoichiometry is found to be the most stable one. The importance of the slab approximation in first-principles calculations is discussed and estimated by combining classical potential techniques and elasticity theory. For the most stable configuration, an estimate of the interface energy is given. Finally, the electronic structure is investigated and discussed in relation with the dislocation array structure. Interface states, localized in the heterostructure gap and located on dislocation cores, are identified

OPTOELECTRONIC IMPLEMENTATION OF ARTIFICIALNEURAL NETWORK: PERCEPTRON LEARNING RULE AND MCATEGORYCLASSIFIER

Single neuron perceptron is designed as a classifier of two different classes using the hardlimiter activation function (i.e. in the absence of light, and presence of light). An example is designed and tested so that the proposed circuit learned different categories and then used as a classifier for two different classes because of the use of single neuron. Additional electronic circuits were used for computation processes. The Computer simulation results indicate stable solution that compares with theoretical results. Single layer perceptron M-category classifier is designed as a classifier for more than two classes. An example is designed and tested for the verification. The example learns after (5) iterations. Computer simulation results indicate stable solution that compares favorably with theoretical results

The electrokinetic impact on heavy metals remediation of Tasik Chini iron ore mine tailings, at Pahang state, Peninsular Malaysia

The improper disposal of mining tailings is a severe threat to the surrounding environment because it comprises high concentrations of heavy metals contamination. Any precious metal extraction (mining) produces millions of tons of waste; iron ore extraction is common globally, unlike other metals extraction. The iron ore tailings contain heavy metals such as Arsenic (As), Cobalt (Co), Manganese (Mn), Lead (Pb), Copper (Cu), and Zinc (Zn). This study focuses on extracting hazardous metals such as As, V, and Zn from the disposed waste and improving its geotechnical properties. Nine samples were collected from Tasik Chini Iron ore mine, Pekan district, Pahang State, Malaysia. The initial data were prepared for elemental analysis by following ICP-OES analysis. The results showed that As, Co, Mn, Pb, Cu, and Zn concentrations exceeded the standard guidelines. In recent years, sustainable remediations techniques (EKR) have attracted extensive attention, including the electrokinetic remediation technique. The (EKR) method was applied to extract these metals from iron ore tailings specimens. A comprehensive approach of EKR shows an outstanding result where the highest removal efficiency of As was 68.4 %, Co 64.5%, Mn 67.8%, Pb 67.1%, and Cu was 64.1% and Zn 64.9% with the voltage gradient of 100 and 150 V for 4 and 8 hours constantly. Increasing the voltage gradient could be a cost-effective long-term solution for the remediation of iron ore tailings. The existing method was experienced as an effective and green technique for extracting heavy metals and recycling the mining waste materials

Proton and molecular permeation through the basal plane of monolayer graphene oxide

Two-dimensional (2D) materials offer a prospect of membranes that combine negligible gas permeability with high proton conductivity and could outperform the existing proton exchange membranes used in various applications including fuel cells. Graphene oxide (GO), a well-known 2D material, facilitates rapid proton transport along its basal plane but proton conductivity across it remains unknown. It is also often presumed that individual GO monolayers contain a large density of nanoscale pinholes that lead to considerable gas leakage across the GO basal plane. Here we show that relatively large, micrometer-scale areas of monolayer GO are impermeable to gases, including helium, while exhibiting proton conductivity through the basal plane which is nearly two orders of magnitude higher than that of graphene. These findings provide insights into the key properties of GO and demonstrate that chemical functionalization of 2D crystals can be utilized to enhance their proton transparency without compromising gas impermeability

A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

In recent years, there has been a dramatic increase in the use of unmanned aerial vehicles (UAVs), particularly for small UAVs, due to their affordable prices, ease of availability, and ease of operability. Existing and future applications of UAVs include remote surveillance and monitoring, relief operations, package delivery, and communication backhaul infrastructure. Additionally, UAVs are envisioned as an important component of 5G wireless technology and beyond. The unique application scenarios for UAVs necessitate accurate air-to-ground (AG) propagation channel models for designing and evaluating UAV communication links for control/non-payload as well as payload data transmissions. These AG propagation models have not been investigated in detail when compared to terrestrial propagation models. In this paper, a comprehensive survey is provided on available AG channel measurement campaigns, large and small scale fading channel models, their limitations, and future research directions for UAV communication scenarios