Searching for Charged Higgs Boson in Polarized Top Quark

The charged Higgs boson is quite common in many new physics models. In this study we examine the potential of observing a heavy charged Higgs boson in its decay mode of top-quark and bottom-quark in the Type-II Two-Higgs-Doublet-Model. In this model, the chirality structure of the coupling of charged Higgs boson to the top- and bottom-quark is very sensitive to the value of $\tan\beta$. As the polarization of the top-quark can be measured experimentally from the top-quark decay products, one could make use of the top-quark polarization to determine the value of $\tan\beta$. We preform a detailed analysis of measuring top-quark polarization in the production channels $gb\to tH^-$ and $g\bar{b}\to \bar{t}H^+$. We calculate the helicity amplitudes of the charged Higgs boson production and decay.Our calculation shows that the top-quark from the charged Higgs boson decay provides a good probe for measuring $\tan\beta$, especially for the intermediate $\tan\beta$ region. On the contrary, the top-quark produced in association with the charged Higgs boson cannot be used to measure $\tan\beta$ because its polarization is highly contaminated by the $t$-channel kinematics.Comment: 21 pages, 12 figures, 2 table

Bis(1H-benzotriazole-7-sulfonato-κO)bis­(1,10-phenanthroline-κ2 N,N′)cadmium dihydrate

In the title complex, [Cd(C6H4N3O3S)2(C12H8N2)2]·2H2O, the Cd2+ cation is located on an inversion center and is coordinated by four N atoms from two symmetry-related 1,10-phenanthroline ligands and two sulfonate O atoms from two benzotriazole-7-sulfonate anions, displaying a distorted CdN4O2 octa­hedral geometry. In the crystal, O—H⋯N, O—H⋯O, N—H⋯O, C—H⋯N and C—H⋯O hydrogen bonds occur. The lattice water mol­ecules and sulfonate O atoms as donor or acceptor atoms play important roles in the formation of these inter­actions

Bis(1H-benzotriazole-4-sulfonato-κ2 N 3,O)(2,2′-bipyridyl-κ2 N,N′)cadmium

In the title complex, [Cd(C6H4N3O3S)2(C10H8N2)], the Cd2+ cation is located on a twofold rotation axis and is coordinated by two N and two O atoms from two symmetry-related benzotriazole-4-sulfonate anions and two N atoms from a 2,2-bipyridyl ligand, displaying a distorted CdN4O2 octa­hedral geometry. The crystal structure is stabilized by N—H⋯O and C—H⋯O hydrogen-bonding inter­actions

Optimal Systemic Risk Bailout: A PGO Approach Based on Neural Network

The bailout strategy is crucial to cushion the massive loss caused by systemic risk in the financial system. There is no closed-form formulation of the optimal bailout problem, making solving it difficult. In this paper, we regard the issue of the optimal bailout (capital injection) as a black-box optimization problem, where the black box is characterized as a fixed-point system that follows the E-N framework for measuring the systemic risk of the financial system. We propose the so-called ``Prediction-Gradient-Optimization'' (PGO) framework to solve it, where the ``Prediction'' means that the objective function without a closed-form is approximated and predicted by a neural network, the ``Gradient'' is calculated based on the former approximation, and the ``Optimization'' procedure is further implemented within a gradient projection algorithm to solve the problem. Comprehensive numerical simulations demonstrate that the proposed approach is promising for systemic risk management

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Glass matrix composite material prepared with waste foundry sand

The technology of glass matrix of the composite material manufactured through a sintering process and using waste foundry sand and waste glass as the main raw materials was studied. The effects of technological factors on the performance of this material were studied. The results showed that this composite material is formed with glass as matrix, core particulate as strengthening material, it has the performance of glass and ceramics, and could be used to substitute for stone

Ozone and haze pollution weakens net primary productivity in China

Atmospheric pollutants have both beneficial and detrimental effects on carbon uptake by land ecosystems. Surface ozone (O3) damages leaf photosynthesis by oxidizing plant cells, while aerosols promote carbon uptake by increasing diffuse radiation and exert additional influences through concomitant perturbations to meteorology and hydrology. China is currently the world’s largest emitter of both carbon dioxide and short-lived air pollutants. The land ecosystems of China are estimated to provide a carbon sink, but it remains unclear whether air pollution acts to inhibit or promote carbon uptake. Here, we employ Earth system modeling and multiple measurement datasets to assess the separate and combined effects of anthropogenic O3 and aerosol pollution on net primary productivity (NPP) in China. In the present day, O3 reduces annual NPP by 0.6 Pg C (14 %) with a range from 0.4 Pg C (low O3 sensitivity) to 0.8 Pg C (high O3 sensitivity). In contrast, aerosol direct effects increase NPP by 0.2 Pg C (5 %) through the combination of diffuse radiation fertilization, reduced canopy temperatures, and reduced evaporation leading to higher soil moisture. Consequently, the net effects of O3 and aerosols decrease NPP by 0.4 Pg C (9 %) with a range from 0.2 Pg C (low O3 sensitivity) to 0.6 Pg C (high O3 sensitivity). However, precipitation inhibition from combined aerosol direct and indirect effects reduces annual NPP by 0.2 Pg C (4 %), leading to a net air pollution suppression of 0.8 Pg C (16 %) with a range from 0.6 Pg C (low O3 sensitivity) to 1.0 Pg C (high O3 sensitivity). Our results reveal strong dampening effects of air pollution on the land carbon uptake in China today. Following the current legislation emission scenario, this suppression will be further increased by the year 2030, mainly due to a continuing increase in surface O3. However, the maximum technically feasible reduction scenario could drastically relieve the current level of NPP damage by 70 % in 2030, offering protection of this critical ecosystem service and the mitigation of long-term global warming

3-D Positioning and Resource Allocation for Multi-UAV Base Stations Under Blockage-Aware Channel Model

In this paper, we propose to deploy multiple unmanned aerial vehicle (UAV) mounted base stations to serve ground users in outdoor environments with obstacles. In particular, the geographic information is employed to capture the blockage effects for air-to-ground (A2G) links caused by buildings, and a realistic blockage-aware A2G channel model is proposed to characterize the continuous variation of the channels at different locations. Based on the proposed channel model, we formulate the joint optimization problem of UAV three-dimensional (3-D) positioning and resource allocation, by power allocation, user association, and subcarrier allocation, to maximize the minimum achievable rate among users. To solve this non-convex combinatorial programming problem, we introduce a penalty term to relax it and develop a suboptimal solution via a penalty-based double-loop iterative optimization framework. The inner loop solves the penalized problem by employing the block successive convex approximation (BSCA) technique, where the UAV positioning and resource allocation are alternately optimized in each iteration. The outer loop aims to obtain proper penalty multipliers to ensure the solution of the penalized problem converges to that of the original problem. Simulation results demonstrate the superiority of the proposed algorithm over other benchmark schemes in terms of the minimum achievable rate