Thrust distribution in Higgs decays at the next-to-leading order and beyond

We present predictions for the thrust distribution in hadronic decays of the Higgs boson at the next-to-leading order and the approximate next-to-next-to-leading order. The approximate NNLO corrections are derived from a factorization formula in the soft/collinear phase-space regions. We find large corrections, especially for the gluon channel. The scale variations at the lowest orders tend to underestimate the genuine higher order contributions. The results of this paper is therefore necessary to control the perturbative uncertainties of the theoretical predictions. We also discuss on possible improvements to our results, such as a soft-gluon resummation for the 2-jets limit, and an exact next-to-next-to-leading order calculation for the multi-jets region

Phase transitions and thermodynamics of the two-dimensional Ising model on a distorted Kagom\'{e} lattice

The two-dimensional Ising model on a distorted Kagom\'{e} lattice is studied by means of exact solutions and the tensor renormalisation group (TRG) method. The zero-field phase diagrams are obtained, where three phases such as ferromagnetic, ferrimagnetic and paramagnetic phases, along with the second-order phase transitions, have been identified. The TRG results are quite accurate and reliable in comparison to the exact solutions. In a magnetic field, the magnetization ($m$), susceptibility and specific heat are studied by the TRG algorithm, where the $m=1/3$ plateaux are observed in the magnetization curves for some couplings. The experimental data of susceptibility for the complex Co(N$_3$)$_2$(bpg)$\cdot$ DMF$_{4/3}$ are fitted with the TRG results, giving the couplings of the complex $J=22K$ and $J'=33K$

Development of Chinese Strategic Petroleum Reserves Base on Comparable Research about Japanese Situation and Policies

AbstractPetroleum is one of the most important energy which can’t be regenerated. During the increasing development of world economy, petroleum has become significant energy to keep a county's economy survival. Japan which has almost no petroleum capacity had started strategic petroleum reserve since last century. Abundant reserve helped Japan conquering twice petroleum crises. As the biggest and fastest developing country, China has the main purpose to protect economy rise consecutively. Petroleum reserve also is emphasized more by Chinese government in present time. According to research base on Japanese and Chinese petroleum situation, legislation, policies, and response measures, the article put forward three stage reserve system of China, and discussed some relative issues about development of Chinese strategic petroleum reserve

Emergent spin-1 trimerized valence bond crystal in the spin-1/2 Heisenberg model on the star lattice

We explore the frustrated spin-$1/2$ Heisenberg model on the star lattice with antiferromagnetic (AF) couplings inside each triangle and ferromagnetic (FM) inter-triangle couplings ($J_e<0$), and calculate its magnetic and thermodynamic properties. We show that the FM couplings do not sabotage the magnetic disordering of the ground state due to the frustration from the AF interactions inside each triangle, but trigger a fully gapped inversion-symmetry-breaking trimerized valence bond crystal (TVBC) with emergent spin-1 degrees of freedom. We discover that with strengthening $J_e$, the system scales exponentially, either with or without a magnetic field $h$: the order parameter, the five critical fields that separate the $J_e$-$h$ ground-state phase diagram into six phases, and the excitation gap obtained by low-temperature specific heat, all depend exponentially on $J_e$. We calculate the temperature dependence of the specific heat, which can be directly compared with future experiments.Comment: 7 pages, 6 figure

Linearized Tensor Renormalization Group Algorithm for Thermodynamics of Quantum Lattice Models

A linearized tensor renormalization group (LTRG) algorithm is proposed to calculate the thermodynamic properties of one-dimensional quantum lattice models, that is incorporated with the infinite time-evolving block decimation technique, and allows for treating directly the two-dimensional transfer-matrix tensor network. To illustrate its feasibility, the thermodynamic quantities of the quantum XY spin chain are calculated accurately by the LTRG, and the precision is shown to be comparable with (even better than) the transfer matrix renormalization group (TMRG) method. Unlike the TMRG scheme that can only deal with the infinite chains, the present LTRG algorithm could treat both finite and infinite systems, and may be readily extended to boson and fermion quantum lattice models.Comment: published versio

A New Method for Conflict Resoluton Based on Multi-Agent Reinforcement Learning Algorithms

Conflict resolution is a research topic for game theory (GT) and conflict analysis. A decision support system (DSS) is very helpful for conflict decision making. Reinforcement learning (RL) is an efficient method to learn knowledge by agents themselves. Although successful applications of RL have been reported in single-agent domain, a lot of work should be done in the case of multi-agent domain. Nash Q-learning is a famous learning algorithm for multi-agent RL. Based on the Nash Q-learning, a novel DSS: multi-agent RL based DSS (MRLDSS) is proposed in this paper and is tested by using several typical examples of conflict resolution. Experimental results show that the proposed architecture and algorithm can solve conflict resolution problems correctly and efficiently

Benzyl 3-dehydr­oxy-1,2,5-oxadiazolo[3′,4′:2,3]oleanolate

The title compound, C37H50N2O3, is a benzyl ester derivative of oleanolic acid, a penta­cyclic triterpene, with a five-membered oxadiazole ring fused to the ring A. The triterpene A and C rings adopt slightly distorted half-chair conformations, whereas the remaining three six-membered rings are in chair forms

A Framework on Complex Matrix Derivatives with Special Structure Constraints for Wireless Systems

Matrix-variate optimization plays a central role in advanced wireless system designs. In this paper, we aim to explore optimal solutions of matrix variables under two special structure constraints using complex matrix derivatives, including diagonal structure constraints and constant modulus constraints, both of which are closely related to the state-of-the-art wireless applications. Specifically, for diagonal structure constraints mostly considered in the uplink multi-user single-input multiple-output (MU-SIMO) system and the amplitude-adjustable intelligent reflecting surface (IRS)-aided multiple-input multiple-output (MIMO) system, the capacity maximization problem, the mean-squared error (MSE) minimization problem and their variants are rigorously investigated. By leveraging complex matrix derivatives, the optimal solutions of these problems are directly obtained in closed forms. Nevertheless, for constant modulus constraints with the intrinsic nature of element-wise decomposability, which are often seen in the hybrid analog-digital MIMO system and the fully-passive IRS-aided MIMO system, we firstly explore inherent structures of the element-wise phase derivatives associated with different optimization problems. Then, we propose a novel alternating optimization (AO) algorithm with the aid of several arbitrary feasible solutions, which avoids the complicated matrix inversion and matrix factorization involved in conventional element-wise iterative algorithms. Numerical simulations reveal that the proposed algorithm can dramatically reduce the computational complexity without loss of system performance

Exploring the longitudinal relationship between lockdown policy stringency and public negative emotions among 120 countries during the COVID-19 pandemic: mediating role of population mobility

Background: To limit the spread of COVID-19, governments worldwide have implemented a series of lockdown policies to restrict the social activities of people. Although scholars suggest that such policies may produce negative effects on public emotions, the existing research is limited because it only provides a cross-sectional snapshot of the effect of lockdown policies in small and local samples. Using large-scale longitudinal cross-country data, the current study aims to gain a better understanding of the dynamic effect of lockdown policies on public emotions and their underlying mechanisms. Methods: Drawing on a large-scale longitudinal data from multiple sources, the study employs fixed-effects models to analyze the association between lagged lockdown policy stringency and public negative emotions among 120 countries from February to July 2020 (N = 9,141 country-day observations). The bootstrapping mediation test is used to examine the mediation effects of increased population mobility in residential areas. Results: The results show a statistically significant and positive association between lagged lockdown policy stringency and general public negative emotion (standardized coefficient = 0.32, CI = 0.30–0.35, p < 0.001). This pattern remains similar to other specific negative emotions, such as depression, anxiety, hopelessness, and helplessness. Moreover, the negative health effects of lockdown policy stringency are significantly mediated by increased mobility in residential areas (51–74% points, p < 0.001). Conclusion: The findings confirm that stringent lockdown policies have a negative effect on public emotions via confining population mobility residential areas. To tackle the COVID-19, future public health policies should pay more attention to the unintended negative consequences of lockdown measures on public emotions