QCD corrections to single slepton production at hadron colliders

We evaluate the cross section for single slepton production at hadron colliders in supersymmetric theories with R-parity violating interactions to the next-to-leading order in QCD. We obtain fully differential cross section by using the phase space slicing method. We also perform soft-gluon resummation to all order in $\alpha_s$ of leading logarithm to obtain a complete transverse momentum spectrum of the slepton. We find that the full transverse momentum spectrum is peaked at a few GeV, consistent with the early results for Drell-Yan production of lepton pairs. We also consider the contribution from gluon fusion via quark-triangle loop diagrams dominated by the $b$-quark loop. The cross section of this process is significantly smaller than that of the tree-level process induced by the initial $b\bar{b}$ annihilation.Comment: one new reference is adde

Dyson-Schwinger Equations with a Parameterized Metric

We construct and solve the Dyson-Schwinger equation (DSE) of quark propagator with a parameterized metric, which connects the Euclidean metric with the Minkowskian one. We show, in some models, the Minkowskian vacuum is different from the Euclidean vacuum. The usual analytic continuation of Green function does not make sense in these cases. While with the algorithm we proposed and the quark-gluon vertex ansatz which preserves the Ward-Takahashi identity, the vacuum keeps being unchanged in the evolution of the metric. In this case, analytic continuation becomes meaningful and can be fully carried out.Comment: 10 pages, 7 figures. To appear in Physical Review

Optimal Sizing of a Grid Independent Renewable Heating System for Building Decarbonisation

As the use of fossil fuels has led to global climate change due to global warming, most countries are aiming to reduce greenhouse gas emissions through the application of renewable energies. Due to the distributed and seasonal heating demand, the decarbonisation of heating is more challenging, especially for countries that are cold in winters. Electrically powered heat pumps are considered as an attractive solution for decarbonising heating sector. Since grid-powered heat pumps may significantly increase the power demand of the grid, this paper considers using local renewable energy to provide power for heat pumps, which is known as the grid independent renewable heating system including photovoltaic, wind turbine, battery storage system and thermal energy storage. This paper investigates a complete renewable heating system (RHS) framework and sizing the components to decarbonise building heating. The relationship between the reduction of gas consumption and the requirement of battery storage system (BSS) under the corresponding installation capacity of renewable components is analysed with their technical requirements. Then, according to different investment plans, this paper uses the particle swarm optimisation algorithm for optimal sizing of each component in the RHS to find a solution to minimise CO2 emissions. The results verify that the RHS with optimal sizing can minimise CO2 emissions and reduce the operational cost of natural gas. This work provides a feasible solution of how to invest the RHS to replace the existing heating system based on gas boilers and CHPs

Phase diagram and critical endpoint for strongly-interacting quarks

We introduce a method based on the chiral susceptibility, which enables one to draw a phase diagram in the chemical-potential/temperature plane for strongly-interacting quarks whose interactions are described by any reasonable gap equation, even if the diagrammatic content of the quark-gluon vertex is unknown. We locate a critical endpoint (CEP) at (\mu^E,T^E) ~ (1.0,0.9)T_c, where T_c is the critical temperature for chiral symmetry restoration at \mu=0; and find that a domain of phase coexistence opens at the CEP whose area increases as a confinement length-scale grows.Comment: 4 pages, 3 figure

Multimorbidity and catastrophic health expenditure among patients with diabetes in China : a nationwide population-based study

Introduction Multimorbidity is common among patients with diabetes and can lead to catastrophic health expenditure (CHE) for their families. This study aims to investigate the prevalence of multimorbidity and CHE among people with diabetes in China, and the association between multimorbidity and CHE and whether this is influenced by socioeconomic status and health insurance type. Methods A national survey was conducted in China in 2013 that included 8471 people aged ≥18 years who were living with diabetes. The concentration curve and concentration index were used to measure socioeconomic-related inequalities. Factors influencing CHE and the impact of multimorbidity on CHE according to socioeconomic status and health insurance type were examined by logistic regression. Results There were 5524 (65.2%) diabetes patients with multimorbidity. The prevalence of CHE was 56.6%, with a concentration index of-0.030 (95% CI-0.035 to-0.026). For each additional chronic disease, the probability of CHE increased by 39% (OR=1.39, 95% CI 1.31 to 1.47). Factors that were positively associated (p<0.05) with CHE included older age; male sex; lower educational level; being retired, unemployed or jobless; being a non-smoker and non-drinker; having had no physical examination; lower socioeconomic status; being in an impoverished family; and residing in the central or western regions. Among participants with Urban Employee Basic Medical Insurance, Urban Resident Basic Medical Insurance, and New Rural Cooperative Medical Scheme, the probability of CHE increased by 32% (OR=1.32, 95% CI 1.23 to 1.43), 43% (OR=1.43, 95% CI 1.24 to 1.65) and 47% (OR=1.47, 95% CI 1.33 to 1.63), respectively, with each additional chronic disease. The association between multimorbidity and CHE was observed across all health insurance types irrespective of socioeconomic status. Conclusions Multimorbidity affects about two-thirds of Chinese patients with diabetes. Current health insurance schemes offer limited protection against CHE to patients' families

Sensitivity Analysis to Reduce Duplicated Features in ANN Training for District Heat Demand Prediction

Artificial neural network (ANN) has become an important method to model the nonlinear relationships between weather conditions, building characteristics and its heat demand. Due to the large amount of training data required for ANN training, data reduction and feature selection are important to simplify the training. However, in building heat demand prediction, many weather-related input variables contain duplicated features. This paper develops a sensitivity analysis approach to analyse the correlation between input variables and to detect the variables that have high importance but contain duplicated features. The proposed approach is validated in a case study that predicts the heat demand of a district heating network containing tens of buildings at a university campus. The results show that the proposed approach detected and removed several unnecessary input variables and helped the ANN model to reduce approximately 20% training time compared with the traditional methods while maintaining the prediction accuracy. It indicates that the approach can be applied for analysing large number of input variables to help improving the training efficiency of ANN in district heat demand prediction and other applications