Primordial Gravitational Waves Measurements and Anisotropies of CMB Polarization Rotation

Searching for the signal of primordial gravitational waves in the B-modes (BB) power spectrum is one of the key scientific aims of the cosmic microwave background (CMB) polarization experiments. However, this could be easily contaminated by several foreground issues, such as the thermal dust emission. In this paper we study another mechanism, the cosmic birefringence, which can be introduced by a CPT-violating interaction between CMB photons and an external scalar field. Such kind of interaction could give rise to the rotation of the linear polarization state of CMB photons, and consequently induce the CMB BB power spectrum, which could mimic the signal of primordial gravitational waves at large scales. With the recent polarization data of BICEP2 and the joint analysis data of BICEP2/Keck Array and Planck, we perform a global fitting analysis on constraining the tensor-to-scalar ratio $r$ by considering the polarization rotation angle which can be separated into a background isotropic part and a small anisotropic part. Since the data of BICEP2 and Keck Array experiments have already been corrected by using the "self-calibration" method, here we mainly focus on the effects from the anisotropies of CMB polarization rotation angle. We find that including the anisotropies in the analysis could slightly weaken the constraints on $r$, when using current CMB polarization measurements. We also simulate the mock CMB data with the BICEP3-like sensitivity. Very interestingly, we find that if the effects of the anisotropic polarization rotation angle can not be taken into account properly in the analysis, the constraints on $r$ will be dramatically biased. This implies that we need to break the degeneracy between the anisotropies of the CMB polarization rotation angle and the CMB primordial tensor perturbations, in order to measure the signal of primordial gravitational waves accurately.Comment: 7 pages, 5 figure

A multi-criteria decision-making method based on single-valued trapezoidal neutrosophic preference relations with complete weight information

Single-valued trapezoidal neutrosophic numbers (SVTNNs) have a strong capacity to depict uncertain, inconsistent, and incomplete information about decisionmaking problems. Preference relations represent a practical tool for presenting decision makers’ preference information regarding various alternatives

Bituminous Coal Combustion with New Insights

As one of the most important primary energy, bituminous coal has been widely applied in many fields. The combustion studies of bituminous coal have attracted a lot of attention due to the releases of hazardous emissions. This work focuses on the investigation of combustion characteristics of Shenmu bituminous pulverized coal as a representative bituminous coal in China with a combined TG-MS-FTIR system by considering the effect of particle size, heating rate, and the total flow rate. The combustion products were accurately quantified by normalization and numerical analysis of MS results. The results indicate that the decrease of the particle size, heating rate, and the total flow rate result in lower ignition and burnout temperatures. The activation energy tends to be lower with smaller particle size, faster heating rate, and lower total flow rate. The MS and FTIR results demonstrate that lower concentrations of different products, such as NO, NO2, HCN, CH4, and SO2, were produced with smaller particle size, slower heating rate, and lower total flow rate. This work will guide to understand the combustion kinetics of pulverized coals and be beneficial to control the formation of pollutants

Semantic lifting and reasoning on the personalised activity big data repository for healthcare research

The fast growing markets of smart health monitoring devices and mobile applications provide opportunities for common citizens to have capability for understanding and managing their own health situations. However, there are many challenges for data engineering and knowledge discovery research to enable efficient extraction of knowledge from data that is collected from heterogonous devices and applications with big volumes and velocity. This paper presents research that initially started with the EC MyHealthAvatar project and is under continual improvement following the project’s completion. The major contribution of the work is a comprehensive big data and semantic knowledge discovery framework which integrates data from varied data resources. The framework applies hybrid database architecture of NoSQL and RDF repositories with introductions for semantic oriented data mining and knowledge lifting algorithms. The activity stream data is collected through Kafka’s big data processing component. The motivation of the research is to enhance the knowledge management, discovery capabilities and efficiency to support further accurate health risk analysis and lifestyle summarisation