LAMP: a micro-satellite based soft X-ray polarimeter for astrophysics

The Lightweight Asymmetry and Magnetism Probe (LAMP) is a micro-satellite mission concept dedicated for astronomical X-ray polarimetry and is currently under early phase study. It consists of segmented paraboloidal multilayer mirrors with a collecting area of about 1300 cm^2 to reflect and focus 250 eV X-rays, which will be detected by position sensitive detectors at the focal plane. The primary targets of LAMP include the thermal emission from the surface of pulsars and synchrotron emission produced by relativistic jets in blazars. With the expected sensitivity, it will allow us to detect polarization or place a tight upper limit for about 10 pulsars and 20 blazars. In addition to measuring magnetic structures in these objects, LAMP will also enable us to discover bare quark stars if they exist, whose thermal emission is expected to be zero polarized, while the thermal emission from neutron stars is believed to be highly polarized due to plasma polarization and the quantum electrodynamics (QED) effect. Here we present an overview of the mission concept, its science objectives and simulated observational results

Comparative Genomics Study of Multi-Drug-Resistance Mechanisms in the Antibiotic-Resistant Streptococcus suis R61 Strain

BACKGROUND: Streptococcus suis infections are a serious problem for both humans and pigs worldwide. The emergence and increasing prevalence of antibiotic-resistant S. suis strains pose significant clinical and societal challenges. RESULTS: In our study, we sequenced one multi-drug-resistant S. suis strain, R61, and one S. suis strain, A7, which is fully sensitive to all tested antibiotics. Comparative genomic analysis revealed that the R61 strain is phylogenetically distinct from other S. suis strains, and the genome of R61 exhibits extreme levels of evolutionary plasticity with high levels of gene gain and loss. Our results indicate that the multi-drug-resistant strain R61 has evolved three main categories of resistance. CONCLUSIONS: Comparative genomic analysis of S. suis strains with diverse drug-resistant phenotypes provided evidence that horizontal gene transfer is an important evolutionary force in shaping the genome of multi-drug-resistant strain R61. In this study, we discovered novel and previously unexamined mutations that are strong candidates for conferring drug resistance. We believe that these mutations will provide crucial clues for designing new drugs against this pathogen. In addition, our work provides a clear demonstration that the use of drugs has driven the emergence of the multi-drug-resistant strain R61

Research Article Several Existence Theorems of Nonlinear m-Point BVP for an Increasing Homeomorphism and Homomorphism on Time Scales

Several existence theorems of positive solutions are established for nonlinear m-point boundary value problem for the following dynamic equations on time scales �φ�uΔ� � ∇ � a�t�f�t, u�t� � � 0, t ∈ �0,T�, φ�uΔ�0� � � ∑m−2 i�1 aiφ�uΔ�ξi��, u�T � � ∑m−2 i�1 biu�ξi�, where φ: R→R is an increasing homeomorphism and homomorphism and φ�0 � �0. As an application, an example to demonstrate our results is given. Copyright q 2008 Yanbin Sang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1

A Hybrid of NARX and Moving Average Structures for Exhaust Gas Temperature Prediction of Gas Turbine Engines

Aiming at engine health management, a novel hybrid prediction method is proposed for exhaust gas temperature (EGT) prediction of gas turbine engines. This hybrid model combines a nonlinear autoregressive with exogenous input (NARX) model and a moving average (MA) model. A feature attention mechanism-enhanced long short-term memory network (FAE-LSTM) is first developed to construct the NARX model, which is used for identifying the aircraft engine using condition parameters and gas path measurement parameters that correlate to the EGT. A vanilla LSTM is then used for constructing the MA model, which is used for improving the difference between the actual EGT and the predicted EGT given by the NARX model. The proposed method is evaluated using real flight process data and compared to several dynamic prediction techniques. The results show that our hybrid model reduces the predicted RMSE and MAE by at least 13.23% and 18.47%, respectively. The developed FAE-LSTM network can effectively deal with dynamic data. Overall, the present work demonstrates a promising performance and provides a positive guide for predicting engine parameters