Schwarzschild-de Sitter Metric and Inertial Beltrami Coordinates

Under consideration of coordinate conditions, we get the Schwarzschild-Beltrami-de Sitter (S-BdS) metric solution of the Einstein field equations with a cosmological constant $\Lambda$. A brief review to the de Sitter invariant special relativity (dS-SR), and de Sitter general relativity (dS-GR, or GR with a $\Lambda$) is presented. The Beltrami metric $B_{\mu\nu}$ provides inertial reference frame for the dS-spacetime. By examining the Schwarzschild-de Sitter (S-dS) metric $g_{\mu\nu}^{(M)}$ existed in literatures since 1918, we find that the existed S-dS metric $g_{\mu\nu}^{(M)}$ describes some mixing effects of gravity and inertial-force, instead of a pure gravity effect arisen from "solar mass" $M$ in dS-GR. In this paper, we solve the vacuum Einstein equation of dS-GR, with the requirement of gravity-free metric $g_{\mu\nu}^{(M)}|_{M\rightarrow 0}=B_{\mu\nu}$. In this way we find S-BdS solution of dS-GR, written in inertial Beltrami coordinates. This is a new form of S-dS metric. Its physical meaning and possible applications are discussed.Comment: 16 pages, 1 figur

A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data

Background: In the studies of genomics, it is essential to select a small number of genes that are more significant than the others for the association studies of disease susceptibility. In this work, our goal was to compare computational tools with and without feature selection for predicting chronic fatigue syndrome (CFS) using genetic factors such as single nucleotide polymorphisms ( SNPs). Methods: We employed the dataset that was original to the previous study by the CDC Chronic Fatigue Syndrome Research Group. To uncover relationships between CFS and SNPs, we applied three classification algorithms including naive Bayes, the support vector machine algorithm, and the C4.5 decision tree algorithm. Furthermore, we utilized feature selection methods to identify a subset of influential SNPs. One was the hybrid feature selection approach combining the chi-squared and information-gain methods. The other was the wrapper- based feature selection method. Results: The naive Bayes model with the wrapper-based approach performed maximally among predictive models to infer the disease susceptibility dealing with the complex relationship between CFS and SNPs. Conclusion: We demonstrated that our approach is a promising method to assess the associations between CFS and SNPs

Some Results for a Finite Family of Uniformly -Lipschitzian Mappings in Banach Spaces

The purpose of this paper is to prove a strong convergence theorem for a finite family of uniformly L-Lipschitzian mappings in Banach spaces. The results presented in the paper not only correct some mistakes appeared in the paper by Ofoedu (2006) but also improve and extend some recent results by Chang (2001), Cho et al. (2005), Ofoedu (2006), Schu (1991), and Zeng (2003, 2005)

Conditioning of BPM pickup signals for operations of the Duke storage ring with a wide range of single-bunch current

The Duke storage ring is a dedicated driver for the storage ring based oscillator free-electron lasers (FELs), and the High Intensity Gamma-ray Source (HIGS). It is operated with a beam current ranging from about 1 mA to 100 mA per bunch for various operations and accelerator physics studies. High performance operations of the FEL and gamma-ray source require a stable electron beam orbit, which has been realized by the global orbit feedback system. As a critical part of the orbit feedback system, the electron beam position monitors (BPMs) are required to be able to precisely measure the electron beam orbit in a wide range of the single-bunch current. However, the high peak voltage of the BPM pickups associated with high single-bunch current degrades the performance of the BPM electronics, and can potentially damage the BPM electronics. A signal conditioning method using low pass filters is developed to reduce the peak voltage to protect the BPM electronics, and to make the BPMs capable of working with a wide range of single-bunch current. Simulations and electron beam based tests are performed. The results show that the Duke storage ring BPM system is capable of providing precise orbit measurements to ensure highly stable FEL and HIGS operations

Stimulatory Effects of Androgens on Eel Primary Ovarian Development - from Phenotypes to Genotypes

Androgens stimulate primary ovarian development in Vertebrate. Japanese eels underwent operation to sample the pre- and post-treated ovarian tissues from the same individual. Ovarian phenotypic or genotypic data were mined in a pair. A correlation between the initial ovarian status (determined by kernel density estimation (KDE), presented as a probability density of oocyte size) and the consequence of androgen (17MT) treatment (change in ovary) has been showed. The initial ovarian status appeared to be important to influence ovarian androgenic sensitivity. The initial ovary was important to the outcomes of androgen treatments, and ePAV (expression presence-absence variation) is existing in Japanese eel by analyze DEGs; core, unique, or accessory genes were identified, the sensitivities of initial ovaries were correlated with their gene expression profiles. We speculated the importance of genetic differential expression on the variations of phenotypes by 17MT, and transcriptomic approach seems to allow extracting multiple layers of genomic data

Modeling Air Handling Units to Create a Diverse Fault Dataset for FDD Innovation: Lessons Learned and Recommendations

As energy management and information systems (e.g., automated fault detection and diagnostics [AFDD] tools) become more prevalent in the commercial building stock, it is important to determine the effectiveness of these technologies by benchmarking their performance. The authors have been working to develop the largest publicly available dataset of HVAC fault datasets for performance benchmarking applications, covering the most common HVAC systems and designs including chiller plants, rooftop packaged units, dual duct air handling unit and single duct air handling units. This study covers the development, modeling, and validation of a synthetic fault dataset for the air handling unit (AHU), one of the most common HVAC configurations found in the commercial building stock. Despite this being a common system, real-world time series data are scarce and usually do not span a wide range of weather conditions. Due to this limitation, two detailed AHU models, which included the single duct AHU and dual duct AHU developed in the Modelica language and HVACSIM+ were employed to carry out annual simulations of numerous common sensor faults, mechanical faults, and control sequence faults. The fault inclusive data were then validated by comparing fault effects on system performance to expected symptoms. We summarize the nature of each fault and their impacts under different weather and operation conditions. We report some lessons learnt during the efforts of validating the high volumes of the FDD data sets. Finally, we highlight considerations for FDD developers that may want to use this dataset to assess their algorithms’ performance and their improvement over time

On determination of the geometric cosmological constant from the OPERA experiment of superluminal neutrinos

The recent OPERA experiment of superluminal neutrinos has deep consequences in cosmology. In cosmology a fundamental constant is the cosmological constant. From observations one can estimate the effective cosmological constant $\Lambda_{eff}$ which is the sum of the quantum zero point energy $\Lambda_{dark energy}$ and the geometric cosmological constant $\Lambda$. The OPERA experiment can be applied to determine the geometric cosmological constant $\Lambda$. It is the first time to distinguish the contributions of $\Lambda$ and $\Lambda_{dark energy}$ from each other by experiment. The determination is based on an explanation of the OPERA experiment in the framework of Special Relativity with de Sitter space-time symmetry.Comment: 7 pages, no figure

Ascitic Fluid Analysis in the Differential Diagnosis of Ascites: Focus on Cirrhotic Ascites

Abstract Ascites is the pathologic accumulation of fluid within the peritoneal cavity. Because many diseases can cause ascites, in particular cirrhosis, samples of ascitic fluid are commonly analyzed in order to develop a differential diagnosis. The concept of transudate versus exudate, as determined by total protein measurements, is outdated and the use of serumascites albumin gradient as an indicator of portal hypertension is more accurate. Lactate dehydrogenase (LDH), vascular endothelial growth factor (VEGF), and other tumor markers can be helpful in distinguishing between malignant and benign conditions. Glucose and adenosine deaminase levels may support a diagnosis of tuberculous disease, and amylase level may indicate a diagnosis of pancreatitis. Given the specificity and sensitivity of laboratory results, accurate diagnosis should be based on both laboratory data and clinical judgment