Simultaneous dual-frequency radio observations of S5 0716+714: A search for intraday variability with the Korean VLBI Network

This study aims to search for the existence of intraday variability (IDV) of BL Lac object S5 0716+714 at high radio frequencies for which the interstellar scintillation effect is not significant. Using the 21-meter radio telescope of the Korean VLBI Network (KVN), we present results of multi-epoch simultaneous dual-frequency radio observations. Single-dish observations of S5 0716+714 were simultaneously conducted at 21.7 GHz (K-band) and 42.4 GHz (Q-band), with a high cadence of 30-60 minute intervals.We observed four epochs between December 2009 and June 2010. Over the whole set of observation epochs, S5 0716+714 showed significant inter-month variations in flux density at both the K- and Q-bands, with modulation indices of approximately 19% for the K-band and approximately 36% for the Q-band. In all epochs, no clear intraday variability was detected at either frequency. The source shows monotonic flux density increase in epochs 1 and 3 and monotonic flux density decrease in epochs 2 and 4. In the flux density increasing phases, the flux densities at the Q-band increase more rapidly. In the decreasing phase, no significant flux density difference is seen at the two frequencies. The situation could be different close to flux density peaks that we did not witness in our observations. We find an inverted spectrum with mean spectral indices of -0.57+-0.13 in epoch 1 and -0.15+-0.11 in epoch 3. On the other hand, we find relatively steep indices of +0.24+-0.14 and +0.17+-0.18 in epochs 2 and 4, respectively. We conclude that the frequency dependence of the variability and the change of the spectral index are caused by source-intrinsic effects rather than by any extrinsic scintillation effect.Comment: 6 pages and 4 figures and 4 table

CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network

With recent advances in DNA sequencing technologies, fast acquisition of large-scale genomic data has become commonplace. For cancer studies, in particular, there is an increasing need for the classification of cancer type based on somatic alterations detected from sequencing analyses. However, the ever-increasing size and complexity of the data make the classification task extremely challenging. In this study, we evaluate the contributions of various input features, such as mutation profiles, mutation rates, mutation spectra and signatures, and somatic copy number alterations that can be derived from genomic data, and further utilize them for accurate cancer type classification. We introduce a novel ensemble of machine learning classifiers, called CPEM (Cancer Predictor using an Ensemble Model), which is tested on 7,002 samples representing over 31 different cancer types collected from The Cancer Genome Atlas (TCGA) database. We first systematically examined the impact of the input features. Features known to be associated with specific cancers had relatively high importance in our initial prediction model. We further investigated various machine learning classifiers and feature selection methods to derive the ensemble-based cancer type prediction model achieving up to 84% classification accuracy in the nested 10-fold cross-validation. Finally, we narrowed down the target cancers to the six most common types and achieved up to 94% accuracy

Determination of Refrigerant Path Number for Fin-tube Condenser Considering Heat Transfer Performance and Pumping Power

Fin-tube heat exchangers are widely used in air-conditioners and heat pumps, which are constructed with a lot of tubes. Refrigerant circuit of heat exchanger with numerous pipe can be constructed by many methods. Refrigerant circuit design is usually determined designer’s experience and case by case test without guides. The number of path affects largely on heat exchanger performance. In this paper, design methodology for optimum number of path is suggested by relating convective thermal resistance and pumping power. Suggested methodology is described through an example and verified by various refrigerant circuit simulation results

The Influence of Consumer Experiences on Store Choice Criteria and Patronage Intention: the Case Study of SPA brands

Based on an array of economic and social data as well as business trends, Pine and Gilmore (1999) agree that current consumers are concerned with engaging experiences rather than just buying goods and services. Pine and Gilmore (1999) conceptualized this new focus of consumer demand as the emerging “Experience Economy” (EE). This perspective views experiences as enhancing consumer value beyond that derived from goods and services. Pine and Gilmore (1999) proposed four experience realms of EE—entertainment, educational, escapist, and esthetic

The Progress of PVDF as a Functional Material for Triboelectric Nanogenerators and Self-Powered Sensors

Ever since a new energy harvesting technology, known as a triboelectric nanogenerator (TENG), was reported in 2012, the rapid development of device fabrication techniques and mechanical system designs have considerably made the instantaneous output power increase up to several tens of mW/cm(2). With this innovative technology, a lot of researchers experimentally demonstrated that various portable/wearable devices could be operated without any external power. This article provides a comprehensive review of polyvinylidene fluoride (PVDF)-based polymers as effective dielectrics in TENGs for further increase of the output power to speed up commercialization of the TENGs, as well as the fundamental issues regarding the materials. In the end, we will also review PVDF-based sensors based on the triboelectric and piezoelectric effects of the PVDF polymers