FeAs-based superconductivity: a case study of the effects of transition metal doping on BaFe2As2

The recently discovered FeAs-based superconductors are a new, promising set of materials for both technological as well as basic research. They offer transition temperatures as high as 55 K as well as essentially isotropic and extremely large upper, superconducting critical fields in excess of 40 T at 20 K. In addition they may well provide insight into exotic superconductivity that extends beyond just FeAs-based superconductivity, perhaps even shedding light on the still perplexing CuO-based high-Tc materials. Whereas superconductivity can be induced in the RFeAsO (R = rare earth) and AEFe2As2 (AE = Ba, Sr, Ca)) families by a number of means, transition metal doping of BaFe2As2, e.g. Ba(Fe1-xTMx)2As2, offers the easiest experimental access to a wide set of materials. In this review we present an overview and summary of the effect of TM doping (TM = Co, Ni, Cu, Pd, and Rh) on BaFe2As2. The resulting phase diagrams reveal the nature of the interaction between the structural, magnetic and superconducting phase transitions in these compounds and delineate a region of phase space that allows for the stabilization of superconductivity.Comment: edited and shortened version is accepted to AR:Condensed Matter Physic

Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation

In recent years, many spatial and temporal satellite image fusion (STIF) methods have been developed to solve the problems of trade-off between spatial and temporal resolution of satellite sensors. This study, for the first time, conducted both scene-level and local-level comparison of five state-of-art STIF methods from four categories over landscapes with various spatial heterogeneity and temporal variation. The five STIF methods include the spatial and temporal adaptive reflectance fusion model (STARFM) and Fit-FC model from the weight function-based category, an unmixing-based data fusion (UBDF) method from the unmixing-based category, the one-pair learning method from the learning-based category, and the Flexible Spatiotemporal DAta Fusion (FSDAF) method from hybrid category. The relationship between the performances of the STIF methods and scene-level and local-level landscape heterogeneity index (LHI) and temporal variation index (TVI) were analyzed. Our results showed that (1) the FSDAF model was most robust regardless of variations in LHI and TVI at both scene level and local level, while it was less computationally efficient than the other models except for one-pair learning; (2) Fit-FC had the highest computing efficiency. It was accurate in predicting reflectance but less accurate than FSDAF and one-pair learning in capturing image structures; (3) One-pair learning had advantages in prediction of large-area land cover change with the capability of preserving image structures. However, it was the least computational efficient model; (4) STARFM was good at predicting phenological change, while it was not suitable for applications of land cover type change; (5) UBDF is not recommended for cases with strong temporal changes or abrupt changes. These findings could provide guidelines for users to select appropriate STIF method for their own applications

Crux, space constraints and subdivisions

For a given graph $H$, its subdivisions carry the same topological structure. The existence of $H$-subdivisions within a graph $G$ has deep connections with topological, structural and extremal properties of $G$. One prominent examples of such connections, due to Bollob\'{a}s and Thomason and independently Koml\'os and Szemer\'edi, asserts that the average degree of $G$ being $d$ ensures a $K_{\Omega(\sqrt{d})}$-subdivision in $G$. Although this square-root bound is best possible, various results showed that much larger clique subdivisions can be found in a graph from many natural classes. We investigate the connection between crux, a notion capturing the essential order of a graph, and the existence of large clique subdivisions. This reveals the unifying cause underpinning all those improvements for various classes of graphs studied. Roughly speaking, when embedding subdivisions, natural space constraints arise; and such space constraints can be measured via crux. Our main result gives an asymptotically optimal bound on the size of a largest clique subdivision in a generic graph $G$, which is determined by both its average degree and its crux size. As corollaries, we obtain (1) a characterisation of extremal graphs for which the square-root bound above is tight: they are essentially disjoint union of graphs each of which has the crux size linear in $d$; (2) a unifying approach to find a clique subdivision of almost optimal size in graphs which does not contain a fixed bipartite graph as a subgraph; (3) and that the clique subdivision size in random graphs $G(n,p)$ witnesses a dichotomy: when $p = \omega(n^{-1/2})$, the barrier is the space, while when $p=o( n^{-1/2})$, the bottleneck is the density.Comment: 37 pages, 2 figure

Transcriptional adaptation of Mycobacterium tuberculosis within macrophages: Insights into the phagosomal environment

Little is known about the biochemical environment in phagosomes harboring an infectious agent. To assess the state of this organelle we captured the transcriptional responses of Mycobacterium tuberculosis (MTB) in macrophages from wild-type and nitric oxide (NO) synthase 2–deficient mice before and after immunologic activation. The intraphagosomal transcriptome was compared with the transcriptome of MTB in standard broth culture and during growth in diverse conditions designed to simulate features of the phagosomal environment. Genes expressed differentially as a consequence of intraphagosomal residence included an interferon � – and NO-induced response that intensifies an iron-scavenging program, converts the microbe from aerobic to anaerobic respiration, and induces a dormancy regulon. Induction of genes involved in the activation and �-oxidation of fatty acids indicated that fatty acids furnish carbon and energy. Induction of �E-dependent, sodium dodecyl sulfate–regulated genes and genes involved in mycolic acid modification pointed to damage and repair of the cell envelope. Sentinel genes within the intraphagosomal transcriptome were induced similarly by MTB in the lungs of mice. The microbial transcriptome thus served as a bioprobe of the MTB phagosomal environment

Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data

For a long time, researchers have tried to find a way to analyze tropical cyclone (TC) intensity in real-time. Since there is no standardized method for estimating TC intensity and the most widely used method is a manual algorithm using satellite-based cloud images, there is a bias that varies depending on the TC center and shape. In this study, we adopted convolutional neural networks (CNNs) which are part of a state-of-art approach that analyzes image patterns to estimate TC intensity by mimicking human cloud pattern recognition. Both two dimensional-CNN (2D-CNN) and three-dimensional-CNN (3D-CNN) were used to analyze the relationship between multi-spectral geostationary satellite images and TC intensity. Our best-optimized model produced a root mean squared error (RMSE) of 8.32 kts, resulting in better performance (~35%) than the existing model using the CNN-based approach with a single channel image. Moreover, we analyzed the characteristics of multi-spectral satellite-based TC images according to intensity using a heat map, which is one of the visualization means of CNNs. It shows that the stronger the intensity of the TC, the greater the influence of the TC center in the lower atmosphere. This is consistent with the results from the existing TC initialization method with numerical simulations based on dynamical TC models. Our study suggests the possibility that a deep learning approach can be used to interpret the behavior characteristics of TCs

Energy performance evaluation of a recycled water heat pump system in cool and dry climate zone

This paper presents performance evaluation results for a recycled water heat pump (RWHP) system, which uses the recycled water from a municipal water system as a heat sink and heat source for heat pumps. The temperature of the recycled water, system heat flow, and efficiency were analyzed based on measured data from December 2014 through August 2015. The annual energy consumption of the RWHP system was compared with that of a baseline system-a conventional variable-air-volume system using a water-cooled chiller and a natural gas-fired boiler, both of which meet the minimum energy efficiencies allowed by ASHRAE 90.1-2013. The analysis results indicate that, on an annual basis, the RWHP system has avoided 50% of source energy consumption, reduced CO2 emissions by 41%, and saved 34% in energy costs compared with the baseline system

The Emerging Role of Robotics in Home Health Care

The demand for health care continues to exceed the supply of affordable, accessible care due in large part to the rapidly aging baby boomer population. The advancing field of robotics can provide an effective solution to this problem. This study aimed to develop a set of user requirements for a personal home health care robot. To generate these requirements, we conducted nine interviews with robotics professionals and three different focus groups with current and future caregivers and the elderly. Using this data we identified prominent and desired functionalities of robots, as well as what may influence their acceptance into the home setting. Our findings indicate that monitoring robots have the biggest acceptance potential among elderly and caregivers

Layer thickness dependence of the current induced effective field vector in Ta|CoFeB|MgO

The role of current induced effective magnetic field in ultrathin magnetic heterostructures is increasingly gaining interest since it can provide efficient ways of manipulating magnetization electrically. Two effects, known as the Rashba spin orbit field and the spin Hall spin torque, have been reported to be responsible for the generation of the effective field. However, quantitative understanding of the effective field, including its direction with respect to the current flow, is lacking. Here we show vector measurements of the current induced effective field in Ta|CoFeB|MgO heterostructrures. The effective field shows significant dependence on the Ta and CoFeB layers' thickness. In particular, 1 nm thickness variation of the Ta layer can result in nearly two orders of magnitude difference in the effective field. Moreover, its sign changes when the Ta layer thickness is reduced, indicating that there are two competing effects that contribute to the effective field. The relative size of the effective field vector components, directed transverse and parallel to the current flow, varies as the Ta thickness is changed. Our results illustrate the profound characteristics of just a few atomic layer thick metals and their influence on magnetization dynamics

GaN micro-light-emitting diode arrays with monolithically integrated sapphire microlenses

A microdisk light emitting diode (micro-LED) with a monolithically integrated microlens array was demonstrated. The capability of the lenses in concentrating light emitted from microdisk LEDs was also demonstrated. The focal lengths of the microlenses were determined to be around 44 νm. The emission pattern of the LED emitters was found to be altered by the optical properties of the microlenses. The light emitted by the hybrid device was also found to be less divergent than a broad-area device.published_or_final_versio