Indigenous and Non-Indigenous Paraprofessionals: An Empirical Comparison

This paper provides a comparative description of indigenous and nonindigenous paraprofessionals who were employed in a social service capacity in a large urban setting. Personal interviews were conducted with 88 paraprofessionals employed by the Fulton County Department of Family and Children Servives (Atlanta, Ga.). The primary variables discussed include an assessment of the respondent\u27s background, their present employment situation, experience with and attitudes toward welfare and general attitudinal measures. The results provide a basic demographic profile of the indigenous and non-indigenous paraprofessional and indicate their differing characteristics. Briefly, the indigenous respondents were less anomic, felt more efficacious in terms of helping clients, had a less favorable stance toward welfare, had less training and were considerably more more satisfied with their job than were their non-indigenous counterparts. Additionally, the implications of these findings and considerations which need to be explored in future research are discussed

GRAPHENE: A Precise Biomedical Literature Retrieval Engine with Graph Augmented Deep Learning and External Knowledge Empowerment

Effective biomedical literature retrieval (BLR) plays a central role in precision medicine informatics. In this paper, we propose GRAPHENE, which is a deep learning based framework for precise BLR. GRAPHENE consists of three main different modules 1) graph-augmented document representation learning; 2) query expansion and representation learning and 3) learning to rank biomedical articles. The graph-augmented document representation learning module constructs a document-concept graph containing biomedical concept nodes and document nodes so that global biomedical related concept from external knowledge source can be captured, which is further connected to a BiLSTM so both local and global topics can be explored. Query expansion and representation learning module expands the query with abbreviations and different names, and then builds a CNN-based model to convolve the expanded query and obtain a vector representation for each query. Learning to rank minimizes a ranking loss between biomedical articles with the query to learn the retrieval function. Experimental results on applying our system to TREC Precision Medicine track data are provided to demonstrate its effectiveness.Comment: CIKM 201

Month-Timescale Optical Variability in the M87 Jet

A previously inconspicuous knot in the M87 jet has undergone a dramatic outburst and now exceeds the nucleus in optical and X-ray luminosity. Monitoring of M87 with the Hubble Space Telescope and Chandra X-ray Observatory during 2002-2003, has found month-timescale optical variability in both the nucleus and HST-1, a knot in the jet 0.82'' from the nucleus. We discuss the behavior of the variability timescales as well as spectral energy distribution of both components. In the nucleus, we see nearly energy-independent variability behavior. Knot HST-1, however, displays weak energy dependence in both X-ray and optical bands, but with nearly comparable rise/decay timescales at 220 nm and 0.5 keV. The flaring region of HST-1 appears stationary over eight months of monitoring. We consider various emission models to explain the variability of both components. The flares we see are similar to those seen in blazars, albeit on longer timescales, and so could, if viewed at smaller angles, explain the extreme variability properties of those objects.Comment: 4 pages, 3 figures, ApJ Lett., in pres

Low Mass Printable Devices for Energy Capture, Storage, and Use for Space Exploration Missions

The energy-efficient, environmentally friendly technology that will be presented is the result of a Space Act Agreement between -Technologies Worldwide, Inc., and the National Aeronautics and Space Administration s (NASA s) Marshall Space Flight Center (MSFC). This work combines semiconductor and printing technologies to advance lightweight electronic and photonic devices having excellent potential for commercial and exploration applications, and is an example of industry and government cooperation that leads to novel inventions. Device development involves three energy generation and consumption projects: 1) a low mass efficient (low power, low heat emission) micro light-emitting diode (LED) area lighting device; 2) a low-mass omni-directional efficient photovoltaic (PV) device with significantly improved energy capture; and 3) a new approach to building supercapacitors. These three technologies - energy capture, storage, and usage (e.g., lighting) - represent a systematic approach for building efficient local micro-grids that are commercially feasible; furthermore, these same technologies will be useful for lightweight power generation that enables inner planetary missions using smaller launch vehicles and facilitates surface operations. The PV device model is a two-sphere, light-trapped sheet approximately 2-mm thick. The model suggests a significant improvement over current thin film systems. All three components may be printed in line by printing sequential layers on a standard screen or flexographic direct impact press using the threedimensional printing technique (3DFM) patented by NthDegree. MSFC is testing the robustness of prototype devices in the harsh space and lunar surface environments, and available results will be reported. Unlike many traditional light sources, this device does not contain toxic compounds, and the LED component has passed stringent off-gassing tests required for potential manifesting on spacecraft such as the International Space Station. Future exploration missions will benefit from "green" technology lighting devices such as this, which show great promise for both terrestrial use and space missions

Experimental Demonstration of Time-Delay Interferometry for the Laser Interferometer Space Antenna

We report on the first demonstration of time-delay interferometry (TDI) for LISA, the Laser Interferometer Space Antenna. TDI was implemented in a laboratory experiment designed to mimic the noise couplings that will occur in LISA. TDI suppressed laser frequency noise by approximately 10^9 and clock phase noise by 6x10^4, recovering the intrinsic displacement noise floor of our laboratory test bed. This removal of laser frequency noise and clock phase noise in post-processing marks the first experimental validation of the LISA measurement scheme.Comment: 4 pages, 4 figures, to appear in Physical Review Letters end of May 201

Effects of Virtual Reality During Rowing Ergometry on Metabolic and Performance Parameters

Physical activity and moderate or intense exercise improve musculoskeletal and metabolic health; however, approximately 80% of Americans do not meet the minimum exercise recommendations from the American College of Sports Medicine (ACSM) or the Centers for Disease Control (CDC). Exercise intensity may be the most important factor in eliciting positive physical outcomes with exercise. PURPOSE: To assess the effectiveness of a proprietary virtual reality (VR) interface to increase metabolic and physical performance during rowing ergometry. METHODS: A novel VR software program for rowing ergometry was developed. Subsequently, sixteen apparently healthy, recreationally active individuals (12M, 4F; 35.5 ± 13.9 y; 174.5 ± 10.1 cm; 80.4 ± 12.8 kg; VO2max: 38.1 ± 5.6 mL/kg/min) were familiarized with the rowing ergometer and VR software, and then completed a VO2max test during two separate sessions. Finally, subjects performed four, 30-min rowing sessions in a randomized, counterbalanced order at maximal voluntary intensity in four different conditions: 1) no augmented visual or audio stimuli (CON), 2) no augmented visual stimuli with self-selected music (MUS), 3) screen-based environmental display (SB), and 4) a virtual reality environment (VR). Oxygen consumption, ventilation, heart rate, and the respiratory exchange ratio (RER) were measured continuously during the four experimental sessions; these data were then averaged over each 30-min testing period. Power output (W) and distance rowed (m) were measured and similarly reduced. Data (mean ± SD) were analyzed by repeated measures ANOVA and appropriate Tukey’s post hoc tests. Alpha was set at P \u3c 0.05. RESULTS: Oxygen consumption (CON: 2.23 ± 0.63 L/min; MUS: 2.30 ± 0.63 L/min; SB: 2.23 ± 0.71 L/min; VR: 2.19 ± 0.69 L/min), ventilation (CON: 74.2 ± 21.0 L/min; MUS: 77.5 ± 20.5 L/min; SB: 73.4 ± 23.9 L/min; VR: 71.7 ± 23.8 L/min), heart rate (CON: 154 ± 16 bpm; MUS: 156 ± 17 bpm; SB: 152 ± 23 bpm; VR: 154 ± 17 bpm), and RER (CON: 0.94 ± 0.04; MUS: 0.95 ± 0.04; SB: 0.94 ± 0.04; VR: 0.93 ± 0.05) were not different between conditions (all P \u3e 0.05). Performance outcomes also did not differ between conditions (CON: 126 ± 40 W, 6337 ± 763 m; MUS: 130 ± 42 W, 6486 ± 617 m; SB: 128 ± 46 W, 6358 ± 862 m; VR: 124 W ± 44 W, 6294 ± 849 m; all P \u3e 0.05). CONCLUSION: The pilot version of the VR software for rowing ergometry did not increase voluntary effort as determined by metabolic or physical performance outputs. Added features, such as greater immersion for reluctant exercisers, and competitive elements for highly motivated individuals, may elicit greater voluntary exertion with VR in rowing ergometry. Moreover, such applications may be more beneficial and improve exercise enjoyment in less experienced exercises who are not accustomed to high exercise intensities

A comparison of different optical instruments and machine learning techniques to identify sprouting activity in potatoes during storage

The quality of potato tubers is dependent on several attributes been maintained at appropriate levels during storage. One of these attributes is sprouting activity that is initiated from meristematic regions of the tubers (eyes). Sprouting activity is a major problem that contributes to reduced shelf life and elevated sugar content, which affects the marketability of seed tubers as well as fried products. This study compared the capabilities of three different optical systems (1: visible/near-infrared (Vis/NIR) interactance spectroscopy, 2: Vis/NIR hyperspectral imaging, 3: NIR transmittance) and machine learning methods to detect sprouting activity in potatoes based on the primordial leaf count (LC). The study was conducted on Frito Lay 1879 and Russet Norkotah cultivars stored at different temperatures and classification models were developed that considered both cultivars combined and classified the tubers as having either high or low sprouting activity. Measurements were performed on whole tubers and sliced samples to see the effect this would have on identifying sprouting activity. Sequential forward selection was applied for wavelength selection and the classification was carried out using K-nearest neighbor, partial least squares discriminant analysis, and soft independent modeling class analogy. The highest classification accuracy values obtained by the hyperspectral imaging system and was 87.5% and 90% for sliced and whole samples, respectively. Data fusion did not show classification improvement for whole tubers, whereas a 7.5% classification accuracy increase was illustrated for sliced samples. By investigating different optical techniques and machine learning methods, this study provides a first step toward developing a handheld optical device for early detection of sprouting activity, enabling advanced aid potato storage management