Miracle-dz/UniFAR-FTUs huggingface dataset:
Job Satisfaction of Nurse Educators in a Collaborative Baccalaureate Nursing Program in Western Canada.

A study identified the factors that influenced the job satisfaction of nurse educators involved in a change to a collaborative baccalaureate nursing program in the province of Manitoba, Canada. The total population of nurse educators from both hospital-based and university-based institutions (n=42) was survved. Data were collected using an investigator-designed questionnaire and the Work Environment Scale. Descriptive statistics were esed to present the characteristics and associations; independent t-tests measured the differences between group means. Educators in the collaborative baccalaureate nursing program in Manitoba had a positive perception of the organizational climate and identified an increased level of job satisfaction. Respondents expressed concerns about equity between groups in relation to workload, employment benefits, role expectations, and power in decision making. Hospital-based faculty stressed job security, and university-based faculty emphasized a desire for improved peer relationships and supportive leadership. Improving communication mechanisms was identified as a way to expedite joint planning, foster understanding and respect, and enhance perceptions of equity. Recommendations based on th,1 results were made for nursing leaders, political and postsecondary leaders, and health and education researchers. (Appendixes include 126 references, instruments, and correspondence.) (Y LB) *********************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. ***********************************************************************
COMMUNICATING TRUST ACROSS DISTANCE – EMPIRICAL STUDY ON TRUST IN RELATIONSHIP DEVELOPMENT THROUGH TECHNOLOGY-MEDIATED COMMUNICATION OF TWO VIRTUAL TEAMS IN THE ICT SECTOR

Trust has been identified as a key factor leading to successful relationship development in virtual teams. This study concentrates on trust and the challenges and possibilities in building trust through technology-mediated communication in virtual teams focussing on the creation stages of virtual team development. In order to understand the mechanisms and dynamics in trust building two case studies in virtual teams were conducted. The results of the two case studies show that different types of communication behaviour and member actions facilitated trust at different stages of the virtual team’s development. Theoretical contribution of the paper lies mainly in elaborating the role of virtual trust in different stages of the virtual team creation. The study captures some of the dynamics both in virtual team development and trust building, as well as the different role and nature of trust in different stages.
X-ray off states and optical variability in CAL 83

CAL 83 was one of the rst supersoft X-ray binaries (SSBs) to be discovered and is considered to be the prototype of its class. In 15 X-ray observations between 1983{1997 it was observed to have nearly constant X-ray luminosity and temperature, with the exception of one o-state in 1996 (Kahabka et al. 1996). We report on a second X-ray o-state, discovered with a Chandra ACIS-S observation in November 1999. We consider the long-term X-ray and MACHO optical light curves. We nd that, during more than 7 years of monitoring by the MACHO team, CAL 83 has exhibited distinct and well-dened low, intermediate, and high optical states. Transitions between states are not accompanied by color variations. We also nd that both X-ray o states were observed during optical high states and were followed by optical low states within50 days. We discuss possible explanations for the observed optical and X-ray variations. While photospheric adjustments might account for the variations in soft X-ray flux, optical variations can be explained only by invoking changes in the accretion disk, which is the primary source of optical radiation.
Baicalin exerts protective effects against lipopolysaccharide-induced acute lung injury by regulating the crosstalk between the CX3CL1-CX3CR1 axis and NF-κB pathway in CX3CL1-knockout mice

Acute lung injury (ALI) as a serious diseases with high mortality and is considered a threat to human health and life. A number of studies have focused on the treatment and prevention of lung injury. However, the molecular mechanisms responsible for the development of lung injury are not yet fully understood, and this has impeded the development of effective drugs and treatment strategies. Hence, in the present study, mice with lipopolysaccharide (LPS)-induced ALI were used as a model to investigate the crosstalk between the CX3CL1-CX3CR1 axis and the nuclear factor (NF)-κB signaling pathway in the process of lung injury. CX3CL1-knockout (CX3CL1-KO or CX3CL1−/−) mice were used to examine the role of the CX3CL1-CX3CR1 axis in LPS-induced lung injury. We used baicalin, a natural product, to investigate its anti-inflammatory effects and its protective effects against lung injury. Western blot analysis, reverse transcription-quantitavie PCR (RT-qPCR), immunohistochemistry, enzyme-linked immunosorbent assay (ELISA) and the analysis of biochemical indicators were used to determine the key signaling pathway involved in the development of lung injury. The results indicated that, on the one hand, baicalin exerted potent anti-inflammatory effects by inhibiting the activation of the CX3CL1-CX3CR1 axis and NF-κB, thus preventing the the crosstalk between the CX3CL1-CX3CR1 axis and NF-κB pathway. In addition, the phosphorylation of AKT, which was significantly induced by LPS-induced ALI through the CX3CL1-CX3CR1 axis, was inhibited by treatment with baicalin. On the other hand, we further investigated the role of the CX3CL1-CX3CR1 axis in lung injury. We determined the diffrences in the expression levels of CX3CR1 between wild-type (WT) and CX3CL1−/− mice in order to establish its association with lung injury. Our results indicated that CX3CL1 may be the central and major indicator in the process of lung injury, which mediates the CX3CR1 receptor to activate AKT and further promote NF-κB activation. These findings demonstrate that the crosstalk between the CX3CL1-CX3CR1 axis and NF-κB signaling pathway plays a direct role in LPS-induced lung injury. The inhibition of the activation of the CX3CL1-CX3CR1 axis may thus suppress the development of ALI. In addition, baicalin inhibited the crosstalk between the CX3CL1-CX3CR1 axis and NF-κB pathway in mice with LPS-induced ALI. Thus, treatment with baicalin may be a potential therapeutic strategy for ALI.
Digital Neural Network Implementations

This chapter gives an overview of existing digital VLSI implementations and discusses techniques for implementing high performance, high capacity digital neural nets. It presents a set of techniques for estimating chip area, performance, and power consumption in the early stages of design to facilitate architectural exploration. It shows how technology scaling rules can be included in the estimation process. It presents a set of basic building blocks useful in implementing digital networks. It then uses the estimation techniques to predict capacity and performance of a variety of digital architectures. Finally, it discusses implementation strategies for very large networks.
Market Index and Stock Price Direction Prediction using Machine Learning Techniques: An empirical study on the KOSPI and HSI

The prediction of a stock market direction may serve as an early recommendation system for short-term investors and as an early financial distress warning system for long-term shareholders. In this paper, we propose an empirical study on the Korean and Hong Kong stock market with an integrated machine learning framework that employs Principal Component Analysis (PCA) and Support Vector Machine (SVM). We try to predict the upward or downward direction of stock market index and stock price. In the proposed framework, PCA, as a feature selection method, identifies principal components in the stock market movement and SVM, as a classifier for future stock market movement, processes them along with other economic factors in training and forecasting. We present the results of an extensive empirical study of the proposed method on the Korean composite stock price index (KOSPI) and Hangseng index (HSI), as well as the individual constituents included in the indices. In our experiment, ten years data (from January 1st, 2002 to January 1st, 2012) are collected and schemed by rolling windows to predict one-day-ahead directions. The experimental results show notably high hit ratios in predicting the movements of the individual constituents in the KOSPI and HSI. The results also varify the \textit{co-movement} effect between the Korean (Hong Kong) stock market and the American stock market.
Improvement of Phytase Activity by a New Saccharomyces cerevisiae Strain Using Statistical Optimization

Using statistical optimization, we enhanced the activity of phytase by a new Saccharomyces cerevisiae strain cultured in mineral medium. Concentrations of carbon source and inducer of phytase production were optimized using a 22 full factorial CCD and response surface methodology (RSM). Urea was fixed as nitrogen source in culture medium (0.15%, w/v). The culture medium consisting of 2.5% sucrose and 0.5% sodium phytate optimally supported the maximum phytase activity. In addition, we found that culture of the yeast at 35°C with shaking at 150 rpm supports maximum phytase production. The validity of this model was verified by culturing the organisms in flasks on a shaker. Using the optimized media and growth conditions, we obtained a 10-fold improvement in the production of phytase by S. cerevisiae.
Short- and Long-range Neural Synchrony in Grapheme–Color Synesthesia

Grapheme–color synesthesia is a perceptual phenomenon where single graphemes (e.g., the letter “E”) induce simultaneous sensations of colors (e.g., the color green) that were not objectively shown. Current models disagree as to whether the color sensations arise from increased short-range connectivity between anatomically adjacent grapheme- and color-processing brain structures or from decreased effectiveness of inhibitory long-range connections feeding back into visual cortex. We addressed this issue by examining neural synchrony obtained from EEG activity, in a sample of grapheme–color synesthetes that were presented with color-inducing versus non-color-inducing graphemes. For color-inducing graphemes, the results showed a decrease in the number of long-range couplings in the theta frequency band (4–7 Hz, 280–540 msec) and a concurrent increase of short-range phase-locking within lower beta band (13–20 Hz, 380–420 msec at occipital electrodes). Because the effects were both found in long-range synchrony and later within the visual processing stream, the results support the idea that reduced inhibition is an important factor for the emergence of synesthetic colors.
SUBSTRATES OF GLYCOLYSIS IN INTRACRANIAL TUMORS DURING COMPLETE ISCHEMIA.

Quantitative changes in the substrates of glycolysis and other metabolites were measured in a series of completely ischemic human and experimentally induced intracranial tumors. Serial portions of the same tumor were maintained under nitrogen at 37°C and then rapidly frozen. Initial rates of lactate production by all tumors were slower than in normal brain, but the final amounts produced at equilibrium were greater. In each tumor, the total amount of lactate produced significantly exceeded the disappearance of glucose and glycogen, and in most cases the increases in Pi exceeded the disappearance of adenosine triphosphate (ATP), P-creatine, and measured phosphorylated intermediates. The source or sources of the extra Pi and lactate was not determined. ATP was maintained at substantial levels despite prolonged ischemia except in 1 tumor, an oligodendroglioma. The significance of these observations in relation to the survival of the neoplastic cell is discussed.
Expression Patterns of Circular RNAs from Primary Kinase Transcripts in the Mammary Glands of Lactating Rats

Purpose Circular RNAs (circRNAs), a novel class of RNAs, perform important functions in biological processes. However, the role of circRNAs in the mammary gland remains unknown. The present study is aimed at identifying and characterizing the circRNAs expressed in the mammary gland of lactating rats. Methods Deep sequencing of RNase R-enriched rat lactating mammary gland samples was performed and circRNAs were predicted using a previously reported computational pipeline. Gene ontology terms of circRNA-producing genes were also analyzed. Results A total of 6,824 and 4,523 circRNAs were identified from rat mammary glands at two different lactation stages. Numerous circRNAs were specifically expressed at different lactation stages, and only 1,314 circRNAs were detected at both lactation stages. The majority of the candidate circRNAs map to noncoding intronic and intergenic regions. The results demonstrate a circular preference or specificity of some genes. DAVID analysis revealed an enrichment of protein kinases and related proteins among the set of genes encoding circRNAs. Interestingly, four protein-coding genes (Rev3l, IGSF11, MAML2, and LPP) that also transcribe high levels of circRNAs have been reported to be involved in cancer. Conclusion Our findings provide the basis for comparison between breast cancer profiles and for selecting representative circRNA candidates for future functional characterization in breast development and breast cancer.
Systematic development of hybrid cybernetic models: Application to recombinant yeast co‐consuming glucose and xylose

The hybrid cybernetic modeling approach of Kim et al. (Kim et al. [2008] Biotechnol. Prog., in press) views the substrate uptake flux in microorganisms as being distributed in a regulated way among different elementary modes (EMs) of a metabolic network, which intracellular fluxes related to the uptake rates by the pseudo‐steady‐state approximation on intracellular metabolites. While the conceptual development has been demonstrated by Kim et al. (Kim et al. [2008] Biotechnol. Prog., in press) using a rather simple example (i.e., Escherichia coli metabolizing a single substrate), its extension to a larger scale network involving multiple substrates results in serious overparameterization (which implies an excessive number of parameters relative to the measurements available to determine them). Through the case study of recombinant Saccharomyces yeast co‐consuming glucose and xylose, we present a systematic way of formulating a minimal order hybrid cybernetic model (HCM) for a general metabolic network. The overparameterization problem mostly arising from a large number of EMs is avoided using a model reduction technique developed by Song and Ramkrishna (Song and Ramkrishna [2009a] Biotechnol. Bioeng. 102(2):554–568) where an original set of EMs is condensed to a much smaller subset. Detailed discussions follow on the issue of determining the minimal set of active modes needed for the description of the simultaneous consumption of multiple substrates. The developed HCM is compared with other metabolic models: macroscopic bioreaction models (Provost et al. [2006] Bioprocess Biosyt. Eng. 29(5–6):349–366), and dynamic flux balance analysis. It is shown that the HCM outperforms the other two as validated using various sets of fermentation data. The difference among the models is more dramatic in a situation such as the sequential utilization of glucose and xylose, which is observed under realistic fermentation conditions. Biotechnol. Bioeng. 2009;103: 984–1002. © 2009 Wiley Periodicals, Inc.
Modelling tourism flows through gravity models: a quantile regression approach

Gravity models are widely used to study tourism flows. The peculiarities of the segmented international demand for agritourism in Italy are examined by means of a novel approach: a panel data quantile regression. We characterize the international demand for Italian agritourism with a large data set, by considering data of 33 countries of origin, from 1998 to 2010. Distance and income are the major determinants, but we also found that mutual agreements and high urbanization rates in countries of origin are associated with larger flows of incoming tourists.
Adjoint Algorithm for CAD-Based Shape Optimization Using a Cartesian Method

Adjoint solutions of the governing flow equations are becoming increasingly important for the development of efficient analysis and optimization algorithms. A well-known use of the adjoint method is gradient-based shape optimization. Given an objective function that defines some measure of performance, such as the lift and drag functionals, its gradient is computed at a cost that is essentially independent of the number of design variables (geometric parameters that control the shape). More recently, emerging adjoint applications focus on the analysis problem, where the adjoint solution is used to drive mesh adaptation, as well as to provide estimates of functional error bounds and corrections. The attractive feature of this approach is that the mesh-adaptation procedure targets a specific functional, thereby localizing the mesh refinement and reducing computational cost. Our focus is on the development of adjoint-based optimization techniques for a Cartesian method with embedded boundaries.12 In contrast t o implementations on structured and unstructured grids, Cartesian methods decouple the surface discretization from the volume mesh. This feature makes Cartesian methods well suited for the automated analysis of complex geometry problems, and consequently a promising approach to aerodynamic optimization. Melvin et developed an adjoint formulation for the TRANAIR code, which is based on the full-potential equation with viscous corrections. More recently, Dadone and Grossman presented an adjoint formulation for the Euler equations. In both approaches, a boundary condition is introduced to approximate the effects of the evolving surface shape that results in accurate gradient computation. Central to automated shape optimization algorithms is the issue of geometry modeling and control. The need to optimize complex, "real-life" geometry provides a strong incentive for the use of parametric-CAD systems within the optimization procedure. In previous work, we presented an effective optimization framework that incorporates a direct-CAD interface. In this work, we enhance the capabilities of this framework with efficient gradient computations using the discrete adjoint method. We present details of the adjoint numerical implementation, which reuses the domain decomposition, multigrid, and time-marching schemes of the flow solver. Furthermore, we explain and demonstrate the use of CAD in conjunction with the Cartesian adjoint approach. The final paper will contain a number of complex geometry, industrially relevant examples with many design variables to demonstrate the effectiveness of the adjoint method on Cartesian meshes.
Multifrequency Studies of Active Galactic Nuclei in the Planck Satellite Era

The multi-epoch single-survey Planck satellite data have given a rare glimpse into how the radio spectra of active galactic nuclei (AGN) evolve in time. Using Planck and simultaneous auxiliary radio data ranging from 1 GHz to 857 GHz, spectra for 104 bright northern extragalactic radio sources (most of them AGN) have been assembled; in these, the various stages of flare development can be identified. The results are compared with theoretical models describing relativistic jets. Evidence for particularly flat high-frequency radio spectra is found, indicating a harder accelerated electron energy spectrum than usually assumed. A set of sources also shows signs of intrinsic cold dust.
Later-life mental health in Europe: a country-level comparison.

OBJECTIVES To investigate the influence of country of residence on depression and well-being among older Europeans, after establishing the between-country measurement invariance of both constructs. METHODS We used data from a cross-sectional nationally representative population-based sample of older Europeans, the Survey of Health, Ageing, and Retirement in Europe (SHARE). The analysis sample comprised 13,498 older Europeans from nine countries. The EURO-D was used to measure depression, and a well-being outcome was derived from self-report items available in SHARE. The between-country measurement invariance of both mental health outcomes was established using modern psychometric modeling techniques. RESULTS After adjustment for demographic characteristics and the presence of chronic illness, Spain was the country scoring highest on depression and Denmark highest on well-being. Optimal mental health was associated with higher educational attainment and being married. DISCUSSION There is considerable between-country heterogeneity in later-life mental health in Europe. The Scandinavian countries, the Netherlands, and Austria, do best (low depression/high well-being), followed by Germany and France, whereas residents of Spain, Italy, and Greece report the worst mental health.
Reinforcement learning approach to learning human experience in tuning cavity filters

Owing to the rapid development of the communication industry, various kinds of radio frequency components are in great demand and put into mass production. Among them, passive devices such as microwave cavity filters, duplexers and combiners have experienced fast and unexpected upgrades. However, the tuning process of these products, which is always manually operated, still seems hard to be automatically replaced or improved because of the difficulties in extracting human experience. In this study, we make deep investigations into some previous automatic cavity filter tuning solutions, especially the ones using intelligent algorithms. In addition, we propose the method of intelligent tuning based on the reinforcement learning algorithm which dynamically extracts the human strategies during the tuning process. The experimental results prove the powerful performance of reinforcement learning in mastering human skills.
Efficient Parallel Evaluation of Straight-Line Code and Arithmetic Circuits

A new parallel algorithm is given to evaluate a straight line program. The algorithm evaluates a program over a commutative semi-ring R of degree d and size n in time O(log n(log nd)) using M(n) processors, where M(n) is the number of processors required for multiplying n×n matrices over the semi-ring R in O (log n) time.