Measurement of neutron flow with the external target experimental device on CSR

With the construction of the neutron detection wall at the external target position on Heavy Ion Research Facility in Lanzhou-Cooling Storage Ring (HIRFL-CSR), it will be possible to detect high energy neutron. A BUU model is applied to simulate the flow in both symmetric (Ni+Ni, Pb+Pb) and asymmetric(Pb+Ni) systems. It is shown that at above several hundreds MeV/u, the flow signals are very obvious and depend clearly on the centrality of the collisions. Based on the products in the forward angle less than 20 degrees, the simulation also reveals that the determination of the reaction plane and the selection of the impact parameter, both of which are essential in the flow measurement, are well implemented. The double event and its influence on the determination of the neutron flow are also simulated.

Measurement of K*(892)(0) and K-0 mesons in Al plus Al collisions at 1.9A GeV

A new measurement of subthreshold K*(892)(0) and K-0 production is presented. The experimental data complete the measurement of strange particles produced in Al + Al collisions at 1.9A GeV measured with the FOPI detector at SIS at GSI (Darmstadt). The K*(892)(0)/K-0 yield ratio is found to be 0.0315 +/- 0.006(stat.) +/- 0.012(syst.) and is in good agreement with the transport model prediction. These measurements provide information on the in-medium cross section of K+-pi(-) fusion, which is the dominant process in subthreshold K*(892)(0) production.

Measurement of the total reaction cross section for the mirror nuclei N-12 and B-12

The mirror nuclei N-12 and B-12 are separated by the Radioactive Ion Beam Line in Lanzhou (RIBLL) at HIRFL from the breakup of 78.6 MeV/u N-14 on a Be target. The total reaction cross-sections of N-12 at 34.9 MeV/u and B-12 at 54.4 MeV/u on a Si target have been measured by using the transmission method. Assuming N-12 consists of a C-11 core plus one halo proton, the excitation function of N-12 and B-12 on a Si target and a C target were calculated with the Glauber model. It can fit the experimental data very well. The characteristic halo structure for N-12 was found with a large diffusion of the protons density distribution.

Stratigraphic Model Predictions of Geoacoustic Properties

Geoacoustic properties of the seabed have a controlling role in the propagation and reverberation of sound in shallow-water environments. Several techniques are available to quantify the important properties but are usually unable to adequately sample the region of interest. In this paper, we explore the potential for obtaining geotechnical properties from a process-based stratigraphic model. Grain-size predictions from the stratigraphic model are combined with two acoustic models to estimate sound speed with distance across the New Jersey continental shelf and with depth below the seabed. Model predictions are compared to two independent sets of data: 1) Surficial sound speeds obtained through direct measurement using in situ compressional wave probes, and 2) sound speed as a function of depth obtained through inversion of seabed reflection measurements. In water depths less than 100 m, the model predictions produce a trend of decreasing grain-size and sound speed with increasing water depth as similarly observed in the measured surficial data. In water depths between 100 and 130 m, the model predictions exhibit an increase in sound speed that was not observed in the measured surficial data. A closer comparison indicates that the grain-sizes predicted for the surficial sediments are generally too small producing sound speeds that are too slow. The predicted sound speeds also tend to be too slow for sediments 0.5-20 m below the seabed in water depths greater than 100 m. However, in water depths less than 100 m, the sound speeds between 0.5-20-m subbottom depth are generally too fast. There are several reasons for the discrepancies including the stratigraphic model was limited to two dimensions, the model was unable to simulate biologic processes responsible for the high sound-speed shell material common in the model area, and incomplete geological records necessary to accurately predict grain-size

A new empirical model for retrieving sea surface salinity in L band

Sea surface salinity is a key physical parameter in ocean science. It is important in the ocean remote sensing to retrieve sea surface salinity by the microwave probe technology. Based on the in situ measurement data and remote sensing data of the Yellow Sea, we have built a new empirical model in this paper, which can be used to retrieve sea surface salinity of the Yellow Sea by means of the brightness temperature of the sea water at L-band. In this model, the influence of the roughness of the sea surface is considered, and the retrieved result is in good agreement with the in situ measurement data, where the mean absolute error of the retrieved sea surface salinity is about 0.288 psu. This result shows that our model has greater retrieval precision compared with similar models.

(Semi-)Predictive Discretization During Model Selection

In this paper, we present an approach to discretizing multivariate continuous data while learning the structure of a graphical model. We derive the joint scoring function from the principle of predictive accuracy, which inherently ensures the optimal trade-off between goodness of fit and model complexity (including the number of discretization levels). Using the so-called finest grid implied by the data, our scoring function depends only on the number of data points in the various discretization levels. Not only can it be computed efficiently, but it is also independent of the metric used in the continuous space. Our experiments with gene expression data show that discretization plays a crucial role regarding the resulting network structure.

Robust 2-D Model-Based Object Recognition

Techniques, suitable for parallel implementation, for robust 2D model-based object recognition in the presence of sensor error are studied. Models and scene data are represented as local geometric features and robust hypothesis of feature matchings and transformations is considered. Bounds on the error in the image feature geometry are assumed constraining possible matchings and transformations. Transformation sampling is introduced as a simple, robust, polynomial-time, and highly parallel method of searching the space of transformations to hypothesize feature matchings. Key to the approach is that error in image feature measurement is explicitly accounted for. A Connection Machine implementation and experiments on real images are presented.

Capturing the overarm throw in darts employing wireless inertial measurement

This work employs a custom built body area network of wireless inertial measurement technology to conduct a biomechanical analysis of precision targeted throwing in competitive and recreational darts. The solution is shown to be capable of measuring key biomechanical factors including speed, acceleration and timing. These parameters are subsequently correlated with scoring performance to determine the affect each variable has on outcome. For validation purposes an optical 3D motion capture system provides a complete kinematic model of the subject and enables concurrent benchmarking of the 'gold standard' optical inertial measurement system with the more affordable and proactive wireless inertial measurement solution developed as part of this work.

Factors relating to motivation to change behaviour in individuals who are overweight: application of the health belief model

Background: Obesity is the most important health challenge faced at a global level and represents a rapidly growing problem to the health of populations. Given the escalating global health problem of obesity and its co-morbidities, the need to re-appraise its management is more compelling than ever. The normalisation of obesity within our society and the acceptance of higher body weights have led to individuals being unaware of the reality of their weight status and gravity of this situation. Recognition of the problem is a key component of obesity management and it remains especially crucial to address this issue. A large amount of research has been undertaken on obesity however, limited research has been undertaken using the Health Belief Model. Aim: The aim of the research was to determine factors relating to motivation to change behaviour in individuals who perceive themselves to be overweight and investigate whether the constructs of the Health Belief Model help to explain motivation to change behaviour. Method: The research design was quantitative, correlational and cross-sectional. The design was guided by the Health Belief Model. Data Collection: Data were collected online using a multi-section and multi-item questionnaire, developed from a review of the theoretical and empirical research. Descriptive and inferential statistical analyses were employed to describe relationships between variables. Sample: A sample of 202 men and women who perceived themselves to be overweight participated in the research. Results: Following multivariate regression analysis, perceived barriers to weight loss and perceived benefits of weight loss were significant predictors of motivation to change behaviour. The perceived barriers to weight loss which were significant were psychological barriers to weight loss (p =<0.019) and environmental barriers to physical activity (p=<0.032).The greatest predictor of motivation to change behaviour was the perceived benefits of weight loss (p<0.001). Perceived susceptibility to obesity and perceived severity of obesity did not emerge as significant predictors in this model. Total variance explained by the model was 33.5%. Conclusion: Perceived barriers to weight loss and perceived benefits of weight loss are important determinants of motivation to change behaviour. The current study demonstrated the limited applicability of the Health Belief Model constructs to motivation to change behaviour, as not all core dimensions proved significant predictors of the dependant variable.

A multivariate generalized ARCH approach to modeling risk premia in forward foreign exchange rate markets

Assuming that daily spot exchange rates follow a martingale process, we derive the implied time series process for the vector of 30-day forward rate forecast errors from using weekly data. The conditional second moment matrix of this vector is modelled as a multivariate generalized ARCH process. The estimated model is used to test the hypothesis that the risk premium is a linear function of the conditional variances and covariances as suggested by the standard asset pricing theory literature. Little supportt is found for this theory; instead lagged changes in the forward rate appear to be correlated with the 'risk premium.'. © 1990.

An evaluation of remifentanil-sevoflurane response surface models in patients emerging from anesthesia: model improvement using effect-site sevoflurane concentrations.

INTRODUCTION: We previously reported models that characterized the synergistic interaction between remifentanil and sevoflurane in blunting responses to verbal and painful stimuli. This preliminary study evaluated the ability of these models to predict a return of responsiveness during emergence from anesthesia and a response to tibial pressure when patients required analgesics in the recovery room. We hypothesized that model predictions would be consistent with observed responses. We also hypothesized that under non-steady-state conditions, accounting for the lag time between sevoflurane effect-site concentration (Ce) and end-tidal (ET) concentration would improve predictions. METHODS: Twenty patients received a sevoflurane, remifentanil, and fentanyl anesthetic. Two model predictions of responsiveness were recorded at emergence: an ET-based and a Ce-based prediction. Similarly, 2 predictions of a response to noxious stimuli were recorded when patients first required analgesics in the recovery room. Model predictions were compared with observations with graphical and temporal analyses. RESULTS: While patients were anesthetized, model predictions indicated a high likelihood that patients would be unresponsive (> or = 99%). However, after termination of the anesthetic, models exhibited a wide range of predictions at emergence (1%-97%). Although wide, the Ce-based predictions of responsiveness were better distributed over a percentage ranking of observations than the ET-based predictions. For the ET-based model, 45% of the patients awoke within 2 min of the 50% model predicted probability of unresponsiveness and 65% awoke within 4 min. For the Ce-based model, 45% of the patients awoke within 1 min of the 50% model predicted probability of unresponsiveness and 85% awoke within 3.2 min. Predictions of a response to a painful stimulus in the recovery room were similar for the Ce- and ET-based models. DISCUSSION: Results confirmed, in part, our study hypothesis; accounting for the lag time between Ce and ET sevoflurane concentrations improved model predictions of responsiveness but had no effect on predicting a response to a noxious stimulus in the recovery room. These models may be useful in predicting events of clinical interest but large-scale evaluations with numerous patients are needed to better characterize model performance.

Preoperative pain level and patient expectation predict hospital length of stay after total hip arthroplasty.

The purpose of this study was to identify preoperative predictors of length of stay after primary total hip arthroplasty in a patient population reflecting current trends toward shorter hospitalization and using readily obtainable factors that do not require scoring systems. A retrospective review of 112 consecutive patients was performed. High preoperative pain level and patient expectation of discharge to extended care facilities (ECFs) were the only significant multivariable predictors of hospitalization extending beyond 2 days (P=0.001 and P<0.001 respectively). Patient expectation remained significant after adjusting for Medicare's 3-day requirement for discharge to ECFs (P<0.001). The study was adequately powered to analyze the variables in the multivariable logistic regression model, which had a concordance index of 0.857.

The Golden Rule Ethic, its Measurement, and Relationships with Well-Being and Prosocial Values Across Four Religions in India

As a psychological principle, the golden rule represents an ethic of universal empathic concern. It is, surprisingly, present in the sacred texts of virtually all religions, and in philosophical works across eras and continents. Building on the literature demonstrating a positive impact of prosocial behavior on well-being, the present study investigates the psychological function of universal empathic concern in Indian Hindus, Christians, Muslims and Sikhs.

I develop a measure of the centrality of the golden rule-based ethic, within an individual’s understanding of his or her religion, that is applicable to all theistic religions. I then explore the consistency of its relationships with psychological well-being and other variables across religious groups.

Results indicate that this construct, named Moral Concern Religious Focus, can be reliably measured in disparate religious groups, and consistently predicts well-being across them. With measures of Intrinsic, Extrinsic and Quest religious orientations in the model, only Moral Concern and religiosity predict well-being. Moral Concern alone mediates the relationship between religiosity and well-being, and explains more variance in well-being than religiosity alone. The relationship between Moral Concern and well-being is mediated by increased preference for prosocial values, more satisfying interpersonal relationships, and greater meaning in life. In addition, across religious groups Moral Concern is associated with better self-reported physical and mental health, and more compassionate attitudes toward oneself and others.

Two additional types of religious focus are identified: Personal Gain, representing the motive to use religion to improve one’s life, and Relationship with God. Personal Gain is found to predict reduced preference for prosocial values, less meaning in life, and lower quality of relationships. It is associated with greater interference of pain and physical or mental health problems with daily activities, and lower self-compassion. Relationship with God is found to be associated primarily with religious variables and greater meaning in life.

I conclude that individual differences in the centrality of the golden rule and its associated ethic of universal empathic concern may play an important role in explaining the variability in associations between religion, prosocial behavior and well-being noted in the literature.

Local examination of skin diffusion using FTIR spectroscopic imaging and multivariate target factor analysis

In the context of trans-dermal drug delivery it is very important to have mechanistic insight into the barrier function of the skin's stratum corneum and the diffusion mechanisms of topically applied drugs. Currently spectroscopic imaging techniques are evolving which enable a spatial examination of various types of samples in a dynamic way. ATR-FTIR imaging opens up the possibility to monitor spatial diffusion profiles across the stratum corneum of a skin sample. Multivariate data analyses methods based on factor analysis are able to provide insight into the large amount of spectroscopically complex and highly overlapping signals generated. Multivariate target factor analysis was used for spectral resolution and local diffusion profiles with time through stratum corneum. A model drug, 4-cyanophenol in polyethylene glycol 600 and water was studied. Results indicate that the average diffusion profiles between spatially different locations show similar profiles despite the heterogeneous nature of the biological sample and the challenging experimental set-up.

The role of space charge formation in the generation of thermally stimulated current (TSC) spectroscopy data for a model amorphous drug system

Thermally stimulated current (TSC) spectroscopy is attracting increasing attention as a means of materials characterization, particularly in terms of measuring slow relaxation processes in solid samples. However, wider use of the technique within the pharmaceutical field has been inhibited by difficulties associated with the interpretation of TSC data, particularly in terms of deconvoluting dipolar relaxation processes from charge distribution phenomena. Here, we present evidence that space charge and electrode contact effects may play a significant role in the generation of peaks that have thus far proved difficult to interpret. We also introduce the use of a stabilization temperature in order to control the space charge magnitude. We have studied amorphous indometacin as a model drug compound and have varied the measurement parameters (stabilization and polarization temperatures), interpreting the changes in spectral composition in terms of charge redistribution processes. More specifically, we suggested that charge drift and diffusion processes, charge injection from the electrodes and high activation energy charge redistribution processes may all contribute to the appearance of shoulders and 'spurious' peaks. We present recommendations for eliminating or reducing these effects that may allow more confident interpretation of TSC data.