Associations between perceived discrimination and health status among frequent Emergency Department users

Objective: Frequent Emergency Department (ED) users are vulnerable individuals and discrimination is usually associated with increased vulnerability. The aim of this study was to investigate frequent ED users' perceptions of discrimination and to test whether they were associated with increased vulnerability. Methods: In total, 250 adult frequent ED users were interviewed in Lausanne University Hospital. From a previously published questionnaire, we assessed 15 dichotomous sources of perceived discrimination. Vulnerability was assessed using health status: objective health status (evaluation by a healthcare practitioner including somatic, mental health, behavioral, and social issues - dichotomous variables) and subjective health status [self-evaluation including health-related quality of life (WHOQOL) and quality of life (EUROQOL) - mean-scores]. We computed the prevalence rates of perceived discrimination and tested associations between perceived discrimination and health status (Fischer's exact tests, Mann-Whitney U-tests)

Generalization transitions in Hidden-Layer neural networks for third-order feature discrimination

Stochastic learning processes for a specific feature detector are studied. This technique is applied to nonsmooth multilayer neural networks requested to perform a discrimination task of order 3 based on the ssT-block¿ssC-block problem. Our system proves to be capable of achieving perfect generalization, after presenting finite numbers of examples, by undergoing a phase transition. The corresponding annealed theory, which involves the Ising model under external field, shows good agreement with Monte Carlo simulations.

HouSI: Heuristic for delimitation of housing submarkets and price homogeneous areas

This paper seeks to address the problem of the empirical identification of housing market segmentation,once we assume that submarkets exist. The typical difficulty in identifying housing submarkets when dealing with many locations is the vast number of potential solutions and, in such cases, the use of the Chow test for hedonic functions is not a practical solution. Here, we solve this problem by undertaking an identification process with a heuristic for spatially constrained clustering, the"Housing Submarket Identifier" (HouSI). The solution is applied to the housing market in the city of Barcelona (Spain), where we estimate a hedonic model for fifty thousand dwellings aggregated into ten groups. In order to determine the utility of the procedure we seek to verify whether the final solution provided by the heuristic is comparable with the division of the city into ten administrative districts.

The skill paradox: Explaining and reducing employment discrimination against skilled immigrants

Using a social identity theory approach, we theorized that recruiters might be particularly biased against skilled immigrant applicants. We refer to this phenomenon as a skill paradox, according to which immigrants are more likely to be targets of employment discrimination the more skilled they are. Furthermore, building on the common ingroup identity model, we proposed that this paradox can be resolved through human resource management (HRM) strategies that promote inclusive hiring practices (e.g., by emphasizing fit with a diverse clientele). The results from a laboratory experiment were consistent with our predictions: Local recruiters preferred skilled local applicants over skilled immigrant applicants, but only when these applicants were qualified for a specific job. This bias against qualified and skilled immigrant applicants was attenuated when fit with a diverse clientele was emphasized, but not when fit with a homogeneous clientele was emphasized or when the hiring strategy was not explained. We discuss the implications of our findings for research on employment discrimination against skilled immigrants, including the role of inclusiveness for reducing discriminatory biases.

Comovement of stock price risk in strategic alliance — Case: mobile internet

The focus of this study has been comovement of stock price risk level between two companies as they form strategic alliance. Thus the main reason has been to shed more light to possible increased risk level that the stockholder confronts when a company he owns forms a strategic alliance with another company. This study has centralized to interfirm cooperation between mobile and internet companies, which have furthered the development of mobile internet. The study has been divided into theoretical and empirical part. In theoretical part the main concepts riskiness of a stock (volatility), comovement and strategic alliance have been run through. In empirical part seven strategic alliances formed by mobile internet companies have been examined. Based on this, strategic alliance seems to increase comovement of stock price risk in some degree. This comovement seems to be stronger when core businesses or operating environments of cooperating companies differ more from each other.

Asymptotic analysis of stock price densities and implied volatilities in mixed stochastic models

In this paper, we obtain sharp asymptotic formulas with error estimates for the Mellin con- volution of functions de ned on (0;1), and use these formulas to characterize the asymptotic behavior of marginal distribution densities of stock price processes in mixed stochastic models. Special examples of mixed models are jump-di usion models and stochastic volatility models with jumps. We apply our general results to the Heston model with double exponential jumps, and make a detailed analysis of the asymptotic behavior of the stock price density, the call option pricing function, and the implied volatility in this model. We also obtain similar results for the Heston model with jumps distributed according to the NIG law.

Commodity Price Driven Risk Management from Viewpoint of High Grade Steel Alloys

The study of price risk management concerning high grade steel alloys and their components was conducted. This study was focused in metal commodities, of which nickel, chrome and molybdenum were in a central role. Also possible hedging instruments and strategies for referred metals were studied. In the literature part main themes are price formation of Ni, Cr and Mo, the functioning of metal exchanges and main hedging instruments for metal commodities. This section also covers how micro and macro variables may affect metal prices from the viewpoint of short as well as longer time period. The experimental part consists of three sections. In the first part, multiple regression model with seven explanatory variables was constructed to describe price behavior of nickel. Results were compared after this with information created with comparable simple regression model. Additionally, long time mean price reversion of nickel was studied. In the second part, theoretical price of CF8M alloy was studied by using nickel, ferro-chrome and ferro-molybdenum as explanatory variables. In the last section, cross hedging possibilities for illiquid FeCr -metal was studied with five LME futures. Also this section covers new information concerning possible forthcoming molybdenum future contracts as well. The results of this study confirm, that linear regression models which are based on the assumption of market rationality, are not able to reliably describe price development of metals at issue. Models fulfilling assumptions for linear regression may though include useful information of statistical significant variables which have effect on metal prices. According to the experimental part, short futures were found to incorporate the most accurate information concerning the price movements in the future. However, not even 3M futures were able to predict turning point in the market before the faced slump. Cross hedging seemed to be very doubtful risk management strategy for illiquid metals, because correlations coefficients were found to be very sensitive for the chosen time span.

¿Puede un modelo educativo intercultural combatir la discriminación y la xenofobia? = Can an intercultural education model combat discrimination and xenophobia?

In this article we present a qualitative study conducted with six indigenous and six mestizos from Intercultural University of Chiapas. The aim of the study is to exemplify the mutual perception between different ethno-linguistic groups, as well as the possible change occurred after the admission to the University. That is, opinions about the other group after and before entering the University. We conclude that a higher education intercultural model can promote mutual understanding and relationship between indigenous and mestizos and thus combat prejudices and stereotypes

Discrimination of native wood charcoal by infrared spectroscopy

Brazil is one of the largest producers and consumers of charcoal in the world. About 50% of its charcoal comes from native forests, with a large part coming from unsustainable operations. The anatomic identification of charcoal is subjective; an instrumental technique would facilitate the monitoring of forests. This study aimed to verify the feasibility of using medium and near infrared reflectance spectroscopy to discriminate native (ipê) from plantation charcoals (eucalyptus). Principal Components Analysis, followed by Discriminant Factorial Analysis formed two different groups indicated by Mahalanobis distances of 40.6 and 80.3 for near and mid infrared, respectively. Validation of the model showed 100% efficacy.

Cognitive Auditory Evoked Potentials in Investigation of Hearing Discrimination

Preattentive perception of occasional deviating stimuli in the stream of standard stimuli can be recorded with cognitive event-related potential (ERP) mismatch negativity (MMN). The earlier detection of stimuli at the auditory cortex can be examined with N1 and P2 ERPs. The MMN recording does not require co-operation, it correlates with perceptual threshold, and even complex sounds can be used as stimuli. The aim of this study was to examine different aspects that should be considered when measuring discrimination of hearing with ERPs. The MMN was found to be stimulusintensity- dependent. As the intensity of sine wave stimuli was increased from 40 to 80 dB HL, MMN mean amplitudes increased. The effect of stimulus frequency on the MMN was studied so that the pitch difference would be equal in each stimulus block according to the psychophysiological mel scale or the difference limen of frequency (DLF). However, the blocks differed from each other. The contralateral white noise masking (50 dB EML) was found to attenuate the MMN amplitude when the right ear was stimulated. The N1 amplitude was attenuated and, in contrast, P2 amplitude was not affected by contralateral white noise masking. The perception and production of vowels by four postlingually deafened patients with a cochlear implant were studied. The MMN response could be elicited in the patient with the best vowel perception abilities. The results of the studies show that concerning the MMN recordings, the stimulus parameters and recording procedure design have a great influence on the results.

Price spike forecasting in a competitive day-ahead energy market

Electricity price forecasting has become an important area of research in the aftermath of the worldwide deregulation of the power industry that launched competitive electricity markets now embracing all market participants including generation and retail companies, transmission network providers, and market managers. Based on the needs of the market, a variety of approaches forecasting day-ahead electricity prices have been proposed over the last decades. However, most of the existing approaches are reasonably effective for normal range prices but disregard price spike events, which are caused by a number of complex factors and occur during periods of market stress. In the early research, price spikes were truncated before application of the forecasting model to reduce the influence of such observations on the estimation of the model parameters; otherwise, a very large forecast error would be generated on price spike occasions. Electricity price spikes, however, are significant for energy market participants to stay competitive in a market. Accurate price spike forecasting is important for generation companies to strategically bid into the market and to optimally manage their assets; for retailer companies, since they cannot pass the spikes onto final customers, and finally, for market managers to provide better management and planning for the energy market. This doctoral thesis aims at deriving a methodology able to accurately predict not only the day-ahead electricity prices within the normal range but also the price spikes. The Finnish day-ahead energy market of Nord Pool Spot is selected as the case market, and its structure is studied in detail. It is almost universally agreed in the forecasting literature that no single method is best in every situation. Since the real-world problems are often complex in nature, no single model is able to capture different patterns equally well. Therefore, a hybrid methodology that enhances the modeling capabilities appears to be a possibly productive strategy for practical use when electricity prices are predicted. The price forecasting methodology is proposed through a hybrid model applied to the price forecasting in the Finnish day-ahead energy market. The iterative search procedure employed within the methodology is developed to tune the model parameters and select the optimal input set of the explanatory variables. The numerical studies show that the proposed methodology has more accurate behavior than all other examined methods most recently applied to case studies of energy markets in different countries. The obtained results can be considered as providing extensive and useful information for participants of the day-ahead energy market, who have limited and uncertain information for price prediction to set up an optimal short-term operation portfolio. Although the focus of this work is primarily on the Finnish price area of Nord Pool Spot, given the result of this work, it is very likely that the same methodology will give good results when forecasting the prices on energy markets of other countries.

Stock Price Reactions on M&A, Dividends and Game Releases. Evidence from Gaming Industry.

A rapidly growing gaming industry, which specializes on PC, console, online and other games, attracts attention of investors and analysts, who try to understand what drives changes of the gaming industry companies’ stock prices. This master thesis shows the evidence that, besides long-established types of events (M&A and dividend payments), the companies’ stock price changes depend on industry-specific events. I analyzed specific for gaming industry events - game releases with respect to its subdivisions: new games-sequels, games ratings and subdivision according to a developer of a game (self-developed by publisher or outsourced). The master thesis analyzes stock prices of 55 companies from gaming industry from all over the world. The research period covers 5 year, spreading from April 2008 to April 2013. Executed with an event study method, results of the research show that all the analyzed events types have significant influence on the stock prices of the gaming industry companies. The current master thesis suggests that acquisitions in the industry affect positively bidders’ and targets’ stock prices. Mergers events cause positive stock price reactions as well. But dividends payments and game releases events influence negatively on the stock prices. Game releases’ effect is up to -2.2% of cumulative average abnormal return (CAAR) drop during the first ten days after the game releases. Having researched different kinds of events and identified the direction of their impact, the current paper can be of high value for investors, seeking profits in the gaming industry, and other interested parties.

Economical potential of controlling charging of electric vehicles by using price signal

The number of electric vehicles grows continuously and the implementation of charging electric vehicles is an important issue for the future. Increasing amount of electric vehicles can cause problems to distribution grid by increasing peak load. Currently charging of electric vehicles is uncontrolled, but as the amount of electric vehicles grows, smart charg-ing (controlled charging) will be one possible solution to handle this situation. In this thesis smart charging of electric vehicles is examined from electricity retailers` point of view. The purpose is to find out plausible saving potentials of smart charging, when it´s controlled by price signal. Saving potential is calculated by comparing costs of price signal controlled charging and uncontrolled charging.