H.B. 491: Ohio\u27s New Transactional Immunity Statute

Governmental power to compel persons to testify in court is firmly established in American law. Both the constitutions of the United States and Ohio, however, set forth a privilege against compulsory self-incrimination. Statutes which empower a court to compel testimony over a claim of privilege against self-incrimination in return for protection against criminal punishment are called immunity statutes. Such statutes seek a rational accommodation between the constitutional imperatives of the privilege against self-incrimination and the governmental need for testimony. The United States Supreme Court has characterized immunity statutes as essential to the efficient enforcement of certain criminal statutes. Mr. Justice Frankfurter observed that immunity statutes have become part of our constitutional fabric. In Kastigar v. United States, the Supreme Court of the United States held that in order to compel testimony over a witness\u27 claim of privilege against self-incrimination, a court must grant use and derivative use immunity to the witness. Use and derivative use immunity means that neither the actual compelled testimony nor information directly or indirectly derived from the compelled testimony may be used as evidence against the witness in any subsequent criminal action

Bankruptcy prediction of engineering companies in the EU using classification methods

This article focuses on the problem of binary classification of 902 small- and medium-sized engineering companies active in the EU, together with additional 51 companies which went bankrupt in 2014. For classification purposes, the basic statistical method of logistic regression has been selected, together with a representative of machine learning (support vector machines and classification trees method) to construct models for bankruptcy prediction. Different settings have been tested for each method. Furthermore, the models were estimated based on complete data and also using identified artificial factors. To evaluate the quality of prediction we observe not only the total accuracy with the type I and II errors but also the area under ROC curve criterion. The results clearly show that increasing distance to bankruptcy decreases the predictive ability of all models. The classification tree method leads us to rather simple models. The best classification results were achieved through logistic regression based on artificial factors. Moreover, this procedure provides good and stable results regardless of other settings. Artificial factors also seem to be a suitable variable for support vector machines models, but classification trees achieved better results using original data.O

Predicting financial distress of agriculture companies in EU

The objective of this paper is prediction of financial distress (default of payment or insolvency) of 250 agriculture business companies in EU from which 62 companies defaulted in 2014 with respect to lag of the used attributes. From many types of classification models we chose Logistic regression, Support vector machines method with RBF ANOVA kernel, Decision trees and Adaptive boosting based on decision trees to acquire the best results. From the results it is obvious that with the rising distance to the bankruptcy there drops average accuracy of financial distress prediction and there is a greater difference between active and distressed companies in terms of liquidity, rentability and debt ratios. The Decision trees and Adaptive boosting offer better accuracy for distress prediction than SVM and logit methods, what is comparable to previous studies. From overall of 15 accounting variables, we construct classification trees by Decision trees with inner feature selection method for better vizualization, what reduce full data set only to 1 or 2 attributes: ROA and Long-term debt to Total assets ratio in 2011, ROA and Current ratio in 2012, ROA in 2013 for discrimination of distressed companies.O

Optimal threshold of data envelopment analysis in bankruptcy prediction

Data envelopment analysis is not typically used for bankruptcy prediction. However, this paper shows that a correctly set up a model for this approach can be very useful in that context. A superefficiency model was applied to classify bankrupt and actively manufactured companies in the European Union. To select an appropriate threshold, the Youden index and the distance from the corner were used in addition to the total accuracy. The results indicate that selecting a suitable threshold improves specificity visibly with only a small reduction in the total accuracy. The thresholds of the best models appear to be robust enough for predictions in different time and economic sectors

The probability of default under ifrs 9: Multi-period estimation and macroeconomic forecast

In this paper we propose a straightforward, flexible and intuitive computational framework for the multi-period probability of default estimation incorporating macroeconomic forecasts. The concept is based on Markov models, the estimated economic adjustment coefficient and the official economic forecasts of the Czech National Bank. The economic forecasts are taken into account in a separate step to better distinguish between idiosyncratic and systemic risk. This approach is also attractive from the interpretational point of view. The proposed framework can be used especially when calculating lifetime expected credit losses under IFRS 9.O

Micro-Data Efficiency Evaluation of Forest Companies: The Case of Central Europe

The forestry sector is facing critical challenges due to climate change. Decision-making support based on efficiency evaluation using non-parametric methods could provide important information for both forest managers and policymakers. However, such advanced technical analysis is scarce in forestry science. When applied, its application has been primarily based on aggregated, macro-level data, and efficiency was analysed for the forestry sector as a whole. There is a lack of studies from the company-level perspective, which are needed to provide sound decision support. In this paper, we focus on the micro-data level and offer the data envelopment analysis model settings and interpretations for an efficiency evaluation based on the financial data of individual forestry companies. The aim is to provide an original analysis of the company-level driving forces of forestry sector efficiency. The results for central European countries show that efficiency is driven by company size and country of operation. The study also confirms that, generally, German companies are the »efficiency leaders« in the region, while Czech companies may serve as an efficiency reference for east-central European forestry companies

The Effect of Phytogenic Additive on Behavior During Mild - Moderate Heat Stress in Broilers

The aim of this study was to evaluate the effect of phytogenic additive with anti-inflammatory and antioxidant properties on thermoregulatory behavior (lifting of the wings, high respiratory rate defined as open beaks) and water and feed intake in mild-moderate heat stressed Cobb 500 broiler chickens. From 28th day of chickens' age experimental group was supplemented with 0.1 % phytogenic additive based on Scutellaria baicalensis L. extract in the diet. At the age 30 days temperature was increased to 27 oC and kept the same till the end of experiment at 34 days of age. Ethological observation was performed 31st, 32nd and 33th days of experiment. Monitoring was performed in three observation periods at the start, in the middle and at the end of 18h day length, always for three consecutive hours. Changes in chickens' behavior among days and observation periods, as well as the influence of phytogenic additive were evaluated. Manifestations of thermoregulatory behavior were significantly higher (p < 0.001) in the middle and at the end of photoperiod. Significantly the highest number of chickens fed diets (p < 0.001) at the end of day-light, water intake was the lowest in the middle of the day (p < 0.001). Significantly the oftenest lifting of the wings (p < 0.001), high respiratory rate (p < 0.001) and feed intake (p = 0.017) were found in 33 days of chickens' age. Feeding of phytogenic additive did not affect water intake and feed intake, anyway significantly reduced (p < 0.001) thermoregulatory behavior.O

On the Investment Attractiveness of Ukrainian Companies

The geographical location of Ukraine provides a multitude of possibilities for successful investment activity. There are rich natural resources, a fertile soil and a qualified low-cost labour force. On the other hand, investors have to deal with historical ties to the Soviet Union, corruption, and political instability exacerbated by occupation of part of the territory by Russia. This paper deals with the possibility of identifying the investment attractiveness of the particular sectors in Ukraine by the level of concentration measured by the Herfindahl-Hirschman index. Accounting data of companies taken from the Orbis database are evaluated by ABC analysis and the general linear model. The results point to significant dependency of variables representing investment attractiveness on the Herfindahl-Hirschman index, where deviations are explained by sectoral specifics.O

White matter differences between healthy young ApoE4 carriers and non-carriers identified with tractography and support vector machines.

The apolipoprotein E4 (ApoE4) is an established risk factor for Alzheimer's disease (AD). Previous work has shown that this allele is associated with functional (fMRI) changes as well structural grey matter (GM) changes in healthy young, middle-aged and older subjects. Here, we assess the diffusion characteristics and the white matter (WM) tracts of healthy young (20-38 years) ApoE4 carriers and non-carriers. No significant differences in diffusion indices were found between young carriers (ApoE4+) and non-carriers (ApoE4-). There were also no significant differences between the groups in terms of normalised GM or WM volume. A feature selection algorithm (ReliefF) was used to select the most salient voxels from the diffusion data for subsequent classification with support vector machines (SVMs). SVMs were capable of classifying ApoE4 carrier and non-carrier groups with an extremely high level of accuracy. The top 500 voxels selected by ReliefF were then used as seeds for tractography which identified a WM network that included regions of the parietal lobe, the cingulum bundle and the dorsolateral frontal lobe. There was a non-significant decrease in volume of this WM network in the ApoE4 carrier group. Our results indicate that there are subtle WM differences between healthy young ApoE4 carriers and non-carriers and that the WM network identified may be particularly vulnerable to further degeneration in ApoE4 carriers as they enter middle and old age

Mutual Unbiasedness in Coarse-grained Continuous Variables

The notion of mutual unbiasedness for coarse-grained measurements of quantum continuous variable systems is considered. It is shown that while the procedure of "standard" coarse graining breaks the mutual unbiasedness between conjugate variables, this desired feature can be theoretically established and experimentally observed in periodic coarse graining. We illustrate our results in an optics experiment implementing Fraunhofer diffraction through a periodic diffraction grating, finding excellent agreement with the derived theory. Our results are an important step in developing a formal connection between discrete and continuous variable quantum mechanics.Comment: 5 pages, 3 figures + Supplemental Material (1 page) v2: Introduction expanded, minor typos correcte