Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of feeding raw and extruded peas on ileal digestibility, pancreatic enzymes and plasma glucose and insulin in early weaned pigs

Zusammenfassung Der Einflus von rohen und extrudierten Erbsen auf die ileale Verdaulichkeit, Pankreasenzyme und Plasma-Glucose- und Insulinkonzentrationen bei fruhabgesetzten Ferkeln Zwei Pisum sativum L. -Varietaten wurden zu 45 % roh oder extrudiert in Starterrationen als Proteinquelle fur fruhabgesetzte Ferkel verwendet. Um die ileale Verdaulichkeit der Hauptnahrstoffe, von Starke und α-Galaktosiden messen zu konnen, erhielten 24 mannliche Ferkel ein termino-terminale, ileorektale Anastomose. Die Operation wurde 3 Tage nach dem Absetzen im Alter von 3 Wochen durchgefuhrt. Die ileale Verdaulichkeit wurde in zwei aufeinanderfolgenden 7-tagigen Sammelperioden bestimmt. Die Gesamtverdaulichkeit wurde zusatzlich an 24 intakten Ferkeln in einem 2 × faktoriellen Versuch mit sechs Wiederholungen gemessen. Die Operation der Tiere verursachte keine Erhohung des Pankreasgewichtes und Veranderungen der Pankreasenzymaktivitaten. Die Extrudierung der Erbsen fuhrte in vitro zu einer hoheren Hydrolyserate von Starke durch Pankreas-Amylase und stimulierte die Aktivitaten von Chymotrypsin und Carboxypeptidase A im Pankreasgewebe. Dadurch betrug die ileale Stickstoffverdaulichkeit 65,7% bei rohen Erbsen und 78,4% bei extrudierten Erbsen, die entsprechenden Werte fur Starke lagen bei 94,4% und 99,1 %. Die ileale Verdaulichkeit der α-Galaktoside betrug 75,7% und wurde durch die Behandlung nicht beeinflust. Nach der Futterung von extrudierten Erbsen konnte ein deutlicher postprandialer Anstieg der Glucose- und Insulin-Konzentrationen im Serum festgestellt werden. Beides konnte die Biosynthese von Pankreasenzymen stimulieren. Die scheinbare ileale Verdaulichkeit von Starke und Stickstoff bei Ferkeln kann als aussagefahiger Parameter zur Bewertung von Proteinen mit ANFs, wie sie Leguminosensaaten enthalten, herangezogen werden.
Impact of Capital Account Liberalization on Stock Market Crashes

This paper examines the effect of capital account liberalization on stock market crashes using both de jure and de facto measures of capital account liberalization. Using a sample of 64 countries over the time period of 1973-2016, we show that with restricted de jure capital account liberalization, an increase in capital flows leads to a decrease in stock market crashes. On the other hand, with a more liberalized de jure capital account liberalization, an increase in capital flows leads to an increase in stock market crashes. The main finding supports the hypothesis that free, unregulated flow of capital induces more volatility in the stock market and crashes in these markets are more likely to happen. These results are also robust to different sub samples comprising of high-income OECD countries and non-OECD countries and changes in the specification of stock market crashes and different definitions of capital flows.
Is the EMU Government Bond Market a Playground for Asymmetries?

We investigate the volatility dynamics of some major European Monetary Union sovereign bond markets. We provide an endogenous characterization in terms of two Markov switching regimes for market volatility and analyze the impact of some institutional sector imbalances and policy actions on the persistence of volatility swings. The empirical findings indicate that net foreign assets’ positions and trade balance developments were a matter of minor importance for these European Monetary Union sovereign bond markets. On the contrary, central banks’ liquidity provision measures have important but asymmetrical effects on the persistence of the European Monetary Union’s bond market volatility swings. Whether countries’ specific government liquidity management affected volatility dynamics in a systematic and consistent way remains to a large extent inconclusive.
Rethinking and Expanding the Study of Administrative Rules: Report of the 2010 Red Tape Research Workshop

This document presents the views of the participants of a two day workshop discussing the future of red tape research. To the greatest extent possible, the authors have tried to accurately represent the consensus view that emerged. But as is in any discourse of a moderately large number of scholars, there will be claims that not all participants subscribe to, and aspects of the discussion that are not captured here. INTRODUCTION While the topic of red tape is of long-standing interest to both scholars and the public, it is only in the last two decades that scholars have begun to develop empirical knowledge on this topic. Scholars working in this area recently convened a workshop to consider a research agenda for the next decade. The 2010 Red Tape Research Workshop: Rethinking and Expanding the Study of Administrative Rules was hosted by the La Follette School of Public Affairs at the University of Wisconsin-Madison, and supported by the University of Wisconsin Center for World Affairs and the Global Economy, Center for European Studies, and European Union Center of Excellence. The meeting included more than 20 researchers and PhD students from 14 universities and five countries (see appendix 1 for a full list of participants). GOALS The aim of the meeting was to engage in an enthusiastic discussion about expanding and improving red tape research and to develop a promising future for collaboration. The red tape research community is a good example of how the sociology of science works, with researchers continually making small, informal steps toward better measures. The 2010 Red Tape Research Workshop offers the first opportunity to try to aim at broader planning that can move the field forward in a more concerted effort. Participants reviewed the development of red tape theory and research, identifying gaps in the field, as well as possible avenues for future research and funding opportunities. This included considering ways to improve measures, data, and methods researchers use, and identifying a core list of central questions that would form a 10 year research agenda for the field. The meeting also sought to more formally establish networks of interested researchers that go beyond US and UK based scholars, or scholars who consciously identify themselves as red-tape researchers.
Central Bank’s Macroeconomic Projections and Learning

We study the impact of the publication of central bank’s macroeconomic projections on the dynamic properties of an economy where: (i) private agents have incomplete information and form their expectations using recursive learning algorithms, (ii) the short-term nominal interest rate is set as a linear function of the deviations of inflation and real output from their target level and (iii) the central bank, ignoring the exact mechanism used by private agents to form expectations, assumes that it can be reasonably approximated by perfect rationality and releases macroeconomic projections consistent with this assumption. Results in terms of stability of the equilibrium and speed of convergence of the learning process crucially depend on the set of macroeconomic projections released by the central bank. In particular, while the publication of inflation and output gap projections enlarges the set of interest rate rules associated with stable equilibria under learning and helps agents to learn faster, the announcement of the interest rate path exerts the opposite effect. In the latter case, in order to stabilize expectations and to speed up the learning process the response of the policy instrument to inflation should be stronger than under no announcement.
Dissolution and Permeation Properties of Naproxen From Solid-State Systems With Chitosan

The purpose of this study was to investigate the influence of different types of chitosan and of the preparation technique of the drug–polymer combination in improving the dissolution and permeation abilities of naproxen, a very poorly water-soluble anti-inflammatory drug. Drug–chitosan systems were prepared by simple physical mixing, kneading, cogrinding, or coevaporation using five types of chitosan (base and glutamate or hydrochloride salts, both at two different molecular weights). The products were tested for drug-dissolution behavior and for permeation properties through both Caco-2 cell monolayers and artificial lipophilic membranes. All combinations with chitosan base were significantly (p < .01) more effective in enhancing drug-dissolution rate than those with both its salts, probably in virtue of its higher amorphizing effect toward the drug, as observed in solid-state studies. A different rank order was found in permeation experiments in which chitosan glutamate was the most powerful partner in improving the drug-apparent permeability (p < .01), followed by the hydrochloride salt (p < .05), whereas no significant effect was obtained with chitosan base. Cogrinding was the most powerful technique in promoting both dissolution and permeation properties of the drug, thus pointing out the importance of the preparation method in obtaining efficacious drug-carrier systems. Finally, the good correspondence between permeation experiments with Caco-2 cells and those with the artificial lipophilic membrane indicated the suitability of this latter in preformulation studies for a rapid screening of the best carrier and the most efficient drug-carrier preparation method for improving the biopharmaceutical properties of drugs.
Deep Option Pricing - Term Structure Models

This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options within the setting of interest rate term structure models. This aims to accelerate existing numerical methods which is important for applications like historical VaR or exposure calculation being used in financial institutions. With ANNs being a universal function approximation method, this method trains an ANN on synthetically generated data including term structures of yield and volatility. Then, within an VaR or exposure calculation instead of applying costly numerical methods for the financial model, the engine runs the trained ANN. This is faster and more efficient and allows (a) considering term structures of yields, (b) term structures of volatilities and (c) trade interpolation. We outline the generation of the training data, the neural net selection and propose further methods for optimization. In particular we consider a control variate method and the application of no-arbitrage conditions and regularization to the cost function used for learning and calibration. Finally, we test our approach on the Hull-White model with time-dependent term structure for volatility and the the Trolle-Schwartz model. The latter adds an un-spanned stochastic volatility to the rates dynamic. The numerical results show that the ANN solution, especially the one with the control variate, is accurate and reduces the computing time significantly.
Economic Condition and Financial Cognition

Abstract Are low-income individuals trapped because they are unable to make good financial decisions? We use a randomized controlled survey experiment to examine how prompting individuals to think about their personal economic condition (priming) affects their scores on a financial literacy quiz. We find that the marginal effect of poverty on financial literacy scores is 3.7 times higher for primed (treated) respondents compared to nonprimed ones. Priming not only worsens the financial literacy scores of low-income individuals, but also improves the scores of high-income individuals. Anxiety and shame are key explanations for our baseline results. Our findings shed light on how economic condition affects financial cognition, especially with regard to cognitive impediments resulting from negative emotions.
The Association between Bank Credit Terms and Conditions and the Business Cycle in the Euro-Area

We explore the trajectory of bank loan Terms and Conditions over the business cycle, where the latter is decomposed into its long-run (trend) and short-run (cyclical) components. We find that deterioration of each business cycle component leads to a significant tightening of credit terms and conditions. We found mixed results concerning the symmetry of impacts of the short and long run components. Symmetry was found between the terms and conditions on loans for small vs. large enterprises. Our findings provide very useful information to policy makers and thus they should be taken into consideration when monetary policies are designed.
Modified Whittle Estimation of Multilateral Models on a Lattice

In the estimation of parametric models for stationary spatial or spatio-temporal data on a d-dimensional lattice, for d >= 2, the achievement of asymptotic efficiency under Gaussianity, and asymptotic normality more generally, with standard convergence rate, faces two obstacles. One is the "edge effect", which worsens with increasing d. The other is the possible difficulty of computing a continuous-frequency form of Whittle estimate or a time domain Gaussian maximum likelihood estimate, due mainly to the Jacobian term. This is especially a problem in "multilateral" models, which are naturally expressed in terms of lagged values in both directions for one or more of the d dimensions. An extension of the discrete-frequency Whittle estimate from the time series literature deals conveniently with the computational problem, but when subjected to a standard device for avoiding the edge effect has disastrous asymptotic performance, along with finite sample numerical drawbacks, the objective function lacking a minimum-distance interpretation and losing any global convexity properties. We overcome these problems by first optimizing a standard, guaranteed non-negative, discrete-frequency, Whittle function, without edge-effect correction, providing an estimate with a slow convergence rate, then improving this by a sequence of computationally convenient approximate Newton iterations using a modified, almost-unbiased periodogram, the desired asymptotic properties being achieved after finitely many steps. The asymptotic regime allows increase in both directions of all d dimensions, with the central limit theorem established after re-ordering as a triangular array. However our work offers something new for "unilateral" models also. When the data are non-Gaussian, asymptotic variances of all parameter estimates may be affected, and we propose consistent, non-negative definite estimates of the asymptotic variance matrix.
Life histories, neighbourhood sizes, and variance structure in some North American conifers

Neighourhood sizes and variance structure were determined for ten North American coniferous forest tree species, using information on seed dispersal, patterns of life history, and demography. A synthetic cohort of reproductive individuals belonging to a critical age group was constructed using normal yield tables. Data on reproductive individuals in a synthetic cohort were used to determine the neighbourhood sizes for each species. The neighbourhood sizes varied from 1244 to 50 118 individuals. Effective population sizes were calculated from data on neighbourhood sizes and replacement patterns of individuals in various species, the interdeme differentiation, Fst ranged from 0.0002 to 0.007. The results suggest that large neighbourhood and effective population sizes, and hence panmixia, may be virtually common among the species examined. These conclusions are consistent with recent findings on population differentiation and estimates of gene flow in confers using allozyme markers.
Calculating and Using Second Order Accurate Solutions of Discrete Time Dynamic Equilibrium Models

It is now widely understood how to obtain first-order accurate approximations to the solution to a dynamic, stochastic general equilibrium model (DSGE model). Such solutions are fairly easy to construct and useful for a wide variety of purposes. They are likely to be accurate enough to be a basis for fitting the models to data, for example. However, for some purposes first-order accuracy is not enough. This is true in particular for comparing welfare across policies that do not have first-order effects on the model's deterministic steady state, for example. It is also true for attempts to study asset pricing in the context of DSGE models. This paper describes the algorithm for computing a second order approximation and shows how to apply it to calculating forecasts and impulse responses in dynamic models and to evaluating welfare in DSGE models. It points out some necessary regularity conditions for application of the method and discusses the sense in which the approximate solutions are locally accurate
The Impact of the TRIPS Agreement on the Access to Antiretroviral Therapy in Sub-Saharan Africa

This dissertation analyses the impact of the prevailing international intellectual property (IP) system on the access to antiretroviral treatment in Sub-Saharan Africa. <br><br>For the past decades, HIV/AIDS have been causing an unprecedented public health crisis in the region, where approximately 1.2 million people die every year of AIDS-related diseases. Despite the fact that a life-saving treatment exists, the high prices on antiretroviral drugs make them inaccessible to a vast number of patients. Arguably, this is the direct consequence of the World Trade Organization’s Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS), by virtue of which signatory states agreed to implement minimum standards of IP protection within their domestic laws. Now, multinational pharmaceutical corporations are entitled to obtain and enforce patents in the jurisdiction of all WTO members states. Since generic competition is prevented during the patent period, these are entitled to arbitrarily set the prices for their new drugs, which tend to be excessively high. <br><br>The TRIPS agreement establishes a set of opt-out clauses for those cases of national emergencies caused by a public health crisis (such as the HIV/AIDS epidemic). Governments are entitled to grant compulsory licenses and to allow for parallel imports of patented drugs, in order to make treatment available at affordable prices. However, the U.S. and the E.U. have continuously opposed the use of TRIPS-get out clauses under the menace of trade retaliations. Further, multinational pharmaceutical corporations have threaten to file law suits against governments issuing compulsory licenses and allowing for parallel imports of their drugs. <br><br>The WTO Ministerial Conference Declaration on the TRIPS Agreement and Public Health, agreed during the Doha Round of Negotiations in 2001, reinforced the right of member states to issue compulsory licenses and to freely determine the grounds under which they might be granted. Nonetheless, the U.S and the E.U. have been celebrating free trade agreements with developing countries containing provisions that increase the protection and enforcement of IP, which hinder the use of TRIPS-get out clauses. <br><br>Despite the attempts by the international community and civil society to enhance the access to antiretroviral treatment in the developing world, at the present date many patients cannot afford the high prices of antiretroviral drugs.
The Promises and Pitfalls of Machine Learning for Predicting Stock Returns

Recent research suggests that machine learning models dominate traditional linear models in predicting cross-sectional stock returns. The authors confirm this finding when predicting one-month-forward-looking returns based on a set of common stock characteristics, including predictors such as short-term reversal. Despite the statistical advantage of machine learning model predictions, the authors demonstrate that the economic gains tend to be more limited and critically dependent on the ability to take risk and implement trades efficiently. Unlike traditional models, machine learning models have been somewhat more effective over the past decade at discerning valuable predictions from cross-sectional equity characteristics. TOPICS: Security analysis and valuation, big data/machine learning Key Findings ▪ The authors compare a nonlinear machine learning model called gradient boosting machine (GBM) with traditional linear models in predicting cross-sectional stock returns based on well-known equity characteristics. ▪ They demonstrate how to rationalize the mechanics and outcome of GBM to alleviate its black-box characteristics. ▪ The extent to which the statistical advantage of GBM’s performance over that of linear models can be translated into economic gains depends critically on one’s ability to take risk and implement trades efficiently.
The Role of Social Interactions on Preferences for Redistribution

This paper investigates the role of social influences in preferences for redistribution using data from the General Social Survey. We employ social interaction models with a socioeconomic network structure and intertemporal feedbacks during the impressionable years. We find substantial evidence of both lagged endogenous and contextual effects that imply that the redistributive preferences are intertemporally dependent. Interestingly, controlling for individual income, the contextual effect of income is negative and strongly significant. Our results highlight the key role of fathers' education in the structure of the social network. We interpret our findings as suggestive evidence that social identity can explain attitudes towards redistribution. We also uncover evidence of threshold effects in preferences for redistribution that are consistent with the predictions of theoretical models that exhibit multiple equilibria. Finally, we show that our results extend to a range of other attitudes and beliefs, including politics, religion, and ethics.