Total Factor Productivity and the Mongolian Transition

Total Factor Productivity (TFP)is often used on the macro-economic level as an indicator of changes in efficiency of a country. In many transition economies TFP is seen to have been negative the last decade of the plan economy and starts increasing and become positive after a (quite a) few years of transition. Many authors conclude that this is a gain in efficiency due to the structural changes –such as privatisation and liberalisation – carried out in order to establish a market economy in those countries. In the case of Mongolia, not only non-viable enterprises closed down, but many possibly viable enterprises with potential closed down as well. This raises the question whether changes in TFP were really attributable to increases in efficiency. To investigate this, the mathematical properties of TFP are analysed in order to generate new insights into the development of TFP in Mongolia. Simulations are performed to see what happens with TFP if not the le! ast efficient, but a certain percentage of enterprises in a (closed) economy randomly close down. The robustness of Total Factor Productivity of Mongolia was tested not only for errors in all estimated values but also for measurement errors in the data. It was concluded that in many commonly occurring cases it is not necessary to estimate alpha; that a random closure of enterprises fits the data of Mongolia much more closely than closing only the least efficient enterprises; and that measurement errors in the data influence the estimated TFP significantly

Answering attitudinal questions: modelling the response process underlying contrastive questions

We analyse reaction time distributions and responses for attitudinal survey questions, which were part of a self-administered questionnaire about medical and ethical issues. Two contrastive versions of each question were asked, whether an issue should be forbidden or whether the government should allow it. Logically, the answers to these contrastive questions should oppose, but numerous investigations have shown that they result in the so-called forbid/allow asymmetry : respondents tend to rather say ‘no’ to both questions. We present a mathematical model, based on point process theory, which formalises attitude representations in memory and different stages in the response process. The data of the allow questions are used for parameter estimation, while the forbid data are used to test the predictive power of different model versions. The result is a model that describes the cognitive processes underlying the asymmetry. It indicates that the forbid/allow asymmetry is caused by the use of an increased response threshold in forbid answers, and that the asymmetry size varies due to both respondent characteristics and the issue at hand. This model is capable of simultaneously predicting the asymmetry in the reaction time distributions and in the response scores for the answering categories. Copyright © 2007 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/55953/1/1337_ftp.pd

Crack roughness and avalanche precursors in the random fuse model

We analyze the scaling of the crack roughness and of avalanche precursors in the two dimensional random fuse model by numerical simulations, employing large system sizes and extensive sample averaging. We find that the crack roughness exhibits anomalous scaling, as recently observed in experiments. The roughness exponents ($\zeta$, $\zeta_{loc}$) and the global width distributions are found to be universal with respect to the lattice geometry. Failure is preceded by avalanche precursors whose distribution follows a power law up to a cutoff size. While the characteristic avalanche size scales as $s_0 \sim L^D$, with a universal fractal dimension $D$, the distribution exponent $\tau$ differs slightly for triangular and diamond lattices and, in both cases, it is larger than the mean-field (fiber bundle) value $\tau=5/2$

Near-optimal combination of disparity across a log-polar scaled visual field

The human visual system is foveated: we can see fine spatial details in central vision, whereas resolution is poor in our peripheral visual field, and this loss of resolution follows an approximately logarithmic decrease. Additionally, our brain organizes visual input in polar coordinates. Therefore, the image projection occurring between retina and primary visual cortex can be mathematically described by the log-polar transform. Here, we test and model how this space-variant visual processing affects how we process binocular disparity, a key component of human depth perception. We observe that the fovea preferentially processes disparities at fine spatial scales, whereas the visual periphery is tuned for coarse spatial scales, in line with the naturally occurring distributions of depths and disparities in the real-world. We further show that the visual system integrates disparity information across the visual field, in a near-optimal fashion. We develop a foveated, log-polar model that mimics the processing of depth information in primary visual cortex and that can process disparity directly in the cortical domain representation. This model takes real images as input and recreates the observed topography of human disparity sensitivity. Our findings support the notion that our foveated, binocular visual system has been moulded by the statistics of our visual environment

Application of a Limited-Area Short-Range Ensemble Forecast System to a Case of Heavy Rainfall in the Mediterranean Region

n/

Analysis of 22 mutations within milk protein genes in Italian Friesian cattle

The bovine milk protein caseins, αS1-CN, β-CN, αS2-CN, and κ-CN are codified by four well characterized genes, named CSN1S1, CSN2, CSN1S2, and CSN3 respectively and clustered in a region of 250-kb of chromosome 6. A recent revision of milk protein nomenclature considering only protein polymorphisms indicates 8 αS1-CN, 4 αS2-CN, 12 β-CN, and 11 κ-CN variants within the genus Bos. Other mutations were found in the non-coding regions of the cluster, such as the promoter regions or the 3'UTR. Many of these polymorphisms, together with others in various genes, such as the one coding for β-lactoglobulin (LGB), show important associations with different milk quality traits. Analyzing all these polymorphisms could help clarify the role of both the casein haplotype and the other polymorphisms in milk composition and cheese-making properties, and could explain which polymorphisms are really or mostly involved. The mPCR-LDR-UA approach recently developed to test simultaneously 22 SNPs in DNA regions responsible for milk protein expression was used to type 250 Italian Friesian cattle. In perfect agreement with literature, the most frequent alleles were CSN1S1*B, CSN2*A2, CSN3*A, variant 2 of CSN1S1 promoter, and variant A of Bov-A2 element. A quite balanced frequency was observed for the LGB*A and LGB*B. No CSN2*C, CSN3*C, and CSN3*H alleles were found. The CSN1S1*C, CSN2*A3, CSN2*I alleles were detected only at the heterozygous condition and at a frequency lower than 2%. The method allowed also finding some unusual intragenic haplotype, such as the Bov-A2 element-CSN3 haplotypes A-B and B-E. As to LGB one of the four SNPs tested was always homozygous for the same mutation, as already noticed. This finding confirms that this synonymous SNP is probably a sequencing mistake or a rare mutation not decisive for the LGB typing in the Italian Friesian. Reducing cost and time for typing simultaneously many SNPs, the method will be applied to a greater number of individuals and to other breeds, aiming to find out a number of animals for each haplotype sufficient for accurate statistical analysis to give a better understanding of the significance of milk protein polymorphism

Green power grids: How energy from renewable sources affects networks and markets

The increasing attention to environmental issues is forcing the implementation of novel energy models based on renewable sources. This is fundamentally changing the configuration of energy management and is introducing new problems that are only partly understood. In particular, renewable energies introduce fluctuations which cause an increased request for conventional energy sources to balance energy requests at short notice. In order to develop an effective usage of low-carbon sources, such fluctuations must be understood and tamed. In this paper we present a microscopic model for the description and for the forecast of short time fluctuations related to renewable sources in order to estimate their effects on the electricity market. To account for the inter-dependencies in the energy market and the physical power dispatch network, we use a statistical mechanics approach to sample stochastic perturbations in the power system and an agent based approach for the prediction of the market players' behavior. Our model is data-driven; it builds on one-dayahead real market transactions in order to train agents' behaviour and allows us to deduce the market share of different energy sources. We benchmarked our approach on the Italian market, finding a good accordance with real data

In vitro evaluation of caseinophosphopeptides from different genetic variants on bone mineralization

Casein phosphopeptides (CPPs) have been shown to enhance calcium solubility and to increase the calcification by in vitro analyses. The aim of our study was to investigate the effects of four selected casein peptides, which differ in the number of phosphorylated serines, on osteoblast mineralization in vitro. The chosen peptides, related to different casein genetic variants, were obtained by chemical synthesis and tested on murine osteoblast cell line (MC3T3-E1). Our results suggest that the distinct peptides in protein hydrolysates may differentially affect calcium deposition in the extracellular matrix and that the genetic variation within the considered peptides is involved in their differential effect