Privatization in Bolivia: The Impact on Firm Performance

This report studies the change in performance of Bolivian State Owned Enterprises (SOEs) that have been transferred to the private sector. The paper focuses particularly on how ownership affects management by measuring the characteristics of management and relating them to both ownership structure and performance. It argues that the characteristics of private management that follow privatization are a key factor in determining the effects of privatization on performance. To determine the impact of privatization on the performance of the 31 firms studied, the authors performed two ratio analyses, one with unadjusted and the other with adjusted ratios. For this purpose, they undertook two regression analyses, one with panel data and the other with a cross section analysis. Two methods were used to conduct privatization of Bolivian SOEs, and the process took place in three stages. The methods were traditional, or standard, privatization and capitalization. Traditional privatization consisted of the complete transfer (assets and shares) to the private sector of companies operating in competitive markets. Capitalization involved attracting private firms to invest in and manage key SOEs. SOEs were not sold outright. Instead, private investors gained managerial control but no more than 50 percent of equity.

Lower Extremity Power and its Relationship to Qualitative and Quantitative Measures of Landing Performance

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Practical Applications of Electromyography for Strength Coaches: A Case Study of the Isometric Squat

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Thirteen years of integrated precipitable water derived by GPS at Mario Zucchelli Station, Antarctica

Since 1998, the Italian Antarctic Programme is funding space geodetic activities based on the use of episodic and permanent GPS observations. Beside their exploitation in geodynamics, the data can be used to sense the atmosphere and retrieve the water vapour content and variation. The surface pressure and temperature at the GPS tracking sites are necessary to compute the precipitable water; at sites where no information is available, the values can be retrieved from a global grid model. We process the data series of the permanent GPS site TNB1 (Mario Zucchelli Station, Antarctica) from 1998 up to 2010 comparing the use of grid values to the implementation of real surface records. With both approaches, we estimate almost 70000 hourly values of precipitable water over 13 years and we find discrepancies varying between (1.8 ± 0.2) mm in summer and (3.3 ± 0.5) mm in winter. In addition, the discrepancies of the two solutions exhibit a clear seasonal dependency. We validate our results using radio soundings measurements. They agree better with the precipitable water values derived from real surface data. Nevertheless, these latter exhibit dry biases and detect the (77±21) % of the content of moisture measured by the radio soundings. Both GPS and radio sounding observations are processed adopting the most up-to-date strategies to reduce and dominate known systematic errors

Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning (DL) is already present in many applications ranging from computer vision for medicine to autonomous driving of modern cars as well as other sectors in security, healthcare, and finance. However, to achieve impressive performance, these algorithms employ very deep networks, requiring a significant computational power, both during the training and inference time. A single inference of a DL model may require billions of multiply-and-accumulated operations, making the DL extremely compute-and energy-hungry. In a scenario where several sophisticated algorithms need to be executed with limited energy and low latency, the need for cost-effective hardware platforms capable of implementing energy-efficient DL execution arises. This paper first introduces the key properties of two brain-inspired models like Deep Neural Network (DNN), and Spiking Neural Network (SNN), and then analyzes techniques to produce efficient and high-performance designs. This work summarizes and compares the works for four leading platforms for the execution of algorithms such as CPU, GPU, FPGA and ASIC describing the main solutions of the state-of-the-art, giving much prominence to the last two solutions since they offer greater design flexibility and bear the potential of high energy-efficiency, especially for the inference process. In addition to hardware solutions, this paper discusses some of the important security issues that these DNN and SNN models may have during their execution, and offers a comprehensive section on benchmarking, explaining how to assess the quality of different networks and hardware systems designed for them

The Geant4-DNA project

The Geant4-DNA project proposes to develop an open-source simulation software based and fully included in the general-purpose Geant4 Monte Carlo simulation toolkit. The main objective of this software is to simulate biological damages induced by ionising radiation at the cellular and sub-cellular scale. This project was originally initiated by the European Space Agency for the prediction of deleterious effects of radiation that may affect astronauts during future long duration space exploration missions. In this paper, the Geant4-DNA collaboration presents an overview of the whole ongoing project, including its most recent developments already available in the last Geant4 public release (9.3 BETA), as well as an illustration example simulating the direct irradiation of a chromatin fibre. Expected extensions involving several research domains, such as particle physics, chemistry and cellular and molecular biology, within a fully interdiciplinary activity of the Geant4 collaboration are also discussed.Comment: presented by S. Incerti at the ASIA SIMULATION CONFERENCE 2009, October 7-9, 2009, Ritsumeikan University, Shiga, Japa

Amniotic microvesicles impact hatching and pregnancy percentages of in vitro bovine embryos and blastocyst microRNA expression versus in vivo controls

Embryo development and implantation are dynamic processes, responsive to external signals, and can potentially be influenced by many environmental factors. The aims of this study were to evaluate the effects of a culture medium supplemented with amniotic-derived microvesicles (MVs) on in vitro embryo hatching after cryopreservation, and pregnancy rate following embryo transfer. In addition, miRNA profiling of blastocysts produced in vitro, with or without (control; CTR) amniotic MV supplementation, was also evaluated using blastocysts produced in vivo. In vitro embryos were cultured with and without amniotic MV supplementation. In vivo blastocysts were obtained from superovulated cows. Samples for RNA isolation were obtained from three pools of 10 embryos each (in vivo, in vitro-CTR and in vitro + MVs). Our results show that the hatching percentage of cryopreserved in vitro + MVs embryos is higher (P < 0.05) than in vitro-CTR embryos and the pregnancy rate with fresh and cryopreserved in vitro + MVs embryos is higher than in vitro-CTR embryos. In addition, the analysis of differently expressed (DE) microRNAs showed that embryos produced in vivo are clearly different from those produced in vitro. Moreover, in vitro-CTR and in vitro + MVs embryos differ significantly for expression of two miRNAs that were found in higher concentrations in in vitro-CTR embryos. Interestingly, these two miRNAs were also reported in degenerated bovine embryos compared to good quality blastocysts. In conclusion, MV addition during in vitro production of embryos seems to counteract the adverse effect of in vitro culture and partially modulate the expression of specific miRNAs involved in successful embryo implantation

Genomic and transcriptomic comparison between Staphylococcus aureus strains associated with high and low within herd prevalence of intra-mammary infection

Background: Staphylococcus aureus (Staph. aureus) is one of the major pathogens causing mastitis in dairy ruminants worldwide. The chronic nature of Staph. aureus infection enhances the contagiousness risk and diffusion in herds. In order to identify the factors involved in intra-mammary infection (IMI) and diffusion in dairy cows, we investigated the molecular characteristics of two groups of Staph. aureus strains belonging to ST8 and ST398, differing in clinical properties, through comparison of whole genome and whole transcriptome sequencing. Results: The two groups of strains, one originated from high IMI prevalence herds and the other from low IMI prevalence herds, present a peculiar set of genes and polymorphisms related to phenotypic features, such as bacterial invasion of mammary epithelial cells and host adaptation. Transcriptomic analysis supports the high propensity of ST8 strain to chronicity of infection and to a higher potential cytotoxicity. Conclusions: Our data are consistent with the invasiveness and host adaptation feature for the strains GTB/ST8 associated to high within-herd prevalence of mastitis. Variation in genes coding for surface exposed proteins and those associated to virulence and defence could constitute good targets for further research

Chemical modelling of Alkali Silica reaction: Influence of the reactive aggregate size distribution

International audienceThis article presents a new model which aims at the prediction of the expansion induced by Alkali Silica Reaction (ASR) and the description of the chemical evolution of affected concretes. It is based on the description of the transport and reaction of alkalis and calcium ions within a Relative Elementary Volume (REV). It takes into account the influence of the reactive aggregate size grading on ASR, i.e. the effect of the simultaneous presence of different sized reactive aggregates within concrete. The constitutive equations are detailed and fitted using experimental results. Results from numerical simulations are presented and compared with experiments.Cet article présente un modèle qui a pour but la prédiction du gonflement induit par la réaction alcali-silice et la description de l'évolution chimique des bétons affectés. Il est basé sur la description du transport et de la réaction des alcalins et des ions calcium dans un Volume Elémentaire Représentatif. Il permet notamment de tenir compte de l'influence de la granulométrie réactive, c'est-à-dire de l'influence de la présence simultanée de granulats réactifs de différentes tailles dans le béton. Les équations constitutives du modèle sont détaillées puis calées à partir de résultats expérimentaux. Les résultats des simulations numériques sont présentés et comparés aux valeurs expérimentales