2,010 research outputs found

    Understanding the impact of economic shocks on labor market outcomes in developing countries : an application to Indonesia and Mexico

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    In this paper the authors use a search and matching model of multi-sector labor markets, to understand the channels through which economic shocks affect labor market outcomes in developing countries. In the model workers can be employed in agriculture, formal or informal urban jobs, or unemployed. Economic shocks are manifested as either increased turbulence in the formal/informal sectors or a decrease in overall sectoral productivity. By calibrating the model to Indonesia and Mexico, the authors are able to understand how the 1998 Indonesian crisis and the 2001 Mexican recession translated into labor market outcomes. They then venture to simulate how the current financial crisis might affect the allocation of labor and earnings across sectors, in these countries. The results suggest that in both countries past crises have increased the degree of turbulence of the formal sector, increasing job destruction. However, while in Indonesia the crisis affected the overall formal sector productivity, this was not the case in Mexico. This explains the larger blow to formal wages -- relative to the size of the shock- witnessed by Indonesian workers. The response of the informal sector was also different: In both countries the informal sector was able to act as a buffer, as relative earnings increased. However, while in Mexico it became much harder to find informal sector opportunities and easier to keep the job once found; in Indonesia turbulence in the informal sector increased substantially increasing the job destruction rate of informal jobs andlimiting the cushioning role that the informal sector might have played. The agricultural sector was spared from the shock in both countries. In Indonesia, it actually benefited from an unusual exogenous increase in the price of rise. The simulations show that if either the informal or agricultural sectors are spared from the shocks, large reallocations of labor might occur, and the overall effect of the shock is smaller. Instead, if these sectors can’t buffer the shock, the reallocation of labor is much smaller, but earnings in the formal sector drop substantially. The authors also explore the impact of alternative policies. They find that in relatively flexible markets where informality can be seen more as a choice rather than as queuing, unemployment benefits and informal employment subsidies may have paradoxical effects, by discouraging formal search. Instead, policies targeted at creating informal employment and boosting formal TFP growth have the desired effects.Labor Markets,Labor Policies,Markets and Market Access,Banks&Banking Reform,Economic Theory&Research

    Isotope effects in the Hubbard-Holstein model within dynamical mean-field theory

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    We study the isotope effects arising from the coupling of correlated electrons with dispersionless phonons by considering the Hubbard-Holstein model at half-filling within the dynamical mean-field theory. In particular we calculate the isotope effects on the quasi-particle spectral weight ZZ, the renormalized phonon frequency, and the static charge and spin susceptibilities. In the weakly correlated regime U/t1.5U/t \lesssim 1.5, where UU is the Hubbard repulsion and tt is the bare electron half-bandwidth, the physical properties are qualitatively similar to those characterizing the Holstein model in the absence of Coulomb repulsion, where the bipolaronic binding takes place at large electron-phonon coupling, and it reflects in divergent isotope responses. On the contrary in the strongly correlated regime U/t1.5U/t \gtrsim 1.5, where the bipolaronic metal-insulator transition becomes of first order, the isotope effects are bounded, suggesting that the first order transition is likely driven by an electronic mechanism, rather then by a lattice instability. These results point out how the isotope responses are extremely sensitive to phase boundaries and they may be used to characterize the competition between the electron-phonon coupling and the Hubbard repulsion.Comment: 10 pages, 8 figures. The paper has been already accepted on Phys. Rev.

    Nonadiabatic high-Tc superconductivity in hole-doped fullerenes

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    In this paper we address the possibility of high-T-c superconductivity (T(c)similar to100 K) in hypothetical hole doped C-60 within the context of the nonadiabatic theory of superconductivity. Our analysis shows that electron doped fullerenes, represented by the A(3)C(60) family, are characterized by relatively small values of the electron-phonon coupling constant lambda, which can thus be further increased by hole doping before lattice instabilities occur. In particular we show that T-c larger than 100 K are compatible in the nonadiabatic context with microscopic parameters lambda(h)similar or equal to0.5-1.0, mu(*)similar or equal to0.3-0.5 and phonon frequencies omega(ph)similar or equal to1500-2000 K. These results provide a stimulus for material engineering and optimization along the lines indicated

    Polaronic and nonadiabatic phase diagram from anomalous isotope effects

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    Isotope effects (IEs) are powerful tool to probe directly the dependence of many physical properties on the lattice dynamics. In this paper we invenstigate the onset of anomalous IEs in the spinless Holstein model by employing the dynamical mean field theory. We show that the isotope coefficients of the electron effective mass and of the dressed phonon frequency are sizeable also far away from the strong coupling polaronic crossover and mark the importance of nonadiabatic lattice fluctuations in the weak to moderate coupling region. We characterize the polaronic regime by the appearence of huge IEs. We draw a nonadiabatic phase diagram in which we identify a novel crossover, not related to polaronic features, where the IEs attain their largest anomalies.Comment: 5 pages, 4 figure

    Allyl sulfur compounds and cellular detoxification system: effects and perspectives in cancer therapy

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    Natural organosulfur compounds (OSCs) have been shown to have chemopreventive effects and to suppress the proliferation of tumor cells in vitro through the induction of apoptosis. The biochemical mechanisms underlying the antitumorigenic and anti-proliferative effects of garlic-derived OSCs are not fully understood. Several modes of action of these compounds have been proposed, and it seems likely that the rate of clearance of allyl sulfur groups from cells is a determinant of the overall response. The aim of this review is to focus attention on the effects of natural allyl sulfur compounds on the cell detoxification system in normal and tumor cells. It has been already reported that several natural allyl sulfur compounds induce chemopreventive effects by affecting xenobiotic metabolizing enzymes and inducing their down-activation. Moreover, different effects of water- and oil-soluble allyl sulfur compounds on enzymes involved in the detoxification system of rat tissues have been observed. A direct interaction of the garlic allyl sulfur compounds with proteins involved in the detoxification system was studied in order to support the hypothesis that proteins possessing reactive thiol groups and that are involved in the detoxification system and in the cellular redox homeostasis, are likely the preferential targets of these compounds. The biochemical transformation of the OSCs in the cell and their adducts with thiol functional groups of these proteins, could be considered relevant events to uncover the anticancer properties of the allyl sulfur compounds. Although additional studies, using proteomic approaches and transgenic models, are needed to identify the molecular targets and modes of action of these natural compounds, the allyl sulfur compounds can represent potential ideal agents in anticancer therapy, either alone or in association with other antitumor drugs

    Effect of dietary turmeric powder (Curcuma longa L.) on cooked pig meat quality

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    The study was carried out on raw meat samples derived from pigs fed with a control diet and a diet supplemented with daily 4.5 g of turmeric powder per pig. After slaughter raw meat was stored for 7 days at 4°C. At Day 0 and Day 7 samples were cooked in a preheated oven at 163°C to the internal temperature of 71°C. Colour parameters, Warner Bratzler shear force, TBARS and antioxidant capacity (ABTS, DPPH and FRAP) were determined at Day 0 and Day 7. Dietary turmeric powder induced an increase in cooked meat of L* value (P < 0.001) and reductions in a*, b* indexes and in C* value (P < 0.01, P < 0.001 and P < 0.001, respectively). Colour modifications in cooked meat were correlated with colour parameters of raw samples. The Curcuma longa powder dietary supplementation did not affect lipid oxidation, Warner Bratzler shear force and antioxidant capacity of cooked meat (P > 0.05)

    The Role of High-Dimensional Diffusive Search, Stabilization, and Frustration in Protein Folding

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    Proteins are polymeric molecules with many degrees of conformational freedom whose internal energetic interactions are typically screened to small distances. Therefore, in the high-dimensional conformation space of a protein, the energy landscape is locally relatively flat, in contrast to low-dimensional representations, where, because of the induced entropic contribution to the full free energy, it appears funnel-like. Proteins explore the conformation space by searching these flat subspaces to find a narrow energetic alley that we call a hypergutter and then explore the next, lower-dimensional, subspace. Such a framework provides an effective representation of the energy landscape and folding kinetics that does justice to the essential characteristic of high-dimensionality of the search-space. It also illuminates the important role of nonnative interactions in defining folding pathways. This principle is here illustrated using a coarse-grained model of a family of three-helix bundle proteins whose conformations, once secondary structure has formed, can be defined by six rotational degrees of freedom. Two folding mechanisms are possible, one of which involves an intermediate. The stabilization of intermediate subspaces (or states in low-dimensional projection) in protein folding can either speed up or slow down the folding rate depending on the amount of native and nonnative contacts made in those subspaces. The folding rate increases due to reduced-dimension pathways arising from the mere presence of intermediate states, but decreases if the contacts in the intermediate are very stable and introduce sizeable topological or energetic frustration that needs to be overcome. Remarkably, the hypergutter framework, although depending on just a few physically meaningful parameters, can reproduce all the types of experimentally observed curvature in chevron plots for realizations of this fold

    Feature transforms for image data augmentation

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    A problem with convolutional neural networks (CNNs) is that they require large datasets to obtain adequate robustness; on small datasets, they are prone to overfitting. Many methods have been proposed to overcome this shortcoming with CNNs. In cases where additional samples cannot easily be collected, a common approach is to generate more data points from existing data using an augmentation technique. In image classification, many augmentation approaches utilize simple image manipulation algorithms. In this work, we propose some new methods for data augmentation based on several image transformations: the Fourier transform (FT), the Radon transform (RT), and the discrete cosine transform (DCT). These and other data augmentation methods are considered in order to quantify their effectiveness in creating ensembles of neural networks. The novelty of this research is to consider different strategies for data augmentation to generate training sets from which to train several classifiers which are combined into an ensemble. Specifically, the idea is to create an ensemble based on a kind of bagging of the training set, where each model is trained on a different training set obtained by augmenting the original training set with different approaches. We build ensembles on the data level by adding images generated by combining fourteen augmentation approaches, with three based on FT, RT, and DCT, proposed here for the first time. Pretrained ResNet50 networks are finetuned on training sets that include images derived from each augmentation method. These networks and several fusions are evaluated and compared across eleven benchmarks. Results show that building ensembles on the data level by combining different data augmentation methods produce classifiers that not only compete competitively against the state-of-the-art but often surpass the best approaches reported in the literature
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