Labor Migration and Social Networks Participation: Evidence from Southern Mozambique

This paper investigates how social networks in poor developing settings are af- fected if people migrate. By using an unique household survey from two southern regions in Mozambique, we test the role of labor mobility in shaping participation in groups and social networks by migrant sending households in village economies at origin. We find that households with successful migrants (i.e. those receiving either remittances or return migration) engage more in community based social networks. Our findings are robust to alternative definitions of social interaction and to endogeneity concerns suggesting that stable migration ties and higher income stability through remittances may decrease participation constraints and increase household commitment in cooperative arrangements in migrant-sending communities.International Migration, Social Capital, Networks, Group Participation, Mozambique

The impact of family size and sibling structure on the great Mexico-USA migration

We investigate the impact of fertility and demographic factors on the Great Mexico-USA immigration by assessing the causal effects of sibship size and structure on migration decisions within the household. We use a rich demographic survey on the population of Mexico and exploit presumably exogenous variation in family size induced by biological fertility and infertility shocks. We further exploit cross-sibling differences to identify the effects of birth order, siblings' sex, and siblings' ages on migration. We find that large families per se do not boost offspring's emigration. However, the likelihood of migrating is not equally distributed within a household. It is higher for sons and decreases sharply with birth order. The female migration disadvantage also varies with sibling composition by age and gender

The impact of family size and sibling structure on the great Mexico–USA migration

We investigate the impact of fertility and demographic factors on the Great Mexico\u2013USA immigration by assessing the causal effects of sibship size and structure on migration decisions within the household. We use a rich demographic survey on the population of Mexico and exploit presumably exogenous variation in family size induced by biological fertility and infertility shocks. We further exploit cross-sibling differences to identify the effects of birth order, siblings\u2019 sex, and siblings\u2019 ages on migration. We find that large families per se do not boost offspring\u2019s emigration. However, the likelihood of migrating is not equally distributed within a household. It is higher for sons and decreases sharply with birth order. The female migration disadvantage also varies with sibling composition by age and gender

Identification of Chimera using Machine Learning

Chimera state refers to coexistence of coherent and non-coherent phases in identically coupled dynamical units found in various complex dynamical systems. Identification of Chimera, on one hand is essential due to its applicability in various areas including neuroscience, and on other hand is challenging due to its widely varied appearance in different systems and the peculiar nature of its profile. Therefore, a simple yet universal method for its identification remains an open problem. Here, we present a very distinctive approach using machine learning techniques to characterize different dynamical phases and identify the chimera state from given spatial profiles generated using various different models. The experimental results show that the performance of the classification algorithms varies for different dynamical models. The machine learning algorithms, namely random forest, oblique random forest based on tikhonov, parallel-axis split and null space regularization achieved more than $96\%$ accuracy for the Kuramoto model. For the logistic-maps, random forest and tikhonov regularization based oblique random forest showed more than $90\%$ accuracy, and for the H\'enon-Map model, random forest, null-space and axis-parallel split regularization based oblique random forest achieved more than $80\%$ accuracy. The oblique random forest with null space regularization achieved consistent performance (more than $83\%$ accuracy) across different dynamical models while the auto-encoder based random vector functional link neural network showed relatively lower performance. This work provides a direction for employing machine learning techniques to identify dynamical patterns arising in coupled non-linear units on large-scale, and for characterizing complex spatio-temporal patterns in real-world systems for various applications.Comment: 20 Pages, 4 Figures; Comments welcom

Numerical Modelling of the Constitutive Behaviour of FRCM Composites through the Use of Truss Elements

The modeling of the mechanical behavior of Fabric Reinforced Cementitious Matrix (FRCM) composites is a difficult task due to the complex mechanisms established at the fibre-matrix and composite-support interface level. Recently, several modeling approaches have been proposed to simulate the mechanical response of FRCM strengthening systems, however a simple and reliable procedure is still missing. In this paper, two simplified numerical models are proposed to simulate the tensile and shear bond behavior of FRCM composites. Both models take advantage of truss and non-linear spring elements to simulate the material components and the interface. The proposed approach enables us to deduce the global mechanical response in terms of stress-strain or stress-slip relations. The accuracy of the proposed models is validated against the experimental benchmarks available in the literature

The role of ecotypic variation and the environment on biomass and nitrogen in a dominant prairie grass

Citation: Mendola, M. L., Baer, S. G., Johnson, L. C., & Maricle, B. R. (2015). The role of ecotypic variation and the environment on biomass and nitrogen in a dominant prairie grass. Ecology, 96(9), 2433-2445. doi:10.1890/14-1492.1Knowledge of the relative strength of evolution and the environment on a phenotype is required to predict species responses to environmental change and decide where to source plant material for ecological restoration. This information is critically needed for dominant species that largely determine the productivity of the central U.S. grassland. We established a reciprocal common garden experiment across a longitudinal gradient to test whether ecotypic variation interacts with the environment to affect growth and nitrogen (N) storage in a dominant grass. We predicted plant growth would increase from west to east, corresponding with increasing precipitation, but differentially among ecotypes due to local adaptation in all ecotypes and a greater range of growth response in ecotypes originating from west to east. We quantified aboveground biomass, root biomass, belowground net primary production (BNPP), root C:N ratio, and N storage in roots of three ecotypes of Andropogon gerardii collected from and reciprocally planted in central Kansas, eastern Kansas, and southern Illinois. Only the ecotype from the most mesic region (southern Illinois) exhibited more growth from west to east. There was evidence for local adaptation in the southern Illinois ecotype by means of the local vs. foreign contrast within a site and the home vs. away contrast when growth in southern Illinois was compared to the most distant site in central Kansas. Root biomass of the eastern Kansas ecotype was higher at home than at either away site. The ecotype from the driest region, central Kansas, exhibited the least response across the environmental gradient, resulting in a positive relationship between the range of biomass response and precipitation in ecotype region of origin. Across all sites, ecotypes varied in root C: N ratio (highest in the driest-origin ecotype) and N storage in roots (highest in the most mesic-origin ecotype). The low and limited range of biomass, higher C: N ratio of roots, and lower N storage in the central Kansas ecotype relative to the southern Illinois ecotype suggests that introducing ecotypes of A. gerardii from much drier regions into highly mesic prairie would reduce productivity and alter belowground ecosystem processes under a wide range of conditions

Application of a predictive model of axillary lymph node status in patients with sentinel node metastasis from breast cancer. A retrospective cohort study

Background and objectives The Axillary Lymph Node Dissection (ALND) is the standard treatment in patients with invasive breast cancer and sentinel node metastasis, but in 60% of the cases there is no further axillary neoplastic involvement, so this invasive intervention represents an overtreatment. The purpose of the study is to identify patients with low risk of additional nodal metastases, to omit ALND. Methods The MSKCC Additional nodal metastasis nomogram was applied on a sample of 175 patients with invasive breast cancer who underwent ALND after detection of macrometastasis with the extemporaneous examination of the sentinel lymph node. Patients were classified as “low risk” when the result of the nomogram was ≤50%. Sensitivity, specificity, positive and negative predictive values and AUC (Area Under Curve) of the ROC curve of the nomogram were then calculated. Results A cut-off by 50% yielded 92.3% sensitivity, 81,4% specificity, 80% positive predictive value and 92.9% negative predictive value. The ROC curve AUC in these patients was 0.885. Conclusions The MSKCC nomogram has proven to be an effective tool in estimating the axillary lymph node status and it can potentially be used to better select the patients with sentinel node macrometastasis who can actually benefit from ALND