The generalized finite point method

In this paper we propose a new mesh-less method based on a sub-domain collocation approach. By reducing the size of the sub-domains the method becomes similar to the well-known finite point method (FPM) and thus it can be regarded as the generalized form of finite point method (GFPM). However, unlike the FPM, the equilibrium equations are weakly satisfied on the sub-domains. It is shown that the accuracy of the results is dependent on the sizes of the sub-domains. To find an optimal size for a sub-domain we propose a patch test procedure in which a set of polynomials of higher order than those chosen for the approximations/interpolations are used as the exact solution and a suitable error norm is minimized through a size tuning procedure. In this paper we have employed the GFPM in elasto-static problems. We give the results of the size optimization in a series of tables for further use. Also the results of the integrations on a generic sub-domain are given as a series of library functions for those who want to use GFPM as a cheap and fast integral-based mesh-less method. The performance of GFPM has been demonstrated by solving several sample problems

More about QCD on compact spaces

We present some results about spontaneous breaking of global symmetries for four-flavor, three color QCD on compact spaces with two short directions. When the two short directions have equal length and identical boundary conditions, there is a single transition. When the two short directions have boundary conditions of opposite parity and are of roughly equal extent, the C-breaking and deconfinement transitions separate. When the two short dimensions are of different length, the transitions are modified in qualitative agreement with expectations from dimensional reduction. These features resemble the situation in pure gauge simulations at small and large number of colors.Comment: 9 pages, 8 figures, JHEP styl

Landslide susceptibility modeling: An integrated novel method based on machine learning feature transformation

Landslide susceptibility modeling, an essential approach to mitigate natural disasters, has witnessed considerable improvement following advances in machine learning (ML) techniques. However, in most of the previous studies, the distribution of input data was assumed as being, and treated, as normal or Gaussian; this assumption is not always valid as ML is heavily dependent on the quality of the input data. Therefore, we examine the effectiveness of six feature transformations (minimax normalization (Std-X), logarithmic functions (Log-X), reciprocal function (Rec-X), power functions (Power-X), optimal features (Opt-X), and one-hot encoding (Ohe-X) over the 11conditioning factors (i.e., altitude, slope, aspect, curvature, distance to road, distance to lineament, distance to stream, terrain roughness index (TRI), normalized difference vegetation index (NDVI), land use, and vegetation density). We selected the frequent landslide-prone area in the Cameron Highlands in Malaysia as a case study to test this novel approach. These transformations were then assessed by three benchmark ML methods, namely extreme gradient boosting (XGB), logistic regression (LR), and artificial neural networks (ANN). The 10-fold cross-validation method was used for model evaluations. Our results suggest that using Ohe-X transformation over the ANN model considerably improved performance from 52.244 to 89.398 (37.154% improvement)

Assessing Phytoplankton Nutritional Status and Potential Impact of Wet Deposition in Seasonally Oligotrophic Waters of the Mid-Atlantic Bight

To assess phytoplankton nutritional status in seasonally oligotrophic waters of the southern Mid-Atlantic Bight, and the potential for rain to stimulate primary production in this region during summer, shipboard bioassay experiments were performed using natural seawater and phytoplankton collected north and south of the Gulf Stream. Bioassay treatments comprised iron, nitrate, iron + nitrate, iron + nitrate + phosphate, and rainwater. Phytoplankton growth was inferred from changes in chlorophyll a, inorganic nitrogen, and carbon-13 uptake, relative to unamended control treatments. Results indicated the greatest growth stimulation by iron + nitrate + phosphate, intermediate growth stimulation by rainwater, modest growth stimulation by nitrate and iron + nitrate, and no growth stimulation by iron. Based on these data and analysis of seawater and atmospheric samples, nitrogen was the proximate limiting nutrient, with a secondary limitation imposed by phosphorus. Our results imply that summer rain events increase new production in these waters by contributing nitrogen and phosphorus, with the availability of the latter setting the upper limit on rain-stimulated new production. Plain Language Summary Human activities have substantially increased the atmospheric loading and deposition of biologically available nitrogen, an essential nutrient, to the surface ocean. Such atmospheric inputs to the ocean will likely impact on oceanic primary production by phytoplankton, and thus the marine ecosystem and ocean carbon cycling, although the scale and spatial distribution of such impacts are not well known. In this study, we used shipboard experiments, observations, and laboratory measurements to assess the potential impacts of atmospheric nitrogen deposition in rainfall on oceanic waters of the Mid-Atlantic Bight, off the U.S. eastern seaboard, during the summer. We find that the growth of phytoplankton in these waters is limited by the availability of nitrogen during summer, such that nitrogen added to the ocean by summer rain events can considerably stimulate phytoplankton primary production. However, the biological impact of these rainwater nitrogen inputs appears to be limited by the availability of another essential nutrient, phosphorus, which is present at relatively low concentrations in rainwater. This is the first study to directly examine the nutritional status of phytoplankton in relation to the impacts of rainwater nitrogen addition on primary production in oceanic waters off the U.S. East Coast