Enhanced default risk models with SVM+

Default risk models have lately raised a great interest due to the recent world economic crisis. In spite of many advanced techniques that have extensively been proposed, no comprehensive method incorporating a holistic perspective has hitherto been considered. Thus, the existing models for bankruptcy prediction lack the whole coverage of contextual knowledge which may prevent the decision makers such as investors and financial analysts to take the right decisions. Recently, SVM+ provides a formal way to incorporate additional information (not only training data) onto the learning models improving generalization. In financial settings examples of such non-financial (though relevant) information are marketing reports, competitors landscape, economic environment, customers screening, industry trends, etc. By exploiting additional information able to improve classical inductive learning we propose a prediction model where data is naturally separated into several structured groups clustered by the size and annual turnover of the firms. Experimental results in the setting of a heterogeneous data set of French companies demonstrated that the proposed default risk model showed better predictability performance than the baseline SVM and multi-task learning with SVM.     

Replacement of Pontesei Bridge, Italy

Pontesei Dam is a major concrete dam built after World War II across the canyon of Maè Creek (Valle di Zoldo) in the Italian Eastern Alps. Just upstream from the dam, a gully discharges water from steep mountains. The only road serving the dam and the entire valley upstream of the dam runs along the mountainside and crosses the gully. In 1959, an exceptionally rainy season caused a flood, which destroyed the bridge. A temporary Bailey bridge was subsequently built by the army, but in 1990 it was decided to design a new bridge. The main challenges posed to the designer included building the new deck and the abutments underneath the Bailey bridge without disrupting traffic, hoisting the deck just to the bottom of the Bailey bridge, and finally substituting the new deck for the Bailey bridge in one day. Other problems included the instability of the rock mass at the abutments and the gravitative convergence of the two sides of the gully. This paper describes the design, construction, and testing of the bridge replacement and the bridge abutments.     

A Dynamic Harmonic Regression Approach to Power System Modal Identification and Prediction

Abstract—Time series models provide a powerful tool to extract nonstationary features from measured data. In this article, a statistical framework based upon a dynamic harmonic regression model for examining modal behavior is provided. In this model, temporal patterns in measured data are modeled within a stochastic state space setting. Estimates of the states or time-varying parameters are then obtained using an optimal estimation method based on the Kalman filter. Techniques to estimate future values of the unobserved signal are also analyzed. The widely applicable technique is illustrated on both simulated and measured data. Factors that affect the performance of the method are discussed, including the effects of non-linear trends, data quality, and sampling design. Connections with other modal identification methods are also investigated.     

Revamping SiO2 Spheres by Core–Shell Porosity Endowment to Construct a Mazelike Nanoreactor for Enhanced Catalysis in CO2 Hydrogenation to Methanol

Beyond the catalytic activity of nanocatalysts, the support with architectural design and explicit boundary could also promote the overall performance through improving the diffusion process, highlighting additional support for the morphology-dependent activity. To delineate this, herein, a novel mazelike-reactor framework, namely multi-voids mesoporous silica sphere (MVmSiO₂), is carved through a top-down approach by endowing core-shell porosity premade Stöber SiO₂ spheres. The precisely-engineered MVmSiO₂ with peripheral one-dimensional pores in the shell and interconnecting compartmented voids in the core region is simulated to prove combined hierarchical and structural superiority over its analogous counterparts. Supported with CuZn-based alloys, mazelike MVmSiO₂ nanoreactor experimentally demonstrated its expected workability in model gas-phase CO₂ hydrogenation reaction where enhanced CO₂ activity, good methanol yield, and more importantly, a prolonged stable performance are realized. While tuning the nanoreactor composition besides morphology optimization could further increase the catalytic performance, it is accentuated that the morphological architecture of support further boosts the reaction performance apart from comprehensive compositional optimization. In addition to the found morphological restraints and size-confinement effects imposed by MVmSiO₂, active sites of catalysts are also investigated by exploring the size difference of the confined CuZn alloy nanoparticles in CO₂ hydrogenation employing both in-situ experimental characterizations and density functional theory calculations.     

Ethanol dehydration activity on hydrothermally stable LaPxOy catalysts synthesized using CTAB template

Nanocrystalline LaP_xO_y with various starting P to La ratios from 0.5 to 2.0 catalysts were prepared by a sol?Cgel method using cetyl trimethylammonium bromide (CTAB) as template. The catalysts were thoroughly characterized by N₂ physisorption, powder X-ray diffraction (XRD), temperature programmed desorption (TPD) of NH₃, solid state ³¹P and ¹H nuclear magnetic resonance (NMR), and transmission electron microscopy (TEM) techniques. XRD results indicate the presence of predominantly monazite LaPO₄ with minor amounts of (??3.0 wt%) rhabdophane LaPO₄ phase in the samples with starting P/La ratios of 1.0 and 1.5. NH₃-TPD results show an increasing trend in the total acidity with increase in P/La ratio. These catalysts were tested in the selective ethanol dehydration in the temperature range between 250 and 400?°C. The catalyst activity (??mol/h/m²) is increased with P/La ratio and the catalyst with highest P/La ratio of 2.0 exhibiting the highest ethanol dehydration activity. The ethanol conversion increased with reaction temperature, reaching 100% at 350?°C and remains unchanged at higher temperatures. On the other hand, the ethylene selectivity is also increased up to 350?°C and then decreased with further increase of reaction temperature. At a P/La ratio of 2, the CTAB templated LaP_xO_y catalyst showed higher catalytic activities compared to the LaP_xO_y by hydrothermal method without any template.