Structures of ethylenediaminetetraacetato (3-) metal complexes. I complexes with Co, Mg and Cd metals

(I): Hexaaquacobalt(II) aqua[ethylenediaminetetraacetato(3-)]cobaltate(II) dihydrate, [Co(H2O)6][Co(C10H13N2O8)(H2O)]2.2H2O (Ibis): Hexaaquamagnesium(II) aqua[ethylenediaminetetraacetato(3-)]magnesiate(II) dihydrate, [Mg(H2O)6][Mg(C10H13N2O8)(H2O)]2.2H2O (II):Tetraaquabis{aqua[ethylenediaminetetraacetato(3-)]cadmium(II)-O-O'}Cadmium(II) tetrahydrate

Structures of ethylenediaminetetraacetato (3-) metal complexes. I complexes with Co, Mg and Cd metals

(I): Hexaaquacobalt(II) aqua[ethylenediaminetetraacetato(3-)]cobaltate(II) dihydrate, [Co(H2O)6][Co(C10H13N2O8)(H2O)]2.2H2O (Ibis): Hexaaquamagnesium(II) aqua[ethylenediaminetetraacetato(3-)]magnesiate(II) dihydrate, [Mg(H2O)6][Mg(C10H13N2O8)(H2O)]2.2H2O (II):Tetraaquabis{aqua[ethylenediaminetetraacetato(3-)]cadmium(II)-O-O'}Cadmium(II) tetrahydrate

Structures of ethylenediaminetetraacetato (3-) metal complexes. I complexes with Co, Mg and Cd metals

(I): Hexaaquacobalt(II) aqua[ethylenediaminetetraacetato(3-)]cobaltate(II) dihydrate, [Co(H2O)6][Co(C10H13N2O8)(H2O)]2.2H2O (Ibis): Hexaaquamagnesium(II) aqua[ethylenediaminetetraacetato(3-)]magnesiate(II) dihydrate, [Mg(H2O)6][Mg(C10H13N2O8)(H2O)]2.2H2O (II):Tetraaquabis{aqua[ethylenediaminetetraacetato(3-)]cadmium(II)-O-O'}Cadmium(II) tetrahydrate

Iowa State Plan on Aging: Balancing Long-Term Community Support Services for Aging Iowans, FY 2014–2015

This plan outlines the activities and strategies that the IDA will purse to achieve its goals, objectives, and expected outcomes in modernizing Iowa’s aging network. The goals that will move Iowa’s state plan.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Stereoselective Acetate Aldol Reactions From Metal Enolates

This review deals with metal enolate-mediated stereoselective acetate aldol reactions. It summarizes recent advances on aldol additions of unsubstituted metal enolates from chiral auxiliaries, stoichiometric and catalytic Lewis acids, or acting in substrate- controlled reactions, which provide stereocontrolled aldol transformations that allow the efficient synthesis of structurally complex natural products.

Autism Support Program Annual Report, December 2015

The Autism Support Program provides funding for applied behavioral analysis services for children under the age of nine who meet certain diagnostic and financial eligibility criteria.

Family Support Subsidy and Children at Home Program SFY 2015, October 30, 2015

The Family Support Subsidy (FSS) program provides a monthly payment to help families with the cost of raising a child with a developmental disability. Parents of children with disabilities were very active in getting state and federal policy makers to look at how they could divert some of the funds going to institutional care. Families with severely disabled children wanted to raise their children at home but were met with a lot of resistance and policy barriers when they tried to get home-based support.

Prospective study of the barriers to nutritional support in a paediatric intensive care unit : P16

Introduction and aim: Children hospitalised in a paediatric intensive care unit (PICU) are mainly fed by nutritional support (NS) which may often be interrupted. The aims of the study were to verify the relationship between prescribed (PEI) and actual energy intake (AEI) and to identify the reasons for NS interruption. Methods: Prospective study in a PICU. PEI and AEI from day 1 to 15, type of NS (enteral, parenteral, mixed), position of the feeding tube, interruptions in NS and reasons for these were noted. Inter - ruptions were classified in categories of barriers and their frequency and duration were analysed. Results: Fifteen children (24 ± 25.2 months) were studied for 84 days. The NS was exclusively enteral (69%) or mixed (31%). PEI were significantly higher than AEI (54.7 ± 32.9 vs 49.2 ± 33.6 kcal/kg, p = 0.0011). AEI represented 93% of the PEI. Ninety-eight interruptions were noted and lasted 189 h, i.e. 9.4% of the evaluated time. The most frequent barriers were nursing procedures, respiratory physiotherapy and unavailability of intravenous access. The longest were caused by the necessity to stop NS for surgery or diagnostic studies, to treat burns or to carry out medical procedures. Conclusion: AEI in PICU were inferior by 7% to PEI, considerably lower than in adult studies. Making these results available to medical staff for greater anticipation and compensation could reduce NS interruptions. Starving protocols should be reconsidered.

Evaluation of the multicriteria approval method for timber-harvesting group decision support

Abstract