Orientationability analyses of a special class of the Stewart–Gough parallel manipulators using the unit quaternion representation

This paper addresses the orientation-singularity and orientationability analyses of a special class of the Stewart–Gough parallel manipulators whose moving and base platforms are two similar semi-symmetrical hexagons. Employing a unit quaternion to represent the orientation of the moving platform, an analytical expression representing the singularity locus of this class of parallel manipulators in a six-dimensional Cartesian space is obtained. It shows that for a given orientation, the position-singularity locus is a cubic polynomial expression in the moving platform position parameters, and for a given position, the orientation-singularity locus is an analytical expression but not a polynomial directly with respect to the mobile platform orientation parameters. Further inspection shows that for the special class of parallel manipulators, there must exist a nonsingular orientation void in the orientation space around the orientation origin for each position in the position-workspace. Therefore, a new performance index referred to as orientationability is introduced to describe the orientation capability of the special class of manipulators at a given position. A discretization algorithm is proposed for the computation of the orientationability of the special class of manipulators. Moreover, effects of the design parameters and position parameters on the orientationability are investigated in details. Based on the orientationability performance index, another novel performance index referred to as practical orientationability is presented which represents the practical orientation capability of the manipulator at a given position. The practical orientationability not only can satisfy all the kinematic demands and constraints of such class of manipulators, but also can guarantee that the manipulator is nonsingular.     

Exciton storage by Mn(2+) in colloidal Mn(2+)-doped CdSe quantum dots

Colloidal Mn (2+)-doped CdSe quantum dots showing long excitonic photoluminescence decay times of up to tau exc = 15 mus at temperatures over 100 K are described. These decay times exceed those of undoped CdSe quantum dots by approximately 10 (3) and are shown to arise from the creation of excitons by back energy transfer from excited Mn (2+) dopant ions. A kinetic model describing thermal equilibrium between Mn (2+ 4)T 1 and CdSe excitonic excited states reproduces the experimental observations and reveals that, for some quantum dots, excitons can emit with near unity probability despite being approximately 100 meV above the Mn (2+ 4)T 1 state. The effect of Mn (2+) doping on CdSe quantum dot luminescence at high temperatures is thus completely opposite from that at low temperatures described previously.     

Structural, chemical, and optical properties of tin sulfide thin films as controlled by the growth temperature during co-evaporation and subsequent annealing

Tin sulfide thin films on soda-lime glass substrate were prepared by co-evaporation. This technique uses a vapor phase procedure involving chemical reactions between the precursor species evaporated simultaneously. The influence of the substrate temperature in the crystal structure and chemical composition were determined by X-ray diffraction and energy dispersive analysis of X-rays, showing that thin films crystallized in SnS, SnS₂, and Sn₂S₃ phases. Scanning electron microscope shows thin films with homogenous and uniform surface. Some of the samples were annealed to study the variation of structural, chemical, and optical properties. The variation of refractive index (n), extinction coefficient (k), and dielectric constant (ε) with wavelength and photon energy are reported. The energy band gap was calculated from optical transmittance and reflectance measurements in the range 300–1500 nm. The calculated energy band gap values were between 1.75 and 2.3 eV, depending on the phase in which crystallized the different thin films.     

Can porous polymeric scaffolds be functionalized by stem cells leading to osteogenic differentiation? A systematic review of in vitro studies

The aim of this study was to analyze the influence of nanoporous structure of polymeric biomaterials on the in vitro osteogenic induction of human stem cells. An electronic search in three databases (MEDLINE, SCOPUS, and Web of Science) was performed for articles that were published before May 2018. In vitro studies were included if they met the following criteria: (1) the use of polymeric scaffolds (natural or synthetic); (2) the co-culture of human stem cells with the scaffold; and (3) cell viability, proliferation, and osteogenic differentiation assays. The main characteristics of the published studies were summarized, and a quality assessment tool was used to analyze methodological features. Eighty-eight potential articles were firstly retrieved. Thirteen were eligible for qualitative analysis. Only three studies characterized cell stemness. Nanostructure of the scaffolds showed a significant influence on viability, proliferation, and osteogenic differentiation of human stem cells. Combination of porosity between 72 and 93% and a large range diameter between 50 and 224 μm resulted in more remarkable cellular proliferation and differentiation. Porous polymeric scaffolds can be functionalized by stem cells leading to osteogenic induction. High standards of laboratory practice and accurate methodological reporting are essential for the credibility of the results.     

Learning Structure from Data and Its Application to Ozone Prediction

In this paper we propose an algorithm for structure learning in predictive expert systems based on a probabilistic network representation. The idea is to have the simplest structure (minimum number of links) with acceptable predictive capability. The algorithm starts by building a tree structure based on measuring mutual information between pairs of variables, and then it adds links as necessary to obtain certain predictive performance. We have applied this method for ozone prediction in México City, where the ozone level is used as a global indicator for the air quality in different parts of the city. It is important to predict the ozone level a day, or at least several hours in advance, to reduce the health hazards and industrial losses that occur when the ozone reaches emergency levels. We obtained as a first approximation a tree-structured dependency model for predicting ozone in one part of the city. We observe that even with only three parameters, its estimations are acceptable.A causal network representation and the structure learning techniques produced some very interesting results for the ozone prediction problem. Firstly, we got some insight into the dependence structure of the phenomena. Secondly, we got an indication of which are the important and not so important variables for ozone forecasting. Taking this into account, the measurement and computational costs for ozone prediction could be reduced. And thirdly, we have obtained satisfactory short term ozone predictions based on a small set of the most important parameters.     

Is my model any good: differentially private regression diagnostics

Linear and logistic regression are popular statistical techniques for analyzing multi-variate data. Typically, analysts do not simply posit a particular form of the regression model, estimate its parameters, and use the results for inference or prediction. Instead, they first use a variety of diagnostic techniques to assess how well the model fits the relationships in the data and how well it can be expected to predict outcomes for out-of-sample records, revising the model as necessary to improve fit and predictive power. In this article, we develop \(\epsilon \)-differentially private diagnostics tools for regression, beginning to fill a gap in privacy-preserving data analysis. Specifically, we create differentially private versions of residual plots for linear regression and of receiver operating characteristic (ROC) curves as well as binned residual plot for logistic regression. The residual plot and binned residual plot help determine whether or not the data satisfy the assumptions underlying the regression model, and the ROC curve is used to assess the predictive power of the logistic regression model. These diagnostics improve the usefulness of algorithms for computing differentially private regression output, which alone does not allow analysts to assess the quality of the posited model. Our empirical studies show that these algorithms can be effective tools for allowing users to evaluate the quality of their models.     

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