Estimation of the eddy thermal conductivity for lake Botonega

This paper presents a part of a computer model that is suitable for limited temperature prediction and its application for Lake Botonega, which is located in Istria, Croatia. The main assumption of this study is that the heat transfer can be described by the eddy diffusivity model to formulate the model of the heating and cooling of a lake using a series of water and air temperature measurements. The coefficient of thermal diffusion, which is a function of the lake depth, is determined using the inverse model of eddy thermal diffusivity. The inverse model is linearized using the finite element approach. The model of lake thermal diffusivity consists of a conductive part and a radiative part, with the latter part being replaced with the heat flux on the boundary. The model parameters are calculated in two steps—a predictor step and a corrector step—and the coefficient of conduction is calculated instead of the diffusion. The estimated parameters are intended for inclusion in a simple three-dimensional thermal model, which provides the lake temperature prediction that is based on previous temperature measurements, as demonstrated in the examples

Near-field electrospinning of conjugated polymer light-emitting nanofibers

The authors report on the realization of ordered arrays of light-emitting conjugated polymer nanofibers by near-field electrospinning. The fibers, made by poly[2-methoxy-5-(2-ethylhexyloxy)-1,4-phenylenevinylene], have diameters of few hundreds of nanometers and emission peaked at 560 nm. The observed blue-shift compared to the emission from reference films is attributed to different polymer packing in the nanostructures. Optical confinement in the fibers is also analyzed through self-waveguided emission. These results open interesting perspectives for realizing complex and ordered architectures by light-emitting nanofibers, such as photonic circuits, and for the precise positioning and integration of conjugated polymer fibers into light-emitting devices.Comment: 11 pages, 6 figures Nanoscale, 201

Antigone e le altre. Figure mitiche al femminile nei saggi di Margarete Susman

In Margarete Susman’s theoretical and political essays and studies, classical and mythological figures such as Antigone, Pythia, Diotima and Undine become symbols for women’s situation as well as the broader cultural, political and social sphere. The connection between these mythological themes and the intimate, spiritual and intellectual evolution of the author herself signals the originality of her work in women’s studies in general, in women’s literature theory and in mythological studies. This article aims to illustrate the symbolic reading of female mythical figures in Margarete Susman’s writings, also focusing on the connection between mythos and the cultural importance of the maternal function. The icons of the feminine are also objects of Margarete Susman’s writing, addressing questions about the connection of religious attitudes and socio-cultural determined thought

Penalized Orthogonal Iteration for Sparse Estimation of Generalized Eigenvalue Problem

We propose a new algorithm for sparse estimation of eigenvectors in generalized eigenvalue problems (GEP). The GEP arises in a number of modern data-analytic situations and statistical methods, including principal component analysis (PCA), multiclass linear discriminant analysis (LDA), canonical correlation analysis (CCA), sufficient dimension reduction (SDR) and invariant co-ordinate selection. We propose to modify the standard generalized orthogonal iteration with a sparsity-inducing penalty for the eigenvectors. To achieve this goal, we generalize the equation-solving step of orthogonal iteration to a penalized convex optimization problem. The resulting algorithm, called penalized orthogonal iteration, provides accurate estimation of the true eigenspace, when it is sparse. Also proposed is a computationally more efficient alternative, which works well for PCA and LDA problems. Numerical studies reveal that the proposed algorithms are competitive, and that our tuning procedure works well. We demonstrate applications of the proposed algorithm to obtain sparse estimates for PCA, multiclass LDA, CCA and SDR. Supplementary materials are available online