Asymptotic normality of the Parzen-Rosenblatt density estimator for strongly mixing random fields

We prove the asymptotic normality of the kernel density estimator (introduced by Rosenblatt (1956) and Parzen (1962)) in the context of stationary strongly mixing random fields. Our approach is based on the Lindeberg's method rather than on Bernstein's small-block-large-block technique and coupling arguments widely used in previous works on nonparametric estimation for spatial processes. Our method allows us to consider only minimal conditions on the bandwidth parameter and provides a simple criterion on the (non-uniform) strong mixing coefficients which do not depend on the bandwith.Comment: 16 page

Confucian Principles: A Study of Chinese Americans’ Interpersonal Relationships in Selected Children’s Picturebooks

[[abstract]]There has not been enough critical analysis of children’s literature by and about Chinese Americans, especially when compared to other minority groups in the United States. In particular, Chinese American historical books lack extensive analysis. It is important to reflect cultural accuracy in literature and to help children develop clear concepts of self and others by providing precise cultural and physical characteristics of people. While cultural authenticity allows children the opportunity to see a reflection of real experiences within a book instead of seeing stereotypes or misrepresentations, obtaining correct information about a certain time period can help children to see images of immigration accurately represented in literature. Using the Confucian delineation of interpersonal relationships as the major criterion of cultural authenticity, this article examines three currently available children’s picturebooks set in the historical period between 1848 and 1885. In addition to exploring how Chinese Americans’ interpersonal relationships are portrayed in these children’s historical books, this article argues for more proactive inclusion of the diversity in selection of picturebooks.[[notice]]補正完

Modeling electrolytically top gated graphene

We investigate doping of a single-layer graphene in the presence of electrolytic top gating. The interfacial phenomena is modeled using a modified Poisson-Boltzmann equation for an aqueous solution of simple salt. We demonstrate both the sensitivity of graphene's doping levels to the salt concentration and the importance of quantum capacitance that arises due to the smallness of the Debye screening length in the electrolyte.Comment: 7 pages, including 4 figures, submitted to Nanoscale Research Letters for a special issue related to the NGC 2009 conference (http://asdn.net/ngc2009/index.shtml

Electron-Phonon Scattering in Metallic Single-Walled Carbon Nanotubes

Electron scattering rates in metallic single-walled carbon nanotubes are studied using an atomic force microscope as an electrical probe. From the scaling of the resistance of the same nanotube with length in the low and high bias regimes, the mean free paths for both regimes are inferred. The observed scattering rates are consistent with calculations for acoustic phonon scattering at low biases and zone boundary/optical phonon scattering at high biases.Comment: 4 pages, 5 figure

Vimentin is a novel AKT1 target mediating motility and invasion.

The PI3K/AKT signaling pathway is aberrant in a wide variety of cancers. Downstream effectors of AKT are involved in survival, growth and metabolic-related pathways. In contrast, contradictory data relating to AKT effects on cell motility and invasion, crucial prometastatic processes, have been reported pointing to a potential cell type and isoform type-specific AKT-driven function. By implication, study of AKT signaling should optimally be conducted in an appropriate intracellular environment. Prognosis in soft-tissue sarcoma (STS), the aggressive malignancies of mesenchymal origin, is poor, reflecting our modest ability to control metastasis, an effort hampered by lack of insight into molecular mechanisms driving STS progression and dissemination. We examined the impact of the cancer progression-relevant AKT pathway on the mesenchymal tumor cell internal milieu. We demonstrate that AKT1 activation induces STS cell motility and invasiveness at least partially through a novel interaction with the intermediate filament vimentin (Vim). The binding of AKT (tail region) to Vim (head region) results in Vim Ser39 phosphorylation enhancing the ability of Vim to induce motility and invasion while protecting Vim from caspase-induced proteolysis. Moreover, vimentin phosphorylation was shown to enhance tumor and metastasis growth in vivo. Insights into this mesenchymal-related molecular mechanism may facilitate the development of critically lacking therapeutic options for these devastating malignancies

Hope, optimism, and other business assets: Why “psychological capital” is so valuable to your company

Entrevista com Fred Luthans,1 coauthor of Psychological Capital: Developing the Human Competitive EdgeInterview with Fred Luthans,1 coauthor of Psychological Capital: Developing the Human Competitive Edg

Neural networks in petroleum geology as interpretation tools

Abstract Three examples of the use of neural networks in analyses of geologic data from hydrocarbon reservoirs are presented. All networks are trained with data originating from clastic reservoirs of Neogene age located in the Croatian part of the Pannonian Basin. Training always included similar reservoir variables, i.e. electric logs (resistivity, spontaneous potential) and lithology determined from cores or logs and described as sandstone or marl, with categorical values in intervals. Selected variables also include hydrocarbon saturation, also represented by a categorical variable, average reservoir porosity calculated from interpreted well logs, and seismic attributes. In all three neural models some of the mentioned inputs were used for analyzing data collected from three different oil fields in the Croatian part of the Pannonian Basin. It is shown that selection of geologically and physically linked variables play a key role in the process of network training, validating and processing. The aim of this study was to establish relationships between log-derived data, core data, and seismic attributes. Three case studies are described in this paper to illustrate the use of neural network prediction of sandstone-marl facies (Case Study # 1, Okoli Field), prediction of carbonate breccia porosity (Case Study # 2, Beničanci Field), and prediction of lithology and saturation (Case Study # 3, Kloštar Field). The results of these studies indicate that this method is capable of providing better understanding of some clastic Neogene reservoirs in the Croatian part of the Pannonian Basin