Things to Do while Trapped in an Elevator

Pages 60-6

Speech Training Provides Solid Background for Industrial Relations

Since graduating from college in 1930 I have been a teacher, a lawyer, and now the director of industrial relations for a company. I can say without hesitation chat the preparatory work in college which has helped me most in each of these professions was my training in public speaking and debating

El regadío en el área andina central.

Sin resume

Perovskite nitrides: A new playground for functional materials

The perovskite crystal is a favorite playground for electroceramists across a wide variety of applications, and recent developments on hybrid metallorganic perovskite photovoltaics has renewed interest in expanding the chemical space of this flexible and multifunctional crystal structure. A survey of experimentally confirmed simple perovskite compounds (ABX3) finds no reports of pure X=N anion chemistries. One challenge of forming nitride perovskite materials is the high valence cations needed to satisfy the high valency of nitrogen; another is limiting oxygen impurities. Computational predictions of energetically favorable nitride perovskites have been reported[1] and DFT+LDA methods[2] suggest that the lowest energy state of LaWN3 is a non-centrosymmetric R3c type distorted perovskite structure with a spontaneous polarization of approximately 60µC/cm2 along the \u3c111\u3e polar axis. A relatively low energy barrier predicted for polarization reversal raises the possibility of ferroelectricity as well. Developing a ferroelectric nitride would greatly simplify integration of a number of functional (e.g., ferroelectric, piezoelectric, and more) properties directly with nitride semiconductors for a variety of integrated sensing and energy conversion applications. Here we report the experimental confirmation of oxygen-free LaWN3 as a perovskite (Fig. 1) using multiple fabrication approaches. Calculations show 5 different symmetries with very similar lattice energies (3 polar and 2 non-polar); refinements of x-ray and electron diffraction in conjunction with property measurements document the complexity of the LaWN3 system in addition to other closely-related perovskite nitrides. [1] R. Sarmiento-Pérez et. al., Chemistry of Materials, 27, 5957 (2015) [2] Y. Fang et. al., Physical Review B,95, 014111 (2017) Please click Additional Files below to see the full abstract

A Descriptive Study of Cyber Charter Schools in Pennsylvania

The purpose of this descriptive, qualitative study was to explore what the cyber directors believed the present state of cyber schools in Pennsylvania to be and what were their views of cyber schools for the future. Through this investigation, the researcher analyzed the impact of the developments that are currently taking place in the cyber schools and across the state of Pennsylvania. The researcher interviewed nine of the eleven cyber charter school C.E.O.'s in regard to their cyber school and what the future holds for all cyber charter schools across the state. Some of the major findings in the study appeared as three distinct themes. The C.E.O.'s use internal and external sources for professional development on a wide variety of topics, which differed from school to school. That there are statewide changes occurring that could affect cyber charter schools and the money that they receive per student. House Bill 446, if passed, would put a limit on the amount of money cyber charter schools receive for regular and special education students. Lastly, the C.E.O.'s foresee changes in traditional brick and mortar schools so that they can compete with cyber charter schools. Some of these changes could be an increase in the amount of technology used in brick and mortar schools or others such as the offering of online courses

Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar

In this work, we first describe a framework for the application of Reinforcement Learning (RL) control to a radar system that operates in a congested spectral setting. We then compare the utility of several RL algorithms through a discussion of experiments performed on Commercial off-the-shelf (COTS) hardware. Each RL technique is evaluated in terms of convergence, radar detection performance achieved in a congested spectral environment, and the ability to share 100MHz spectrum with an uncooperative communications system. We examine policy iteration, which solves an environment posed as a Markov Decision Process (MDP) by directly solving for a stochastic mapping between environmental states and radar waveforms, as well as Deep RL techniques, which utilize a form of Q-Learning to approximate a parameterized function that is used by the radar to select optimal actions. We show that RL techniques are beneficial over a Sense-and-Avoid (SAA) scheme and discuss the conditions under which each approach is most effective.Comment: Accepted for publication at IEEE Intl. Radar Conference, Washington DC, Apr. 2020. This is the author's version of the wor

Detection and classification of buried dielectric anomalies using a separated aperture sensor and a neural network discriminator

Includes bibliographical references.The problem of detection and classification of buried dielectric anomalies using a separated aperture microwave sensor and an artificial neural network discriminator was considered. Several methods for training and data representation were developed to study the trainability and generalization capabilities of the networks. The effect of the architectural variation on the network performance was also studied. The principal component method was used to reduce the volume of the data and also the dimension of the weight space. Simulation results on two types of targets were obtained which indicated superior detection and classification performance when compared with the conventional methods

Metabolic Syndrome Is Associated with Greater Histologic Severity, Higher Carbohydrate, and Lower Fat Diet in Patients with NAFLD

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73077/1/j.1572-0241.2006.00719.x.pd

Multimodal neuroimaging in patients with disorders of consciousness showing "functional hemispherectomy".

Beside behavioral assessment of patients with disorders of consciousness, neuroimaging modalities may offer objective paraclinical markers important for diagnosis and prognosis. They provide information on the structural location and extent of brain lesions (e.g., morphometric MRI and diffusion tensor imaging (DTI-MRI) assessing structural connectivity) but also their functional impact (e.g., metabolic FDG-PET, hemodynamic fMRI, and EEG measurements obtained in "resting state" conditions). We here illustrate the role of multimodal imaging in severe brain injury, presenting a patient in unresponsive wakefulness syndrome (UWS; i.e., vegetative state, VS) and in a "fluctuating" minimally conscious state (MCS). In both cases, resting state FDG-PET, fMRI, and EEG showed a functionally preserved right hemisphere, while DTI showed underlying differences in structural connectivity highlighting the complementarities of these neuroimaging methods in the study of disorders of consciousness.Peer reviewe

Building connectomes using diffusion MRI: why, how and but

Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically-relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments