Google Chrome: More than a Browser

Chrome is more than a browser. It’s an operating system in itself, and it’s transforming the educational computing experience. Come learn about Chrome’s features such as data syncing across computers (bookmarks, passwords, settings, etc), and most importantly, Chrome-specific apps. Learn about Chromebooks as well - those powerful, fast, and cheap laptops that are spreading like wildfire

A Touch of Glass

Learn more about Google Glass! We will demonstrate how Glass works, provide details on the features, functions, and limitations, and explain where Glass is moving in the future. Why do educators need to know about it? What are the implications of such technology? How is it being used in STEM education

STEM Apps for Android Devices

We will showcase free STEM related apps for Android devices (and indicate which are also available for iOS). Android tablets will be provided so that visitors can try the apps themselves

FREE STEM Apps for Common Core

We will highlight FREE apps for Computers, Chrome browser and mobile devices (Android and iOS) that align to Common Core standards. A website will be provided with a full list for participants to peruse after the conference. Science will be the primary focus but there will also be apps for Math & Engineering as well as apps that are generally useful for teachers as well. Audience are any K-16 teachers. Participants are welcome to bring mobile devices and to suggest their own favorite apps

The Impact of Insurance on the Law of Torts

An experimental study of "Model-on-Demand" (MoD) identification is made on a pilot-scale brine-water mixing tank. MoD estimation is compared against semi-physical modeling techniques using identification data generated from a systematically designed m-level Pseudo Random Sequence (PRS) input. The estimated models are the basis for evaluating the usefulness of MoD-based Model Predictive Control (MPC). For this application, MoD-MPC is shown to provide better performance at high bandwidths compared to a linear MPC controller

A Risk-Based Model Predictive Control Approach to Adaptive Interventions in Behavioral Health

This brief examines how control engineering and risk management techniques can be applied in the field of behavioral health through their use in the design and implementation of adaptive behavioral interventions. Adaptive interventions are gaining increasing acceptance as a means to improve prevention and treatment of chronic, relapsing disorders, such as abuse of alcohol, tobacco, and other drugs, mental illness, and obesity. A risk-based model predictive control (MPC) algorithm is developed for a hypothetical intervention inspired by Fast Track, a real-life program whose long-term goal is the prevention of conduct disorders in at-risk children. The MPC-based algorithm decides on the appropriate frequency of counselor home visits, mentoring sessions, and the availability of after-school recreation activities by relying on a model that includes identifiable risks, their costs, and the cost/benefit assessment of mitigating actions. MPC is particularly suited for the problem because of its constraint-handling capabilities, and its ability to scale to interventions involving multiple tailoring variables. By systematically accounting for risks and adapting treatment components over time, an MPC approach as described in this brief can increase intervention effectiveness and adherence while reducing waste, resulting in advantages over conventional fixed treatment. A series of simulations are conducted under varying conditions to demonstrate the effectiveness of the algorithm

Foreword Identification and Control in Biomedical Applications

Control engineering (in the broad sense of the term) has become an important enabling technology in many areas of medicine. Prominent examples include the artificial pancreas, closed-loop anesthesia, and personalized drug dosing strategies in neurology, oncology, endocrinology, and psychiatry. It is a testament to the power of control systems that allow individualizing treatment by providing mechanisms for linking treatment goals to treatment regimens, thus achieving a desired therapeutic effect. Consequently, the arrival of control systems engineering to the clinic enables the visionary concept of "treat the patient, not the disease" technologically and economically feasible

The Lyman Alpha Reference Sample: V. The impact of neutral ISM kinematics and geometry on Lyman Alpha escape

We present high-resolution far-UV spectroscopy of the 14 galaxies of the Lyman Alpha Reference Sample; a sample of strongly star-forming galaxies at low redshifts ($0.028 < z < 0.18$). We compare the derived properties to global properties derived from multi band imaging and 21 cm HI interferometry and single dish observations, as well as archival optical SDSS spectra. Besides the Lyman $\alpha$ line, the spectra contain a number of metal absorption features allowing us to probe the kinematics of the neutral ISM and evaluate the optical depth and and covering fraction of the neutral medium as a function of line-of-sight velocity. Furthermore, we show how this, in combination with precise determination of systemic velocity and good Ly$\alpha$ spectra, can be used to distinguish a model in which separate clumps together fully cover the background source, from the "picket fence" model named by Heckman et al. (2011). We find that no one single effect dominates in governing Ly$\alpha$ radiative transfer and escape. Ly$\alpha$ escape in our sample coincides with a maximum velocity-binned covering fraction of $\lesssim 0.9$ and bulk outflow velocities of $\gtrsim 50$ km s$^{-1}$, although a number of galaxies show these characteristics and yet little or no Ly$\alpha$ escape. We find that Ly$\alpha$ peak velocities, where available, are not consistent with a strong backscattered component, but rather with a simpler model of an intrinsic emission line overlaid by a blueshifted absorption profile from the outflowing wind. Finally, we find a strong anticorrelation between H$\alpha$ equivalent width and maximum velocity-binned covering factor, and propose a heuristic explanatory model.Comment: 28 pages, 19 figures, 5 table