Assessing the Capacity to Make Everyday Decisions: A Guide for Clinicians and an Agenda for Future Research

Assessing the capacity of patients to make decisions about their functional problems has substantial ethical, clinical, and financial implications. The growing population of older adults with cognitive impairment either in the community or in long-term care and medical facilities increase the importance of adequately assessing this capacity. This review examines the current approaches to making this assessment, demonstrates how they are incomplete, and considers potential approaches for improving these evaluations. Future research should develop and validate methods to identify patients with impaired capacity to make everyday decisions. These data will supplement functional, cognitive, and medical assessments

GraphCrunch: A tool for large network analyses

<p>Abstract</p> <p>Background</p> <p>The recent explosion in biological and other real-world network data has created the need for improved tools for large network analyses. In addition to well established <it>global </it>network properties, several new mathematical techniques for analyzing <it>local </it>structural properties of large networks have been developed. Small over-represented subgraphs, called network <it>motifs</it>, have been introduced to identify simple building blocks of complex networks. Small induced subgraphs, called <it>graphlets</it>, have been used to develop "network signatures" that summarize network topologies. Based on these network signatures, two new highly sensitive measures of network local structural similarities were designed: the <it>relative graphlet frequency distance </it>(<it>RGF-distance</it>) and the <it>graphlet degree distribution agreement </it>(<it>GDD-agreement</it>).</p> <p>Finding adequate null-models for biological networks is important in many research domains. Network properties are used to assess the fit of network models to the data. Various network models have been proposed. To date, there does not exist a software tool that measures the above mentioned local network properties. Moreover, none of the existing tools compare real-world networks against a series of network models with respect to these local as well as a multitude of global network properties.</p> <p>Results</p> <p>Thus, we introduce GraphCrunch, a software tool that finds well-fitting network models by comparing large real-world networks against random graph models according to various network structural similarity measures. It has unique capabilities of finding computationally expensive RGF-distance and GDD-agreement measures. In addition, it computes several standard global network measures and thus supports the largest variety of network measures thus far. Also, it is the first software tool that compares real-world networks against a series of network models and that has built-in parallel computing capabilities allowing for a user specified list of machines on which to perform compute intensive searches for local network properties. Furthermore, GraphCrunch is easily extendible to include additional network measures and models.</p> <p>Conclusion</p> <p>GraphCrunch is a software tool that implements the latest research on biological network models and properties: it compares real-world networks against a series of random graph models with respect to a multitude of local and global network properties. We present GraphCrunch as a comprehensive, parallelizable, and easily extendible software tool for analyzing and modeling large biological networks. The software is open-source and freely available at <url>http://www.ics.uci.edu/~bio-nets/graphcrunch/</url>. It runs under Linux, MacOS, and Windows Cygwin. In addition, it has an easy to use on-line web user interface that is available from the above web page.</p

A Low-Cost Soft-Switched DC/DC Converter for Solid-Oxide Fuel Cells

A highly efficient DC to DC converter has been developed for low-voltage high-current solid oxide fuel cells. The newly developed 'V6' converter resembles what has been done in internal combustion engine that split into multiple cylinders to increase the output capacity without having to increase individual cell size and to smooth out the torque with interleaving operation. The development was started with topology overview to ensure that all the DC to DC converter circuits were included in the study. Efficiency models for different circuit topologies were established, and computer simulations were performed to determine the best candidate converter circuit. Through design optimization including topology selection, device selection, magnetic component design, thermal design, and digital controller design, a bench prototype rated 5-kW, with 20 to 50V input and 200/400V output was fabricated and tested. Efficiency goal of 97% was proven achievable through hardware experiment. This DC to DC converter was then modified in the later stage to converter 35 to 63 V input and 13.8 V output for automotive charging applications. The complete prototype was tested at Delphi with their solid oxide fuel cell test stand to verify the performance of the modified DC to DC converter. The output was tested up to 3-kW level, and the efficiency exceeded 97.5%. Multiple-phase interleaving operation design was proved to be reliable and ripple free at the output, which is desirable for the battery charging. Overall this is a very successful collaboration project between the SECA Core Technology Team and Industrial Team

Project Blue Ocean report

A project was undertaken for Synlait Milk Limited in partial fulfilment of the Master in Engineering Management degree at the University of Canterbury. Project Blue Ocean aimed to discover opportunities for new technology adoption to improve the business operation. The project was initiated to drive the Manufacturing Excellence framework which contains three strong pillars: Safety, Reliability and People. The project began with the discovery of the current issues, mainly focused on manual handling (critical risk activities), repetitive and low-value tasks. The technology solutions were generated respectively to each issue and a high level concept study was developed for each of the top three technology solutions. Design Thinking methodology was applied throughout the project to understand the problems, define the underlying issues, generate unconstrained technology ideas, and prototype the most feasible solution. Justification methods such as the NTCP Diamond Model, the Total Application Model and the Technology Category Model were combined to create an evaluation matrix to find out the top three technology solutions: Vacuum System at Fluid Bed, Collaborative Robots and Fob Key Integration. Preliminary economic evaluation and recommendation plans were made, based on a high level concept study of each solution

Testing MSW effect in Supernova Explosion with Neutrino event rates

Flavor transitions in supernova neutrinos are yet to be determined. We present a method to probe whether or not the Mikheyev-Smirnov-Wolfenstein effects occur as SN neutrinos propagate outward from the SN core by investigating time evolutions of neutrino event rates for different flavors in different kinds of detectors. As the MSW effect occurs, the $\nu_e$ flux swaps with the $\nu_x$ flux, which represents any one of $\nu_{\mu}$, $\nu_{\tau}$, $\bar{\nu}_{\mu}$, and $\bar{\nu}_{\tau}$ flux, either fully or partially depending on the neutrino mass hierarchy. During the neutronization burst, the $\nu_e$ emission evolves in a much different shape from the emissions of $\bar{\nu}_e$ and $\nu_x$ while the latter two evolve in a similar pattern. Meanwhile, the luminosity of the the $\nu_e$ emission is much larger than those of the $\bar{\nu}_e$ and $\nu_x$ emissions while the latter two are roughly equal. As a consequence, the time-evolution pattern of the $\nu_e{\rm Ar}$ event rates in the absence of the MSW effect will be much different from that in the occurrence of the MSW effect, in either mass hierarchy. With the simulated SN neutrino emissions, the $\nu_e{\rm Ar}$ and inverse beta decay event rates are evaluated. The ratios of the two cumulative event rates are calculated for different progenitor masses up to $100~{\rm ms}$. We show that the time evolutions of this cumulative ratios can effectively determine whether MSW effects really occur for SN neutrinos or not.Comment: 13 pages, 4 figure

Techniques for Deblurring Faces in Images by Utilizing Multi-Camera Fusion

This publication describes techniques for deblurring faces in images by utilizing multi-camera (e.g., dual-camera) fusion processes. In the techniques, multiple cameras of a computing device (e.g., wide-angle camera, an ultrawide-angle camera) concurrently capture a scene. A multi-camera fusion technique is utilized to fuse the captured images together to generate an image with increased sharpness while preserving the brightness of the scene and other details under a motion scene. The images are processed by a Deblur Module, which includes an optical flow machine-learned model for generating a warped ultrawide-angle image, a subject mask trained to identify and mask faces detected in the wide-angle image, and an occlusion mask for handling occlusion artifacts. The warped ultrawide-angle image, the raw wide-angle image (with blurred faces), the sharp ultrawide-angle image, the subject mask, and the occlusion map are then stacked and merged (fused) using a machine-learning model to output a sharp image without the presence of motion blur. This publication further describes techniques utilizing adaptive multi-streaming to optimize power consumption and dual camera usage on computing devices

Stochastic Simulation of Carbonaceous Nanoparticle Precursor Formation in Combustion.

Combustion-generated nanoparticles (diameter less than or equal to 100 nm) are prevalent in modern society. Carbonaceous nanoparticles (CNPs) are especially important, finding applications as pigments in ink, in composite materials, or as catalysts. Despite such useful applications, CNPs have attracted the most attention as hazardous emissions from combustion sources like automotive engines, especially as aggregate particles known as soot, as their primary constituents are carcinogenic polycyclic aromatic hydrocarbons (PAHs). Critically, the formation of CNPs in combustion environments remains an area of considerable uncertainty, particularly the growth from gas phase precursors through the nucleation of solid phase particles. Towards elucidating PAH formation via chemical reactions, a significant element of the growth process, a novel simulation software was developed, named the Stochastic Nanoparticle Simulator (SNAPS), along with a corresponding PAH chemical reaction mechanism. This software was then applied to investigate the chemical and physical properties of PAHs formed in combustion. SNAPS simulations were corroborated through comparisons with existing experimental measurements in flames utilizing a variety of fuels. Furthermore, simulations provided molecular-level detail that revealed key aspects of a complex chemical growth process. Importantly, these simulations provided insights into chemical reaction and composition details beyond those typically inaccessible by experiment. For all studied flames, analysis of the major chemical reactions and PAH species involved in simulations contrasted with conventional theories. Simulations showed that PAH growth is characterized by complex sequences of highly reversible reactions, leading to a variety of species that far exceeds the relatively narrow range that has traditionally been the focus of investigations. SNAPS therefore represents an important tool for synthesizing experimental observations and theoretical predictions, towards building a comprehensive and accurate description of CNP growth. Most importantly, the current work is only one application of the software. The extensibility of SNAPS will enable modeling of many different systems involving heterogeneous nucleation and growth of nanoparticles, which illustrates its potential for wide impact. Altogether, this work represents a strong framework that will support and drive future investigations of nanoparticle growth and contribute to the development of novel combustion technologies that will positively impact society.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107094/1/jasonlai_1.pd