Building Scalable Communities from International Knowledge Networks

Small organizations frequently implement knowledge networks where people use their personal uncoordinated connections to transfer information. Although small knowledge networks may be very effective, they often experience problems as they grow. One way organizations can counteract this is by developing communities of practice. Cycling Without Age (CWA) is an organization that faces issues with knowledge sharing due to a rapidly expanding network. The goal of this project was to help CWA develop a community of practice that allows for more effective knowledge transfer. We investigated CWA’s knowledge network structure and analyzed their social learning group behavior in order to develop a governance structure around the network and implement a new platform

3D Swarm Construction

Robotic construction can drastically improve the efficiency and safety of construction. However, current robotic construction methods are limited by the types of structures robots can build and the ability for multiple robots to work collaboratively to build structures. This project creates an autonomous collective construction system in which two types of robots cooperate: construction robots and smart scaffolding robots. The latter robot type integrates electronics into building materials to create intelligent structures and allows for dynamic reassembling of existing components. In addition, we present a novel multi-robot collaborative building algorithm that showcases construction both with real and simulated robots

Skilled Care Requirements for Elderly Patients After Coronary Artery Bypass Grafting

To examine the extent to which elderly individuals use various skilled care facilities after coronary artery bypass grafting (CABG). Design : Retrospective cohort study. Setting : State of Michigan from 1997 to 1998. Participants : Residents aged 65 and older enrolled in Medicare who underwent CABG. Measurements : Cumulative incidence of admission within 100 days of hospital discharge, relative risk (RR) of admission, readmission or extended stay at a skilled care facility, and length of stay in a skilled care facility. Results : Fifty percent of patients aged 80 and older used a skilled care facility after CABG, with most requiring admission to a skilled nursing facility (SNF) or readmission to an acute-care hospital within 100 days after discharge. Patients aged 80 and older had a significantly higher risk of admission to a SNF (adjusted RR=3.3, 95% confidence interval (CI)=2.8–4.0) than did those aged 65 to 69, as did patients aged 75 to 79 (adjusted RR=2.2, 95% CI=1.8–2.6) and those aged 70 to 74 (adjusted RR=1.5, 95% CI=1.3–1.8). The length of time spent in skilled care facilities significantly increased with age (mean days=13.3 for aged 65–69, 16.9 for 70–74, 19.6 for 75–79, and 22.9 for 80 and older; P< .001). Conclusion : Older patients are more likely to be admitted to a SNF, be readmitted to an acute-care hospital, and have longer institutional stays after CABG. When balancing the risks and benefits of CABG, physicians, patients, families, and policy-makers need to carefully consider the likelihood of follow-up institutional care in elderly patients.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66314/1/j.1532-5415.2005.53356.x.pd

What drives acquisitions in the EU banking industry? The role of bank regulation and supervision framework, bank specific and market specific factors

We investigate the determinants of commercial bank acquisitions in the former fifteen countries of the European Union by evaluating the impact of bank-specific measures, such as size, growth and efficiency of banks, and external influences reflecting industry level differences in the regulatory and supervision framework, market environment and economic conditions. Our empirical analysis involves multinomial logit estimation at various levels in order to identify those characteristics that most consistently predict targets and acquirers from a sample of over 1400 commercial banks. The overall results indicate that, relative to banks that were not involved in the acquisitions, (i) targets and acquirers were significantly larger, less well capitalized and less cost efficient, (ii) targets were less profitable with lower growth prospects, and acquirers more profitable with higher growth prospects, (iii) external factors have affected targets and acquirers differently, and their effects have not been consistent or robust to sample size changes. © 2011 New York University Salomon Center and Wiley Periodicals, Inc.

Hospital-level associations with 30-day patient mortality after cardiac surgery: a tutorial on the application and interpretation of marginal and multilevel logistic regression

Background: Marginal and multilevel logistic regression methods can estimate associations between hospital-level factors and patient-level 30-day mortality outcomes after cardiac surgery. However, it is not widely understood how the interpretation of hospital-level effects differs between these methods. Methods. The Australasian Society of Cardiac and Thoracic Surgeons (ASCTS) registry provided data on 32,354 patients undergoing cardiac surgery in 18 hospitals from 2001 to 2009. The logistic regression methods related 30-day mortality after surgery to hospital characteristics with concurrent adjustment for patient characteristics. Results: Hospital-level mortality rates varied from 1.0% to 4.1% of patients. Ordinary, marginal and multilevel regression methods differed with regard to point estimates and conclusions on statistical significance for hospital-level risk factors; ordinary logistic regression giving inappropriately narrow confidence intervals. The median odds ratio, MOR, from the multilevel model was 1.2 whereas ORs for most patient-level characteristics were of greater magnitude suggesting that unexplained between-hospital variation was not as relevant as patient-level characteristics for understanding mortality rates. For hospital-level characteristics in the multilevel model, 80% interval ORs, IOR-80%, supplemented the usual ORs from the logistic regression. The IOR-80% was (0.8 to 1.8) for academic affiliation and (0.6 to 1.3) for the median annual number of cardiac surgery procedures. The width of these intervals reflected the unexplained variation between hospitals in mortality rates; the inclusion of one in each interval suggested an inability to add meaningfully to explaining variation in mortality rates. Conclusions: Marginal and multilevel models take different approaches to account for correlation between patients within hospitals and they lead to different interpretations for hospital-level odds ratios. © 2012 Sanagou et al; licensee BioMed Central Ltd

Handling missing data in multivariate time series using a vector autoregressive model-imputation (VAR-IM) algorithm

Imputing missing data from a multivariate time series dataset remains a challenging problem. There is an abundance of research on using various techniques to impute missing, biased, or corrupted values to a dataset. While a great amount of work has been done in this field, most imputing methodologies are centered about a specific application, typically involving static data analysis and simple time series modelling. However, these approaches fall short of desired goals when the data originates from a multivariate time series. The objective of this paper is to introduce a new algorithm for handling missing data from multivariate time series datasets. This new approach is based on a vector autoregressive (VAR) model by combining an expectation and minimization (EM) algorithm with the prediction error minimization (PEM) method. The new algorithm is called a vector autoregressive imputation method (VAR-IM). A description of the algorithm is presented and a case study was accomplished using the VAR-IM. The case study was applied to a real-world data set involving electrocardiogram (ECG) data. The VAR-IM method was compared with both traditional methods list wise deletion and linear regression substitution; and modern methods Multivariate Auto-Regressive State-Space (MARSS) and expectation maximization algorithm (EM). Generally, the VAR-IM method achieved significant improvement of the imputation tasks as compared with the other two methods. Although an improvement, a summary of the limitations and restrictions when using VAR-IM is presented

Report from the EPAA workshop: In vitro ADME in safety testing used by EPAA industry sectors

AbstractThere are now numerous in vitro and in silico ADME alternatives to in vivo assays but how do different industries incorporate them into their decision tree approaches for risk assessment, bearing in mind that the chemicals tested are intended for widely varying purposes? The extent of the use of animal tests is mainly driven by regulations or by the lack of a suitable in vitro model. Therefore, what considerations are needed for alternative models and how can they be improved so that they can be used as part of the risk assessment process? To address these issues, the European Partnership for Alternative Approaches to Animal Testing (EPAA) working group on prioritisation, promotion and implementation of the 3Rs research held a workshop in November, 2008 in Duesseldorf, Germany. Participants included different industry sectors such as pharmaceuticals, cosmetics, industrial- and agro-chemicals. This report describes the outcome of the discussions and recommendations (a) to reduce the number of animals used for determining the ADME properties of chemicals and (b) for considerations and actions regarding in vitro and in silico assays. These included: standardisation and promotion of in vitro assays so that they may become accepted by regulators; increased availability of industry in vivo kinetic data for a central database to increase the power of in silico predictions; expansion of the applicability domains of in vitro and in silico tools (which are not necessarily more applicable or even exclusive to one particular sector) and continued collaborations between regulators, academia and industry. A recommended immediate course of action was to establish an expert panel of users, developers and regulators to define the testing scope of models for different chemical classes. It was agreed by all participants that improvement and harmonization of alternative approaches is needed for all sectors and this will most effectively be achieved by stakeholders from different sectors sharing data