A Preliminary Study of Applying Lean Six Sigma Methods to Machine Tool Measurement

Many manufacturers aim to increase their levels of high-quality production in order to improve their market competitiveness. Continuous improvement of maintenance strategies is a key factor to be capable of delivering high quality products and services on-time with minimal operating costs. However, the cost of maintaining quality is often perceived as a non-added-value task. Improving the efficiency and effectiveness of the measurement procedures necessary to guarantee accuracy of production is a more complex task than many other maintenance functions and so deserves particular analysis. This paper investigates the feasibility of producing a concise yet effective framework that will provide a preliminary approach for integrating Lean and Six Sigma philosophies to the specific goal of reducing unnecessary downtime on manufacturing machines while maintaining its ability to machine to the required tolerance. The purpose of this study is to show how a Six Sigma infrastructure is used to investigate the root causes of complication occurring during the machine tool measurement. This work recognises issues of the uncertainty of data, and the measurement procedures in parallel with the main tools of Six Sigma’s Define-Measure-Analyse-Improve-Control (DMAIC). The significance of this work is that machine tool accuracy is critical for high value manufacturing. Over-measuring the machine to ensure accuracy potentially reduces production volume. However, not measuring them or ignoring accuracy aspects possibly lead to production waste. This piece of work aims to present a lean guidance to lessen measurement uncertainties and optimise the machine tool benchmarking procedures, while adopting the DMAIC strategy to reduce unnecessary downtime

Doing Good Today and Better Tomorrow: A Roadmap to High Impact Philanthropy Through Outcome-Focused Grantmaking

Describes Hewlett's experience with implementing the outcome-focused grantmaking (OFG) process in its environment program as a guide for identifying a portfolio of grants with maximum impact. Outlines trials and errors, recent innovations, and challenges

Preventing Emergency Department Overutilization for Florida’s Seasonal Resident Population

Background/Local Problem: Seasonal migration of elderly patients to Lee County, Florida result in overcrowding and prolonged wait times in emergency departments. Many of these seasonal residents dissociate the management of their chronic health conditions with a local provider, therefore utilizing the emergency department for non-urgent needs. Purpose: The Seasonal Resident Navigator Program was intended to enhance the coordination of primary care services for elderly seasonal residents by establishing appointments with local primary care providers (PCP) in order to reduce the overutilization of emergency services and improve patient throughput. Methods: A residency and provider assessment tool was incorporated into the Healthpark Medical Center Emergency Department (ED) nurse triage workflow between November 2017-February 2018 in order to identify seasonal residents, age 65 or greater, without an assigned local provider and facilitate proper follow up appointments. Interventions: The percentage of all seasonal resident encounters at Healthpark Medical Center ED pre-and-post intervention were evaluated as well as the percentage of all seasonal residents that maintained their assigned PCP follow up appointment. Open commentary from patients was evaluated to identify perceived barriers from outpatient follow up. Results/Conclusion: The Seasonal Resident Navigator program will contribute to future trends in emergency department utilization and seasonal resident access to care through enhanced coordination between the acute care and primary care sector

DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences.

Modeling the properties and functions of DNA sequences is an important, but challenging task in the broad field of genomics. This task is particularly difficult for non-coding DNA, the vast majority of which is still poorly understood in terms of function. A powerful predictive model for the function of non-coding DNA can have enormous benefit for both basic science and translational research because over 98% of the human genome is non-coding and 93% of disease-associated variants lie in these regions. To address this need, we propose DanQ, a novel hybrid convolutional and bi-directional long short-term memory recurrent neural network framework for predicting non-coding function de novo from sequence. In the DanQ model, the convolution layer captures regulatory motifs, while the recurrent layer captures long-term dependencies between the motifs in order to learn a regulatory 'grammar' to improve predictions. DanQ improves considerably upon other models across several metrics. For some regulatory markers, DanQ can achieve over a 50% relative improvement in the area under the precision-recall curve metric compared to related models. We have made the source code available at the github repository http://github.com/uci-cbcl/DanQ

Hydrologic verification: A call for action and collaboration

Traditionally, little attention has been focused on the systematic verification of operational hydrologic forecasts. This paper summarizes the results of forecasts verification from 15 river basins in the United States. The verification scores for these forecast locations do not show improvement over the periods of record despite a number of forecast process improvements. In considering a root cause for these results, the authors note that the current paradigm for designing hydrologic forecast process improvements is driven by expert opinion and not by objective verification measures. The authors suggest that this paradigm should be modified and objective verification metrics should become the primary driver for hydrologic forecast process improvements. ©2007 American Meteorological Society

Detecting and quantifying stellar magnetic fields -- Sparse Stokes profile approximation using orthogonal matching pursuit

In the recent years, we have seen a rapidly growing number of stellar magnetic field detections for various types of stars. Many of these magnetic fields are estimated from spectropolarimetric observations (Stokes V) by using the so-called center-of-gravity (COG) method. Unfortunately, the accuracy of this method rapidly deteriorates with increasing noise and thus calls for a more robust procedure that combines signal detection and field estimation. We introduce an estimation method that provides not only the effective or mean longitudinal magnetic field from an observed Stokes V profile but also uses the net absolute polarization of the profile to obtain an estimate of the apparent (i.e., velocity resolved) absolute longitudinal magnetic field. By combining the COG method with an orthogonal-matching-pursuit (OMP) approach, we were able to decompose observed Stokes profiles with an overcomplete dictionary of wavelet-basis functions to reliably reconstruct the observed Stokes profiles in the presence of noise. The elementary wave functions of the sparse reconstruction process were utilized to estimate the effective longitudinal magnetic field and the apparent absolute longitudinal magnetic field. A multiresolution analysis complements the OMP algorithm to provide a robust detection and estimation method. An extensive Monte-Carlo simulation confirms the reliability and accuracy of the magnetic OMP approach.Comment: A&A, in press, 15 pages, 14 figure

Framework for Evaluation of the IT&C Audit Metrics Impact

The paper defines an assessment system for performance of IT&C audit process. The analytical models of performance indicators are provided together with the interpretation of their results. Performance levels catch the quality characteristics of the audit processes carried out for distributed informatics systems. Also, the paper presents a performance assessment framework for audit processes and a performance audit methodology. The impact of performance indicators is defined as the organization’s income after performance audit recommendation implementing. Methods and techniques for performance assessment are provided for audit processes of the distributed informatics system. The impact levels of performance indicators are calculated before implementation of the performance recommendation and after that to establish whether the performance audit increases the quality of IT&C audit processes.Performance Metrics, Metric Impact, Audit Process

Impacts of Homeownership Education and Counseling on Homebuyer Purchasing Power: Summary of Findings

In addition to reducing defaults and foreclosures, homeownership education and counseling is often claimed to help families achieve homeownership in the first place by helping them to navigate the homebuying process, improve their credit, and access favorable financing products. This study tests an approach to quantifying this benefit by estimating the amount of increased purchasing power that results from homeownership education and counseling. While the results are preliminary, they provide early suggestive evidence that high-performing homeownership education and counseling agencies may provide quantifiable benefits that exceed their costs of assistance. The study also makes recommendations for how data could be collected on a more systematic basis to track and assess these benefits

Quantifying the Detrimental Impacts of Land-Use and Management Change on European Forest Bird Populations

The ecological impacts of changing forest management practices in Europe are poorly understood despite European forests being highly managed. Furthermore, the effects of potential drivers of forest biodiversity decline are rarely considered in concert, thus limiting effective conservation or sustainable forest management. We present a trait-based framework that we use to assess the detrimental impact of multiple land-use and management changes in forests on bird populations across Europe. Major changes to forest habitats occurring in recent decades, and their impact on resource availability for birds were identified. Risk associated with these changes for 52 species of forest birds, defined as the proportion of each species' key resources detrimentally affected through changes in abundance and/or availability, was quantified and compared to their pan-European population growth rates between 1980 and 2009. Relationships between risk and population growth were found to be significantly negative, indicating that resource loss in European forests is an important driver of decline for both resident and migrant birds. Our results demonstrate that coarse quantification of resource use and ecological change can be valuable in understanding causes of biodiversity decline, and thus in informing conservation strategy and policy. Such an approach has good potential to be extended for predictive use in assessing the impact of possible future changes to forest management and to develop more precise indicators of forest health