This article presents a novel approach for evaluating laser scribing quality through cross-section profiles generated from a three-dimensional optical profiler. Existing methods for assessing scribing quality only consider the width and depth of a scribe profile. The proposed method uses a cubic spline model for cross-section profiles. Two quality characteristics are proposed to assess scribing accuracy and consistency. Accuracy is measured by the ratio of the actual laser-scribed area to the target area (RA), which reflects the deviation from the desired profile. The mean square error (MSE) is a measure of how close each scribed cross-section under the same scribing conditions is to the fitted cubic spline model. Over 1370 cross-section profiles were generated under 171 scribing conditions. Two response surface polynomial models for RA and MSE were built with 18 scribing conditions with acceptable scribing depth and RA values. Both RA and MSE were considered simultaneously via contour plots. A scatter plot of RA and MSE was then used for Pareto optimization. It was found that the cross-sectional profile of a laser scribe could be accurately represented by a cubic spline model. A multivariate nonlinear regression model for RA and MSE identified pulse energy and repetition rate as the two dominant laser parameters. A Pareto optimization analysis further established a Pareto front, where the best compromised solution could be found.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10672879 | PMC |
http://dx.doi.org/10.3390/mi14112020 | DOI Listing |
Food Chem
December 2024
Sensors and Biosensors Group, Analytical Chemistry and Electrochemistry Lab (LR99ES15), University of Tunis El Manar, Tunis El Manar, 2092 Tunis, Tunisia. Electronic address:
Improper use and harmful effects of nitrite ions pose a significant risk to human health. To address this concern, the use of carbon-based materials for electrochemical sensing is regarded as one of the most promising detection tools for ensuring the quality of drinking water and food products. In this context, we developed laser-ablated graphene electrodes (LAGEs) by direct laser scribing on a polyimide substrate, which were subsequently modified by electrochemical deposition of a redox-active melanin-like film (MeLF/LAGEs).
View Article and Find Full Text PDFJ Am Med Inform Assoc
December 2024
Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, United States.
Objectives: To quantify utilization and impact on documentation time of a large language model-powered ambient artificial intelligence (AI) scribe.
Materials And Methods: This prospective quality improvement study was conducted at a large academic medical center with 45 physicians from 8 ambulatory disciplines over 3 months. Utilization and documentation times were derived from electronic health record (EHR) use measures.
J Am Med Inform Assoc
December 2024
Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, United States.
Objective: This study evaluates the pilot implementation of ambient AI scribe technology to assess physician perspectives on usability and the impact on physician burden and burnout.
Materials And Methods: This prospective quality improvement study was conducted at Stanford Health Care with 48 physicians over a 3-month period. Outcome measures included burden, burnout, usability, and perceived time savings.
Nat Commun
December 2024
Innovative Genomics Institute, University of California, Berkeley, CA, USA.
Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. RNA structure prediction is not yet possible due to a lack of high-quality reference data associated with organismal phenotypes that could inform RNA function. We present GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB).
View Article and Find Full Text PDFUrology
November 2024
Department of Surgery, Division of Urology, McMaster University, Hamilton, Ontario, Canada. Electronic address:
Objective: To assess the quality of artificial intelligence (AI) scribes and to evaluate their impact on urologic practice.
Methods: Standardized reference consultation notes were created for common urologic referrals (urolithiasis, benign prostate hyperplasia, and prostate-specific antigen screening) were created. Audio recordings of these simulated patient encounters were played for five freely accessible AI scribes.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!