Development of LaserPro System for External Corrosion Mapping With Integrated Assessment

2004 ◽  
Author(s):  
Paul Holloway ◽  
Vaughn Inman ◽  
Duane Cronin
Keyword(s):  
Position Measurement ◽  
Data Set ◽  
Depth Measurement ◽  
Assessment Techniques ◽  
Corrosion Defects ◽  
Automatic Tool ◽  
External Corrosion ◽  
The University ◽  
Online Feedback ◽  
Automated Methods

This paper presents the design and application of a new semi-automatic tool for mapping external pipeline corrosion. The device hardware is complemented by the implementation of current corrosion assessment techniques via online software. The development of this device is based on over 10 years of experience in external corrosion mapping and automated scanner development, in support of an experimental research program at the University of Waterloo. The mapping device hardware consists of a novel position measurement system which is coupled to a laser-based depth measurement device to generate a surface map of corrosion defects. Direct comparison with manual (pit gauge) and fully automated measurements has shown that a given defect can be quickly and accurately mapped using the new system. Automated data capture and evaluation has demonstrated significant improvements over fully manual pit gauge-based methods. In addition, the manual position of the measurement head allows the user to map only those areas which are pertinent to the assessment. This is in contrast to fully automated methods which require continuous measurements over predefined areas of a pipe. Online feedback compliments this system and allows the operator to determine if the data set is complete by providing information on the convergence of the predicted failure pressure. Using this new mapping device, measured surface maps of natural complex corrosion defects are shown to compare favorably with other mapping devices and manually measured defect dimensions.

Diagnostics of professional competence of IT students based on digital footprint data

2020 ◽  
pp. 4-11
Author(s):  
I. G. Zakharova ◽  
Yu. V. Boganyuk ◽  
M. S. Vorobyova ◽  
E. A. Pavlova
Keyword(s):  
Information Technology ◽  
Educational Program ◽  
Professional Competence ◽  
Objective Data ◽  
Text Data ◽  
Data Set ◽  
Job Requirements ◽  
Digital Footprint ◽  
Graduate Employment ◽  
The University

The article goal is to demonstrate the possibilities of the approach to diagnosing the level of IT graduates’ professional competence, based on the analysis of the student’s digital footprint and the content of the corresponding educational program. We describe methods for extracting student professional level indicators from digital footprint text data — courses’ descriptions and graduation qualification works. We show methods of comparing these indicators with the formalized requirements of employers, reflected in the texts of vacancies in the field of information technology. The proposed approach was applied at the Institute of Mathematics and Computer Science of the University of Tyumen. We performed diagnostics using a data set that included texts of courses’ descriptions for IT areas of undergraduate studies, 542 graduation qualification works in these areas, 879 descriptions of job requirements and information on graduate employment. The presented approach allows us to evaluate the relevance of the educational program as a whole and the level of professional competence of each student based on objective data. The results were used to update the content of some major courses and to include new elective courses in the curriculum.

scholarly journals SURG-29. A SINGLE CENTRE EXPERIENCE OF USING INTRA-OPERATIVE MRI IN MANAGING PEDIATRIC CRANIAL NEURO-ONCOLOGY CASES

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii464-iii464
Author(s):  
Dharmendra Ganesan ◽  
Nor Faizal Ahmad Bahuri ◽  
Revathi Rajagopal ◽  
Jasmine Loh PY ◽  
Kein Seong Mun ◽  
...  
Keyword(s):  
Standard Operating Procedure ◽  
Ferromagnetic Material ◽  
Extent Of Resection ◽  
Kuala Lumpur ◽  
Data Set ◽  
Single Centre Experience ◽  
Physical Setting ◽  
Paediatric Case ◽  
High Level ◽  
The University

Abstract The University of Malaya Medical Centre, Kuala Lumpur had acquired a intraoperative MRI (iMRI) brain suite via a public private initiative in September 2015. The MRI brain suite has a SIEMENS 1.5T system with NORAS coil system and NORAS head clamps in a two room solution. We would like to retrospectively review the cranial paediatric neuro-oncology cases that had surgery in this facility from September 2015 till December 2019. We would like to discuss our experience with regard to the clear benefits and the challenges in using such technology to aid in the surgery. The challenges include the physical setting up the paediatric case preoperatively, the preparation and performing the intraoperative scan, the interpretation of intraoperative images and making a decision and the utilisation of the new MRI data set to assist in the navigation to locate the residue safely. Also discuss the utility of the intraoperative images in the decision of subsequent adjuvant management. The use of iMRI also has other technical challenges such as ensuring the perimeter around the patient is free of ferromagnetic material, the process of transfer of the patient to the scanner and as a consequence increased duration of the surgery. CONCLUSION: Many elements in the use of iMRI has a learning curve and it improves with exposure and experience. In some areas only a high level of vigilance and SOP (Standard operating procedure) is required to minimize mishaps. Currently, the iMRI gives the best means of determining extent of resection before concluding the surgery.

scholarly journals 136. Don’t Sweat the Small Stuff: Solutions for Large-Scale Stewardship Obstacles

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S79-S80
Author(s):  
Joanne Huang ◽  
Zahra Kassamali Escobar ◽  
Rupali Jain ◽  
Jeannie D Chan ◽  
John B Lynch ◽  
...  
Keyword(s):  
Urgent Care ◽  
Complex Data ◽  
Open Communication ◽  
University Of Washington ◽  
Data Set ◽  
Constructive Feedback ◽  
Diagnostic Coding ◽  
Icd 10 ◽  
Tract Infections ◽  
The University

Abstract Background In an effort to support stewardship endeavors, the MITIGATE (a Multifaceted Intervention to Improve Prescribing for Acute Respiratory Infection for Adult and Children in Emergency Department and Urgent Care Settings) Toolkit was published in 2018, aiming to reduce unnecessary antibiotics for viral respiratory tract infections (RTIs). At the University of Washington, we have incorporated strategies from this toolkit at our urgent care clinics. This study aims to address solutions to some of the challenges we experienced. Challenges and Solutions Methods This was a retrospective observational study conducted at Valley Medical Center (Sept 2019-Mar 2020) and the University of Washington (Jan 2019-Feb 2020) urgent care clinics. Patients were identified through ICD-10 diagnosis codes included in the MITIGATE toolkit. The primary outcome was identifying challenges and solutions developed during this process. Results We encountered five challenges during our roll-out of MITIGATE. First, using both ICD-9 and ICD-10 codes can lead to inaccurate data collection. Second, technical support for coding a complex data set is essential and should be accounted for prior to beginning stewardship interventions of this scale. Third, unintentional incorrect diagnosis selection was common and may require reeducation of prescribers on proper selection. Fourth, focusing on singular issues rather than multiple outcomes is more feasible and can offer several opportunities for stewardship interventions. Lastly, changing prescribing behavior can cause unintended tension during implementation. Modifying benchmarks measured, allowing for bi-directional feedback, and identifying provider champions can help maintain open communication. Conclusion Resources such as the MITIGATE toolkit are helpful to implement standardized data driven stewardship interventions. We have experienced some challenges including a complex data build, errors with diagnostic coding, providing constructive feedback while maintaining positive stewardship relationships, and choosing feasible outcomes to measure. We present solutions to these challenges with the aim to provide guidance to those who are considering using this toolkit for outpatient stewardship interventions. Disclosures All Authors: No reported disclosures

Retrievals of Atmospheric Carbonyl Sulfide from IASI

2021 ◽  
Author(s):  
Michael P. Cartwright ◽  
Jeremy J. Harrison ◽  
David P. Moore
Keyword(s):  
Gross Primary Production ◽  
Carbonyl Sulfide ◽  
Optimal Estimation ◽  
Stratospheric Aerosol ◽  
Data Set ◽  
Spatial Coverage ◽  
Retrieval Scheme ◽  
The University ◽  
Fresh Insight

<p>Carbonyl sulfide (OCS) is the most abundant sulfur containing gas in the atmosphere and is an important source of stratospheric aerosol. Furthermore, it has been shown that OCS can be used as a proxy for photosynthesis, which is a powerful tool in quantifying global gross primary production. While considerable improvements have been made in our understanding of the location and magnitude of OCS fluxes over the past few decades, recent studies highlight the need for a new satellite dataset to help reduce the uncertainties in current estimations. The Infrared Atmospheric Sounding Interferometer (IASI) instruments on-board the MetOp satellites offer over 14 years of nadir viewing radiance measurements with excellent spatial coverage. Given that there are currently three IASI instruments in operation, there is the potential for a significantly larger OCS dataset than is currently available elsewhere. Retrievals of OCS from these IASI radiances have been made using an adapted version of the University of Leicester IASI Retrieval Scheme (ULIRS). OCS total column amounts are calculated from profiles retrieved on a 31-layer equidistant pressure grid, using an optimal estimation approach for microwindows in the range 2000 – 2100 cm<sup>-1</sup> wavenumbers. Sensitivity of the measurements peak in the mid-troposphere, between 5 – 10 km.</p><p>The outlook of this work is to produce a long-term OCS satellite observational data set that provides fresh insight to the spatial distribution and trend of atmospheric OCS. Here, we present subsets of data in the form of case studies for different geographic regions and time periods.</p>

scholarly journals Convolutional Neural Networks Model in Premature Detection of Melanoma

2019 ◽  
Vol 8 (12S2) ◽  
pp. 6-11 ◽  
Keyword(s):  
Neural Networks ◽  
Skin Cancer ◽  
Convolutional Neural Networks ◽  
Additional Data ◽  
Data Set ◽  
Non Invasive ◽  
Convolution Filter ◽  
The University ◽  
Improved Accuracy

The objective of this research is provide to the specialists in skin cancer, a premature, rapid and non-invasive diagnosis of melanoma identification, using an image of the lesion, to apply to the treatment of a patient, the method used is the architecture contrast of Convolutional neural networks proposed by Laura Kocobinski of the University of Boston, against our architecture, which reduce the depth of the convolution filter of the last two convolutional layers to obtain maps of more significant characteristics. The performance of the model was reflected in the accuracy during the validation, considering the best result obtained, which is confirmed with the additional data set. The findings found with the application of this base architecture were improved accuracy from 0.79 to 0.83, with 30 epochs, compared to Kocobinski's AlexNet architecture, it was not possible to improve the accuracy of 0.90, however, the complexity of the network played an important role in the results we obtained, which was able to balance and obtain better results without increasing the epochs, the application of our research is very helpful for doctors, since it will allow them to quickly identify if an injury is melanoma or not and consequently treat it efficiently.

scholarly journals Multi-Classification of Fetal Health Status Using Extreme Learning Machine

2021 ◽  
Vol 5 (2) ◽  
pp. 62-70
Author(s):  
Ömer KASIM
Keyword(s):  
Extreme Learning Machine ◽  
Classification Accuracy ◽  
Binary Classification ◽  
Clinical Decision ◽  
Support Vector ◽  
Data Set ◽  
Multiple Classification ◽  
Learning Machine ◽  
Multi Class Classification ◽  
The University

Cardiotocography (CTG) is used for monitoring the fetal heart rate signals during pregnancy. Evaluation of these signals by specialists provides information about fetal status. When a clinical decision support system is introduced with a system that can automatically classify these signals, it is more sensitive for experts to examine CTG data. In this study, CTG data were analysed with the Extreme Learning Machine (ELM) algorithm and these data were classified as normal, suspicious and pathological as well as benign and malicious. The proposed method is validated with the University of California International CTG data set. The performance of the proposed method is evaluated with accuracy, f1 score, Cohen kappa, precision, and recall metrics. As a result of the experiments, binary classification accuracy was obtained as 99.29%. There was only 1 false positive.  When multi-class classification was performed, the accuracy was obtained as 98.12%.  The amount of false positives was found as 2. The processing time of the training and testing of the ELM algorithm were quite minimized in terms of data processing compared to the support vector machine and multi-layer perceptron. This result proved that a high classification accuracy was obtained by analysing the CTG data both binary and multiple classification.

scholarly journals The BernClim plant phenological data set from the canton of Bern (Switzerland) 1970–2018

2019 ◽  
Vol 11 (4) ◽  
pp. 1645-1654 ◽  
Author(s):  
This Rutishauser ◽  
François Jeanneret ◽  
Robert Brügger ◽  
Yuri Brugnara ◽  
Christian Röthlisberger ◽  
...  
Keyword(s):  
Growing Season ◽  
Data Set ◽  
Urban And Regional Planning ◽  
Phenological Data ◽  
Observation Network ◽  
Good Internal Consistency ◽  
Change Impact ◽  
University Of Bern ◽  
The University

Abstract. In 1970, the Institute of Geography of the University of Bern initiated the phenological observation network BernClim. Seasonality information from plants, fog and snow was originally available for applications in urban and regional planning and agricultural and touristic suitability and is now a valuable data set for climate change impact studies. Covering the growing season, volunteer observers record the dates of key development stages of hazel (Corylus avellana), dandelion (Taraxacum officinale), apple tree (Pyrus malus) and beech (Fagus sylvatica). All observations consist of detailed site information, including location, altitude, exposition (aspect) and inclination, that makes BernClim unique in its richness in detail on decadal timescales. Quality control (QC) by experts and statistical analyses of the data have been performed to flag impossible dates, dates outside the biologically plausible range, repeated dates in the same year, stretches of consecutive identical dates and statistically inconsistent dates (outliers in time or in space). Here, we report BernClim data of 7414 plant phenological observations from 1970 to 2018 from 1304 sites at 110 stations, the QC procedure and selected applications (Rutishauser et al., 2019: https://doi.org/10.1594/PANGAEA.900102). The QC points to very good internal consistency (only 0.2 % were flagged as internally inconsistent) and likely high quality of the data. BernClim data indicate a trend towards an extended growing season. They also track the regime shift in the late 1980s well to pronounced earlier dates like numerous other phenological records across the Northern Hemisphere.

Professional community and student engagement in higher education: rethinking the contributions of professional staff

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yaw Owusu-Agyeman ◽  
Enna Moroeroe
Keyword(s):  
Higher Education ◽  
Student Engagement ◽  
Teaching And Learning ◽  
Academic Staff ◽  
Professional Community ◽  
Professional Staff ◽  
Data Set ◽  
Content Type ◽  
Service Culture ◽  
The University

PurposeScholarly studies on student engagement are mostly focused on the perceptions of students and academic staff of higher education institutions (HEIs) with a few studies concentrating on the perspectives of professional staff. To address this knowledge gap, this paper aims to examine how professional staff who are members of a professional community perceive their contributions to enhancing student engagement in a university.Design/methodology/approachData for the current study were gathered using semi-structured face-to-face interviews among 41 professional staff who were purposively sampled from a public university in South Africa. The data gathered were analysed using thematic analysis that involved a process of identifying, analysing, organising, describing and reporting the themes that emerged from the data set.FindingsAn analysis of the narrative data revealed that when professional staff provide students with prompt feedback, support the development of their social and cultural capital and provide professional services in the area of teaching and learning, they foster student engagement in the university. However, the results showed that poor communication flow and delays in addressing students’ concerns could lead to student disengagement. The study further argues that through continuous interaction and shared norms and values among members of a professional community, a service culture can be developed to address possible professional knowledge and skills gaps that constrain quality service delivery.Originality/valueThe current paper contributes to the scholarly discourse on student engagement and professional community by showing that a service culture of engagement is developed among professional staff when they share ideas, collaborate and build competencies to enhance student engagement. Furthermore, the collaboration between professional staff and academics is important to addressing the academic issues that confront students in the university.

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