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This content will become publicly available on July 9, 2024

Title: Laser-Induced Liquid Deformation Driven by the Marangoni Effect
We studied laser-induced liquid indentations generated by the Marangoni effect. We showed experimental results along with the simulation model based on the lubrication theory.  more » « less
Award ID(s):
1809622
NSF-PAR ID:
10476265
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
Optica Publishing Group
Date Published:
Page Range / eLocation ID:
JTu4A.1
Format(s):
Medium: X
Location:
Busan
Sponsoring Org:
National Science Foundation
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  1. Abstract Purpose The ability to identify the scholarship of individual authors is essential for performance evaluation. A number of factors hinder this endeavor. Common and similarly spelled surnames make it difficult to isolate the scholarship of individual authors indexed on large databases. Variations in name spelling of individual scholars further complicates matters. Common family names in scientific powerhouses like China make it problematic to distinguish between authors possessing ubiquitous and/or anglicized surnames (as well as the same or similar first names). The assignment of unique author identifiers provides a major step toward resolving these difficulties. We maintain, however, that in and of themselves, author identifiers are not sufficient to fully address the author uncertainty problem. In this study we build on the author identifier approach by considering commonalities in fielded data between authors containing the same surname and first initial of their first name. We illustrate our approach using three case studies. Design/methodology/approach The approach we advance in this study is based on commonalities among fielded data in search results. We cast a broad initial net—i.e., a Web of Science (WOS) search for a given author’s last name, followed by a comma, followed by the first initial of his or her first name (e.g., a search for ‘John Doe’ would assume the form: ‘Doe, J’). Results for this search typically contain all of the scholarship legitimately belonging to this author in the given database (i.e., all of his or her true positives), along with a large amount of noise, or scholarship not belonging to this author (i.e., a large number of false positives). From this corpus we proceed to iteratively weed out false positives and retain true positives. Author identifiers provide a good starting point—e.g., if ‘Doe, J’ and ‘Doe, John’ share the same author identifier, this would be sufficient for us to conclude these are one and the same individual. We find email addresses similarly adequate—e.g., if two author names which share the same surname and same first initial have an email address in common, we conclude these authors are the same person. Author identifier and email address data is not always available, however. When this occurs, other fields are used to address the author uncertainty problem. Commonalities among author data other than unique identifiers and email addresses is less conclusive for name consolidation purposes. For example, if ‘Doe, John’ and ‘Doe, J’ have an affiliation in common, do we conclude that these names belong the same person? They may or may not; affiliations have employed two or more faculty members sharing the same last and first initial. Similarly, it’s conceivable that two individuals with the same last name and first initial publish in the same journal, publish with the same co-authors, and/or cite the same references. Should we then ignore commonalities among these fields and conclude they’re too imprecise for name consolidation purposes? It is our position that such commonalities are indeed valuable for addressing the author uncertainty problem, but more so when used in combination. Our approach makes use of automation as well as manual inspection, relying initially on author identifiers, then commonalities among fielded data other than author identifiers, and finally manual verification. To achieve name consolidation independent of author identifier matches, we have developed a procedure that is used with bibliometric software called VantagePoint (see www.thevantagepoint.com) While the application of our technique does not exclusively depend on VantagePoint, it is the software we find most efficient in this study. The script we developed to implement this procedure is designed to implement our name disambiguation procedure in a way that significantly reduces manual effort on the user’s part. Those who seek to replicate our procedure independent of VantagePoint can do so by manually following the method we outline, but we note that the manual application of our procedure takes a significant amount of time and effort, especially when working with larger datasets. Our script begins by prompting the user for a surname and a first initial (for any author of interest). It then prompts the user to select a WOS field on which to consolidate author names. After this the user is prompted to point to the name of the authors field, and finally asked to identify a specific author name (referred to by the script as the primary author) within this field whom the user knows to be a true positive (a suggested approach is to point to an author name associated with one of the records that has the author’s ORCID iD or email address attached to it). The script proceeds to identify and combine all author names sharing the primary author’s surname and first initial of his or her first name who share commonalities in the WOS field on which the user was prompted to consolidate author names. This typically results in significant reduction in the initial dataset size. After the procedure completes the user is usually left with a much smaller (and more manageable) dataset to manually inspect (and/or apply additional name disambiguation techniques to). Research limitations Match field coverage can be an issue. When field coverage is paltry dataset reduction is not as significant, which results in more manual inspection on the user’s part. Our procedure doesn’t lend itself to scholars who have had a legal family name change (after marriage, for example). Moreover, the technique we advance is (sometimes, but not always) likely to have a difficult time dealing with scholars who have changed careers or fields dramatically, as well as scholars whose work is highly interdisciplinary. Practical implications The procedure we advance has the ability to save a significant amount of time and effort for individuals engaged in name disambiguation research, especially when the name under consideration is a more common family name. It is more effective when match field coverage is high and a number of match fields exist. Originality/value Once again, the procedure we advance has the ability to save a significant amount of time and effort for individuals engaged in name disambiguation research. It combines preexisting with more recent approaches, harnessing the benefits of both. Findings Our study applies the name disambiguation procedure we advance to three case studies. Ideal match fields are not the same for each of our case studies. We find that match field effectiveness is in large part a function of field coverage. Comparing original dataset size, the timeframe analyzed for each case study is not the same, nor are the subject areas in which they publish. Our procedure is more effective when applied to our third case study, both in terms of list reduction and 100% retention of true positives. We attribute this to excellent match field coverage, and especially in more specific match fields, as well as having a more modest/manageable number of publications. While machine learning is considered authoritative by many, we do not see it as practical or replicable. The procedure advanced herein is both practical, replicable and relatively user friendly. It might be categorized into a space between ORCID and machine learning. Machine learning approaches typically look for commonalities among citation data, which is not always available, structured or easy to work with. The procedure we advance is intended to be applied across numerous fields in a dataset of interest (e.g. emails, coauthors, affiliations, etc.), resulting in multiple rounds of reduction. Results indicate that effective match fields include author identifiers, emails, source titles, co-authors and ISSNs. While the script we present is not likely to result in a dataset consisting solely of true positives (at least for more common surnames), it does significantly reduce manual effort on the user’s part. Dataset reduction (after our procedure is applied) is in large part a function of (a) field availability and (b) field coverage. 
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  2. Obeid, Iyad ; Picone, Joseph ; Selesnick, Ivan (Ed.)
    The Neural Engineering Data Consortium (NEDC) is developing a large open source database of high-resolution digital pathology images known as the Temple University Digital Pathology Corpus (TUDP) [1]. Our long-term goal is to release one million images. We expect to release the first 100,000 image corpus by December 2020. The data is being acquired at the Department of Pathology at Temple University Hospital (TUH) using a Leica Biosystems Aperio AT2 scanner [2] and consists entirely of clinical pathology images. More information about the data and the project can be found in Shawki et al. [3]. We currently have a National Science Foundation (NSF) planning grant [4] to explore how best the community can leverage this resource. One goal of this poster presentation is to stimulate community-wide discussions about this project and determine how this valuable resource can best meet the needs of the public. The computing infrastructure required to support this database is extensive [5] and includes two HIPAA-secure computer networks, dual petabyte file servers, and Aperio’s eSlide Manager (eSM) software [6]. We currently have digitized over 50,000 slides from 2,846 patients and 2,942 clinical cases. There is an average of 12.4 slides per patient and 10.5 slides per case with one report per case. The data is organized by tissue type as shown below: Filenames: tudp/v1.0.0/svs/gastro/000001/00123456/2015_03_05/0s15_12345/0s15_12345_0a001_00123456_lvl0001_s000.svs tudp/v1.0.0/svs/gastro/000001/00123456/2015_03_05/0s15_12345/0s15_12345_00123456.docx Explanation: tudp: root directory of the corpus v1.0.0: version number of the release svs: the image data type gastro: the type of tissue 000001: six-digit sequence number used to control directory complexity 00123456: 8-digit patient MRN 2015_03_05: the date the specimen was captured 0s15_12345: the clinical case name 0s15_12345_0a001_00123456_lvl0001_s000.svs: the actual image filename consisting of a repeat of the case name, a site code (e.g., 0a001), the type and depth of the cut (e.g., lvl0001) and a token number (e.g., s000) 0s15_12345_00123456.docx: the filename for the corresponding case report We currently recognize fifteen tissue types in the first installment of the corpus. The raw image data is stored in Aperio’s “.svs” format, which is a multi-layered compressed JPEG format [3,7]. Pathology reports containing a summary of how a pathologist interpreted the slide are also provided in a flat text file format. A more complete summary of the demographics of this pilot corpus will be presented at the conference. Another goal of this poster presentation is to share our experiences with the larger community since many of these details have not been adequately documented in scientific publications. There are quite a few obstacles in collecting this data that have slowed down the process and need to be discussed publicly. Our backlog of slides dates back to 1997, meaning there are a lot that need to be sifted through and discarded for peeling or cracking. Additionally, during scanning a slide can get stuck, stalling a scan session for hours, resulting in a significant loss of productivity. Over the past two years, we have accumulated significant experience with how to scan a diverse inventory of slides using the Aperio AT2 high-volume scanner. We have been working closely with the vendor to resolve many problems associated with the use of this scanner for research purposes. This scanning project began in January of 2018 when the scanner was first installed. The scanning process was slow at first since there was a learning curve with how the scanner worked and how to obtain samples from the hospital. From its start date until May of 2019 ~20,000 slides we scanned. In the past 6 months from May to November we have tripled that number and how hold ~60,000 slides in our database. This dramatic increase in productivity was due to additional undergraduate staff members and an emphasis on efficient workflow. The Aperio AT2 scans 400 slides a day, requiring at least eight hours of scan time. The efficiency of these scans can vary greatly. When our team first started, approximately 5% of slides failed the scanning process due to focal point errors. We have been able to reduce that to 1% through a variety of means: (1) best practices regarding daily and monthly recalibrations, (2) tweaking the software such as the tissue finder parameter settings, and (3) experience with how to clean and prep slides so they scan properly. Nevertheless, this is not a completely automated process, making it very difficult to reach our production targets. With a staff of three undergraduate workers spending a total of 30 hours per week, we find it difficult to scan more than 2,000 slides per week using a single scanner (400 slides per night x 5 nights per week). The main limitation in achieving this level of production is the lack of a completely automated scanning process, it takes a couple of hours to sort, clean and load slides. We have streamlined all other aspects of the workflow required to database the scanned slides so that there are no additional bottlenecks. To bridge the gap between hospital operations and research, we are using Aperio’s eSM software. Our goal is to provide pathologists access to high quality digital images of their patients’ slides. eSM is a secure website that holds the images with their metadata labels, patient report, and path to where the image is located on our file server. Although eSM includes significant infrastructure to import slides into the database using barcodes, TUH does not currently support barcode use. Therefore, we manage the data using a mixture of Python scripts and manual import functions available in eSM. The database and associated tools are based on proprietary formats developed by Aperio, making this another important point of community-wide discussion on how best to disseminate such information. Our near-term goal for the TUDP Corpus is to release 100,000 slides by December 2020. We hope to continue data collection over the next decade until we reach one million slides. We are creating two pilot corpora using the first 50,000 slides we have collected. The first corpus consists of 500 slides with a marker stain and another 500 without it. This set was designed to let people debug their basic deep learning processing flow on these high-resolution images. We discuss our preliminary experiments on this corpus and the challenges in processing these high-resolution images using deep learning in [3]. We are able to achieve a mean sensitivity of 99.0% for slides with pen marks, and 98.9% for slides without marks, using a multistage deep learning algorithm. While this dataset was very useful in initial debugging, we are in the midst of creating a new, more challenging pilot corpus using actual tissue samples annotated by experts. The task will be to detect ductal carcinoma (DCIS) or invasive breast cancer tissue. There will be approximately 1,000 images per class in this corpus. Based on the number of features annotated, we can train on a two class problem of DCIS or benign, or increase the difficulty by increasing the classes to include DCIS, benign, stroma, pink tissue, non-neoplastic etc. Those interested in the corpus or in participating in community-wide discussions should join our listserv, nedc_tuh_dpath@googlegroups.com, to be kept informed of the latest developments in this project. You can learn more from our project website: https://www.isip.piconepress.com/projects/nsf_dpath. 
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  3. In this theory paper, we set out to consider, as a matter of methodological interest, the use of quantitative measures of inter-coder reliability (e.g., percentage agreement, correlation, Cohen’s Kappa, etc.) as necessary and/or sufficient correlates for quality within qualitative research in engineering education. It is well known that the phrase qualitative research represents a diverse body of scholarship conducted across a range of epistemological viewpoints and methodologies. Given this diversity, we concur with those who state that it is ill advised to propose recipes or stipulate requirements for achieving qualitative research validity and reliability. Yet, as qualitative researchers ourselves, we repeatedly find the need to communicate the validity and reliability—or quality—of our work to different stakeholders, including funding agencies and the public. One method for demonstrating quality, which is increasingly used in qualitative research in engineering education, is the practice of reporting quantitative measures of agreement between two or more people who code the same qualitative dataset. In this theory paper, we address this common practice in two ways. First, we identify instances in which inter-coder reliability measures may not be appropriate or adequate for establishing quality in qualitative research. We query research that suggests that the numerical measure itself is the goal of qualitative analysis, rather than the depth and texture of the interpretations that are revealed. Second, we identify complexities or methodological questions that may arise during the process of establishing inter-coder reliability, which are not often addressed in empirical publications. To achieve this purposes, in this paper we will ground our work in a review of qualitative articles, published in the Journal of Engineering Education, that have employed inter-rater or inter-coder reliability as evidence of research validity. In our review, we will examine the disparate measures and scores (from 40% agreement to 97% agreement) used as evidence of quality, as well as the theoretical perspectives within which these measures have been employed. Then, using our own comparative case study research as an example, we will highlight the questions and the challenges that we faced as we worked to meet rigorous standards of evidence in our qualitative coding analysis, We will explain the processes we undertook and the challenges we faced as we assigned codes to a large qualitative data set approached from a post positivist perspective. We will situate these coding processes within the larger methodological literature and, in light of contrasting literature, we will describe the principled decisions we made while coding our own data. We will use this review of qualitative research and our own qualitative research experiences to elucidate inconsistencies and unarticulated issues related to evidence for qualitative validity as a means to generate further discussion regarding quality in qualitative coding processes. 
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  4. Early in the pandemic we gathered a group of educators to create and share at-home educational opportunities for families to design and make STEAM projects while at home. As this effort, CoBuild19, continued, we decided to extend our offerings to include basic computer programming. To accomplish this, we created an offering called the Design with Code Club (DwCC). We structured DwCC to be different from other common coding offerings in that we wanted the main focus to be on kids designing solutions to problems that might include the use of technology and coding. We were purposeful in this decision for two main reasons. First, we wanted to make our coding club more interesting to girls, where previous research demonstrates their interest in designing solutions. Second, we wanted this effort to be different from most programming instruction, where coding activities use programming as the core of instruction and application in authentic and student-selected contexts plays a secondary role. DwCC was set up so that each of the first four weeks had a different larger challenge that was COVID-19 related and sessions unfolded with alternating smaller challenges, discussion around design and coding instruction that would develop their skills and knowledge of micro:bit capabilities. We culminated DwCC with an open-ended project where the kids were given the challenge of coming up with their own problem for which they might incorporate micro:bit as part of the solution. Because we were doing all of this online, we used the micro:bit interface through Microsoft MakeCode, which includes a functional simulator. From our experiences we realized that simulations are not as enticing as physical computing with a tangible device, so we set up an incentive where youth who participated in at least three sessions of the club would receive a physical micro:bit. We advertised DwCC through Facebook and twitter and had nearly 200 families register their kids to participate. In the end, a total of 52 micro:bits were sent to youth participants. Based on this success, we sought to expand the effort and increase accessibility for groups that are traditionally underrepresented in STEM. In spring 2021, we offered a Girls DwCC. This was a redesigned version of the club where the focus was even more on problem-solving through design. The club was run by all women, including one from the US, an Industrial Engineer from Mexico and a computer programmer from Albania. More than 50 girls from 17 countries participated in the club! We are working on another version of GDwCC that will be offered in Spanish and focus on Latina girls in the US and Mexico. In the most recent iteration of DwCC we are working with an educator at a school for deaf students to create a version of the club that works for their students. We are doing some modification of activities and recreating videos that involve sign language interpretation. In this presentation we will report on the variants of DwCC, results from participant feedback surveys and plans for future versions. 
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  5. Our NSF-funded project, CoBuild19, sought to address the large-scale shift to at-home learning based on nationwide school closures that occurred during COVID-19 through creating making/STEM activities for families with children in grades K-6. Representing multiple organizations, our CoBuild19 project team developed approximately 60 STEM activities that make use of items readily available in most households. From March through June 2020, we produced and shared videos and activity guides, averaging 3+ new activities per week. Initially, the activities consisted of whatever team members could pull together, but we soon created weekly themes with associated activities, including Design and Prototype Week, Textiles Week, Social and Emotional Learning Week, and one week which highlighted kids sharing cooking and baking recipes for other kids. All activities were delivered fully online. To do so, our team started a Facebook group on March 13, 2020. Membership grew to 3490 followers by April 1st, to 4245 by May 1st, and leveled off at approximately 5100 members since June 2020. To date, 22 of our videos have over 1000 views, with the highest garnering 23K views. However, we had very little participation in the form of submitted videos, images, or text from families sharing what they were creating, limiting our possible analyses. While we had some initial participation by members, as the FB group grew, substantive evidence of participation faded. To better understand this drop, we polled FB group members about their use of the activities. Responses (n = 101) were dominated by the option, "We are glad to know the ideas are available, but we are not using much" (49%), followed by, "We occasionally do activities" (35%). At this point, we had no data about home participation, so we decided to experiment with different approaches. Our next efforts focused on conducting virtual maker/STEM camps. Leveraging the content produced in the first months of CoBuild19, we hosted two rounds of Camp CoBuild by the end of July, serving close to 100 campers. The camps generated richer data in the form of recorded Zoom camp sessions where campers made synchronously with educators and youth-created Flipgrid videos where campers shared their process and products for each activity. We also collected post-camp surveys and some caregiver interviews. Preliminary analyses have focused on the range of participant engagement and which malleable factors may be associated with deeper engagement. Initial feedback from caregivers indicated that their children gained confidence to experiment with simple materials through engaging in these activities. This project sought to fill what we perceived as a developing need in the community at a large scale (e.g., across the US). Although we have not achieved the level of success we expected, the project achieved quick growth that took us in a different direction than we originally intended. Overall, we created content that educators and families can use to engage kids with minimal materials. Additionally, we have a few models of extended engagement (e.g., Camp CoBuild) that we can develop further into future offerings. 
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