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10th International Conference on Barkhausen and Micro-Magnetics (ICBM)

作者： Aldrin, John C. Coughlin, Chris Forsyth, David S. Welter, John T. Air Force Res Lab AFRL RXCA Wright Patterson AFB OH 45433 USA Computat Tool Gurnee IL 60031 USA TRI Austin Austin TX 78746 USA

ISBN: (纸本)9780735412125

Progress is presented on the development and implementation of automated data analysis (ADA) software to address the burden in interpreting ultrasonic inspection data for large composite structures. The automated data analysis algorithm is presented in detail, which follows standard procedures for analyzing signals for time-of-flight indications and backwall amplitude dropout. ADA processing results are presented for test specimens that include inserted materials and discontinuities produced under poor manufacturing conditions.

关键词： automated data analysis Composites Image Processing Ultrasonic NDE

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A Scoring Algorithm for the automated analysis of Glycosaminoglycan MS/MS data

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JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2019年第12期30卷 2692-2703页

作者： Duan, Jiana Pepi, Lauren Amster, I. Jonathan Univ Georgia Dept Chem Athens GA 30602 USA

The role of glycosaminoglycans (GAGs) in major biological functions is numerous and diverse, yet structural characterization of them by mass spectrometric techniques proves to be challenging. Characterization of GAG structure from tandem mass spectrometry is a tedious and time-consuming process but one that can be automated in a database-independent, high-throughput fashion through the assistance of software implementing a genetic algorithm (J. Am. Soc. Mass Spectrom. 29, 1802-1911, 2018). This work presents the manner in which this data is interpreted by the software, specifically addressing the development of a scoring algorithm. The significance of glycosidic and cross-ring fragment ions and the implications that specific fragments provide for assigning the positions of modifications are discussed. The scoring algorithm is tested for statistical merit using the widely accepted expectation value as the criterion for quality. Using MS/MS data for well-characterized standards, this scoring approach is shown to assign the correct structure, with a low likelihood (1 in 10(12) chances) that the assigned structure matches the data due to random chance. The integrated software that automates the structure assignment is called Glycosaminoglycan-Unambiguous Identification Technology (G-UNIT).

关键词： Glycosaminoglycans Fourier transform mass spectrometry Automation Software automated data analysis

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Towards fully automated post-event data collection and analysis: Pre-event and post-event information fusion

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ENGINEERING STRUCTURES 2020年 208卷 109884-000页

作者： Lenjani, Ali Dyke, Shirley J. Bilionis, Ilias Yeum, Chul Min Kamiya, Kenzo Choi, Jongseong Liu, Xiaoyu Chowdhury, Arindam G. Purdue Univ Sch Mech Engn W Lafayette IN 47907 USA Purdue Univ Lyles Sch Civil Engn W Lafayette IN 47907 USA Univ Waterloo Dept Civil & Environm Engn Waterloo ON Canada Florida Int Univ Dept Civil & Environm Engn Miami FL 33199 USA

In post-event reconnaissance missions, engineers and researchers collect perishable information about damaged buildings in the affected geographical region to learn from the consequences of the event. A typical post-event reconnaissance mission is conducted by first doing a preliminary survey, followed by a detailed survey. The objective of the preliminary survey is to develop an understanding of the overall situation in the field, and use that information to plan the detailed survey. The preliminary survey is typically conducted by driving slowly along a pre-determined route, observing the damage, and noting where further detailed data should be collected. This involves several manual, time-consuming steps that can be accelerated by exploiting recent advances in computer vision and artificial intelligence. The objective of this work is to develop and validate an automated technique to support post-event reconnaissance teams in the rapid collection of reliable and sufficiently comprehensive data, for planning the detailed survey. The focus here is on residential buildings. The technique incorporates several methods designed to automate the process of categorizing buildings based on their key physical attributes, and rapidly assessing their post-event structural condition. It is divided into pre-event and post-event streams, each intending to first extract all possible information about the target buildings using both pre-event and post-event images. Algorithms based on convolutional neural networks (CNNs) are implemented for scene (image) classification. A probabilistic approach is developed to fuse the results obtained from analyzing several images to yield a robust decision regarding the attributes and condition of a target building. We validate the technique using post-event images captured during reconnaissance missions that took place after hurricanes Harvey and Irma. The validation data were collected by a structural wind and coastal engineering reconnaissance

关键词： Artificial intelligence data-driven decision making Post-event reconnaissance Resilience Convolutional neural networks Machine learning Bayesian information fusion automated data analysis

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Validation of a suite of ERP and QEEG biomarkers in a pre-competitive, industry-led study in subjects with schizophrenia and healthy volunteers

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SCHIZOPHRENIA RESEARCH 2023年第1期254卷 178-189页

作者： Cecchi, M. Adachi, M. Basile, A. Buhl, D. L. Chadchankar, H. Christensen, S. Christian, E. Doherty, J. Fadem, K. C. Farley, B. Forman, M. S. Honda, S. Johannesen, J. Kinon, B. J. Klamer, D. Marino, M. J. Missling, C. O'Donnell, P. Piser, T. Puryear, C. B. Quirk, M. C. Rotte, M. Sanchez, C. Smith, D. G. Uslaner, J. M. Javitt, D. C. Keefe, R. S. E. Mathalon, D. Potter, W. Z. Walling, D. P. Ereshefsky, L. Astellas Pharma San Diego CA USA

Objective: Complexity and lack of standardization have mostly limited the use of event-related potentials (ERPs) and quantitative EEG (QEEG) biomarkers in drug development to small early phase trials. We present results from a clinical study on healthy volunteers (HV) and patients with schizophrenia (SZ) that assessed test-retest, group differences, variance, and correlation with functional assessments for ERP and QEEG measures collected at clinical and commercial trial sites with standardized instrumentation and methods, and analyzed through an automated data analysis ***: 81 HV and 80 SZ were tested at one of four study sites. Subjects were administered two ERP/EEG testing sessions on separate visits. Sessions included a mismatch negativity paradigm, a 40 Hz auditory steady-state response paradigm, an eyes-closed resting state EEG, and an active auditory oddball paradigm. SZ subjects were also tested on the Brief Assessment of Cognition (BAC), Positive and Negative Syndrome Scale (PANSS), and Virtual Reality Functional Capacity Assessment Tool (VRFCAT).Results: Standardized ERP/EEG instrumentation and methods ensured few test failures. The automated data analysis pipeline allowed for near real-time analysis with no human intervention. Test-retest reliability was fair -to-excellent for most of the outcome measures. SZ subjects showed significant deficits in ERP and QEEG measures consistent with published academic literature. A subset of ERP and QEEG measures correlated with functional assessments administered to the SZ ***: With standardized instrumentation and methods, complex ERP/EEG testing sessions can be reliably performed at clinical and commercial trial sites to produce high-quality data in near real-time.

关键词： ERP Biomarker Qualification Consortium Event related potentials Quantitative EEG Schizophrenia Test-retest reliability automated data analysis

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Development of Tools for Studying the Practices of Using AI by Residents of a Megalopolis: Methodology and analysis

Development of Tools for Studying the Practices of Using AI ...

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2025 Communication Strategies in Digital Society Seminar, ComSDS 2025

作者： Nizomutdinov, Boris A. Institute of Design and Urban Studies ITMO University Saint-Petersburg Russia

ISBN: (纸本)9798331536121

There are many aspects of social interaction associated with the use of artificial intelligence technologies in everyday life, which makes the study of such practices among residents of a megalopolis an important area of research. In a megalopolis, the use of AI covers a variety of areas, which requires an integrated approach to data collection and analysis. The article discusses a methodological approach to studying the practices of using artificial intelligence (AI) technologies among the residents of a megalopolis, based on automated analysis of open data. Based on the pilot study, key sources of information and their analytical potential for further in-depth study are evaluated. The authors present a combined method aimed at identifying the differentiation of practices in the application of AI. To study the practices of using AI, an approach was applied that includes automated data collection and analysis: open data research using parsing and machine learning methods, which allows us to identify trends and features of the use of AI. Main data sources for the analysis include: Telegram bots and channels dedicated to AI;Mobile app from the Store Market using AI technology, the company profiles on Google Maps and *** that demonstrate the use of AI, social networks, and media outlets that publish information about AI. As part of the pilot testing, data was collected from these sources. The results confirmed that each source provides valuable data for the analysis of AI-based tools and services. This confirms the expediency of using these sources to identify differences in AI usage practices. The pilot study showed that automated data analysis technologies can be effective for studying the practices of using AI in a megacity. In the future, it is planned to conduct a more in-depth data analysis to identify patterns and trends in the use of AI technologies among various social groups in the metropolis. © 2025 IEEE.

关键词： artificial intelligence automated data analysis megacity social differentiation usage practices

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Mass Dynamics 1.0: A Streamlined, Web-Based Environment for Analyzing, Sharing, and Integrating Label-Free data

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JOURNAL OF PROTEOME RESEARCH 2021年第11期20卷 5180-5188页

作者： Bloom, Joseph Triantafyllidis, Aaron Quaglieri, Anna Ngov, Paula Burton Infusini, Giuseppe Webb, Andrew Mass Dynam Melbourne Vic 3000 Australia Walter & Eliza Hall Inst Med Res Parkville Vic 3052 Australia Univ Melbourne Dept Med Biol Melbourne Vic 3010 Australia

Label-free quantification (LFQ) of shotgun proteomics data is a popular and robust method for the characterization of relative protein abundance between samples. Many analytical pipelines exist for the automation of this analysis, and some tools exist for the subsequent representation and inspection of the results of these pipelines. Mass Dynamics 1.0 (MD 1.0) is a web-based analysis environment that can analyze and visualize LFQ data produced by software such as MaxQuant. Unlike other tools, MD 1.0 utilizes a cloud-based architecture to enable researchers to store their data, enabling researchers to not only automatically process and visualize their LFQ data but also annotate and share their findings with collaborators and, if chosen, to easily publish results to the community. With a view toward increased reproducibility and standardization in proteomics data analysis and streamlining collaboration between researchers, MD 1.0 requires minimal parameter choices and automatically generates quality control reports to verify experiment integrity. Here, we demonstrate that MD 1.0 provides reliable results for protein expression quantification, emulating Perseus on benchmark datasets over a wide dynamic range. The MD 1.0 platform is available globally via: https://***/.

关键词： MaxQuant automated data analysis label free quantification benchmarking web-based software tool

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Tool automation and computer vision methodologies for faster IC diagnostics

Tool automation and computer vision methodologies for faster...

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IEEE International Symposium on the Physical and Failure analysis of Integrated Circuits (IPFA)

作者： Stellari, Franco Lin, Chung-Ching Lan, Fei Song, Peilin IBM TJ Watson Res Ctr 1101 Kitchawan Rd Yorktown Hts NY 10598 USA

automated data acquisition and analysis using scripting and Computer Vision (CV) allow one to perform repetitive tasks faster with higher accuracy. In this paper, we discuss techniques to automate analytical tools and... 详细信息

ISBN: (纸本)9781665439886

关键词： automated data acquisition automated data analysis computer vision (CV) scripting

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ScreenGarden: a shinyR application for fast and easy analysis of plate-based high-throughput screens

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BMC BIOINFORMATICS 2022年第1期23卷 60-60页

作者： Klemm, Cinzia Howell, Rowan S. M. Thorpe, Peter H. Queen Mary Univ London Sch Biol & Behav Sci Mile End Rd London E1 4NS England UCL UCL Canc Inst 72 Huntley St London WC1E 6DD England

Background Colony growth on solid media is a simple and effective measure for high-throughput genomic experiments such as yeast two-hybrid, synthetic dosage lethality and Synthetic Physical Interaction screens. The development of robotic pinning tools has facilitated the experimental design of these assays, and different imaging software can be used to automatically measure colony sizes on plates. However, comparison to control plates and statistical data analysis is often laborious and pinning issues or plate specific growth effects can lead to the detection of false-positive growth defects. Results We have developed ScreenGarden, a shinyR application, to enable easy, quick and robust data analysis of plate-based high throughput assays. The code allows comparisons of different formats of data and different sized arrays of colonies. A comparison of ScreenGarden with previous analysis tools shows that it performs, at least, equivalently. The software can be run either via a website or offline via the RStudio program;the code is available and can be modified by expert uses to customise the analysis. Conclusions ScreenGarden provides a simple, fast and effective tool to analyse colony growth data from genomic experiments.

关键词： Genomics Yeast automated data analysis

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IRIS: A Modular Platform for Continuous Monitoring and Caretaker Notification in the Intensive Care Unit

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2020年第8期24卷 2389-2397页

作者： Baldassano, Steven N. Roberson, Shawniqua Williams Balu, Ramani Scheid, Brittany Bernabei, John M. Pathmanathan, Jay Oommen, Brian Leri, Damien Echauz, Javier Gelfand, Michael Bhalla, Paulomi Kadakia Hill, Chloe E. Christini, Amanda Wagenaar, Joost B. Litt, Brian Univ Penn Dept Bioengn Philadelphia PA 19104 USA Vanderbilt Univ Dept Neurol Nashville TN 37232 USA Univ Penn Dept Neurol Philadelphia PA 19104 USA UPMC Pinnacle Harrisburg Dept Neurol Harrisburg PA 17101 USA Univ Penn Penn Med Ctr Hlth Care Innovat Philadelphia PA 19104 USA JE Res Inc Alpharetta GA 30022 USA Stanford Univ Dept Neurol Stanford CA 94305 USA Univ Michigan Dept Neurol Ann Arbor MI 48109 USA Blackfynn Inc Philadelphia PA 19107 USA

Objective: New approaches are needed to interpret large amounts of physiologic data continuously recorded in the ICU. We developed and prospectively validated a versatile platform (IRIS) for real-time ICU physiologic monitoring, clinical decision making, and caretaker notification. Methods: IRIS was implemented in the neurointensive care unit to stream multimodal time series data, including EEG, intracranial pressure (ICP), and brain tissue oxygenation (PbtO2), from ICU monitors to an analysis server. IRIS was applied for 364 patients undergoing continuous EEG, 26 patients undergoing burst suppression monitoring, and four patients undergoing intracranial pressure and brain tissue oxygen monitoring. Custom algorithms were used to identify periods of elevated ICP, compute burst suppression ratios (BSRs), and detect faulty or disconnected EEG electrodes. Hospital staff were notified of clinically relevant events using our secure API to route alerts through a password-protected smartphone application. Results: Sustained increases in ICP and concordant decreases in PbtO2 were reliably detected using user-defined thresholds and alert throttling. BSR trends computed by the platform correlated highly with manual neurologist markings (r(2) 0.633-0.781;p < 0.0001). The platform identified EEG electrodes with poor signal quality with 95% positive predictive value, and reduced latency of technician response by 93%. Conclusion: This study validates a flexible real-time platform for monitoring and interpreting ICU data and notifying caretakers of actionable results, with potential to reduce the manual burden of continuous monitoring services on care providers. Significance: This work represents an important step toward facilitating translational medical data analytics to improve patient care and reduce health care costs.

关键词： Monitoring Electroencephalography Electrodes Biomedical monitoring Iris Brain Neurology automated data analysis Continuous data monitoring EEG Intensive care unit Multimodal data

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InSitu Studies of the Formation of Tungsten and Niobium Oxide Nanoparticles: Towards automated analysis of Reaction Pathways from PDF analysis using the Pearson Correlation Coefficient

CHEMISTRY-METHODS

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CHEMISTRY-METHODS 2022年第9期2卷

作者： Kjaer, Emil T. S. Aalling-Frederiksen, Olivia Yang, Long Thomas, Nancy K. Juelsholt, Mikkel Billinge, Simon J. L. Jensen, Kirsten M. O. Univ Copenhagen Dept Chem DK-2100 Copenhagen Denmark Univ Copenhagen Nanosci Ctr DK-2100 Copenhagen Denmark Columbia Univ Dept Appl Phys & Appl Math New York NY 10027 USA Brookhaven Natl Lab Condensed Matter Phys & Mat Sci Dept Upton NY 11973 USA

Using Pair Distribution Function (PDF) analysis of insitu total scattering data, we investigate the formation of tungsten and niobium oxides in a simple solvothermal synthesis. We use Pearson Correlation Coefficient (PCC) analysis of the time resolved PDFs to both map the structural changes taking place throughout the synthesis and identify structural models for precursor and product through PCC-based structure mining. Our analysis first shows that ultra-small tungsten and niobium oxide nanoparticles form instantaneously upon heating, with sizes between 1.5 and 2nm. We show that the main structural motifs in the nanoparticles can be described with structures containing pentagonal columns, which is characteristic for many bulk tungsten and niobium oxides. We furthermore elucidate the structure of the precursor complex as clusters of octahedra with O- and Cl-ligands. The PCC based methodology automates the structure characterization and proves useful for analysis of large datasets of for example, time resolved X-ray scattering studies. The PCC is implemented in 'PDF in the cloud', a web platform for PDF analysis.

关键词： automated data analysis insitu nanoparticles Pearson correlation total scattering pair distribution function analysis

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