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Добірка наукової літератури з теми "Changes detection"

Автор: Grafiati

Опубліковано: 7 грудня 2024

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Статті в журналах з теми "Changes detection"

1

Kouda, Takaharu. "Detection of Blink and Facial Expression Changes using DCT Signs." Journal of the Institute of Industrial Applications Engineers 2, no. 2 (April 25, 2014): 70–73. http://dx.doi.org/10.12792/jiiae.2.70.

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2

Yang, Le, Yiming Chen, Shiji Song, Fan Li, and Gao Huang. "Deep Siamese Networks Based Change Detection with Remote Sensing Images." Remote Sensing 13, no. 17 (August 26, 2021): 3394. http://dx.doi.org/10.3390/rs13173394.

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Although considerable success has been achieved in change detection on optical remote sensing images, accurate detection of specific changes is still challenging. Due to the diversity and complexity of the ground surface changes and the increasing demand for detecting changes that require high-level semantics, we have to resort to deep learning techniques to extract the intrinsic representations of changed areas. However, one key problem for developing deep learning metho for detecting specific change areas is the limitation of annotated data. In this paper, we collect a change detection dataset with 862 labeled image pairs, where the urban construction-related changes are labeled. Further, we propose a supervised change detection method based on a deep siamese semantic segmentation network to handle the proposed data effectively. The novelty of the method is that the proposed siamese network treats the change detection problem as a binary semantic segmentation task and learns to extract features from the image pairs directly. The siamese architecture as well as the elaborately designed semantic segmentation networks significantly improve the performance on change detection tasks. Experimental results demonstrate the promising performance of the proposed network compared to existing approaches.

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3

Guépié, Blaise Kévin, Lionel Fillatre, and Igor Nikiforov. "Sequential Detection of Transient Changes." Sequential Analysis 31, no. 4 (October 2012): 528–47. http://dx.doi.org/10.1080/07474946.2012.719443.

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4

Kashiwagi, Nobuhisa. "Bayesian detection of structural changes." Annals of the Institute of Statistical Mathematics 43, no. 1 (March 1991): 77–93. http://dx.doi.org/10.1007/bf00116470.

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5

Gałkowski, Tomasz, Adam Krzyżak, Zofia Patora-Wysocka, Zbigniew Filutowicz, and Lipo Wang. "A New Approach to Detection of Changes in Multidimensional Patterns." Journal of Artificial Intelligence and Soft Computing Research 11, no. 3 (May 29, 2021): 217–27. http://dx.doi.org/10.2478/jaiscr-2021-0013.

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Abstract In the paper we develop an algorithm based on the Parzen kernel estimate for detection of sudden changes in 3-dimensional shapes which happen along the edge curves. Such problems commonly arise in various areas of computer vision, e.g., in edge detection, bioinformatics and processing of satellite imagery. In many engineering problems abrupt change detection may help in fault protection e.g. the jump detection in functions describing the static and dynamic properties of the objects in mechanical systems. We developed an algorithm for detecting abrupt changes which is nonparametric in nature and utilizes Parzen regression estimates of multivariate functions and their derivatives. In tests we apply this method, particularly but not exclusively, to the functions of two variables.

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6

KUROHARA, Akira, Kensuke TERAI, Hiromi TAKEUCHI, and Akio UMEZAWA. "Respiratory changes during detection of deception." Japanese Journal of Physiological Psychology and Psychophysiology 19, no. 2 (2001): 75–86. http://dx.doi.org/10.5674/jjppp1983.19.75.

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7

Lee, T. Y., and D. H. Brainard. "Detection of changes in luminance distributions." Journal of Vision 11, no. 13 (November 15, 2011): 14. http://dx.doi.org/10.1167/11.13.14.

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8

Croy, I., F. Krone, S. Walker, and T. Hummel. "Olfactory Processing: Detection of Rapid Changes." Chemical Senses 40, no. 5 (April 24, 2015): 351–55. http://dx.doi.org/10.1093/chemse/bjv020.

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9

Kramer, David. "DHS changes tack on radiation detection." Physics Today 64, no. 9 (September 2011): 32–33. http://dx.doi.org/10.1063/pt.3.1253.

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10

Nikiforov, Igor V. "Sequential detection/isolation of abrupt changes." Sequential Analysis 35, no. 3 (July 2, 2016): 268–301. http://dx.doi.org/10.1080/07474946.2016.1206354.

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Дисертації з теми "Changes detection"

1

Swe-Hee, Kwet Kon Kian Yen. "Detection of changes in times series." Thesis, University of Cambridge, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.279569.

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2

Braganza, Karl 1971. "Climate change detection and attribution using simple global indices." Monash University, School of Mathematical Sciences, 2002. http://arrow.monash.edu.au/hdl/1959.1/7783.

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3

Malhotra, Ashish. "Detection of abrupt changes and industrial applications." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0005/MQ59842.pdf.

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4

Aviv, David. "Detection of abrupt changes in statistical models." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/43764.

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This dissertation investigates different types of disorder problems by using sequential procedures for on-line implementation. The problem is considered within the framework of detecting abrupt changes in an observed random process when the disorder can occur at unknown times. The focus of this work is on quickest detection methods for cumsum procedures implemented for different parametric and nonparametric nonlinearities and their performance evaluation. Both the non-Bayesian (Maximum-Likelihood) and the Bayesian frameworks are presented but the focus is mainly on non-Bayesian methods for which detailed analysis is provided. The use of Brownian motion approximations is also included and provides an additional viewpoint of analyzing the performance for both the non-Bayesian and Bayesian methods.

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5

Konstantinidis, Dimitrios. "Building detection for monitoring of urban changes." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/57036.

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This thesis addresses the problem of monitoring urban changes by decomposing it to building and change detection. HOG features, in combination with several other discriminative features, such as NDVI, FAST and LBP features, are employed in a search for the development of a robust and accurate building detector. Furthermore, a novel cosine-based distance function is introduced for the computation of distances between the SVM feature vectors in order to suppress the sensitivity of the SVM classifier to the presence of noise and outliers. Moreover, the transformation of SVM scores to probabilities and the definition of a better threshold that differentiates positive from negative feature vectors are proposed. To allow the transition from the object-based building detection to the pixel-based building delineation, a set of novel region refinement processes that includes an unsupervised image segmentation technique and the construction of building candidates by employing the most probable to correspond to buildings image regions based on a novel scoring procedure is proposed. Taking advantage of the ability of the CNNs to automatically generate discriminative features, another approach to the problem of building detection involves the introduction of a Modular-CNN architecture. Two novel layers are proposed and added to the Modular-CNN architecture in order to improve its generalisation power and robustness. The change detection task is approached by a top-down approach that employs the computed building masks in order to identify building changes and a bottom-up approach that initially detects changes prior to their modelling using the computed building masks. In the change detection framework, we propose a novel change amplification algorithm that enhances the differences between the compared images in order to be more easily recognised and extracted. Finally, we propose a new robust change detector based on CNNs with the ability to automatically detect building changes, while discarding all other changes.

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6

Tavakkol, Behnam. "Real-time detection of wave profile changes." Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/14135.

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Master of Science
Department of Industrial and Manufacturing Systems Engineering
Shing I. Chang
This research studies a few methodologies for real-time detection of wave profile changes. In regular profile monitoring, change detection takes place at the end of time period when a complete profile is available. In real-time change detection of profiles, a potential profile change takes place between the beginning and the end of the time period. The decision involves the identification whether a process is in control or out of control before the entire profile is generated. In this regard, five proposed methodologies were developed and tested in this thesis. Earthquake waves, manufacturing processes, and heart beat rate are a few examples of profiles with different natures that the proposed methodologies can be applied to. Water temperature profiles generated during a curing process are considered as an example in this study. Successful implementation of the proposed work on these profiles would cause saving great amounts of time and money. Five methods are studied for monitoring the water control process of a curing process. The first four proposed methodologies are based on an univariate approach where the statistic used for process monitoring is the enclosed area between the profiles and their fitted cutting lines. A multivariate approach is also proposed. A simulation study is also conducted when the best method is chosen based on it performance and simplicity of operations. Various types of acceptable and unacceptable profiles are simulated for the best proposed method identified in the preliminary study. The best method has a satisfactory performance in detecting the changes in the unacceptable profiles. In addition, the false alarm rate in identifying acceptable profiles as bad profiles is lower than 10%.

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7

Graham, Neil. "Automatic detection of authorship changes within single documents." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0017/MQ49736.pdf.

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8

Bustamante, Mariana. "Detection and Quantification of Small Changes in MRI Volumes." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-219487.

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The focus of this research is to attempt to solve the problem of comparing two MRI brain volumes of the same subject taken at different times, and detect the location and size of the differences between them, especially when such differences are too small to be perceived with the naked eye. The research focuses on a combination of registration and morphometry techniques in order to create two different possible solutions: A voxel-based method and a tensor-based method. The first method uses Affine or B-Spline registration combined with voxel-by-voxel subtraction of the volumes; the second method uses Demons registration and analysis of the Jacobian determinants at each point of the deformation field obtained. The methods are implemented as modules for 3D Slicer, a software for medical image analysis and visualization. Both methods are tested on two types of experiments: Artificial experiments, in which made-up differences of distinct sizes are added to volumes of healthy subjects; and real experiments, in which MRIs of real patients are compared. The results obtained from the voxel-based method are very useful, since it was able to detect with almost complete accuracy all of the artificial differences and expected real differences during the experiments. The tensor-based method’s results are not as accurate in location or size of the detected differences, and it usually includes more areas of differences where there seems to be none; even though it behaves adequately when the differences are large. Most of the results obtained are useful for the diagnostic of patients with non-severe trauma to the head; especially when using the voxel-based method. However, the results from both methods are just a suggestion of the size and location of injuries; and as a consequence, the procedure requires the presence of a medical practitioner.

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9

Morak, Simone. "Impact of externally forced changes on temperature extremes." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8246.

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This thesis investigates changes in temperature extremes between 1950-2005, analysing gridded data sets of observations and climate model simulations. It focuses on changes in the frequency of extreme temperatures occurring in single days or over periods of six or more consecutive days. The study aims to quantify the significance of changes in extreme temperature events and answer the following questions. Are external or human-induced forcings together with natural forcings responsible for the observed change in temperature extremes or can these changes be explained due to natural climate variability alone? Are the observed changes consistent with those from climate model simulations? And are the changes in extremes linked only to changes in the mean climate, or only to those in climate variability or both? The analysis concentrates on changes from global to regional scale and from annual mean to seasonal scale. A detection method is applied to assess if changes are significantly different with respect to the internal climate variability. Results show that there has been a significant increase in warm daily extremes and a decrease in cold ones, both on large and small spatial scales. The increase in warm extremes has been found to be highly correlated with the increase in mean temperature. The changes in daily extremes are well represented in climate model simulations. Changes in the persistent extremes show a detectable increase in the frequency of warm and a decrease in cold events and are reproducible by models.

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10

Vellekoop, Michel Henri. "Rapid detection and estimation of abrupt changes by nonlinear filtering." Thesis, Imperial College London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286757.

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Книги з теми "Changes detection"

1

L, Telʹksnis, ed. Detection of changes in random processes. New York: Optimization Software, Publications Division, 1986.

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2

Basseville, M. Detection of abrupt changes: Theory and application. Englewood Cliffs, N.J: Prentice Hall, 1993.

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3

Aviv, David. Detection of abrupt changes in statistical models. Monterey, Calif: Naval Postgraduate School, 1991.

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4

Basseville, Michèle, and Albert Benveniste, eds. Detection of Abrupt Changes in Signals and Dynamical Systems. Berlin/Heidelberg: Springer-Verlag, 1985. http://dx.doi.org/10.1007/bfb0006385.

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5

1952-, Basseville M., and Benveniste Albert, eds. Detection of abrupt changes in signals and dynamical systems. Berlin: Springer-Verlag, 1986.

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6

Loehle, Craig. Climate change: Detection and attribution of trends from long-term geologic data. Research Triangle Park, NC: National Council for Air and Stream Improvement, 2003.

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7

Castellano, Carlos. Input design for detection of abrupt changes in dynamic systems. Manchester: UMIST, 1996.

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8

Frederick, John E. The detection and interpretation of long-term changes in ozone from space. [Washington, DC?: National Aeronautics and Space Administration, 1988.

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9

Informal Planning Meeting on Statistical Procedures for Climate Change Detection (1992 Toronto). Report on the Informal Planning Meeting on Statistical Procedures for Climate Change Detection (Toronto, 25 June, 1992). Geneva, Switzerland: World Meteorological Organization, 1992.

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10

CCl Working Group on Climate Change Detection. Session. Report of CCl Working Group on Climate Change Detection (First session), (Geneva, 21-25 October 1991). [Geneva, Switzerland]: World Meteorological Organization, 1991.

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Частини книг з теми "Changes detection"

1

Shekhar, Shashi, and Hui Xiong. "Detection of Changes." In Encyclopedia of GIS, 240. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_282.

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2

Alho, Kimmo, Carles Escera, and Erich Schröger. "Event-Related Brain Potential Indices of Involuntary Attention to Auditory Stimulus Changes." In Detection of Change, 23–40. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0294-4_2.

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3

Tenneson, Karis, John Dilger, Crystal Wespestad, Brian Zutta, Andréa Puzzi Nicolau, Karen Dyson, and Paula Paz. "Change Detection." In Cloud-Based Remote Sensing with Google Earth Engine, 303–16. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_16.

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AbstractThis chapter introduces change detection mapping. It will teach you how to make a two-date land cover change map using image differencing and threshold-based classification. You will use what you have learned so far in this book to produce a map highlighting changes in the land cover between two time steps. You will first explore differences between the two images extracted from these time steps by creating a difference layer. You will then learn how to directly classify change based on the information in both of your images.

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4

Nussbaum, Sven, and Irmgard Niemeyer. "Detection of Changes in Images." In International Safeguards and Satellite Imagery, 147–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-79132-4_11.

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5

Sze, Chwen-Jye, Hong-Yaun Mark Liao, Hai-Lung Hung, Kuo-Chin Fan, and Jun-Wei Hsieh. "Multiscale edge detection via normal changes." In Image Analysis and Processing, 22–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63507-6_180.

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6

Beibel, Martin, and Hans R. Lerche. "Sequential Bayes Detection of Trend Changes." In Contributions to Statistics, 117–30. Heidelberg: Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-642-57410-8_11.

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7

Hudecová, Šárka, Marie Hušková, and Simos Meintanis. "Detection of Changes in INAR Models." In Springer Proceedings in Mathematics & Statistics, 11–18. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13881-7_2.

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8

Darkhovsky, Boris, and Alexandra Piryatinska. "Detection of Changes in Binary Sequences." In Springer Proceedings in Mathematics & Statistics, 157–76. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28665-1_12.

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9

Gallo, Giovanni, Eliana Granata, and Giuseppe Scarpulla. "Sudden Changes Detection in WCE Video." In Image Analysis and Processing – ICIAP 2009, 701–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04146-4_75.

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10

Basseville, M. "Detection of Abrupt Changes in Signal Processing." In inverse problems and theoretical imaging, 99–101. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-75988-8_5.

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Тези доповідей конференцій з теми "Changes detection"

1

Hills, Anthony, Talia Tseriotou, Xenia Miscouridou, Adam Tsakalidis, and Maria Liakata. "Exciting Mood Changes: A Time-aware Hierarchical Transformer for Change Detection Modelling." In Findings of the Association for Computational Linguistics ACL 2024, 12526–37. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.744.

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2

Lakouraj, Mohammad Mansour, Chetan Mishra, Jaime De La Ree, Luigi Vanfretti, Kevin D. Jones, and Hanif Livani. "Detection of Step Changes in Real-World Synchrophasor Measurements." In 2024 IEEE Power & Energy Society General Meeting (PESGM), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/pesgm51994.2024.10688944.

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3

Vecchia, Anna Dalla, Niccolò Marastoni, and Elisa Quintarelli. "Anomaly detection to infer context changes in temporal data." In 2024 IEEE 18th International Conference on Application of Information and Communication Technologies (AICT), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/aict61888.2024.10740439.

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4

Yarmilko, Andrii, Inna Rozlomii, and Yuliia Mysiura. "Highly Sensitive Real-Time Detection of Surface Texture Changes." In 2024 IEEE 17th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), 1–4. IEEE, 2024. http://dx.doi.org/10.1109/tcset64720.2024.10755562.

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5

Hamilton, Dennis E. "Tracked Changes." In DChanges '14: modeling, detection, storage and visualization. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2723147.2723153.

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6

Mitkari, Kavita V., Manoj K. Arora, and Reet K. Tiwari. "Detecting Glacier Surface Changes Using Object-Based Change Detection." In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. http://dx.doi.org/10.1109/igarss.2018.8519230.

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7

Spadini, Elena. "Annotating Document Changes." In DChanges 2015: modeling, detection, storage and visualization. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2881631.2881637.

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8

Vongsy, Karmon, Michael J. Mendenhall, Michael T. Eismann, and Gilbert L. Peterson. "Removing parallax-induced changes in Hyperspectral Change Detection." In IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012. http://dx.doi.org/10.1109/igarss.2012.6350982.

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9

Whitaker, Nigel. "Understanding Changes in n-way Merge." In DChanges '14: modeling, detection, storage and visualization. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2723147.2723150.

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10

Wei Chen, Xifeng Guo, Xinwang Liu, En Zhu, and Jianping Yin. "Appearance changes detection during tracking." In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7899901.

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Звіти організацій з теми "Changes detection"

1

Nachtsheim, Abigael Catherine. Detection and Quantification of Ground Surface Changes from an Underground Explosion. Office of Scientific and Technical Information (OSTI), July 2018. http://dx.doi.org/10.2172/1463515.

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2

Slone, Scott Michael, Marissa Torres, Nathan Lamie, Samantha Cook, and Lee Perren. Automated change detection in ground-penetrating radar using machine learning in R. Engineer Research and Development Center (U.S.), October 2024. http://dx.doi.org/10.21079/11681/49442.

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Ground-penetrating radar (GPR) is a useful technique for subsurface change detection but is limited by the need for a subject matter expert to process and interpret coincident profiles. Use of a machine learning model can automate this process to reduce the need for subject matter expert processing and interpretation. Several machine learning models were investigated for the purpose of comparing coincident GPR profiles. Based on our literature review, a Siamese Twin model using a twinned convolutional network was identified as the optimum choice. Two neural networks were tested for the internal twinned model, ResNet50 and MobileNetV2, with the former historically having higher accuracy and the latter historically having faster processing time. When trained and tested on experimentally obtained GPR profiles with synthetically added changes, ResNet50 had a higher accuracy. Thanks to this higher accuracy, less computational processing was needed, leading to ResNet50 needing only 107 s to make a prediction compared to MobileNetV2 needing 223 s. Results imply that twinned models with higher historical accuracies should be investigated further. It is also recommended to test Siamese Twin models further with experimentally produced changes to verify the changed detection model’s accuracy is not merely specific to synthetically produced changes.

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McCloy, John S., David V. Jordan, James F. Kelly, Douglas L. McMakin, Bradley R. Johnson, and Luke W. Campbell. Electromagnetic material changes for remote detection and monitoring: a feasibility study: Progress report. Office of Scientific and Technical Information (OSTI), September 2009. http://dx.doi.org/10.2172/990598.

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Wagner, Anna, Arthur Gelvin, Jon Maakestad, Thomas Coleman, Dan Forsland, Sam Johansson, Johan Sundin, and Chandler Engel. Initial data collection from a fiber-optic-based dam seepage monitoring and detection system. Engineer Research and Development Center (U.S.), October 2023. http://dx.doi.org/10.21079/11681/47819.

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Visual inspection is the most used method to detect seepage at dams. Early detection can be difficult with this method, and use of appropriate real time monitoring could significantly increase the chances of recognizing possible failure. Seepages can be identified by analyzing changes in water and soil temperature. Optical fiber placed at the embankment’s downstream toe has been proven to be an efficient means of detecting real time changes at short intervals over several kilometers. This study aims to demonstrate how temperatures measured using fiber optic distributed sensing can be used to monitor seepage at Moose Creek Dam, North Pole, Alaska. The fiber optic cable portion of the monitoring system is installed along a section of the embankment where sand boils have occurred. Though no flood event occurred during this monitoring period, routine pumping tests of nearby relief wells resulted in an increase of soil and water temperature (up to 13°C) along a 100 m section where sand boils were detected during the 2014 flood events. Measurements during a flood event are expected to provide a quantitative assessment of seepage and its rate.

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Vranic-Sowers, Svetlana, and Shihab A. Shamma. Representation of Spectral Profiles in the Auditory System. 2. Detection of Spectral Peak Shape Changes. Fort Belvoir, VA: Defense Technical Information Center, January 1994. http://dx.doi.org/10.21236/ada453133.

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Hoffmann, Manuela, Cigdem Soydal, Irene Virgolini, Murat Tuncel, Kalevi Kairemo, Daniel Kapp, and Finn von Eyben. PSMA PET in Prostate Cancer: Detection of Metastases and Changes of Stage, Treatment, and Outcome. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2024. http://dx.doi.org/10.37766/inplasy2024.11.0041.

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Burns, Malcom, and Gavin Nixon. Literature review on analytical methods for the detection of precision bred products. Food Standards Agency, September 2023. http://dx.doi.org/10.46756/sci.fsa.ney927.

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The Genetic Technology (Precision Breeding) Act (England) aims to develop a science-based process for the regulation and authorisation of precision bred organisms (PBOs). PBOs are created by genetic technologies but exhibit changes which could have occurred through traditional processes. This current review, commissioned by the Food Standards Agency (FSA), aims to clarify existing terminologies, explore viable methods for the detection, identification, and quantification of products of precision breeding techniques, address and identify potential solutions to the analytical challenges presented, and provide recommendations for working towards an infrastructure to support detection of precision bred products in the future. The review includes a summary of the terminology in relation to analytical approaches for detection of precision bred products. A harmonised set of terminology contributes towards promoting further understanding of the common terms used in genome editing. A review of the current state of the art of potential methods for the detection, identification and quantification of precision bred products in the UK, has been provided. Parallels are drawn with the evolution of synergistic analytical approaches for the detection of Genetically Modified Organisms (GMOs), where molecular biology techniques are used to detect DNA sequence changes in an organism’s genome. The scope and limitations of targeted and untargeted methods are summarised. Current scientific opinion supports that modern molecular biology techniques (i.e., quantitative real-time Polymerase Chain Reaction (qPCR), digital PCR (dPCR) and Next Generation Sequencing (NGS)) have the technical capability to detect small alterations in an organism’s genome, given specific prerequisites of a priori information on the DNA sequence of interest and of the associated flanking regions. These techniques also provide the best infra-structure for developing potential approaches for detection of PBOs. Should sufficient information be known regarding a sequence alteration and confidence can be attributed to this being specific to a PBO line, then detection, identification and quantification can potentially be achieved. Genome editing and new mutagenesis techniques are umbrella terms, incorporating a plethora of approaches with diverse modes of action and resultant mutational changes. Generalisations regarding techniques and methods for detection for all PBO products are not appropriate, and each genome edited product may have to be assessed on a case-by-case basis. The application of modern molecular biology techniques, in isolation and by targeting just a single alteration, are unlikely to provide unequivocal evidence to the source of that variation, be that as a result of precision breeding or as a result of traditional processes. In specific instances, detection and identification may be technically possible, if enough additional information is available in order to prove that a DNA sequence or sequences are unique to a specific genome edited line (e.g., following certain types of Site-Directed Nucelase-3 (SDN-3) based approaches). The scope, gaps, and limitations associated with traceability of PBO products were examined, to identify current and future challenges. Alongside these, recommendations were made to provide the infrastructure for working towards a toolkit for the design, development and implementation of analytical methods for detection of PBO products. Recognition is given that fully effective methods for PBO detection have yet to be realised, so these recommendations have been made as a tool for progressing the current state-of-the-art for research into such methods. Recommendations for the following five main challenges were identified. Firstly, PBOs submitted for authorisation should be assessed on a case-by-case basis in terms of the extent, type and number of genetic changes, to make an informed decision on the likelihood of a molecular biology method being developed for unequivocal identification of that specific PBO. The second recommendation is that a specialist review be conducted, potentially informed by UK and EU governmental departments, to monitor those PBOs destined for the authorisation process, and actively assess the extent of the genetic variability and mutations, to make an informed decision on the type and complexity of detection methods that need to be developed. This could be further informed as part of the authorisation process and augmented via a publicly available register or database. Thirdly, further specialist research and development, allied with laboratory-based evidence, is required to evaluate the potential of using a weight of evidence approach for the design and development of detection methods for PBOs. This concept centres on using other indicators, aside from the single mutation of interest, to increase the likelihood of providing a unique signature or footprint. This includes consideration of the genetic background, flanking regions, off-target mutations, potential CRISPR/Cas activity, feasibility of heritable epigenetic and epitranscriptomic changes, as well as supplementary material from supplier, origin, pedigree and other documentation. Fourthly, additional work is recommended, evaluating the extent/type/nature of the genetic changes, and assessing the feasibility of applying threshold limits associated with these genetic changes to make any distinction on how they may have occurred. Such a probabilistic approach, supported with bioinformatics, to determine the likelihood of particular changes occurring through genome editing or traditional processes, could facilitate rapid classification and pragmatic labelling of products and organisms containing specific mutations more readily. Finally, several scientific publications on detection of genome edited products have been based on theoretical principles. It is recommended to further qualify these using evidenced based practical experimental work in the laboratory environment. Additional challenges and recommendations regarding the design, development and implementation of potential detection methods were also identified. Modern molecular biology-based techniques, inclusive of qPCR, dPCR, and NGS, in combination with appropriate bioinformatics pipelines, continue to offer the best analytical potential for developing methods for detecting PBOs. dPCR and NGS may offer the best technical potential, but qPCR remains the most practicable option as it is embedded in most analytical laboratories. Traditional screening approaches, similar to those for conventional transgenic GMOs, cannot easily be used for PBOs due to the deficit in common control elements incorporated into the host genome. However, some limited screening may be appropriate for PBOs as part of a triage system, should a priori information be known regarding the sequences of interest. The current deficit of suitable methods to detect and identify PBOs precludes accurate PBO quantification. Development of suitable reference materials to aid in the traceability of PBOs remains an issue, particularly for those PBOs which house on- and off-target mutations which can segregate. Off-target mutations may provide an additional tool to augment methods for detection, but unless these exhibit complete genetic linkage to the sequence of interest, these can also segregate out in resulting generations. Further research should be conducted regarding the likelihood of multiple mutations segregating out in a PBO, to help inform the development of appropriate PBO reference materials, as well as the potential of using off-target mutations as an additional tool for PBO traceability. Whilst recognising the technical challenges of developing and maintaining pan-genomic databases, this report recommends that the UK continues to consider development of such a resource, either as a UK centric version, or ideally through engagement in parallel EU and international activities to better achieve harmonisation and shared responsibilities. Such databases would be an invaluable resource in the design of reliable detection methods, as well as for confirming that a mutation is as a result of genome editing. PBOs and their products show great potential within the agri-food sector, necessitating a science-based analytical framework to support UK legislation, business and consumers. Differentiating between PBOs generated through genome editing compared to organisms which exhibit the same mutational change through traditional processes remains analytically challenging, but a broad set of diagnostic technologies (e.g., qPCR, NGS, dPCR) coupled with pan-genomic databases and bioinformatics approaches may help contribute to filling this analytical gap, and support the safety, transparency, proportionality, traceability and consumer confidence associated with the UK food chain.

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Clausen, Jay, Michael Musty, Anna Wagner, Susan Frankenstein, and Jason Dorvee. Modeling of a multi-month thermal IR study. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41060.

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Inconsistent and unacceptable probability of detection (PD) and false alarm rates (FAR) due to varying environmental conditions hamper buried object detection. A 4-month study evaluated the environmental parameters impacting standoff thermal infra-red(IR) detection of buried objects. Field observations were integrated into a model depicting the temporal and spatial thermal changes through a 1-week period utilizing a 15-minute time-step interval. The model illustrates the surface thermal observations obtained with a thermal IR camera contemporaneously with a 3-d presentation of subsurface soil temperatures obtained with 156 buried thermocouples. Precipitation events and subsequent soil moisture responses synchronized to the temperature data are also included in the model simulation. The simulation shows the temperature response of buried objects due to changes in incoming solar radiation, air/surface soil temperature changes, latent heat exchange between the objects and surrounding soil, and impacts due to precipitation/changes in soil moisture. Differences are noted between the thermal response of plastic and metal objects as well as depth of burial below the ground surface. Nearly identical environmental conditions on different days did not always elicit the same spatial thermal response.

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Bastawros, Ashraf. DTPH56-16H-CAP01 Mechanochemistry-Based Detection of Early Stage Corrosion Degradation of Pipeline Steels. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), May 2020. http://dx.doi.org/10.55274/r0011990.

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The aim of the work is to provide measurable precursor signals associated with the initiation stage of near-surface damage and cracking, as depicted in Fig. 1.1. We have identified many salient features during the early stage of the SCC process (Stages 1, 2 on Fig. 1.1), including residual stress build-up, near-surface (within few microns) defect percolation, and changes of dislocation dynamics and measurable changes of the surface osmic resistance. We developed a model-based prediction of the onset and progression of SCC subsurface damage and assessed the electrochemical impedance spectroscopy (EIS) to measure the extent of surface damage. Such a framework would enable the development of appropriate field-deployable NDE technology with the needed spatial and temporal resolutions.

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Choudhary, Siddhant, Ross Underhill, and Thomas Krause. PR652-203801-R05 The Lab study Effect of Earth�s Field and Line Pressure on Magnetization of Pipeline. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), July 2024. http://dx.doi.org/10.55274/r0000073.

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Large standoff magnetometry is an emerging non-destructive magnetic test method, which is remote, passive, and non-contact. It is based on the inverse magnetostrictive effect and, therefore, has potential for detection of magnetic anomalies produced by elevated stresses in steel pipelines and ferromagnetic structures. Changes in the local pipeline stress state may arise due to corrosion, cracking, mechanical damage, ground movement (geohazards), or external loading. Laboratory measurements were performed on macroscopic steel samples in the elastic stress regime to investigate the potential for stress detection in steel. The effects of the pipe samples' orientation when degaussed, the generation of a reproducible magnetization state, rotation relative to Earth's magnetic north and then pressurization were also investigated. Three of the seven pipe samples were pressurized with water at 21 MPa (3000 psi), and the effects on the magnetic flux density was measured on the samples' surface in different orientations in Earth's magnetic field. Four-point bending and pressurization was applied to two pipe samples. The results indicate that Earth's magnetic field and its relative orientation with respect to the pipe has a measurable effect on the magnetic state of a pipe, and the resulting magnitude of changes in measured flux density when stress is applied. Changing the orientation of a pipe after degaussing and then applying pressure will also cause more significant changes in the measured magnetic flux density. Pressurization is shown to be the cause of larger changes in magnetization when compared to the effects of bending. The implications for detecting potentially damaging elastic stresses on pipelines using above-ground inspection, large standoff magnetometry, are examined.

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