Literatura científica selecionada sobre o tema "Single and Multiple Change Point Detection"
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Artigos de revistas sobre o assunto "Single and Multiple Change Point Detection"
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Qi, Jin Peng, Fang Pu, Ying Zhu e Ping Zhang. "A Weighted Error Distance Metrics (WEDM) for Performance Evaluation on Multiple Change-Point (MCP) Detection in Synthetic Time Series". Computational Intelligence and Neuroscience 2022 (24 de março de 2022): 1–17. http://dx.doi.org/10.1155/2022/6187110.
Texto completo da fonteResumo:
Change-point detection (CPD) is to find abrupt changes in time-series data. Various computational algorithms have been developed for CPD applications. To compare the different CPD models, many performance metrics have been introduced to evaluate the algorithms. Each of the previous evaluation methods measures the different aspects of the methods. Based on the existing weighted error distance (WED) method on single change-point (CP) detection, a novel WED metrics (WEDM) was proposed to evaluate the overall performance of a CPD model across not only repetitive tests on single CP detection, but also successive tests on multiple change-point (MCP) detection on synthetic time series under the random slide window (RSW) and fixed slide window (FSW) frameworks. In the proposed WEDM method, a concept of normalized error distance was introduced that allows comparisons of the distance between the estimated change-point (eCP) position and the target change point (tCP) in the synthetic time series. In the successive MCPs detection, the proposed WEDM method first divides the original time-series sample into a series of data segments in terms of the assigned tCPs set and then calculates a normalized error distance (NED) value for each segment. Next, our WEDM presents the frequency and WED distribution of the resultant eCPs from all data segments in the normalized positive-error distance (NPED) and the normalized negative-error distance (NNED) intervals in the same coordinates. Last, the mean WED (MWED) and MWTD (1-MWED) were obtained and then dealt with as important performance evaluation indexes. Based on the synthetic datasets in the Matlab platform, repetitive tests on single CP detection were executed by using different CPD models, including ternary search tree (TST), binary search tree (BST), Kolmogorov–Smirnov (KS) tests, t-tests (T), and singular spectrum analysis (SSA) algorithms. Meanwhile, successive tests on MCPs detection were implemented under the fixed slide window (FSW) and random slide window (RSW) frameworks. These CPD models mentioned above were evaluated in terms of our WED metrics, together with supplementary indexes for evaluating the convergence of different CPD models, including rates of hit, miss, error, and computing time, respectively. The experimental results showed the value of this WEDM method.
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Li, Zhaoyuan, e Maozai Tian. "Detecting Change-Point via Saddlepoint Approximations". Journal of Systems Science and Information 5, n.º 1 (8 de junho de 2017): 48–73. http://dx.doi.org/10.21078/jssi-2017-048-26.
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AbstractIt’s well-known that change-point problem is an important part of model statistical analysis. Most of the existing methods are not robust to criteria of the evaluation of change-point problem. In this article, we consider “mean-shift” problem in change-point studies. A quantile test of single quantile is proposed based on saddlepoint approximation method. In order to utilize the information at different quantile of the sequence, we further construct a “composite quantile test” to calculate the probability of every location of the sequence to be a change-point. The location of change-point can be pinpointed rather than estimated within a interval. The proposed tests make no assumptions about the functional forms of the sequence distribution and work sensitively on both large and small size samples, the case of change-point in the tails, and multiple change-points situation. The good performances of the tests are confirmed by simulations and real data analysis. The saddlepoint approximation based distribution of the test statistic that is developed in the paper is of independent interest and appealing. This finding may be of independent interest to the readers in this research area.
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Singh, Uday Pratap, e Ashok Kumar Mittal. "Testing reliability of the spatial Hurst exponent method for detecting a change point". Journal of Water and Climate Change 12, n.º 8 (1 de outubro de 2021): 3661–74. http://dx.doi.org/10.2166/wcc.2021.097.
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Abstract The reliability of using abrupt changes in the spatial Hurst exponent for identifying temporal points of abrupt change in climate dynamics is explored. If a spatio-temporal dynamical system undergoes an abrupt change at a particular time, the time series of spatial Hurst exponent obtained from the data of any variable of the system should also show an abrupt change at that time. As expected, spatial Hurst exponents for each of the two variables of a model spatio-temporal system – a globally coupled map lattice based on the Burgers' chaotic map – showed abrupt change at the same time that a parameter of the system was changed. This method was applied for the identification of change points in climate dynamics using the NCEP/NCAR data on air temperature, pressure and relative humidity variables. Different abrupt change points in spatial Hurst exponents were detected for the data of these different variables. That suggests, for a dynamical system, change point detected using the two-dimensional detrended fluctuation analysis method on a single variable alone is insufficient to comment about the abrupt change in the system dynamics and should be based on multiple variables of the dynamical system.
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He, Youxi, Zhenhong Jia, Jie Yang e Nikola K. Kasabov. "Multispectral Image Change Detection Based on Single-Band Slow Feature Analysis". Remote Sensing 13, n.º 15 (28 de julho de 2021): 2969. http://dx.doi.org/10.3390/rs13152969.
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Due to differences in external imaging conditions, multispectral images taken at different periods are subject to radiation differences, which severely affect the detection accuracy. To solve this problem, a modified algorithm based on slow feature analysis is proposed for multispectral image change detection. First, single-band slow feature analysis is performed to process bitemporal multispectral images band by band. In this way, the differences between unchanged pixels in each pair of single-band images can be sufficiently suppressed to obtain multiple feature-difference images containing real change information. Then, the feature-difference images of each band are fused into a grayscale distance image using the Euclidean distance. After Gaussian filtering of the grayscale distance image, false detection points can be further reduced. Finally, the k-means clustering method is performed on the filtered grayscale distance image to obtain the binary change map. Experiments reveal that our proposed algorithm is less affected by radiation differences and has obvious advantages in time complexity and detection accuracy.
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Pillow, Jonathan W., Yashar Ahmadian e Liam Paninski. "Model-Based Decoding, Information Estimation, and Change-Point Detection Techniques for Multineuron Spike Trains". Neural Computation 23, n.º 1 (janeiro de 2011): 1–45. http://dx.doi.org/10.1162/neco_a_00058.
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One of the central problems in systems neuroscience is to understand how neural spike trains convey sensory information. Decoding methods, which provide an explicit means for reading out the information contained in neural spike responses, offer a powerful set of tools for studying the neural coding problem. Here we develop several decoding methods based on point-process neural encoding models, or forward models that predict spike responses to stimuli. These models have concave log-likelihood functions, which allow efficient maximum-likelihood model fitting and stimulus decoding. We present several applications of the encoding model framework to the problem of decoding stimulus information from population spike responses: (1) a tractable algorithm for computing the maximum a posteriori (MAP) estimate of the stimulus, the most probable stimulus to have generated an observed single- or multiple-neuron spike train response, given some prior distribution over the stimulus; (2) a gaussian approximation to the posterior stimulus distribution that can be used to quantify the fidelity with which various stimulus features are encoded; (3) an efficient method for estimating the mutual information between the stimulus and the spike trains emitted by a neural population; and (4) a framework for the detection of change-point times (the time at which the stimulus undergoes a change in mean or variance) by marginalizing over the posterior stimulus distribution. We provide several examples illustrating the performance of these estimators with simulated and real neural data.
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Yang, Chong, Yu Fu, Jianmin Yuan, Min Guo, Keyu Yan, Huan Liu, Hong Miao e Changchun Zhu. "Damage Identification by Using a Self-Synchronizing Multipoint Laser Doppler Vibrometer". Shock and Vibration 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/476054.
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The vibration-based damage identification method extracts the damage location and severity information from the change of modal properties, such as natural frequency and mode shape. Its performance and accuracy depends on the measurement precision. Laser Doppler vibrometer (LDV) provides a noncontact vibration measurement of high quality, but usually it can only do sampling on a single point. Scanning LDV is normally used to obtain the mode shape with a longer scanning time. In this paper, a damage detection technique is proposed using a self-synchronizing multipoint LDV. Multiple laser beams with various frequency shifts are projected on different points of the object, reflected and interfered with a common reference beam. The interference signal containing synchronized temporal vibration information of multiple spatial points is captured by a single photodetector and can be retrieved in a very short period. Experiments are conducted to measure the natural frequencies and mode shapes of pre- and postcrack cantilever beams. Mode shape curvature is calculated by numerical interpolation and windowed Fourier analysis. The results show that the artificial crack can be identified precisely from the change of natural frequencies and the difference of mode shape curvature squares.
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R. Almaddah, Amr Reda, Tauseef Ahmad e Abdullah Dubai. "Detection and Measurement of Displacement and Velocity of Single Moving Object in a Stationary Background". Sir Syed University Research Journal of Engineering & Technology 7, n.º 1 (19 de dezembro de 2018): 6. http://dx.doi.org/10.33317/ssurj.v7i1.41.
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The traditional Harris detector are sensitive to noise and resolution because without the property of scale invariant. In this research, The Harris corner detector algorithm is improved, to work with multi resolution images, the technique has also been working with poor lighting condition by using histogram equalization technique. The work we have done addresses the issue of robustly detection of feature points, detected multiple of local features are characterized by the intensity changes in both horizontal and vertical direction which is called corner features. The goal of this work is to detect the corner of an object through the Harris corner detector with multiple scale of the same image. The scale invariant property applied to the Harris algorithm for improving the corner detection performance in different resolution of the same image with the same interest point. The detected points represented by two independent variables (x, y) in a matrix (x, y) and the dependent variable f are called intensity of interest points. Through these independent variable, we get the displacement and velocity of object by subtracting independent variable f(x,y) at current frame from the previous location f ̀((x,) ̀(y,) ̀) of another frame. For further work, multiple of moving object environment have been taken consideration for developing algorithms.
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R. Almaddah, Amr Reda, Tauseef Ahmad e Abdullah Dubai. "Detection and Measurement of Displacement and Velocity of Single Moving Object in a Stationary Background". Sir Syed University Research Journal of Engineering & Technology 7, n.º 1 (19 de dezembro de 2018): 6. http://dx.doi.org/10.33317/ssurj.41.
Texto completo da fonteResumo:
The traditional Harris detector are sensitive to noise and resolution because without the property of scale invariant. In this research, The Harris corner detector algorithm is improved, to work with multi resolution images, the technique has also been working with poor lighting condition by using histogram equalization technique. The work we have done addresses the issue of robustly detection of feature points, detected multiple of local features are characterized by the intensity changes in both horizontal and vertical direction which is called corner features. The goal of this work is to detect the corner of an object through the Harris corner detector with multiple scale of the same image. The scale invariant property applied to the Harris algorithm for improving the corner detection performance in different resolution of the same image with the same interest point. The detected points represented by two independent variables (x, y) in a matrix (x, y) and the dependent variable f are called intensity of interest points. Through these independent variable, we get the displacement and velocity of object by subtracting independent variable f(x,y) at current frame from the previous location f ̀((x,) ̀(y,) ̀) of another frame. For further work, multiple of moving object environment have been taken consideration for developing algorithms.
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Tanaka, Kanji. "Fault-Diagnosing Deep-Visual-SLAM for 3D Change Object Detection". Journal of Advanced Computational Intelligence and Intelligent Informatics 25, n.º 3 (20 de maio de 2021): 356–64. http://dx.doi.org/10.20965/jaciii.2021.p0356.
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Although image change detection (ICD) methods provide good detection accuracy for many scenarios, most existing methods rely on place-specific background modeling. The time/space cost for such place-specific models is prohibitive for large-scale scenarios, such as long-term robotic visual simultaneous localization and mapping (SLAM). Therefore, we propose a novel ICD framework that is specifically customized for long-term SLAM. This study is inspired by the multi-map-based SLAM framework, where multiple maps can perform mutual diagnosis and hence do not require any explicit background modeling/model. We extend this multi-map-based diagnosis approach to a more generic single-map-based object-level diagnosis framework (i.e., ICD), where the self-localization module of SLAM, which is the change object indicator, can be used in its original form. Furthermore, we consider map diagnosis on a state-of-the-art deep convolutional neural network (DCN)-based SLAM system (instead of on conventional bag-of-words or landmark-based systems), in which the blackbox nature of the DCN complicates the diagnosis problem. Additionally, we consider a three-dimensional point cloud (PC)-based (instead of typical monocular color image-based) SLAM and adopt a state-of-the-art scan context PC descriptor for map diagnosis for the first time.
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R. Almaddah, Amr Reda, Tauseef Ahmad e Abdullah Dubai. "5 Detection and Measurement of Displacement and Velocity of Single Moving Object in a Stationary Background". Sir Syed Research Journal of Engineering & Technology 1, n.º 1 (19 de dezembro de 2018): 6. http://dx.doi.org/10.33317/ssurj.v1i1.41.
Texto completo da fonteResumo:
The traditional Harris detector are sensitive to noise and resolution because without the property of scale invariant. In this research, The Harris corner detector algorithm is improved, to work with multi resolution images, the technique has also been working with poor lighting condition by using histogram equalization technique. The work we have done addresses the issue of robustly detection of feature points, detected multiple of local features are characterized by the intensity changes in both horizontal and vertical direction which is called corner features. The goal of this work is to detect the corner of an object through the Harris corner detector with multiple scale of the same image. The scale invariant property applied to the Harris algorithm for improving the corner detection performance in different resolution of the same image with the same interest point. The detected points represented by two independent variables (x, y) in a matrix (x, y) and the dependent variable f are called intensity of interest points. Through these independent variable, we get the displacement and velocity of object by subtracting independent variable f(x,y) at current frame from the previous location f ̀((x,) ̀(y,) ̀) of another frame. For further work, multiple of moving object environment have been taken consideration for developing algorithms.
Teses / dissertações sobre o assunto "Single and Multiple Change Point Detection"
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Shabarshova, Liudmila. "Geometric functional pruning for change point detection in low-dimensional exponential family models". Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASM026.
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La détection de ruptures est un problème courant d'apprentissage non supervisé dans de nombreux domaines d'application, notamment en biologie, en génomique, en surveillance de réseaux de capteurs et en cybersécurité. En général, soit une détection de ruptures a posteriori, c'est-à-dire hors ligne, soit une détection de ruptures séquentielle, c'est-à-dire en ligne, est envisagée.Des méthodes standard de programmation dynamique pour la détection de ruptures ont été proposées pour optimiser le logarithme du rapport de vraisemblance. Ces méthodes sont exactes et récupèrent des segmentations optimales. Cependant, elles ont une complexité quadratique. Réduire continuellement l'ensemble des candidats potentiels de ruptures, appelé élagage, est un moyen de réduire la complexité computationnelle des méthodes standard de programmation dynamique. Au cours de la dernière décennie, une nouvelle classe de méthodes de programmation dynamique, appelée élagage fonctionnel, a été proposée.Les techniques d'élagage fonctionnel utilisées dans ces méthodes ont déjà prouvé leur efficacité computationnelle pour les modèles de ruptures paramétriques univariés. Étendre les règles d'élagage fonctionnel univarié à des contextes multivariés est difficile si l'on vise l'élagage le plus efficace. Cela conduit à des problèmes d'optimisation non convexes.Cette thèse présente deux nouvelles méthodes de programmation dynamique d'élagage fonctionnel, efficaces en termes de calcul, pour la détection de ruptures dans les modèles de famille exponentielle de faible dimension : la méthode de détection de ruptures multiples hors ligne, GeomFPOP (Kmax = ∞), et la méthode de détection d'une rupture en ligne, MdFOCuS.La géométrie computationnelle est la base des règles d'élagage fonctionnel pour ces méthodes. La règle d'élagage de GeomFPOP (Kmax = ∞) utilise une heuristique géométrique pour mettre à jour et élaguer les candidats potentiels de ruptures au fil du temps. La règle d'élagage de MdFOCuS utilise une connexion avec un problème d'enveloppe convexe qui simplifie la recherche de la rupture à élaguer. De plus, nous démontrons mathématiquement que cette technique d'élagage conduit à une complexité en temps quasi-linéaire.Ces deux règles d'élagage montrent des améliorations significatives de la complexité computationnelle pour les modèles de famille exponentielle de faible dimension dans des études de simulation. En une minute, les implémentations Rcpp de ces méthodes peuvent traiter plus de 2 × 106 observations dans un signal bivarié sans ruptures avec un bruit gaussien i.i.dChange point detection is a common unsupervised learning problem in many application areas, especially in biology, genomics, sensor network monitoring, and cyber-security. Typically, either a posteriori change detection, i.e. offline, or sequential change detection, i.e. online, is considered.Standard dynamic programming methods for change point detection have been proposed to optimise either the likelihood or the log-likelihood ratio of a change point model. These methods are exact and recover optimal segmentations. However, they have quadratic complexity. Continuously reducing the set of potential change point candidates, called pruning, is a way to reduce the computational complexity of standard dynamic programming methods. Over the last decade, a new class of dynamic programming methods, called functional pruning, has been proposed. The functional pruning techniques used in these methods have already proved to be computationally efficient for univariate parametric change point models. Extending univariate functional pruning rules to multivariate settings is difficult if we aim for the most efficient pruning. It leads to non-convex optimisation problems.This thesis introduces two novel, computationally efficient, functional pruning dynamic programming methods for the detection of change points in low-dimensional exponential family models: the offline multiple change point detection method, GeomFPOP (Kmax = ∞), and the online single change point detection method, MdFOCuS.Computational geometry is the basis of the functional pruning rules for these methods. The pruning rule of GeomFPOP (Kmax = ∞) uses a geometric heuristic to update and prune potential change point candidates over time. The pruning rule of MdFOCuS uses a connection to a convex hull problem that simplifies the search for change point location to be pruned. Further we mathematically demonstrate that this pruning technique leads to a quasi-linear runtime complexity.These two pruning rules show significant improvements in computational complexity for low-dimensional exponential family models in simulation studies. In one minute, the Rcpp implementations of these methods can process more than 2 × 106 observations in a bivariate signal without change with i.i.d. Gaussian noise
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Niu, Yue S., Ning Hao e Heping Zhang. "Multiple Change-Point Detection: A Selective Overview". INST MATHEMATICAL STATISTICS, 2016. http://hdl.handle.net/10150/622820.
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Very long and noisy sequence data arise from biological sciences to social science including high throughput data in genomics and stock prices in econometrics. Often such data are collected in order to identify and understand shifts in trends, for example, from a bull market to a bear market in finance or from a normal number of chromosome copies to an excessive number of chromosome copies in genetics. Thus, identifying multiple change points in a long, possibly very long, sequence is an important problem. In this article, we review both classical and new multiple change-point detection strategies. Considering the long history and the extensive literature on the change-point detection, we provide an in-depth discussion on a normal mean change-point model from aspects of regression analysis, hypothesis testing, consistency and inference. In particular, we present a strategy to gather and aggregate local information for change-point detection that has become the cornerstone of several emerging methods because of its attractiveness in both computational and theoretical properties.
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Hunter, Brandon. "Channel Probing for an Indoor Wireless Communications Channel". BYU ScholarsArchive, 2003. https://scholarsarchive.byu.edu/etd/64.
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The statistics of the amplitude, time and angle of arrival of multipaths in an indoor environment are all necessary components of multipath models used to simulate the performance of spatial diversity in receive antenna configurations. The model presented by Saleh and Valenzuela, was added to by Spencer et. al., and included all three of these parameters for a 7 GHz channel. A system was built to measure these multipath parameters at 2.4 GHz for multiple locations in an indoor environment. Another system was built to measure the angle of transmission for a 6 GHz channel. The addition of this parameter allows spatial diversity at the transmitter along with the receiver to be simulated. The process of going from raw measurement data to discrete arrivals and then to clustered arrivals is analyzed. Many possible errors associated with discrete arrival processing are discussed along with possible solutions. Four clustering methods are compared and their relative strengths and weaknesses are pointed out. The effects that errors in the clustering process have on parameter estimation and model performance are also simulated.
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"A composite likelihood-based approach for multiple change-point detection in multivariate time series models". 2014. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1291459.
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This thesis develops a composite likelihood-based approach for multiple change-points estimation in general multivariate time series models. Specifically, we derive a criterion function based on pairwise likelihood and minimum description length principle for estimating the number and locations of change-points and performing model selection in each segment. By the virtue of pairwise likelihood, the number and location of change-points can be consistently estimated under mild conditions. The computation can be conducted efficiently with a pruned dynamic programming algorithm. Simulation studies and real data examples are presented to demonstrate the statistical and computational efficiency of the proposed method.本論文目的為開發一套以複合似然為基礎的多變點估計方法，該方法可應用於一般多變量時間序列模型。具體而言，我們在最小描述長度原理及成對似然的基礎上推導出一個準則函數，用於估計變化點的數量及位置，並在各段進行模型選擇。憑藉成對似然，在適當條件下變點的數量和位置可以一致地估計。透過使用修剪動態規劃算法，相關的運算能有效地進行。模擬研究及真實數據實例都演示出該方法在統計及運算效率。
Ma, Ting Fung.
Thesis M.Phil. Chinese University of Hong Kong 2014.
Includes bibliographical references (leaves 51-54).
Abstracts also in Chinese.
Title from PDF title page (viewed on 05, October, 2016).
Detailed summary in vernacular field only.
Livros sobre o assunto "Single and Multiple Change Point Detection"
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Wright, A. G. The Photomultiplier Handbook. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199565092.001.0001.
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This handbook is aimed at helping users of PMTs who are faced with the challenge of designing sensitive light detectors for scientific and industrial purposes. The raison d’être for photomultipliers (PMTs) stems from four intrinsic attributes: large detection area, high, and noiseless gain, and wide bandwidth. Detection involves a conversion process from photons to photoelectrons at the photocathode. Photoelectrons are subsequently collected and increased in number by the action of an incorporated electron multiplier. Photon detection, charge multiplication, and many PMT applications are statistical in nature. For this reason appropriate statistical treatments are provided and derived from first principles. PMTs are characterized by a range of photocathodes offering detection over UV to infra-red wavelengths, the sensitivities of which can be calibrated by National Laboratories. The optical interface between light sources and PMTs, particularly for diffuse or uncollimated light, is sparsely covered in the scientific literature. The theory of light guides, Winston cones, and other light concentrators points to means for optimizing light collection subject to the constraints of Liouville’s theorem (étandue). Certain PMTs can detect single photons but are restricted by the limitations of unwanted background ranging in magnitude from a fraction of a photoelectron equivalent to hundreds of photoelectrons. These sources, together with their correlated nature, are examined in detail. Photomultiplier biasing requires a voltage divider comprising a series of resistors or active components, such as FETs. Correct biasing provides the key to linear operation and so considerable attention is given to the treatment of this topic. Electronic circuits and modules that perform the functions of charge to voltage conversion, pulse shaping, and impedance matching are analysed in detail.
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Oakes, Lisa M., e David H. Rakison. Developmental Cascades. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780195391893.001.0001.
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Children take their first steps, produce their first words, and become able to solve many new problems seemingly overnight. Yet, each change reflects many other previous developments that occurred in the whole child across a range of domains, and each change, in turn, will provide opportunities for future development. This book proposes that all change can be explained in terms of developmental cascades such that events that occur at one point in development set the stage, or cause a ripple effect, for the emergence or development of different abilities, functions, or behaviors at another point in time. The authors argue that these developmental cascades are influenced by different kinds of constraints that do not have a single foundation: They may originate from the structure of the child’s nervous system and body, the physical or social environment, or knowledge and experience. These constraints occur at multiple levels of processing and change over time, and both contribute to developmental cascades and are the product of them. The book presents an overview of this developmental cascade perspective as a general framework for understanding change throughout the lifespan, although it is applied primarily to cognitive development in infancy. The book also addresses how a cascade approach obviates the dichotomy between domain-general and domain-specific mechanisms. The framework is applied in detail to three domains within infant cognitive development—namely, looking behavior, object representations, and concepts for animacy—as well as two domains unrelated to infant cognition (gender and attachment).
Capítulos de livros sobre o assunto "Single and Multiple Change Point Detection"
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Priyadarshana, Madawa, Tatiana Polushina e Georgy Sofronov. "Hybrid Algorithms for Multiple Change-Point Detection in Biological Sequences". In Signal and Image Analysis for Biomedical and Life Sciences, 41–61. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10984-8_3.
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Wu, Yanhong. "Sequential change point detection and estimation for multiple alternative hypothesis1". In Systems modelling and optimization, 345–53. Boca Raton: Routledge, 2022. http://dx.doi.org/10.1201/9780203737422-43.
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Akbari, Shagufta, e M. Janga Reddy. "Detecting Changes in Regional Rainfall Series in India Using Binary Segmentation-Based Multiple Change-Point Detection Techniques". In Climate Change Impacts, 103–16. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5714-4_8.
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Oke, Masahiro, e Hideyuki Kawashima. "A Multiple Query Optimization Scheme for Change Point Detection on Stream Processing System". In Lecture Notes in Business Information Processing, 150–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46839-5_10.
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Rigaill, G., E. Lebarbier e S. Robin. "Exact Posterior Distributions over the Segmentation Space and Model Selection for Multiple Change-Point Detection Problems". In Proceedings of COMPSTAT'2010, 557–64. Heidelberg: Physica-Verlag HD, 2010. http://dx.doi.org/10.1007/978-3-7908-2604-3_57.
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Qian, Lianfen, e Wei Zhang. "Multiple Change-Point Detection in Piecewise Exponential Hazard Regression Models with Long-Term Survivors and Right Censoring". In Contemporary Developments in Statistical Theory, 289–304. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02651-0_18.
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Quoc Tran, Dai, Yuntae Jeon, Seongwoo Son, Minsoo Park e Seunghee Park. "Identifying Hazards in Construction Sites Using Deep Learning-Based Multimodal with CCTV Data". In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality, 625–33. Florence: Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.61.
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The use of closed-circuit television (CCTV) for safety monitoring is crucial for reducing accidents in construction sites. However, the majority of currently proposed approaches utilize single detection models without considering the context of CCTV video inputs. In this study, a multimodal detection, and depth map estimation algorithm utilizing deep learning is proposed. In addition, the point cloud of the test site is acquired using a terrestrial laser scanning scanner, and the detected object's coordinates are projected into global coordinates using a homography matrix. Consequently, the effectiveness of the proposed monitoring system is enhanced by the visualization of the entire monitored scene. In addition, to validate our proposed method, a synthetic dataset of construction site accidents is simulated with Twinmotion. These scenarios are then evaluated with the proposed method to determine its precision and speed of inference. Lastly, the actual construction site, equipped with multiple CCTV cameras, is utilized for system deployment and visualization. As a result, the proposed method demonstrated its robustness in detecting potential hazards on a construction site, as well as its real-time detection speed
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Quoc Tran, Dai, Yuntae Jeon, Seongwoo Son, Minsoo Park e Seunghee Park. "Identifying Hazards in Construction Sites Using Deep Learning-Based Multimodal with CCTV Data". In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality, 625–33. Florence: Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.61.
Texto completo da fonteResumo:
The use of closed-circuit television (CCTV) for safety monitoring is crucial for reducing accidents in construction sites. However, the majority of currently proposed approaches utilize single detection models without considering the context of CCTV video inputs. In this study, a multimodal detection, and depth map estimation algorithm utilizing deep learning is proposed. In addition, the point cloud of the test site is acquired using a terrestrial laser scanning scanner, and the detected object's coordinates are projected into global coordinates using a homography matrix. Consequently, the effectiveness of the proposed monitoring system is enhanced by the visualization of the entire monitored scene. In addition, to validate our proposed method, a synthetic dataset of construction site accidents is simulated with Twinmotion. These scenarios are then evaluated with the proposed method to determine its precision and speed of inference. Lastly, the actual construction site, equipped with multiple CCTV cameras, is utilized for system deployment and visualization. As a result, the proposed method demonstrated its robustness in detecting potential hazards on a construction site, as well as its real-time detection speed
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Goossens, Alexandre, Johannes De Smedt, Jan Vanthienen e Wil M. P. van der Aalst. "Enhancing Data-Awareness of Object-Centric Event Logs". In Lecture Notes in Business Information Processing, 18–30. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27815-0_2.
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AbstractWhen multiple objects are involved in a process, there is an opportunity for processes to be discovered from different angles with new information that previously might not have been analyzed from a single object point of view. This does require that all the information of event/object attributes and their values are stored within logs including attributes that have a list of values or attributes with values that change over time. It also requires that attributes can unambiguously be linked to an object, an event or both. As such, object-centric event logs are an interesting development in process mining as they support the presence of multiple types of objects. First, this paper shows that the current object-centric event log formats do not support the aforementioned aspects to their full potential since the possibility to support dynamic object attributes (attributes with changing values) is not supported by existing formats. Next, this paper introduces a novel enriched object-centric event log format tackling the aforementioned issues alongside an algorithm that automatically translates XES logs to this Data-aware OCEL (DOCEL) format.
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Fernández, Néstor, Simon Ferrier, Laetitia M. Navarro e Henrique M. Pereira. "Essential Biodiversity Variables: Integrating In-Situ Observations and Remote Sensing Through Modeling". In Remote Sensing of Plant Biodiversity, 485–501. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33157-3_18.
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AbstractEssential biodiversity variables (EBVs) are designed to support the detection and quantification of biodiversity change and to define priorities in biodiversity monitoring. Unlike most primary observations of biodiversity phenomena, EBV products should provide information readily available to produce policy-relevant biodiversity indicators, ideally at multiple spatial scales, from global to subnational. This information is typically complex to produce from a single set of data or type of observation, thus requiring approaches that integrate multiple sources of in situ and remote sensing (RS) data. Here we present an up-to-date EBV concept for biodiversity data integration and discuss the critical components of workflows for EBV production. We argue that open and reproducible workflows for data integration are critical to ensure traceability and reproducibility so that each EBV endures and can be updated as novel biodiversity models are adopted, new observation systems become available, and new data sets are incorporated. Fulfilling the EBV vision requires strengthening efforts to mobilize massive amounts of in situ biodiversity data that are not yet publicly available and taking full advantage of emerging RS technologies, novel biodiversity models, and informatics infrastructures, in alignment with the development of a globally coordinated system for biodiversity monitoring.
Trabalhos de conferências sobre o assunto "Single and Multiple Change Point Detection"
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Sedighi Maman, Zahra, Amir Baghdadi, Fadel Megahed e Lora Cavuoto. "Monitoring and Change Point Estimation of Normal (In-Control) and Fatigued (Out-of-Control) State in Workers". In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-60487.
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This paper presents a fused metric for the assessment of physical workload that can improve fatigue detection using a statistical visualization approach. The goal for considering this combined metric is to concisely reduce the number of variables acquired from multiple sensors. The sensor system gathers data from a heart rate monitor and accelerometers placed at different locations on the body including trunk, wrist, hip and ankle. Two common manufacturing tasks of manual material handling and small parts assembly were tested. Statistical process control was used to monitor the metrics for the workload state of the human body. A cumulative sum (CUSUM) statistical analysis was applied to each of the single metrics and the combined metric of heart rate reserve and acceleration (HRR*ACC). The sensor data were transformed to linear profiles by using the CUSUM plot, which can be monitored by profile monitoring techniques. A significant variation between the lifting replications was observed for the combined metric in comparison to the single metrics, which is an important factor in selecting a fused metric. The results show that the proposed approach can improve the ability to detect different states (i.e., fatigue vs. non-fatigued) in the human body.
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Roy, Arjun, Sangeeta Nundy, Okja Kim e Godine Chan. "Emission Source Detection and Leak Rate Estimation Using Point Measurements of Concentration". In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22377-ea.
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Abstract Introduction With the advent of global climate change, it has become incumbent on governments and industries to monitor and limit greenhouse gas emissions to prevent a catastrophic rise in the average global temperature. The Paris agreement [Paris 2015] aims to lower global greenhouse gas emissions by 40% (in comparison to greenhouse gas levels observed in 1990) by 2030. Methane is a greenhouse gas whose 100- year global warming potential is 25 times that of carbon dioxide [GWP] and whose atmospheric concentration has been increasing since 2007 [Nisbet 2016, Theo Stein, et al. 2021]. Thus, there is an increased requirement on industries from government regulators to detect, localize, quantify and mitigate both fugitive and vented emissions of methane. There are several different technologies that are available for automated methane emissions management. These include arial and ground-based mobile sensing units that are based on optical-gas imaging, satellite-based imagery [Jacob et al. 2016] and stationary metal-oxide based sensors. A key criterion that often needs to be satisfied is continuous monitoring for early detection and mitigation of fugitive leaks. Fixed metal-oxide based sensors [Yuliarto et al. (2015), Zeng et al. (2019), Yunusa et al. (2014), Potyrailo et al. (2020), Wang et al. (2010) and Feng et al. (2019)] are low-cost sensors that can be used for continuous monitoring of a site and are typically used for detection of leaks and alerting. The main challenge is to extend utility of these sensors to not only detect presence of fugitive and vented emissions, but also be able to estimate the number of leak sources and their probable locations and the total volume of hydrocarbon leaked over a period. This paper describes an approach used for detecting anomalies in emission data, identifying possible emission sources, and estimating emission leak rates using point measurements of concentration collected over a period along with measurements of wind speed and direction. This involves multiple analytics that combine concentration and wind-condition time-series data with physics models to predict the different outcomes.
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Mata, Jose, Zunerge Guevara, Luis Quintero, Carlos Vasquez, Hernando Trujillo, Alberto Muñoz e Jorge Falla. "Combination of New Acoustic and Electromagnetic Frequency Technologies Detects Leaks Behind Multiple Casings. Case History". In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206383-ms.
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Abstract Although leakages in well tubulars have always existed, their occurrence has become very frequent as the number of active wells in mature fields increases. The catastrophic risk of these leaks is an increase in the number of environmental accidents in the oil and gas industry. One of the fundamental causes of leaks is corrosion, which plays a negative role in the productive life of the wells. Generally, these environmental events are associated with surface or near-surface sources. Since multiple casing strings exist within this depth range, the identification of the leak location becomes extremely difficult. In view of this, the industry has put much effort in improving and new technology to be more precise and comprehensive in diagnosing these leaks. The evolution of two of such technologies will be addressed in this paper. The first one is a new electromagnetic high-definition frequency tool for pipes and multiples casing for metal loss detection. This state-of-the-art technology is a noticeable improvement over existing tools, due to an important increase in the number of sources, number of detectors and wide range of working frequencies. The combination of these changes allows for the evaluation of metal loss in up to 5 concentric casings in a single run. Furthermore, the tool is small in diameter which makes it compatible with production pipes without the need of a workover rig. This versatility obviously helps in the preworkover diagnosis before deciding to move a rig to location to eventually remedy any leak problems. The electromagnetic technology is complemented, with the latest leak detection acoustic technology. A spontaneous audio source is normally associated with downhole fluid movements. The tool has an array of 8 hydrophones with a working frequency range from 100 Hz to 100 KHz. These two different technologies based on independent fundamental principles, allows for the detection of leaks in multiple concentric pipes with great vertical and radial precision to identify the exact location of leaks as small as to 0.02 L/min. the depth of investigation of the system is up to 10 feet. Therefore, it is possible to detect fluid movement within the formation. Pulsed neutron technology was included in the study to detect water movement behind the casing to establish the flow path to the surface in addition to the leak point. A very complex acquisition program was established that was undoubtedly a key success factor in the results obtained. The electromagnetic tool determined the depth of severe casing metal loss in 7-inch casing, also the acoustic tool detected the noise of fluid movement in the 7-inch annulus, and the pulsed-neutron tool showed the beginning of water movement at the same interval the temperature log, also included in the same tool string showed a considerable change that correlated with all these logs, indicating the point of communication in this well. After establishing the uniqueness of the solution, this diagnosis helped the operator define an intervention plan for this well, and to make the appropriate corrections in the field development strategy.
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Zhang, Wenyu, Nicholas A. James e David S. Matteson. "Pruning and Nonparametric Multiple Change Point Detection". In 2017 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2017. http://dx.doi.org/10.1109/icdmw.2017.44.
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Chalise, Batu K., Jahi Douglas e Kevin T. Wagner. "Multiple Change Point Detection-based Target Detection in Clutter". In 2023 IEEE Radar Conference (RadarConf23). IEEE, 2023. http://dx.doi.org/10.1109/radarconf2351548.2023.10149616.
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Halme, Topi, Eyal Nitzan, H. Vincent Poor e Visa Koivunen. "Bayesian Multiple Change-Point Detection with Limited Communication". In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053654.
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Halme, Topi, Eyal Nitzan e Visa Koivunen. "Bayesian Multiple Change-Point Detection of Propagating Events". In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9413434.
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Nitzan, Eyal, Topi Halme, H. Vincent Poor e Visa Koivunen. "Deterministic Multiple Change-Point Detection with Limited Communication". In 2020 54th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2020. http://dx.doi.org/10.1109/ciss48834.2020.1570627514.
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Barooah, Abinash, Muhammad Saad Khan, Hicham Ferroudji, Mohammad Azizur Rahman, Rashid Hassan, Ibrahim Hassan, Ahmad K. Sleiti, Sina Rezaei Gomari e Matthew Hamilton. "Investigation of Multiphase Flow Leak Detection in Pipeline Using Time Series Analysis Technique". In ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/omae2024-127882.
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Abstract Detecting chronic small leak sizes can be challenging because they may not produce significant or easily noticeable changes in flow rates or pressure differentials. Therefore, specialized techniques are often required to identify and locate chronic small leaks accurately in pipeline systems. The current study aims to address this gap by developing a method to identify multiphase flow leaks in pipelines using time series analysis techniques. An experimental flow loop apparatus, featuring a 2-inch (0.0508 m) diameter and extending 22.6 feet (6.9 m) in length, has been employed to carry out our experiments. The experiments encompass a range of liquid flow rates varying between 170 and 350 kg/min and gas flow rates ranging from 10 to 60 g/min. The system was equipped with three distinct leak opening diameters, measuring 1.8 mm, 2.5 mm, and 3 mm, each separated by 90 mm. Data collected from four dynamic pressure sensors was subjected to time series analysis such as wavelet transforms to detect and pinpoint the location of pipeline leaks. The obtained results indicate that dynamic pressure sensors are effective in detecting leak scenarios, as well as distinguishing between single and multiple leaks. However, for chronic small leaks, analyzing the standalone pressure response over time is generally not sufficient for detection. Time series analysis techniques play a crucial role in accurately identifying chronic small sized pipeline leaks. Discrete Wavelet Transform (DWT) was able to identify the point of leak opening and closing. Furthermore, DWT was able to reduce the false alarms for leak and no leak situations. This study introduces the application of time series analysis on dynamic pressure to detect chronic small sized leaks in multiphase flow pipelines. Additionally, it explores the capacity of wavelet analysis to minimize the occurrence of false alarms for leak and non-leak scenarios thereby addressing crucial safety, environmental, and economic concerns.
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Nath, Samrat, e Jingxian Wu. "BAYESIAN QUICKEST CHANGE POINT DETECTION WITH MULTIPLE CANDIDATES OF POST-CHANGE MODELS". In 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2018. http://dx.doi.org/10.1109/globalsip.2018.8646596.
Texto completo da fonteRelatórios de organizações sobre o assunto "Single and Multiple Change Point Detection"
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Belkin, Shimshon, Sylvia Daunert e Mona Wells. Whole-Cell Biosensor Panel for Agricultural Endocrine Disruptors. United States Department of Agriculture, dezembro de 2010. http://dx.doi.org/10.32747/2010.7696542.bard.
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Objectives: The overall objective as defined in the approved proposal was the development of a whole-cell sensor panel for the detection of endocrine disruption activities of agriculturally relevant chemicals. To achieve this goal several specific objectives were outlined: (a) The development of new genetically engineered wholecell sensor strains; (b) the combination of multiple strains into a single sensor panel to effect multiple response modes; (c) development of a computerized algorithm to analyze the panel responses; (d) laboratory testing and calibration; (e) field testing. In the course of the project, mostly due to the change in the US partner, three modifications were introduced to the original objectives: (a) the scope of the project was expanded to include pharmaceuticals (with a focus on antibiotics) in addition to endocrine disrupting chemicals, (b) the computerized algorithm was not fully developed and (c) the field test was not carried out. Background: Chemical agents, such as pesticides applied at inappropriate levels, may compromise water quality or contaminate soils and hence threaten human populations. In recent years, two classes of compounds have been increasingly implicated as emerging risks in agriculturally-related pollution: endocrine disrupting compounds (EDCs) and pharmaceuticals. The latter group may reach the environment by the use of wastewater effluents, whereas many pesticides have been implicated as EDCs. Both groups pose a threat in proportion to their bioavailability, since that which is biounavailable or can be rendered so is a priori not a threat; bioavailability, in turn, is mediated by complex matrices such as soils. Genetically engineered biosensor bacteria hold great promise for sensing bioavailability because the sensor is a live soil- and water-compatible organism with biological response dynamics, and because its response can be genetically “tailored” to report on general toxicity, on bioavailability, and on the presence of specific classes of toxicants. In the present project we have developed a bacterial-based sensor panel incorporating multiple strains of genetically engineered biosensors for the purpose of detecting different types of biological effects. The overall objective as defined in the approved proposal was the development of a whole-cell sensor panel for the detection of endocrine disruption activities of agriculturally relevant chemicals. To achieve this goal several specific objectives were outlined: (a) The development of new genetically engineered wholecell sensor strains; (b) the combination of multiple strains into a single sensor panel to effect multiple response modes; (c) development of a computerized algorithm to analyze the panel responses; (d) laboratory testing and calibration; (e) field testing. In the course of the project, mostly due to the change in the US partner, three modifications were introduced to the original objectives: (a) the scope of the project was expanded to include pharmaceuticals (with a focus on antibiotics) in addition to endocrine disrupting chemicals, (b) the computerized algorithm was not fully developed and (c) the field test was not carried out. Major achievements: (a) construction of innovative bacterial sensor strains for accurate and sensitive detection of agriculturally-relevant pollutants, with a focus on endocrine disrupting compounds (UK and HUJ) and antibiotics (HUJ); (b) optimization of methods for long-term preservation of the reporter bacteria, either by direct deposition on solid surfaces (HUJ) or by the construction of spore-forming Bacillus-based sensors (UK); (c) partial development of a computerized algorithm for the analysis of sensor panel responses. Implications: The sensor panel developed in the course of the project was shown to be applicable for the detection of a broad range of antibiotics and EDCs. Following a suitable development phase, the panel will be ready for testing in an agricultural environment, as an innovative tool for assessing the environmental impacts of EDCs and pharmaceuticals. Furthermore, while the current study relates directly to issues of water quality and soil health, its implications are much broader, with potential uses is risk-based assessment related to the clinical, pharmaceutical, and chemical industries as well as to homeland security.
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Burns, Malcom, e Gavin Nixon. Literature review on analytical methods for the detection of precision bred products. Food Standards Agency, setembro de 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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Botulinum Neurotoxin-Producing Clostridia, Working Group on. Report on Botulinum Neurotoxin-Producing Clostridia. Food Standards Agency, agosto de 2023. http://dx.doi.org/10.46756/sci.fsa.ozk974.
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In 1992 a working group of the UK Advisory Committee on the Microbiological Safety of Food presented a report on Vacuum Packaging and Associated Processes regarding the microbiological safety of chilled foods. The report supported subsequent guidance provided by the UK Food Standards Agency for the safe manufacture of vacuum packed and modified atmosphere packed chilled foods. In 2021 the ACMSF requested that a new subgroup should update and build on the 1992 report as well as considering, in addition to chilled foods, some foods that are intended to be stored at ambient temperatures. The new subgroup agreed a scope that includes the conditions that support growth and/or neurotoxin formation by C. botulinum, and other clostridia, as well as identification of limiting conditions that provide control. Other foodborne pathogens that need to be considered separately and some foods including raw beef, pork and lamb were explicitly excluded. The subgroup considered the taxonomy, detection, epidemiology, occurrence, growth, survival and risks associated with C. botulinum and other neurotoxin-forming clostridia. There has been no significant change in the nature of foodborne botulism in recent decades except for the identification of rare cases caused by neurotoxigenic C. butyricum, C. baratii and C. sporogenes. Currently evidence indicates that non-clostridia do not pose a risk in relation to foodborne botulism. The subgroup has compiled lists of incidents and outbreaks of botulism, reported in the UK and worldwide, and have reviewed published information concerning growth parameters and control factors in relation to proteolytic C. botulinum, non-proteolytic C. botulinum and the other neurotoxigenic clostridia. The subgroup concluded that the frequency of occurrence of foodborne botulism is very low (very rare but cannot be excluded) with high severity (severe illness: causing life threatening or substantial sequelae or long-term illness). Uncertainty associated with the assessment of the frequency of occurrence, and with the assessment of severity, of foodborne botulism is low (solid and complete data; strong evidence in multiple sources). The vast majority of reported botulism outbreaks, for chilled or ambient stored foods, are identified with proteolytic C. botulinum and temperature abuse is the single most common cause. In the last 30 years, in the UK and worldwide where a cause can be identified, there is evidence that known controls, combined with the correct storage, would have prevented the reported incidents of foodborne botulism. The subgroup recommends that foods should continue to be formulated to control C. botulinum, and other botulinum neurotoxin-producing clostridia, in accordance with the known factors. With regard to these controls, the subgroup recommends some changes to the FSA guidelines that reflect improved information about using combinations of controls, the z-value used to establish equivalent thermal processes and the variable efficacy associated with some controls such as herbs and spices. Current information does not facilitate revision of the current reference process, heating at 90°C for 10 minutes, but there is strong evidence that this provides a lethality that exceeds the target 6 order of magnitude reduction in population size that is widely attributed to the process and the subgroup includes a recommendation that the FSA considers this issue. Early detection and connection of cases and rapid, effective coordinated responses to very rare incidents are identified as crucial elements for reducing risks from foodborne botulism. The subgroup recommends that the FSA works closely with other agencies to establish clear and validated preparedness in relation to potential major incidents of foodborne botulism in the UK.
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Ley, Matt, Tom Baldvins, Hannah Pilkington, David Jones e Kelly Anderson. Vegetation classification and mapping project: Big Thicket National Preserve. National Park Service, 2024. http://dx.doi.org/10.36967/2299254.
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The Big Thicket National Preserve (BITH) vegetation inventory project classified and mapped vegetation within the administrative boundary and estimated thematic map accuracy quantitatively. National Park Service (NPS) Vegetation Mapping Inventory Program provided technical guidance. The overall process included initial planning and scoping, imagery procurement, vegetation classification field data collection, data analysis, imagery interpretation/classification, accuracy assessment (AA), and report writing and database development. Initial planning and scoping meetings took place during May, 2016 in Kountze, Texas where representatives gathered from BITH, the NPS Gulf Coast Inventory and Monitoring Network, and Colorado State University. The project acquired new 2014 orthoimagery (30-cm, 4-band (RGB and CIR)) from the Hexagon Imagery Program. Supplemental imagery for the interpretation phase included Texas Natural Resources Information System (TNRIS) 2015 50 cm leaf-off 4-band imagery from the Texas Orthoimagery Program (TOP), Farm Service Agency (FSA) 100-cm (2016) and 60 cm (2018) National Aerial Imagery Program (NAIP) imagery, and current and historical true-color Google Earth and Bing Maps imagery. In addition to aerial and satellite imagery, 2017 Neches River Basin Light Detection and Ranging (LiDAR) data was obtained from the United States Geological Survey (USGS) and TNRIS to analyze vegetation structure at BITH. The preliminary vegetation classification included 110 United States National Vegetation Classification (USNVC) associations. Existing vegetation and mapping data combined with vegetation plot data contributed to the final vegetation classification. Quantitative classification using hierarchical clustering and professional expertise was supported by vegetation data collected from 304 plots surveyed between 2016 and 2019 and 110 additional observation plots. The final vegetation classification includes 75 USNVC associations and 27 park special types including 80 forest and woodland, 7 shrubland, 12 herbaceous, and 3 sparse vegetation types. The final BITH map consists of 51 map classes. Land cover classes include five types: pasture / hay ground agricultural vegetation; non ? vegetated / barren land, borrow pit, cut bank; developed, open space; developed, low ? high intensity; and water. The 46 vegetation classes represent 102 associations or park specials. Of these, 75 represent natural vegetation associations within the USNVC, and 27 types represent unpublished park specials. Of the 46 vegetation map classes, 26 represent a single USNVC association/park special, 7 map classes contain two USNVC associations/park specials, 4 map classes contain three USNVC associations/park specials, and 9 map classes contain four or more USNVC associations/park specials. Forest and woodland types had an abundance of Pinus taeda, Liquidambar styraciflua, Ilex opaca, Ilex vomitoria, Quercus nigra, and Vitis rotundifolia. Shrubland types were dominated by Pinus taeda, Ilex vomitoria, Triadica sebifera, Liquidambar styraciflua, and/or Callicarpa americana. Herbaceous types had an abundance of Zizaniopsis miliacea, Juncus effusus, Panicum virgatum, and/or Saccharum giganteum. The final BITH vegetation map consists of 7,271 polygons totaling 45,771.8 ha (113,104.6 ac). Mean polygon size is 6.3 ha (15.6 ac). Of the total area, 43,314.4 ha (107,032.2 ac) or 94.6% represent natural or ruderal vegetation. Developed areas such as roads, parking lots, and campgrounds comprise 421.9 ha (1,042.5 ac) or 0.9% of the total. Open water accounts for approximately 2,034.9 ha (5,028.3 ac) or 4.4% of the total mapped area. Within the natural or ruderal vegetation types, forest and woodland types were the most extensive at 43,022.19 ha (106,310.1 ac) or 94.0%, followed by herbaceous vegetation types at 129.7 ha (320.5 ac) or 0.3%, sparse vegetation types at 119.2 ha (294.5 ac) or 0.3%, and shrubland types at 43.4 ha (107.2 ac) or 0.1%. A total of 784 AA samples were collected to evaluate the map?s thematic accuracy. When each AA sample was evaluated for a variety of potential errors, a number of the disagreements were overturned. It was determined that 182 plot records disagreed due to either an erroneous field call or a change in the vegetation since the imagery date, and 79 disagreed due to a true map classification error. Those records identified as incorrect due to an erroneous field call or changes in vegetation were considered correct for the purpose of the AA. As a simple plot count proportion, the reconciled overall accuracy was 89.9% (705/784). The spatially-weighted overall accuracy was 92.1% with a Kappa statistic of 89.6%. This method provides more weight to larger map classes in the park. Five map classes had accuracies below 80%. After discussing preliminary results with the parl, we retained those map classes because the community was rare, the map classes provided desired detail for management or the accuracy was reasonably close to the 80% target. When the 90% AA confidence intervals were included, an additional eight classes had thematic accruacies that extend below 80%. In addition to the vegetation polygon database and map, several products to support park resource management include the vegetation classification, field key to the associations, local association descriptions, photographic database, project geodatabase, ArcGIS .mxd files for map posters, and aerial imagery acquired for the project. The project geodatabase links the spatial vegetation data layer to vegetation classification, plot photos, project boundary extent, AA points, and PLOTS database sampling data. The geodatabase includes USNVC hierarchy tables allowing for spatial queries of data associated with a vegetation polygon or sample point. All geospatial products are projected using North American Datum 1983 (NAD83) in Universal Transverse Mercator (UTM) Zone 15 N. The final report includes methods and results, contingency tables showing AA results, field forms, species list, and a guide to imagery interpretation. These products provide useful information to assist with management of park resources and inform future management decisions. Use of standard national vegetation classification and mapping protocols facilitates effective resource stewardship by ensuring the compatibility and widespread use throughout NPS as well as other federal and state agencies. Products support a wide variety of resource assessments, park management and planning needs. Associated information provides a structure for framing and answering critical scientific questions about vegetation communities and their relationship to environmental processes across the landscape.