Relevant bibliographies by topics / Spatial-temporal CNN

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Spatial-temporal CNN'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Spatial-temporal CNN"

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Zhao, Zhen, Ze Li, Fuxin Li, and Yang Liu. "CNN-LSTM Based Traffic Prediction Using Spatial-temporal Features." Journal of Physics: Conference Series 2037, no. 1 (2021): 012065. http://dx.doi.org/10.1088/1742-6596/2037/1/012065.

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Horváth, András, and Tamás Roska. "Detection of Spatial-Temporal Events with Delayed CNN Templates." IEICE Proceeding Series 2 (March 17, 2014): 378–81. http://dx.doi.org/10.15248/proc.2.378.

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Liu, Yumin, Zheyun Zhao, Shuai Zhang, and Uk Jung. "Identification of Abnormal Processes with Spatial-Temporal Data Using Convolutional Neural Networks." Processes 8, no. 1 (2020): 73. http://dx.doi.org/10.3390/pr8010073.

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Identifying abnormal process operation with spatial-temporal data remains an important and challenging work in many practical situations. Although spatial-temporal data identification has been extensively studied in some domains, such as public health, geological condition, and environment pollution, the challenge associated with designing accurate and convenient recognition schemes is very rarely addressed in modern manufacturing processes. This paper proposes a general recognition framework for identifying abnormal process with spatial-temporal data by employing a convolutional neural networ
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Wang, Changyuan, Ting Yan, and Hongbo Jia. "Spatial-Temporal Feature Representation Learning for Facial Fatigue Detection." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 12 (2018): 1856018. http://dx.doi.org/10.1142/s0218001418560189.

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In order to reduce the serious problems caused by the operators’ fatigue, we propose a novel network model Convolutional Neural Network and Long Short-Term Memory Network (CNN-LSTM) — for fatigue detection in the inter-frame images of video sequences, which mainly consists of CNN and LSTM network. Firstly, in order to improve the accuracy of the deep network structure, the Viola–Jones detection algorithm and the Kernelized Correlation Filter (KCF) tracking algorithm are used in the face detection to normalize the size of the inter-frame images of video sequences. Secondly, we use the CNN and t
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Wang, Zengkai. "Spatial-Temporal Feature-Based Sports Video Classification." International Journal of Ambient Computing and Intelligence 12, no. 4 (2021): 79–97. http://dx.doi.org/10.4018/ijaci.2021100105.

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Video classification has been an active research field of computer vision in last few years. Its main purpose is to produce a label that is relevant to the video given its frames. Unlike image classification, which takes still pictures as input, the input of video classification is a sequence of images. The complex spatial and temporal structures of video sequence incur understanding and computation difficulties, which should be modeled to improve the video classification performance. This work focuses on sports video classification but can be expanded into other applications. In this paper, t
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Meshkini, K., F. Bovolo, and L. Bruzzone. "A 3D CNN APPROACH FOR CHANGE DETECTION IN HR SATELLITE IMAGE TIME SERIES BASED ON A PRETRAINED 2D CNN." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 143–50. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-143-2022.

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Abstract. Over recent decades, Change Detection (CD) has been intensively investigated due to the availability of High Resolution (HR) multi-spectral multi-temporal remote sensing images. Deep Learning (DL) based methods such as Convolutional Neural Network (CNN) have recently received increasing attention in CD problems demonstrating high potential. However, most of the CNN-based CD methods are designed for bi-temporal image analysis. Here, we propose a Three-Dimensional (3D) CNN-based CD approach that can effectively deal with HR image time series and process spatial-spectral-temporal featur
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Wu, Honggang, Jiabi Niu, Yongqiang Li, Yinsheng Wang, and Daohong Qiu. "Landslide Susceptibility Prediction Based on a CNN–LSTM–SAM–Attention Hybrid Model." Applied Sciences 15, no. 13 (2025): 7245. https://doi.org/10.3390/app15137245.

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Accurate prediction of landslide susceptibility is a key component of disaster risk reduction and early warning systems. Traditional landslide susceptibility prediction methods often face challenges in capturing complex nonlinear and spatio-temporal relationships inherent in geospatial data. In this study, we propose a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Spatial Attention Mechanism (SAM) hybrid deep learning model designed for spatial landslide susceptibility prediction. The model is trained on a comprehensive dataset comprising 19,898 samples, constructed from l
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Li, Jianing, Shiliang Zhang, and Tiejun Huang. "Multi-Scale 3D Convolution Network for Video Based Person Re-Identification." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8618–25. http://dx.doi.org/10.1609/aaai.v33i01.33018618.

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This paper proposes a two-stream convolution network to extract spatial and temporal cues for video based person ReIdentification (ReID). A temporal stream in this network is constructed by inserting several Multi-scale 3D (M3D) convolution layers into a 2D CNN network. The resulting M3D convolution network introduces a fraction of parameters into the 2D CNN, but gains the ability of multi-scale temporal feature learning. With this compact architecture, M3D convolution network is also more efficient and easier to optimize than existing 3D convolution networks. The temporal stream further invol
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Yang, Hao, Chunfeng Yuan, Li Zhang, Yunda Sun, Weiming Hu, and Stephen J. Maybank. "STA-CNN: Convolutional Spatial-Temporal Attention Learning for Action Recognition." IEEE Transactions on Image Processing 29 (2020): 5783–93. http://dx.doi.org/10.1109/tip.2020.2984904.

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Liu, Yiqiang, Luming Shen, Xinghui Zhu, Yangfan Xie, and Shaofang He. "Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model." Applied Sciences 14, no. 24 (2024): 11687. https://doi.org/10.3390/app142411687.

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Accurate prediction of soil properties is essential for sustainable land management and precision agriculture. This study presents an LSTM-CNN-Attention model that integrates temporal and spatial feature extraction with attention mechanisms to improve predictive accuracy. Utilizing the LUCAS soil dataset, the model analyzes spectral data to estimate key soil properties, including organic carbon (OC), nitrogen (N), calcium carbonate (CaCO3), and pH (in H2O). The Long Short-Term Memory (LSTM) component captures temporal dependencies, the Convolutional Neural Network (CNN) extracts spatial featur
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Dissertations / Theses on the topic "Spatial-temporal CNN"

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Cowan, Oliver. "Princess Vlei : what diatoms can tell us about spatial and temporal heterogeneity in an urban wetland." Bachelor's thesis, University of Cape Town, 2010. http://hdl.handle.net/11427/24854.

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Globally, wetlands are being degraded and destroyed largely as a result of anthropogenic activity. Monitoring and assessment are required to maintain functional ecosystems and the valuable services they provide. Surface sediment cores were taken from 4 points around an urban wetland (Princess Vlei) on the Cape Flats, Cape Town, South Africa. Diatoms were and used as a proxy for water quality variables to determine whether the diatom flora preserved in organic sediments accumulating in and around an urban wetland is a useful tool in determining the changes in water quality over time. In additio
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Kriel, Gideon Petrus. "Biological indicators of water quality in an urban waterway : can diatoms reflect short term spatial and temporal changes in water quality? / G.P. Kriel." Thesis, North-West University, 2008. http://hdl.handle.net/10394/2137.

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Blanchet, Catherine. "Le contenu en azote de Vallisneria americana : un élément intégrateur de l'hétérogénéité spatiale et temporelle du lac St-Pierre, un lac fluvial du fleuve St-Laurent." Thèse, 2008. http://hdl.handle.net/1866/8139.

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Books on the topic "Spatial-temporal CNN"

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Schulze, Chad C. Can spatial and temporal variation in sampling regime cause the rapid bioassessment protocols to change assessments? Huxley College of Environmental Studies, Western Washington University, 1995.

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Colaresi, Michael, and Jude C. Hays. Spatial and Temporal Interdependence. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190846626.013.301.

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Time and space are two dimensions that are likely to provide the paths—either singly or in tandem—by which international policy decisions are interdependent. There are several reasons to expect international relations processes to be interdependent across space, time, or both dimensions. Theoretical approaches such as rational expectations models, bureaucratic models of decision-making, and psychological explanations of international phenomena at least implicitly assume—and in many cases explicitly predict—dependence structures within data. One approach that researchers can use to test whether
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Callender, Craig. Looking at the World Sideways. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198797302.003.0008.

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When physics tells its story of the world, it writes on spatial pages and we flip pages in the temporal directions. The present moment contains the seeds of what happens next. Relativity challenges many of our pre-theoretical thoughts about time, yet even this would-be destroyer of time adheres to the idea that production or determination runs along the set of temporal directions. We might think of this fact as one of the last remnants left of manifest time in physics. Is even this residue of manifest time safe from physics? Looking at the world sideways, can we march “initial” data from “east
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Lopes, Hedibert, and Nicholas Polson. Analysis of economic data with multiscale spatio-temporal models. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.12.

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This article discusses the use of Bayesian multiscale spatio-temporal models for the analysis of economic data. It demonstrates the utility of a general modelling approach for multiscale analysis of spatio-temporal processes with areal data observations in an economic study of agricultural production in the Brazilian state of Espìrito Santo during the period 1990–2005. The article first describes multiscale factorizations for spatial processes before presenting an exploratory multiscale data analysis and explaining the motivation for multiscale spatio-temporal models. It then examines the temp
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Andresen, Martin. GIS and Spatial Analysis. Edited by Gerben J. N. Bruinsma and Shane D. Johnson. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190279707.013.33.

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The importance of spatial-temporal dimension(s) within environmental criminology has made the use and applications of geographic information systems (GIS) and spatial analysis rather widespread. This chapter covers some of the principles and advancements in the use of crime mapping and spatial analysis to study the spatial distribution of crime, primarily through the lens of environmental criminology. Crime mapping is defined as the spatial representation of crime (in the context of criminal events) on a map. Consequently, in order to do so, one must have geographic coordinates for each crimin
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Congendo, Marco, and Fernando H. Lopes da Silva. Event-Related Potentials. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0039.

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Event-related potentials (ERPs) can be elicited by a variety of stimuli and events in diverse conditions. This chapter covers the methodology of analyzing and quantifying ERPs in general. Basic models (additive, phase modulation and resetting, potential asymmetry) that account for the generation of ERPs are discussed. The principles and requirements of ensemble time averaging are presented, along with several univariate and multivariate methods that have been proposed to improve the averaging procedure: wavelet decomposition and denoising, spatial, temporal and spatio-temporal filtering. We em
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Wikle, Christopher K. Spatial Statistics. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.710.

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The climate system consists of interactions between physical, biological, chemical, and human processes across a wide range of spatial and temporal scales. Characterizing the behavior of components of this system is crucial for scientists and decision makers. There is substantial uncertainty associated with observations of this system as well as our understanding of various system components and their interaction. Thus, inference and prediction in climate science should accommodate uncertainty in order to facilitate the decision-making process. Statistical science is designed to provide the to
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Pattison, George. The Whole Self. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198813507.003.0005.

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This chapter addresses the question as to how the Christian devout life is related to contemporary holistic spirituality, taking C. G. Jung as representative of holistic spirituality’s quest to balance the binary elements of the self. By way of contrast, Christian spirituality might seem to require the hierarchical subordination of one part of the self to another, reinforcing suspicions as to its essentially heteronomous nature. Nevertheless, the devout life can be shown to be a life involving the coordination of ‘body, mind, and spirit’. Where contemporary holism emphasizes the spatial balanc
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Van Hulle, Dirk. Genetic Criticism. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192846792.001.0001.

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As a method to study literary writing processes, genetic criticism is also a reading strategy. The idea behind this book is to introduce this strategy to a broader audience, from undergraduate students and interested readers to early career researchers and literary critics. A work of literature sometimes seems to hit a nerve, but it is more challenging to pinpoint exactly why it ‘works’. This book therefore starts from a basic principle: knowing how something was made can help us understand how and why it works. This strategy is at the basis of many disciplines, including art history. By means
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Maquet, Pierre, and Julien Fanielle. Neuroimaging in normal sleep and sleep disorders. Edited by Sudhansu Chokroverty, Luigi Ferini-Strambi, and Christopher Kennard. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682003.003.0011.

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Functional neuroimaging techniques include methods that probe various aspects of brain function and help derive models of brain organization in health and disease. These techniques can be grouped in two categories. Some are mainly based on electromagnetic signals (electroencephalography, magnetoencephalography), recording brain activity using a large number of sensors with exquisite temporal resolution (usually of the order of a kilohertz) but allowing only indirect characterization of three-dimensional brain activity by resorting to mathematical models. The second type includes different tech
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Book chapters on the topic "Spatial-temporal CNN"

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Sharma, Suchi, Manoj Diwakar, and Durgaprasad Gangodkar. "Person Re-Identification with CNN ResNet Spatial–Temporal Stream." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8329-8_59.

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Xu, Shulin, Nan Pu, Li Qian, and Guoqiang Xiao. "Combination of Pyramid CNN Representation and Spatial-Temporal Representation for Facial Expression Recognition." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7302-1_4.

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Smith, Aaron, James Atlas, and Ali Atharifard. "Correcting Charge Sharing Distortions in Photon Counting Detectors Utilising a Spatial-Temporal CNN." In Image and Vision Computing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25825-1_6.

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Sukhavasi, Vidyullatha, M. Shanmuga Sundari, K. S. Yamini Nithya, and Pragna Bairu. "Spatial Temporal Signatures: A Hybrid CNN-LSTM Architecture for Improved Sign Language Recognition." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-77029-6_2.

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Smith, Aaron, James Atlas, Ali Atharifard, et al. "Correcting Charge Sharing Distortions in Photon Counting Detectors Utilizing a Spatial-Temporal CNN." In Deep Learning for Advanced X-ray Detection and Imaging Applications. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-75653-5_7.

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Zhang, Chao, Pengfei Su, and Xiaopei Wu. "An Efficient CNN Network Utilizing Temporal and Spatial Attention Mechanisms for SSVEP Frequency Recognition." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-8173-0_28.

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Tao, Weijie, Jianlei Liu, and Zheng Li. "CNN-LSTM Networks Based Fault Diagnosis Using Spatial and Temporal Information for ZPW-2000A Track Circuit." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9243-0_50.

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Wei, Wenjing, Xiaoyi Jia, Yang Liu, and Xiaohui Yu. "Travel Time Forecasting with Combination of Spatial-Temporal and Time Shifting Correlation in CNN-LSTM Neural Network." In Web and Big Data. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96890-2_25.

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Soy, Aakansha, and Ahilya Dubey. "Using Feature Extraction Based on Spatial and Temporal Sequence using CNN Algorithm and Comparing the Accuracy with Novel RNN Algorithm for Automatic Facial Expression Recognition." In Recent Developments in Microbiology, Biotechnology and Pharmaceutical Sciences. CRC Press, 2025. https://doi.org/10.1201/9781003618140-70.

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Blake, Randolph, and Sang-Hun Lee. "Temporal Structure in the Input to Vision Can Promote Spatial Grouping." In Biologically Motivated Computer Vision. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45482-9_64.

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Conference papers on the topic "Spatial-temporal CNN"

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Jagruthi, Maddi, Ashima Sindhu Mohanty, and Deshmukh Nageshwar Rao. "Advanced Spatial-Temporal Framework for Deforestation Monitoring with CNN-LSTM Hybrid Architecture." In 2025 Devices for Integrated Circuit (DevIC). IEEE, 2025. https://doi.org/10.1109/devic63749.2025.11012585.

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Vallileka, N., G. Vinoth Rajkumar, R. Santhana Krishnan, S. Vijay Shankar, J. Relin Francis Raj, and M. Saravana Karthikeyan. "Hybrid CNN-LSTM Model for Enhanced Weather Forecasting: Leveraging Spatial and Temporal Dependencies." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933281.

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ZHAO, Jianing, and Qun JIN. "A Two-Stage Depression Recognition Model Based on Improved YOLOv5 and Spatial-Temporal CNN-Transformer Network." In 2024 IEEE Cyber Science and Technology Congress (CyberSciTech). IEEE, 2024. https://doi.org/10.1109/cyberscitech64112.2024.00058.

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Li, Mingai, and Xingqi Wang. "The EEG-based Automatic Epilepsy Detection Using Multi-scale Multi-level Temporal-Spatial Attention CNN model." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662652.

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Ye, Zhengdeng, Jing Chen, Run Li, Zhekai Tu, Peng Wu, and Qibo Yan. "Accurate forecasting method of new energy power in multi-temporal and spatial scales by CNN-BiLSTM." In Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), edited by Pierluigi Siano and Wenbing Zhao. SPIE, 2024. http://dx.doi.org/10.1117/12.3034059.

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Chang, Hao-Hsiang, Yu-Hua Chang, Yi-Lung Shih, Cheng-Hsun Lin, and Huang-Chia Shih. "Basketball Player Action Recognition and Tracking Using R(2+1)D CNN With Spatial-temporal Features." In 2024 IEEE 13th Global Conference on Consumer Electronics (GCCE). IEEE, 2024. https://doi.org/10.1109/gcce62371.2024.10760677.

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Mewada, Hiren, Jawad F. Al-Asad, Himanshu Patel, and Nayeemuddin Mohammed. "Leveraging Spatial and Temporal Features using CNN-LSTM for Improved Bone Fracture Classification from X-ray Images." In 2024 6th International Symposium on Advanced Electrical and Communication Technologies (ISAECT). IEEE, 2024. https://doi.org/10.1109/isaect64333.2024.10799900.

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Ye, Rongkun, Zhiqiang Lv, Zhihao Xu, and Jianbo Li. "MT-CNN: A Lightweight Spatial-Temporal Convolutional Neural Network for Deep Learning of Complex Trajectory Distributions based on Area Partitioning." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651261.

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Yu, Letian, Haoran Xue, Ruixiang Guo, et al. "Dirac mass induced by optical gain and loss." In Frontiers in Optics. Optica Publishing Group, 2024. https://doi.org/10.1364/fio.2024.jd4a.105.

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We employ a synthetic photonic mesh lattice to simulate relativistic Dirac particles. We show how non-Hermiticity can be used to manipulate the particle mass terms in both spatial and temporal regimes.
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Shu, Pengfeng, Ying Sun, Yifan Zhao, and Gangyan Xu. "Spatial-Temporal Taxi Demand Prediction Using LSTM-CNN." In 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). IEEE, 2020. http://dx.doi.org/10.1109/case48305.2020.9217007.

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Reports on the topic "Spatial-temporal CNN"

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Wilkins, Justin, Andrew McQueen, and Burton Suedel. Improving spatial and temporal monitoring of dredging operations incorporating unmanned technologies. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47520.

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The US Army Corps of Engineers (USACE) is responsible for maintaining safe and navigable waterways through the periodic dredging of shoaled sediment from federal navigation channels. While dredging, a portion of the bottom sediments become resuspended creating a sediment plume near the dredging operation. Suspension of sediments during dredging and dredged sediment disposal operations continues to be a primary concern of regulatory agencies charged with the protection of environmental resources. Consequently, almost all dredging projects incorporate some level of regulatory compliance monitori
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Malik, Arun, Andrea López, and Paul E. Carrillo. Pollution or Crime: The Effect of Driving Restrictions on Criminal Activity. Inter-American Development Bank, 2016. http://dx.doi.org/10.18235/0011747.

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Driving restriction programs have been implemented in many cities around the world to alleviate pollution and congestion problems. Enforcement of such programs is costly and can potentially displace policing resources used for crime prevention and crime detection. Hence, driving restrictions may increase crime. To test this hypothesis, this paper exploits both temporal and spatial variation in the implementation of Quito, Ecuador's Pico y Placa program and evaluates its effect on crime. Both difference-in-difference and spatial regression discontinuity estimates provide credible evidence that
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Farahbod, A., and J. F. Cassidy. Spatial and temporal variations in seismic coda Q attenuation in the lower St. Lawrence region, southeastern Quebec. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/332027.

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We investigate seismic attenuation characteristics of the Lower St. Lawrence seismic zone in southeastern Quebec. This zone is located ~400 km downstream from Quebec City and is between the Quebec North Shore and the Lower St. Lawrence. We used earthquake recordings from 5 broadband and 5 short period seismograph stations of the Canadian National Seismic Network (CNSN) across the region. Our dataset is comprised of 847 earthquakes recorded between 1985 and 2022 with magnitudes ranging from 2.0 to 5.1, depths from 0 to 30 km and epicentral distances of 5 to 100 km. This gives a total of 446 hig
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Dudley, J. P., and S. V. Samsonov. SAR interferometry with the RADARSAT Constellation Mission. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329396.

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The RADARSAT Constellation Mission (RCM) is Canada's latest system of C-band Synthetic Aperture Radar (SAR) Earth observation satellites. The system of three satellites, spaced equally in a common orbit, allows for a rapid four-day repeat interval. The RCM has been designed with a selection of stripmap, spotlight, and ScanSAR beam modes which offer varied combinations of spatial resolution and coverage. Using Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques, the growing archive of SAR data gathered by RCM can be used for change detection and ground deformation monitori
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Miller, Andy, Annalise Blum, and Paul Higgins. Opportunities for Forecast-Informed Water Resources Management. American Meteorological Society, 2018. https://doi.org/10.1175/forecast-informed-water-resource-management-2018.

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Greater use of weather and hydrologic forecasts can help us manage our nation’s water resources more effectively and minimize the risk of future extreme floods and droughts. Although forecasts have become more skilled at temporal and spatial scales relevant to water management, these forecasts are often underutilized in management decisions. Based on findings from an AMS workshop in April 2018, this study analyzes how the forecasting and water resource communities could work together to improve water resource management across the US.
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Newmark, Gregory L. Assessing GTFS Accuracy. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2017.

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The promised benefits of the General Transit Feed Specification (GTFS) Schedule and Realtime standards are dependent on the underlying quality of the data. Despite this fundamental reliance, there has been relatively little research on techniques and strategies to assess GTFS accuracy. The need for such assessment is growing as federal and state governments increasingly require transit agencies to make these data available to the public. This research fills this gap by presenting a suite of methods and metrics to assess the temporal accuracy of GTFS Realtime and the spatial accuracy of GTFS Sc
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Eshed, Yuval, and John Bowman. Harnessing Fine Scale Tuning of Endogenous Plant Regulatory Processes for Manipulation of Organ Growth. United States Department of Agriculture, 2005. http://dx.doi.org/10.32747/2005.7696519.bard.

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Background and objectives: Manipulation of plant organ growth is one of the primary reasons for the success of mankind allowing increasing amounts of food for human and livestock consumption. In contrast with the successful selection for desirable growth characteristics using plant breeding, transgenic manipulations with single genes has met limited success. While breeding is based on accumulation of many small alterations of growth, usually arise from slight changes in expression patterns, transgenic manipulations are primarily based on drastic, non-specific up-regulation or knock down of gen
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Brodie, Katherine, Ian Conery, Nicholas Cohn, Nicholas Spore, and Margaret Palmsten. Spatial variability of coastal foredune evolution, part A : timescales of months to years. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41322.

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Coastal foredunes are topographically high features that can reduce vulnerability to storm-related flooding hazards. While the dominant aeolian, hydrodynamic, and ecological processes leading to dune growth and erosion are fairly well-understood, predictive capabilities of spatial variations in dune evolution on management and engineering timescales (days to years) remain relatively poor. In this work, monthly high-resolution terrestrial lidar scans were used to quantify topographic and vegetation changes over a 2.5 year period along a micro-tidal intermediate beach and dune. Three-dimensional
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Nelson, Alex, Stanford A. Gibson, and Alex Sanchez. Development of a two-dimensional HEC-RAS sediment model for the Chippewa River, Wisconsin, for software development and sediment trend analysis. U.S. Army Engineer Research and Development Center, 2022. http://dx.doi.org/10.21079/11681/44561.

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This US Army Corps of Engineers (USACE) Regional Sediment Management technical note (RSM-TN) describes an RSM effort that converted a one-dimensional (1D) sediment transport model of the Chippewa River confluence with the Mississippi River into a two-dimensional (2D) model. This work leveraged recent sediment data collection and tested the new 2D sediment transport capabilities in the Hydrologic Engineering Center, River Analysis System (HEC-RAS) Version 6.0. In addition to the benefits of software testing, the resulting model developed through this effort can provide more accurate spatial and
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Mohammadian, Abolfazl, Ehsan Rahimi, Mohammadjavad Javadinasr, et al. Analyzing the Impacts of a Successful Diffusion of Shared E-Scooters and Other Micromobility Devices and Efficient Management Strategies for Successful Operations in Illinois. Illinois Center for Transportation, 2022. http://dx.doi.org/10.36501/0197-9191/22-006.

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Active transportation can play an important role in promoting more physically active and positive public health outcomes. While walking and biking provide significant physical health benefits, their modal share remains low. As a new form of micromobility service, shared e-scooters can enhance the suite of options available in cities to promote active transportation and fill in the gaps when walking or biking are not preferred. Although e-scooters show potential as a mode of transportation, it is unclear whether people will adopt the technology for everyday use. Furthermore, shared micromobilit
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