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Artykuły w czasopismach na temat "Stream graphs"
Ashrafi-Payaman, Nosratali, Mohammad Reza Kangavari i Amir Mohammad Fander. "A new method for graph stream summarization based on both the structure and concepts". Open Engineering 9, nr 1 (2.11.2019): 500–511. http://dx.doi.org/10.1515/eng-2019-0060.
Pełny tekst źródłaMalik, Avinash, i David Gregg. "Orchestrating stream graphs using model checking". ACM Transactions on Architecture and Code Optimization 10, nr 3 (16.09.2013): 1–25. http://dx.doi.org/10.1145/2512435.
Pełny tekst źródłaDu, Zhihui, Oliver Alvarado Rodriguez, Joseph Patchett i David A. Bader. "Interactive Graph Stream Analytics in Arkouda". Algorithms 14, nr 8 (21.07.2021): 221. http://dx.doi.org/10.3390/a14080221.
Pełny tekst źródłaWang, Ruize, Zhongyu Wei, Piji Li, Qi Zhang i Xuanjing Huang. "Storytelling from an Image Stream Using Scene Graphs". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 05 (3.04.2020): 9185–92. http://dx.doi.org/10.1609/aaai.v34i05.6455.
Pełny tekst źródłaNi, Xiang, Jing Li, Mo Yu, Wang Zhou i Kun-Lung Wu. "Generalizable Resource Allocation in Stream Processing via Deep Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 01 (3.04.2020): 857–64. http://dx.doi.org/10.1609/aaai.v34i01.5431.
Pełny tekst źródłaDeclerck, Philippe. "DETECTION OF CHANGES BY OBSERVER IN TIMED EVENT GRAPHS AND TIME STREAM EVENT GRAPHS". IFAC Proceedings Volumes 40, nr 6 (2007): 49–54. http://dx.doi.org/10.3182/20070613-3-fr-4909.00011.
Pełny tekst źródłaLlaves, Alejandro, Oscar Corcho, Peter Taylor i Kerry Taylor. "Enabling RDF Stream Processing for Sensor Data Management in the Environmental Domain". International Journal on Semantic Web and Information Systems 12, nr 4 (październik 2016): 1–21. http://dx.doi.org/10.4018/ijswis.2016100101.
Pełny tekst źródłaMathieu, Claire, i Michel de Rougemont. "Large very dense subgraphs in a stream of edges". Network Science 9, nr 4 (grudzień 2021): 403–24. http://dx.doi.org/10.1017/nws.2021.17.
Pełny tekst źródłaAjwani, Deepak, Shoukat Ali, Kostas Katrinis, Cheng-Hong Li, Alfred J. Park, John P. Morrison i Eugen Schenfeld. "Generating synthetic task graphs for simulating stream computing systems". Journal of Parallel and Distributed Computing 73, nr 10 (październik 2013): 1362–74. http://dx.doi.org/10.1016/j.jpdc.2013.06.002.
Pełny tekst źródłaLi, Yan, Tingjian Ge i Cindy Chen. "Data stream event prediction based on timing knowledge and state transitions". Proceedings of the VLDB Endowment 13, nr 10 (czerwiec 2020): 1779–92. http://dx.doi.org/10.14778/3401960.3401973.
Pełny tekst źródłaRozprawy doktorskie na temat "Stream graphs"
Gillani, Syed. "Semantically-enabled stream processing and complex event processing over RDF graph streams". Thesis, Lyon, 2016. http://www.theses.fr/2016LYSES055/document.
Pełny tekst źródłaThere is a paradigm shift in the nature and processing means of today’s data: data are used to being mostly static and stored in large databases to be queried. Today, with the advent of new applications and means of collecting data, most applications on the Web and in enterprises produce data in a continuous manner under the form of streams. Thus, the users of these applications expect to process a large volume of data with fresh low latency results. This has resulted in the introduction of Data Stream Processing Systems (DSMSs) and a Complex Event Processing (CEP) paradigm – both with distinctive aims: DSMSs are mostly employed to process traditional query operators (mostly stateless), while CEP systems focus on temporal pattern matching (stateful operators) to detect changes in the data that can be thought of as events. In the past decade or so, a number of scalable and performance intensive DSMSs and CEP systems have been proposed. Most of them, however, are based on the relational data models – which begs the question for the support of heterogeneous data sources, i.e., variety of the data. Work in RDF stream processing (RSP) systems partly addresses the challenge of variety by promoting the RDF data model. Nonetheless, challenges like volume and velocity are overlooked by existing approaches. These challenges require customised optimisations which consider RDF as a first class citizen and scale the processof continuous graph pattern matching. To gain insights into these problems, this thesis focuses on developing scalable RDF graph stream processing, and semantically-enabled CEP systems (i.e., Semantic Complex Event Processing, SCEP). In addition to our optimised algorithmic and data structure methodologies, we also contribute to the design of a new query language for SCEP. Our contributions in these two fields are as follows: • RDF Graph Stream Processing. We first propose an RDF graph stream model, where each data item/event within streams is comprised of an RDF graph (a set of RDF triples). Second, we implement customised indexing techniques and data structures to continuously process RDF graph streams in an incremental manner. • Semantic Complex Event Processing. We extend the idea of RDF graph stream processing to enable SCEP over such RDF graph streams, i.e., temporalpattern matching. Our first contribution in this context is to provide a new querylanguage that encompasses the RDF graph stream model and employs a set of expressive temporal operators such as sequencing, kleene-+, negation, optional,conjunction, disjunction and event selection strategies. Based on this, we implement a scalable system that employs a non-deterministic finite automata model to evaluate these operators in an optimised manner. We leverage techniques from diverse fields, such as relational query optimisations, incremental query processing, sensor and social networks in order to solve real-world problems. We have applied our proposed techniques to a wide range of real-world and synthetic datasets to extract the knowledge from RDF structured data in motion. Our experimental evaluations confirm our theoretical insights, and demonstrate the viability of our proposed methods
Rannou, Léo. "Temporal Connectivity and Path Computation for Stream Graph". Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS418.
Pełny tekst źródłaFor a long time, structured data and temporal data have been analysed separately. Many real world complex networks have a temporal dimension, such as contacts between individuals or financial transactions. Graph theory provides a wide set of tools to model and analyze static connections between entities. Unfortunately, this approach does not take into account the temporal nature of interactions. Stream graph theory is a formalism to model highly dynamic networks in which nodes and/or links arrive and/or leave over time. The number of applications of stream graph theory has risen rapidly, along with the number of theoretical concepts and algorithms to compute them. Several theoretical concepts such as connected components and temporal paths in stream graphs were defined recently, but no algorithm was provided to compute them. Moreover, the algorithmic complexities of these problems are unknown, as well as the insight they may shed on real-world stream graphs of interest. In this thesis, we present several solutions to compute notions of connectivity and path concepts in stream graphs. We also present alternative representations - data structures designed to facilitate specific computations - of stream graphs. We provide implementations and experimentally compare our methods in a wide range of practical cases. We show that these concepts indeed give much insight on features of large-scale datasets. Straph, a python library, was developed in order to have a reliable library for manipulating, analysing and visualising stream graphs, to design algorithms and models, and to rapidly evaluate them
Faleiros, Thiago de Paulo. "Propagação em grafos bipartidos para extração de tópicos em fluxo de documentos textuais". Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-10112016-105854/.
Pełny tekst źródłaHandling large amounts of data is a requirement for modern text mining algorithms. For some applications, documents are published constantly, which demand a high cost for long-term storage. So it is necessary easily adaptable methods for an approach that considers documents flow, and be capable of analyzing the data in one step without requiring the high cost of storage. Another requirement is that this approach can exploit heuristics in order to improve the quality of results. Several models for automatic extraction of latent information in a collection of documents have been proposed in the literature, among them probabilistic topic models are prominent. Probabilistic topic models achieve good practical results, and have been extended to several models with different types of information included. However, properly describe these models, derive them, and then get appropriate inference algorithms are difficult tasks, requiring a rigorous mathematical treatment for descriptions of operations performed in the latent dimensions discovery process. Thus, for the development of a simple and efficient method to tackle the problem of latent dimensions discovery, a proper representation of the data is required. The hypothesis of this thesis is that by using bipartite graph for representation of textual data one can address the task of latent patterns discovery, present in the relationships between documents and words, in a simple and intuitive way. For validation of this hypothesis, we have developed a framework based on label propagation algorithm using the bipartite graph representation. The framework, called PBG (Propagation in Bipartite Graph) was initially applied to the unsupervised context for a static collection of documents. Then a semi-supervised version was proposed which need only a small amount of labeled documents to the transductive classification task. Finally, it was applied in the dynamic context in which flow of textual data was considered. Comparative analyzes were performed, and the results indicated that the PBG is a viable and competitive alternative for tasks in the unsupervised and semi-supervised contexts.
Arnoux, Thibaud. "Prédiction d'interactions dans les flots de liens. Combiner les caractéristiques structurelles et temporelles". Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS229.
Pełny tekst źródłaThe link stream formalism represent an approach allowing to capture the system dynamic while providing a framework to understand the system's behavior. A link stream is a sequence of triplet (t,u,v) indicating that an interaction occurred between u and v at time t. The importance of the system's dynamic during the prediction places it at the crossroads of link prediction in graphs and time series prediction. We will explore several formalizations of the problem of prediction in link streams. In the following we will study the activity prediction, that is to say predicting the number of interactions occurring in the future between each pair of nodes during a given period. We introduce the protocol, allowing to combine the data characteristics to predict the activity. We study the behavior of our protocol during several experiments on four datasets et evaluate the prediction quality. We will look at how the introduction of pair of nodes classes allows to preserve the link diversity in the prediction while improving the prediction. Our goal is to define a general prediction framework allowing in-depth studies of the relationship between temporal and structural characteristics in prediction tasks
Baudin, Alexis. "Cliques statiques et temporelles : algorithmes d'énumération et de détection de communautés". Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS609.
Pełny tekst źródłaGraphs are mathematical objects used to model interactions or connections between entities of various types. A graph can represent, for example, a social network that connects users to each other, a transport network like the metro where stations are connected to each other, or a brain with the billions of interacting neurons it contains. In recent years, the dynamic nature of these structures has been highlighted, as well as the importance of taking into account the temporal evolution of these networks to understand their functioning. While many concepts and algorithms have been developed on graphs to describe static network structures, much remains to be done to formalize and develop relevant algorithms to describe the dynamics of real networks. This thesis aims to better understand how massive graphs are structured in the real world, and to develop tools to extend our understanding to structures that evolve over time. It has been shown that these graphs have particular properties, which distinguish them from theoretical or randomly drawn graphs. Exploiting these properties then enables the design of algorithms to solve certain difficult problems much more quickly on these instances than in the general case. My PhD thesis focuses on cliques, which are groups of elements that are all connected to each other. We study the enumeration of cliques in static and temporal graphs and the detection of communities they enable. The communities of a graph are sets of vertices such that, within a community, the vertices interact strongly with each other, and little with the rest of the graph. Their study helps to understand the structural and functional properties of networks. We are evaluating our algorithms on massive real-world graphs, opening up new perspectives for understanding interactions within these networks. We first work on graphs, without taking into account the temporal component of interactions. We begin by using the clique percolation method of community detection, highlighting its limitations in memory, which prevent it from being applied to graphs that are too massive. By introducing an approximate problem-solving algorithm, we overcome this limitation. Next, we improve the enumeration of maximal cliques in the case of bipartite graphs. These correspond to interactions between groups of vertices of different types, e.g. links between people and viewed content, participation in events, etc. Next, we consider interactions that take place over time, using the link stream formalism. We seek to extend the algorithms presented in the first part, to exploit their advantages in the study of temporal interactions. We provide a new algorithm for enumerating maximal cliques in link streams, which is much more efficient than the state-of-the-art on massive datasets. Finally, we focus on communities in link streams by clique percolation, developing an extension of the method used on graphs. The results show a significant improvement over the state of the art, and we analyze the communities obtained to provide relevant information on the organization of temporal interactions in link streams. My PhD work has provided new insights into the study of massive real-world networks. This shows the importance of exploring the potential of graphs in a real-world context, and could contribute to the emergence of innovative solutions for the complex challenges of our modern society
Wang, Changliang. "Continuous subgraph pattern search over graph streams /". View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20WANG.
Pełny tekst źródłaNavarin, Nicolò <1984>. "Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction". Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amsdottorato.unibo.it/6578/1/navarin_nicolo_tesi.pdf.
Pełny tekst źródłaNavarin, Nicolò <1984>. "Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction". Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amsdottorato.unibo.it/6578/.
Pełny tekst źródłaReyes, Juan C. (Juan Carlos) 1980. "A graph editing framework for the StreamIt language". Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/17980.
Pełny tekst źródłaIncludes bibliographical references (leaves 55-56).
A programming language is more useful if it provides a level of abstraction that makes programming more intuitive and also allows the development of tools that take advantage of the language's internal representation. StreamIt, a language for the development of streaming applications, has a hierarchical and structural nature that lends itself to a graphical programming tool. I created a prototype StreamIt Graph Editor (SGE) to facilitate the development of streaming applications using StreamIt. The SGE provides intuitive visualization tools that allow developers to work more efficiently by automating certain processes. Thus, the programmer can focus more on design issues than on low level details that slow down the development process.
by Juan C. Reyes.
M.Eng.
Karczmarek, Michal 1977. "Constrained and phased scheduling of synchronous data flow graphs for StreamIt language". Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87333.
Pełny tekst źródłaIncludes bibliographical references (p. 107-109).
by Michal Karczmarek.
S.M.
Książki na temat "Stream graphs"
Lees, Timothy. On context stream tuples and higher-order context flow graphs. Edinburgh: University of Edinburgh Department ofComputer Science, 1990.
Znajdź pełny tekst źródłaKirk, Andy. Stream Graph. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2016. http://dx.doi.org/10.4135/9781529777154.
Pełny tekst źródłaLees, Timothy. On context streams and the boundaries of context flow graphs. Edinburgh: University of Edinburgh Department of Computer Science, 1990.
Znajdź pełny tekst źródłaKoivunen-Niemi, Laura. Learn to Create a Stream Graph in R With Data From Our World in Data (2018). 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2021. http://dx.doi.org/10.4135/9781529772005.
Pełny tekst źródłaHart, Susan J. Entering adulthood.: A curriculum for grades 9-12. Santa Cruz, CA: Network Publications, 1990.
Znajdź pełny tekst źródłaHart, Susan J. Entering adulthood.: A curriculum for grades 9-12. Santa Cruz, CA: Network Publications, 1990.
Znajdź pełny tekst źródłaGraphiscape - New York. Crans-Près-Céligny: RotoVision, 2003.
Znajdź pełny tekst źródłaChild, Nancy Fraser. A comparison of the English language and reading achievement of French immersion students with transfer and English stream students (grades 3-6). Regina, Sask: Research Centre, Saskatchewan School Trustees Association, 1987.
Znajdź pełny tekst źródłaDianne, Schilling, red. Less student stress, more school success: Strategies and activities for creating optimal learning environments, grades K-12. Austin, Tex: Pro-Ed, 2010.
Znajdź pełny tekst źródłaCastellucci, Cecil. The plain Janes. New York, N.Y: DC Comics, 2007.
Znajdź pełny tekst źródłaCzęści książek na temat "Stream graphs"
Ganguly, Sumit. "Data Stream Algorithms via Expander Graphs". W Algorithms and Computation, 52–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-92182-0_8.
Pełny tekst źródłaSchiller, Benjamin, Sven Jager, Kay Hamacher i Thorsten Strufe. "StreaM - A Stream-Based Algorithm for Counting Motifs in Dynamic Graphs". W Algorithms for Computational Biology, 53–67. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21233-3_5.
Pełny tekst źródłaParmentier, P., T. Viard, B. Renoust i J. F. Baffier. "Introducing Multilayer Stream Graphs and Layer Centralities". W Complex Networks and Their Applications VIII, 684–96. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36683-4_55.
Pełny tekst źródłaRutten, J. J. M. M. "An Application of Stream Calculus to Signal Flow Graphs". W Formal Methods for Components and Objects, 276–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30101-1_13.
Pełny tekst źródłaRannou, Léo, Clémence Magnien i Matthieu Latapy. "Strongly Connected Components in Stream Graphs: Computation and Experimentations". W Complex Networks & Their Applications IX, 568–80. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65347-7_47.
Pełny tekst źródłaNguyen, Quan, Peter Eades i Seok-Hee Hong. "StreamEB: Stream Edge Bundling." W Graph Drawing, 400–413. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36763-2_36.
Pełny tekst źródłaLatapy, Matthieu, Clémence Magnien i Tiphaine Viard. "Weighted, Bipartite, or Directed Stream Graphs for the Modeling of Temporal Networks". W Computational Social Sciences, 49–64. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23495-9_3.
Pełny tekst źródłaLatapy, Matthieu, Clémence Magnien i Tiphaine Viard. "Weighted, Bipartite, or Directed Stream Graphs for the Modeling of Temporal Networks". W Computational Social Sciences, 49–64. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-30399-9_3.
Pełny tekst źródłaRaab, Marius, Mark Wernsdorfer, Emanuel Kitzelmann i Ute Schmid. "From Sensorimotor Graphs to Rules: An Agent Learns from a Stream of Experience". W Artificial General Intelligence, 333–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22887-2_39.
Pełny tekst źródłaGoh, Alwyn, G. S. Poh i David C. L. Ngo. "Loss-Tolerant Stream Authentication via Configurable Integration of One-Time Signatures and Hash-Graphs". W Communications and Multimedia Security. Advanced Techniques for Network and Data Protection, 239–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45184-6_20.
Pełny tekst źródłaStreszczenia konferencji na temat "Stream graphs"
Shirui Pan, Xingquan Zhu, Chengqi Zhang i P. S. Yu. "Graph stream classification using labeled and unlabeled graphs". W 2013 29th IEEE International Conference on Data Engineering (ICDE 2013). IEEE, 2013. http://dx.doi.org/10.1109/icde.2013.6544842.
Pełny tekst źródłaKakkad, Vasvi, Andrew E. Santosa i Bernhard Scholz. "Migrating operator placement for compositional stream graphs". W the 15th ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2387238.2387261.
Pełny tekst źródłaAmma, Keigo, Shunsuke Wada, Kanto Nakayama, Yuki Akamatsu, Yuichi Yaguchi i Keitaro Naruse. "Visualization of spread of topic words on Twitter using stream graphs and relational graphs". W 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS). IEEE, 2014. http://dx.doi.org/10.1109/scis-isis.2014.7044759.
Pełny tekst źródłaAmini, Lisa, Jorge Lepre i Martin Kienzle. "Distributed stream control for self-managing media processing graphs". W the seventh ACM international conference. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/319878.319905.
Pełny tekst źródłaChavez, Nidia Yadira Vaquera, i Trilce Estrada. "PASCAL-G: a Probabilistic Stream Clustering Analysis on Graphs". W 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671750.
Pełny tekst źródłaNguyen, Dong, i Jongeun Lee. "Communication-aware mapping of stream graphs for multi-GPU platforms". W CGO '16: 14th Annual IEEE/ACM International Symposium on Code Generation and Optimization. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2854038.2854055.
Pełny tekst źródłaHu, Binbin, Zhengwei Wu, Jun Zhou, Ziqi Liu, Zhigang Huangfu, Zhiqiang Zhang i Chaochao Chen. "MERIT: Learning Multi-level Representations on Temporal Graphs". W Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/288.
Pełny tekst źródłaLee, Geon, Minyoung Choe i Kijung Shin. "HashNWalk: Hash and Random Walk Based Anomaly Detection in Hyperedge Streams". W Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/296.
Pełny tekst źródłaAkili, Samira, i Matthias Weidlich. "MuSE Graphs for Flexible Distribution of Event Stream Processing in Networks". W SIGMOD/PODS '21: International Conference on Management of Data. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3448016.3457318.
Pełny tekst źródłaMiddendorf, Lars, i Christian Haubelt. "Scheduling of Recursive and Dynamic Data-Flow Graphs Using Stream Rewriting". W 2014 International Symposium on Computer Architecture and High Performance Computing Workshop (SBAC-PADW). IEEE, 2014. http://dx.doi.org/10.1109/sbac-padw.2014.7.
Pełny tekst źródłaRaporty organizacyjne na temat "Stream graphs"
Tercek, Michael. Climate monitoring in the Mediterranean Coast Network 2020: Cabrillo National Monument. National Park Service, wrzesień 2022. http://dx.doi.org/10.36967/2294406.
Pełny tekst źródłaTercek, Michael. Climate monitoring in the Mediterranean Coast Network 2020: Santa Monica Mountains National Recreation Area. National Park Service, wrzesień 2022. http://dx.doi.org/10.36967/2294435.
Pełny tekst źródłaFait, Aaron, Grant Cramer i Avichai Perl. Towards improved grape nutrition and defense: The regulation of stilbene metabolism under drought. United States Department of Agriculture, maj 2014. http://dx.doi.org/10.32747/2014.7594398.bard.
Pełny tekst źródłaMikhaleva, E., E. Babikova, G. Bezhashvili, M. Ilina i I. Samkova. VALUE STREAM PROGRAM. Sverdlovsk Regional Medical College, grudzień 2022. http://dx.doi.org/10.12731/er0618.03122022.
Pełny tekst źródłaBeckett-Brown, C. E., A. M. McDonald i M. B. McClenaghan. Discovering a porphyry deposit using tourmaline: a case study from Yukon. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331349.
Pełny tekst źródłaWerne, R. The Contact Stress Sensor _ Proposal to the FY2020 Technology Tech Mat Grants Program. Office of Scientific and Technical Information (OSTI), luty 2020. http://dx.doi.org/10.2172/1598958.
Pełny tekst źródłaHolub, Oleksandr, Mykhailo Moiseienko i Natalia Moiseienko. Fluid Flow Modelling in Houdini. [б. в.], listopad 2020. http://dx.doi.org/10.31812/123456789/4128.
Pełny tekst źródłaSchwarte, Kirk A., i James R. Russell. Grazing Management Effects on the Sward and Physical Characteristics Relative to Streams in Cool-Season Grass Pastures. Ames (Iowa): Iowa State University, styczeń 2009. http://dx.doi.org/10.31274/ans_air-180814-71.
Pełny tekst źródłaLight. L52011 Development of Fieldable Magnet and Digital MIVC Stress Measurement Techniques. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), kwiecień 2002. http://dx.doi.org/10.55274/r0010147.
Pełny tekst źródłaFiron, Nurit, Prem Chourey, Etan Pressman, Allen Hartwell i Kenneth J. Boote. Molecular Identification and Characterization of Heat-Stress-Responsive Microgametogenesis Genes in Tomato and Sorghum - A Feasibility Study. United States Department of Agriculture, październik 2007. http://dx.doi.org/10.32747/2007.7591741.bard.
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