Relevant bibliographies by topics / Network sequences

Contents

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

Academic literature on the topic 'Network sequences'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Network sequences"

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Byrnes, Sean, Anthony N. Burkitt, David B. Grayden, and Hamish Meffin. "Learning a Sparse Code for Temporal Sequences Using STDP and Sequence Compression." Neural Computation 23, no. 10 (2011): 2567–98. http://dx.doi.org/10.1162/neco_a_00184.

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A spiking neural network that learns temporal sequences is described. A sparse code in which individual neurons represent sequences and subsequences enables multiple sequences to be stored without interference. The network is founded on a model of sequence compression in the hippocampus that is robust to variation in sequence element duration and well suited to learn sequences through spike-timing dependent plasticity (STDP). Three additions to the sequence compression model underlie the sparse representation: synapses connecting the neurons of the network that are subject to STDP, a competiti
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Wei, Fang-ping, Sheng Li, and Hong-ru Ma. "Network of tRNA gene sequences." Journal of Shanghai Jiaotong University (Science) 13, no. 5 (2008): 611–16. http://dx.doi.org/10.1007/s12204-008-0611-9.

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Nishitani, Yoshi, Chie Hosokawa, Yuko Mizuno-Matsumoto, Tomomitsu Miyoshi, Hajime Sawai, and Shinichi Tamura. "Detection of M-Sequences from Spike Sequence in Neuronal Networks." Computational Intelligence and Neuroscience 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/862579.

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In circuit theory, it is well known that a linear feedback shift register (LFSR) circuit generates pseudorandom bit sequences (PRBS), including an M-sequence with the maximum period of length. In this study, we tried to detect M-sequences known as a pseudorandom sequence generated by the LFSR circuit from time series patterns of stimulated action potentials. Stimulated action potentials were recorded from dissociated cultures of hippocampal neurons grown on a multielectrode array. We could find several M-sequences from a 3-stage LFSR circuit (M3). These results show the possibility of assembli
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Rocco, Claudio M., Kash Barker, Jose Moronta, and Jose E. Ramirez-Marquez. "Community detection and resilience in multi-source, multi-terminal networks." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 232, no. 6 (2018): 616–26. http://dx.doi.org/10.1177/1748006x17751516.

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Many networks, particularly infrastructure networks, have multiple source nodes and multiple terminal nodes. And many such networks exhibit community structures, wherein the network is partitioned into groups of densely connected nodes with sparse connections between groups, based on topology or spatial characteristics, among others. This article proposes an approach for evaluating the effects of disruptive events, or the disconnection of network components due to failures or attacks, to the community structures and to the total network. The approach enables the assessment of resilience, evalu
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Allaby, Robin G., and Terence A. Brown. "Network Analysis Provides Insights Into Evolution of 5S rDNA Arrays in Triticum and Aegilops." Genetics 157, no. 3 (2001): 1331–41. http://dx.doi.org/10.1093/genetics/157.3.1331.

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Abstract We have used network analysis to study gene sequences of the Triticum and Aegilops 5S rDNA arrays, as well as the spacers of the 5S-DNA-A1 and 5S-DNA-2 loci. Network analysis describes relationships between 5S rDNA sequences in a more realistic fashion than conventional tree building because it makes fewer assumptions about the direction of evolution, the extent of sexual isolation, and the pattern of ancestry and descent. The networks show that the 5S rDNA sequences of Triticum and Aegilops species are related in a reticulate manner around principal nodal sequences. The spacer networ
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Ren, Zhuo-Ming, Xiao Pan, and Yi-Cheng Zhang. "Significance of the Nested Structure in Multiplex World Trade Networks." Complexity 2020 (December 9, 2020): 1–9. http://dx.doi.org/10.1155/2020/8827840.

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The hierarchically nested structure is widely observed in a broad range of real systems, encompassing ecological networks, economic and trade networks, communication networks, among many others. However, there remain statistical challenges of the prevalence of nestedness. In response to this problem, we focus on the effect of incomplete information and the inputted matrix size, the role of network density and degree sequences, and the relevance of degree-degree correlation to conduct systematic research on the significance of the nested structure according to multiplex world trade networks. Fi
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Mathur, Rinku, and Neeru Adlakha. "Binary sequences-based approach for construction of evolutionary network." International Journal of Biomathematics 07, no. 02 (2014): 1450012. http://dx.doi.org/10.1142/s1793524514500120.

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Evolutionary studies have been of prime importance to life scientists since ancient times. The advancements in technology has made it possible to make available the massive amounts of genomic data. The abundance of genomic data poses new challenges for biologists, computer scientists and mathematicians to develop approaches for discovery of new relationships in data and evolutionary networks. In this work, nucleotide sequences are converted into binary sequences to explore the network among different species. A new approach based on binary sequences has been proposed to reconstruct the accurat
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Azha Javed and Muhammad Javed Iqbal. "Classification of Biological Data using Deep Learning Technique." NUML International Journal of Engineering and Computing 1, no. 1 (2022): 13–26. http://dx.doi.org/10.52015/nijec.v1i1.10.

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A huge amount of newly sequenced proteins is being discovered on daily basis. The mainconcern is how to extract the useful characteristics of sequences as the input features for thenetwork. These sequences are increasing exponentially over the decades. However, it is veryexpensive to characterize functions for biological experiments and also, it is really necessaryto find the association between the information of datasets to create and improve medicaltools. Recently machine learning algorithms got huge attention and are widely used. Thesealgorithms are based on deep learning architecture and
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MORITA, Kenta, Haruhiko TAKASE, Hiroharu KAWANAKA, and Naoki MORITA. "Extraction of Frequent Sub-Sequences from Long Sequence Using Neural Network." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 31, no. 1 (2019): 592–96. http://dx.doi.org/10.3156/jsoft.31.1_592.

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Weiss, Michael, Henrike Hultsch, Iris Adam, Constance Scharff, and Silke Kipper. "The use of network analysis to study complex animal communication systems: a study on nightingale song." Proceedings of the Royal Society B: Biological Sciences 281, no. 1785 (2014): 20140460. http://dx.doi.org/10.1098/rspb.2014.0460.

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The singing of song birds can form complex signal systems comprised of numerous subunits sung with distinct combinatorial properties that have been described as syntax-like. This complexity has inspired inquiries into similarities of bird song to human language; but the quantitative analysis and description of song sequences is a challenging task. In this study, we analysed song sequences of common nightingales ( Luscinia megarhynchos ) by means of a network analysis. We translated long nocturnal song sequences into networks of song types with song transitions as connectors. As network measure
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Dissertations / Theses on the topic "Network sequences"

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Bazargan-Harandi, Hamid. "Neural network based simulation of sea-state sequences." Thesis, Brunel University, 2006. http://bura.brunel.ac.uk/handle/2438/379.

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The present PhD study, in its first part, uses artificial neural networks (ANNs), an optimization technique called simulated annealing, and statistics to simulate the significant wave height (Hs) and mean zero-up-crossing period ( ) of 3-hourly sea-states of a location in the North East Pacific using a proposed distribution called hepta-parameter spline distribution for the conditional distribution of Hs or given some inputs. Two different seven- network sets of ANNs for the simulation and prediction of Hs and were trained using 20-year observed Hs’s and ’s. The preceding Hs’s and ’s were the
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Gittens, Shaun. "Neural network generation of temporal sequences from single static vector inputs using varying length distal target sequences." College Park, Md. : University of Maryland, 2007. http://hdl.handle.net/1903/6710.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.<br>Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Chakrabarti, Saikat. "EFFICIENT AND SCALABLE NETWORK SECURITY PROTOCOLS BASED ON LFSR SEQUENCES." UKnowledge, 2008. http://uknowledge.uky.edu/gradschool_diss/640.

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The gap between abstract, mathematics-oriented research in cryptography and the engineering approach of designing practical, network security protocols is widening. Network researchers experiment with well-known cryptographic protocols suitable for different network models. On the other hand, researchers inclined toward theory often design cryptographic schemes without considering the practical network constraints. The goal of this dissertation is to address problems in these two challenging areas: building bridges between practical network security protocols and theoretical cryptography. This
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LÖFROTH, BJÖRN. "Mobile traffic dataset comparisons throughcluster analysis of radio network event sequences." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-153914.

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Ericsson regularly collects traffic datasets from different radio networks around the world. These data sets can be used for several research purposes, ranging from general statistics to more specific studies such as system troubleshooting and buffer-level analysis. Currently, a researcher may find it difficult to assess if a certain dataset is useful for aparticular investigation, since there exists no easily accessible overview of the properties of the different data sets.This thesis project aims to make it easier to compare the existing traffic datasets in terms of general statistics, user
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Mondani, Hernan. "Modeling Organizational Dynamics : Distributions, Networks, Sequences and Mechanisms." Doctoral thesis, Stockholms universitet, Sociologiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-139766.

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The study of how social organizations work, change and develop is central to sociology and to our understanding of the social world and its transformations. At the same time, the underlying principles of organizational dynamics are extremely difficult to investigate. This is partly due to the difficulties of tracking organizations, individuals and their interactions over relatively long periods of time. But it is also due to limitations in the kinds of quantitative methods used to tackle these questions, which are for the most part based on regression analysis. This thesis seeks to improve our
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Olofsson, Lars-Gunnar, and Jacob Hellman. "Detecting changes in UERC switches : A sequence analysis of UERC switches in a mobile network." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-227232.

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This thesis investigates the possibility to analyse a mobile network with sequences of UERC switches specific to each user equipment. An UERC is essentially a channel that carries information and a user equipment connects to different UERCs depending on whether they want to talk and/or send data with different qualities. As a major player in the mobile technology industry, Ericsson strives to optimise the use of the UERCs and are looking for an automated way to detect changes. The first task was to identify and retrieve the required events from the network log files in order to create the UERC
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Bhuiyan, Md Hasanuzzaman. "Parallel Algorithms for Switching Edges and Generating Random Graphs from Given Degree Sequences using HPC Platforms." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/80299.

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Networks (or graphs) are an effective abstraction for representing many real-world complex systems. Analyzing various structural properties of and dynamics on such networks reveal valuable insights about the behavior of such systems. In today's data-rich world, we are deluged by the massive amount of heterogeneous data from various sources, such as the web, infrastructure, and online social media. Analyzing this huge amount of data may take a prohibitively long time and even may not fit into the main memory of a single processing unit, thus motivating the necessity of efficient parallel algori
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Chenkov, Nikolay. "Network mechanisms underlying sharp wave ripples and memory replay." Doctoral thesis, Humboldt-Universität zu Berlin, 2017. http://dx.doi.org/10.18452/18491.

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Komplexe Muster neuronaler Aktivität entstehen während der Sharp-wave Ripples (SWRs) im Hippocampus und während der Up States im Neokortex (Zuständen mit hoher Aktivität). Sequenzen von Verhalten, die in der Vergangenheit erlebt wurden, werden während des komplexen Musters abgespielt. Die zugrunde liegenden Mechanismen sind nicht gründlich erforscht: Wie können kleine synaptische Veränderungen die großflächige Netzwerkaktivität während des Gedächtnisabrufes und der Gedächtniskonsolidierung kontrollieren? Im ersten Teil dieser Abhandlung wird die Hypothese aufgestellt, dass eine schwac
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Silva, Vinícius de Oliveira. "Human action recognition in image sequences based on a two-stream convolutional neural network classifier." reponame:Repositório Institucional da UnB, 2017. http://repositorio.unb.br/handle/10482/25201.

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Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2017.<br>Submitted by Priscilla Sousa (priscillasousa@bce.unb.br) on 2017-10-31T12:50:55Z No. of bitstreams: 1 2017_ViníciusdeOliveiraSilva.pdf: 12072182 bytes, checksum: 8abe6dbc46ba985620334c2ebac9ea8a (MD5)<br>Approved for entry into archive by Raquel Viana (raquelviana@bce.unb.br) on 2017-11-07T14:09:45Z (GMT) No. of bitstreams: 1 2017_ViníciusdeOliveiraSilva.pdf: 12072182 bytes, checksum: 8abe6dbc46ba985620334c2ebac9ea8a (MD5)<br>Made available in DSpace on 2017-11-07T14:09:45Z (
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Butts, John Charles. "Tracking the Sequences of Regulatory Linkages and Their Evolution Within a Fruit Fly Gene Regulatory Network." University of Dayton / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1352918524.

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Books on the topic "Network sequences"

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Leader, Jeffery J. Neural network identification of keystream generators. Naval Postgraduate School, 1993.

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1951-, Sun Hsiao, ed. Protocol conformance testing using unique input/output sequences. World Scientific, 1997.

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Hegde, Malati. An experience in using UIO sequences and rural Chinese postman tours for conformance testing of a Q.931 implementation. Dept. of Electrical Communication Engineering, Indian Institute of Science, 1992.

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Graves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24797-2.

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Graves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Springer Berlin Heidelberg, 2012.

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Laan, Dinard van der. The structure and performance of optimal routing sequences. Universiteit Leiden, 2003.

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U, Bastolla, ed. Structural approaches to sequence evolution: Molecules, networks, populations. Springer, 2007.

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Lipo, Wang, and Alkon Daniel L, eds. Artificial neural networks: Oscillations, chaos, and sequence processing. IEEE Computer Society Press, 1993.

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Bozward, David Victor. Capacity improvements for a direct-sequence code division multiple access network. Aston University. Department of Electrical Engineering and Applied Physics, 1995.

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Frishman, Dmitrij. Modern genome annotation: The BioSapiens Network. Springer, 2009.

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Book chapters on the topic "Network sequences"

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Balaniuk, Remis, and Philippe O. Navaux. "Using Local Minima as Sequences Memories." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_159.

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McCulloch, Neil. "Recurrent Networks for Learning Stochastic Sequences." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_177.

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Chen, Mon-Song, Yanghee Choi, and Aaron Kershenbaum. "Minimal Length Test Sequences for Protocol Conformance." In Network Management and Control. Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-1471-4_24.

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Tsoi, Ah Chung. "Recurrent neural network architectures: An overview." In Adaptive Processing of Sequences and Data Structures. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0053993.

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Nenov, Valeriy I. "Rapid Learning of Pattern Sequences: A Novel Network Model." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_131.

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Ravinder Reddy, R., K. Ayyappa Reddy, C. Madan Kumar, and Y. Ramadevi. "Detection of Network Anomaly Sequences Using Deep Recurrent Neural Networks." In Smart Computing Techniques and Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1502-3_60.

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Sun, G. Z., C. L. Giles, and H. H. Chen. "The neural network pushdown automaton: Architecture, dynamics and training." In Adaptive Processing of Sequences and Data Structures. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0054003.

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Wan, Eric A., and Françoise Beaufays. "Diagrammatic methods for deriving and relating temporal neural network algorithms." In Adaptive Processing of Sequences and Data Structures. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0053995.

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Wan, Li, Kai Shu, and Yu Guo. "Sequences Modeling and Analysis Based on Complex Network." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31965-5_29.

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Yellin, Daniel M., and Gail Weiss. "Synthesizing Context-free Grammars from Recurrent Neural Networks." In Tools and Algorithms for the Construction and Analysis of Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72016-2_19.

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AbstractWe present an algorithm for extracting a subclass of the context free grammars (CFGs) from a trained recurrent neural network (RNN). We develop a new framework, pattern rule sets (PRSs), which describe sequences of deterministic finite automata (DFAs) that approximate a non-regular language. We present an algorithm for recovering the PRS behind a sequence of such automata, and apply it to the sequences of automata extracted from trained RNNs using the $$L^{*}$$ L ∗ algorithm. We then show how the PRS may converted into a CFG, enabling a familiar and useful presentation of the learned l
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Conference papers on the topic "Network sequences"

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Yang, Chang-Ming, Che-Jui Hsu, Tao Ban, Takeshi Takahashi, and Hsu-Chun Hsiao. "Uncovering Recurring Vulnerabilities through Taint-Extracted Operator Sequences." In 2024 IEEE Conference on Communications and Network Security (CNS). IEEE, 2024. http://dx.doi.org/10.1109/cns62487.2024.10735703.

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Xu, Yifan, Mengtao Wang, Zhongkui Wang, and Lin Meng. "Recurrent Neural Network-based Modeling Approach for 4D Printing Design : Long sequence prediction based on short sequences." In 2024 International Conference on Advanced Mechatronic Systems (ICAMechS). IEEE, 2024. https://doi.org/10.1109/icamechs63130.2024.10818844.

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Di Caro, Edoardo, Matteo Brina, Nicolas Belletti, Filippo Poltronieri, Mauro Tortonesi, and Cesare Stefanelli. "TimeGraph: Synthetic Generation of Graph Sequences for Realistic Mobile Connectivity Models." In 2025 IEEE 11th International Conference on Network Softwarization (NetSoft). IEEE, 2025. https://doi.org/10.1109/netsoft64993.2025.11080590.

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Zhang, Zhen, Lingling Tan, and Dongchun Lv. "Fine-Grained Passive Operating System Identification Based on Network Traffic Sequences." In 2024 2nd International Conference on Computer, Vision and Intelligent Technology (ICCVIT). IEEE, 2024. https://doi.org/10.1109/iccvit63928.2024.10872491.

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Parfentiev, Kirill V. "Generation of Pseudo-Random Sequences Using Generative Predictive Neural Network Transformers." In 2025 7th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE). IEEE, 2025. https://doi.org/10.1109/reepe63962.2025.10971073.

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Wang, Yan, and Weidi Guo. "Long Short-Term Memory Network (LSTM) is used to Model Action Sequences." In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN). IEEE, 2025. https://doi.org/10.1109/iciscn64258.2025.10934377.

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Hou, Jiaheng, Peter X. Geng, Zhikai Wu, Wenlin Fan, and Huaiqiu Zhu. "Predicting Microbial Community Productivity Based-On Genomic Sequences with Graph Neural Network." In 2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2024. https://doi.org/10.1109/cisp-bmei64163.2024.10906150.

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Luo, Dixin, Haoran Cheng, Qingbin Li, and Hongteng Xu. "Coupled Point Process-based Sequence Modeling for Privacy-preserving Network Alignment." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/678.

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Network alignment aims at finding the correspondence of nodes across different networks, which is significant for many applications, e.g., fraud detection and crime network tracing across platforms. In practice, however, accessing the topological information of different networks is often restricted and even forbidden, considering privacy and security issues. Instead, what we observed might be the event sequences of the networks' nodes in the continuous-time domain. In this study, we develop a coupled neural point process-based (CPP) sequence modeling strategy, which provides a solution to pri
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Le, Duc-Trong, Hady W. Lauw, and Yuan Fang. "Modeling Contemporaneous Basket Sequences with Twin Networks for Next-Item Recommendation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/474.

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Our interactions with an application frequently leave a heterogeneous and contemporaneous trail of actions and adoptions (e.g., clicks, bookmarks, purchases). Given a sequence of a particular type (e.g., purchases)-- referred to as the target sequence, we seek to predict the next item expected to appear beyond this sequence. This task is known as next-item recommendation. We hypothesize two means for improvement. First, within each time step, a user may interact with multiple items (a basket), with potential latent associations among them. Second, predicting the next item in the target sequenc
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Bredereck, Robert, Lilian Jacobs, and Leon Kellerhals. "Maximizing the Spread of an Opinion in Few Steps: Opinion Diffusion in Non-Binary Networks." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/225.

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We consider the setting of asynchronous opinion diffusion with majority threshold: given a social network with each agent assigned to one opinion, an agent will update its opinion if more than half of its neighbors agree on a different opinion. The stabilized final outcome highly depends on the sequence in which agents update their opinion. We are interested in optimistic sequences---sequences that maximize the spread of a chosen opinion. We complement known results for two opinions where optimistic sequences can be computed in time and length linear in the number of agents. We analyze upper a
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Reports on the topic "Network sequences"

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Sundermier, Amy, Rigobert Tibi, and Christopher Young. Applying Waveform Correlation to Aftershock Sequences Using a Global Sparse Network. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1763210.

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Allocchio, C. Text String Notation for Dial Sequences and Global Switched Telephone Network (GSTN) / E.164 Addresses. RFC Editor, 2003. http://dx.doi.org/10.17487/rfc3601.

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Neighbor, Christopher. Minimum Complexity Echo State Networks for Genome and Sequence Analysis. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.7376.

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Pursley, Michael B., and Carl W. Baum. Performance Evaluation of Mobile Distributed Direct-Sequence Spread-Spectrum Communication Networks. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada389815.

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Harriss, Lydia, and Katie Raymer. Online Information and Fake News. Parliamentary Office of Science and Technology, 2017. http://dx.doi.org/10.58248/pn559.

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Internet search engines and social media platforms are an increasingly popular way of accessing news and information. In 2017, the proportion of UK adults consuming news online exceeded those who watched news on TV (74% versus 69%). This note considers how people access news online, how algorithms (sequences of instructions) and social networks influence the content that users see, and options for mitigating any negative impact.
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Lers, Amnon, and Gan Susheng. Study of the regulatory mechanism involved in dark-induced Postharvest leaf senescence. United States Department of Agriculture, 2009. http://dx.doi.org/10.32747/2009.7591734.bard.

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Postharvest leaf senescence contributes to quality losses in flowers and leafy vegetables. The general goal of this research project was to investigate the regulatory mechanisms involved in dark-induced leaf senescence. The regulatory system involved in senescence induction and control is highly complex and possibly involves a network of senescence promoting pathways responsible for activation of the senescence-associated genes. Pathways involving different internal signals and environmental factors may have distinctive importance in different leaf senescence systems. Darkness is known to have
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Pillai, R. S., and A. K. Rathi. MAXBAND Version 3.1: Heuristic and optimal approach for setting the left turn phase sequences in signalized networks. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/34378.

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Fromm, Hillel, Paul Michael Hasegawa, and Aaron Fait. Calcium-regulated Transcription Factors Mediating Carbon Metabolism in Response to Drought. United States Department of Agriculture, 2013. http://dx.doi.org/10.32747/2013.7699847.bard.

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Abstract:
Original objectives: The long-term goal of the proposed research is to elucidate the transcription factors, genes and metabolic networks involved in carbon metabolism and partitioning in response to water deficit. The proposed research focuses on the GTLcalcium/calmodulinbindingTFs and the gene and metabolic networks modulated by these TFs in Arabidopsis thaliana. The specific objectives are as follows. Objective-1 (USA): Physiological analyses of GTL1 loss- and gain-of-function plants under water sufficient and drought stress conditions Objective 2 (USA / Israel-TAU): Characterizion of GTL ta
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Myers, S. C., C. A. Schultz, and F. Ryall. P-wave arrival times for the 1991 racha, Georgia earthquake sequence at stations of a test, sparse network. Office of Scientific and Technical Information (OSTI), 2000. http://dx.doi.org/10.2172/15005702.

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Saxena, Anugrah. A Novel Multi-layered Epitopes and Genetic Sequence Network for Predicting Antigenic Phenotype of H1 Influenza A Virus in Swine. Iowa State University, 2021. http://dx.doi.org/10.31274/cc-20240624-54.

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