Academic literature on the topic 'Network Dependency Analysis'
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Journal articles on the topic "Network Dependency Analysis"
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Woldeyohannes, Yordanos T., and Yuming Jiang. "Measures for Network Structural Dependency Analysis." IEEE Communications Letters 22, no. 10 (2018): 2052–55. http://dx.doi.org/10.1109/lcomm.2018.2864109.
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Kudĕlka, Miloš, Šárka Zehnalová, Zdenĕk Horák, Pavel Krömer, and Václav Snášel. "Local dependency in networks." International Journal of Applied Mathematics and Computer Science 25, no. 2 (2015): 281–93. http://dx.doi.org/10.1515/amcs-2015-0022.
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Abstract Many real world data and processes have a network structure and can usefully be represented as graphs. Network analysis focuses on the relations among the nodes exploring the properties of each network. We introduce a method for measuring the strength of the relationship between two nodes of a network and for their ranking. This method is applicable to all kinds of networks, including directed and weighted networks. The approach extracts dependency relations among the network’s nodes from the structure in local surroundings of individual nodes. For the tasks we deal with in this article, the key technical parameter is locality. Since only the surroundings of the examined nodes are used in computations, there is no need to analyze the entire network. This allows the application of our approach in the area of large-scale networks. We present several experiments using small networks as well as large-scale artificial and real world networks. The results of the experiments show high effectiveness due to the locality of our approach and also high quality node ranking comparable to PageRank.
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Duo, Jiecairang, Quecairang Hua, Keyou Huan, and Rangdangzhi Cai. "Transition based neural network dependency parsing of Tibetan." MATEC Web of Conferences 336 (2021): 06018. http://dx.doi.org/10.1051/matecconf/202133606018.
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In order to improve the performance of Tibetan natural language processing applications such as machine translation, sentiment analysis and other tasks, this article proposes a neural network-based method for syntactic analysis of Tibetan language dependence. Part of the corpus of Qinghai Normal University’s part-of-speech tag set is marked by the corresponding mapping relationship is transformed into the corpus annotated by the national standard part-of-speech tag set. At the same time, the CoNLL format Tibetan language dependency syntax tree library is constructed, and the method of shift-reduce plus neural network is adopted to systematically study and analyze the Tibetan language dependency syntax. Thereby improving the quality of Tibetan dependency syntactic analysis, and its accuracy rate reaches UAS:94.59%
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Wu, Fei, and Xinfu Li. "Local Dependency-Enhanced Graph Convolutional Network for Aspect-Based Sentiment Analysis." Applied Sciences 13, no. 17 (2023): 9669. http://dx.doi.org/10.3390/app13179669.
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The task of aspect-based sentiment analysis (ABSA) is to detect the sentiment polarity toward given aspects. Contemporary methods predominantly utilize graph neural networks and incorporate attention mechanisms to dynamically connect aspect terms with their surrounding contexts, resulting in more informative feature representations. However, these methods only consider whether there are dependencies between words when introducing dependencies, ignoring that dependencies between different sentiment words have different effects. Neglecting this could introduce noise and negatively impact the model’s performance. To overcome this limitation, we introduce a novel approach called the local dependency-enhanced graph convolutional network (LDEGCN). Our method combines semantic information and dependency relationships to better capture the affective relationships between words. Specifically, we integrate sentiment knowledge from SenticNet to enrich the sentence’s dependency graph and thoroughly explore the dependency types between contexts and aspects to focus on particular dependency types. The local context weight (LCW) method is employed on the dependency-enhanced graph to emphasize the importance of local contexts, thereby mitigating the issue of long-distance dependencies. Through extensive evaluations of five public datasets, the LDEGCN model demonstrates significant improvements over mainstream models.
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Ruslan, Mohd Firdaus. "Proposing A Conceptual Model for Asnafpreneur Success: Social Networks and Resource Dependence in Low-Income Regions." International Journal of Research and Innovation in Social Science VIII, no. X (2024): 935–41. http://dx.doi.org/10.47772/ijriss.2024.8100076.
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This paper proposes a conceptual model integrating Social Network Theory (SNT) and Resource Dependence Theory (RDT) to examine the business performance of Asnafpreneurs—entrepreneurs from marginalized communities supported by zakat—in low-income regions. While Asnafpreneurs frequently rely on social networks to access resources, they face significant challenges, including limited network diversity and heavy dependency on external actors like zakat institutions for financial and operational support. The model presented in this study aims to demonstrate how optimizing social networks can help reduce resource dependency and improve business outcomes, such as revenue growth, sustainability, and entrepreneurial autonomy. To guide future empirical validation, the paper suggests a mixed-methods approach, combining qualitative insights from in-depth interviews with quantitative measures, including social network analysis and structural equation modeling, to test the proposed relationships. This approach provides a roadmap for assessing the impact of social networks on business performance and understanding the mediating role of resource dependency. The findings contribute to the theoretical development of SNT and RDT by extending their application to marginalized entrepreneurship within a faith-based context. The paper also offers practical implications for policymakers and zakat institutions, providing strategies for designing more effective entrepreneurial support programs aimed at empowering Asnafpreneurs in low-income communities.
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Musmeci, Nicolò, Vincenzo Nicosia, Tomaso Aste, Tiziana Di Matteo, and Vito Latora. "The Multiplex Dependency Structure of Financial Markets." Complexity 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/9586064.
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We propose here a multiplex network approach to investigate simultaneously different types of dependency in complex datasets. In particular, we consider multiplex networks made of four layers corresponding, respectively, to linear, nonlinear, tail, and partial correlations among a set of financial time series. We construct the sparse graph on each layer using a standard network filtering procedure, and we then analyse the structural properties of the obtained multiplex networks. The study of the time evolution of the multiplex constructed from financial data uncovers important changes in intrinsically multiplex properties of the network, and such changes are associated with periods of financial stress. We observe that some features are unique to the multiplex structure and would not be visible otherwise by the separate analysis of the single-layer networks corresponding to each dependency measure.
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KENETT, DROR Y., TOBIAS PREIS, GITIT GUR-GERSHGOREN, and ESHEL BEN-JACOB. "DEPENDENCY NETWORK AND NODE INFLUENCE: APPLICATION TO THE STUDY OF FINANCIAL MARKETS." International Journal of Bifurcation and Chaos 22, no. 07 (2012): 1250181. http://dx.doi.org/10.1142/s0218127412501817.
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Much effort has been devoted to assess the importance of nodes in complex networks. Examples of commonly used measures of node importance include node degree, node centrality and node vulnerability score (the effect of the node deletion on the network efficiency). Here we present a new approach to compute and investigate the mutual dependencies between network nodes from the matrices of node–node correlations. The dependency network approach provides a new system level analysis of the activity and topology of directed networks. The approach extracts topological relations between the networks nodes (when the network structure is analyzed), and provides an important step towards inference of causal activity relations between the network nodes (when analyzing the network activity). The resulting dependency networks are a new class of correlation-based networks, and are capable of uncovering hidden information on the structure of the network. Here, we present a review of the new approach, and an example of its application to financial markets. We apply the methodology to the daily closing prices of all Dow Jones Industrial Average (DJIA) index components for the period 1939–2010. Investigating the structure and dynamics of the dependency network across time, we find fingerprints of past financial crises, illustrating the importance of this methodology.
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Yang, Jinjie, Anan Dai, Yun Xue, Biqing Zeng, and Xuejie Liu. "Syntactically Enhanced Dependency-POS Weighted Graph Convolutional Network for Aspect-Based Sentiment Analysis." Mathematics 10, no. 18 (2022): 3353. http://dx.doi.org/10.3390/math10183353.
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Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis that presents great benefits to real-word applications. Recently, the methods utilizing graph neural networks over dependency trees are popular, but most of them merely considered if there exist dependencies between words, ignoring the types of these dependencies, which carry important information, as dependencies with different types have different effects. In addition, they neglected the correlations between dependency types and part-of-speech (POS) labels, which are helpful for utilizing dependency imformation. To address such limitations and the deficiency of insufficient syntactic and semantic feature mining, we propose a novel model containing three modules, which aims to leverage dependency trees more reasonably by distinguishing different dependencies and extracting beneficial syntactic and semantic features to further enhance model performance. To enrich word embeddings, we design a syntactic feature encoder (SynFE). In particular, we design Dependency-POS Weighted Graph Convolutional Network (DPGCN) to weight different dependencies by a graph attention mechanism we proposed. Additionally, to capture aspect-oriented semantic information, we design a semantic feature extractor (SemFE). Extensive experiments on five popular benchmark databases validate that our model can better employ dependency information and effectively extract favorable syntactic and semantic features to achieve new state-of-the-art performance.
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Hieu, Nong Minh, Antoine Ledent, Yunwen Lei, and Cheng Yeaw Ku. "Generalization Analysis for Deep Contrastive Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 16 (2025): 17186–94. https://doi.org/10.1609/aaai.v39i16.33889.
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In this paper, we present generalization bounds for the unsupervised risk in the Deep Contrastive Representation Learning framework, which employs deep neural networks as representation functions. We approach this problem from two angles. On the one hand, we derive a parameter-counting bound that scales with the overall size of the neural networks. On the other hand, we provide a norm-based bound that scales with the norms of neural networks' weight matrices. Ignoring logarithmic factors, the bounds are independent of the size of the tuples provided for contrastive learning. To the best of our knowledge, this property is only shared by one other work, which employed a different proof strategy and suffers from very strong exponential dependence on the depth of the network which is due to a use of the peeling technique. Our results circumvent this by leveraging powerful results on covering numbers with respect to uniform norms over samples. In addition, we utilize loss augmentation techniques to further reduce the dependency on matrix norms and the implicit dependence on network depth. In fact, our techniques allow us to produce many bounds for the contrastive learning setting with similar architectural dependencies as in the study of the sample complexity of ordinary loss functions, thereby bridging the gap between the learning theories of contrastive learning and DNNs.
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Jiang, Tingyao, Zilong Wang, Ming Yang, and Cheng Li. "Aspect-Based Sentiment Analysis with Dependency Relation Weighted Graph Attention." Information 14, no. 3 (2023): 185. http://dx.doi.org/10.3390/info14030185.
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Aspect-based sentiment analysis is a fine-grained sentiment analysis that focuses on the sentiment polarity of different aspects of text, and most current research methods use a combination of dependent syntactic analysis and graphical neural networks. In this paper, a graph attention network aspect-based sentiment analysis model based on the weighting of dependencies (WGAT) is designed to address the problem in that traditional models do not sufficiently analyse the types of syntactic dependencies; in the proposed model, graph attention networks can be weighted and averaged according to the importance of different nodes when aggregating information. The model first transforms the input text into a low-dimensional word vector through pretraining, while generating a dependency syntax graph by analysing the dependency syntax of the input text and constructing a dependency weighted adjacency matrix according to the importance of different dependencies in the graph. The word vector and the dependency weighted adjacency matrix are then fed into a graph attention network for feature extraction, and sentiment polarity is predicted through the classification layer. The model can focus on syntactic dependencies that are more important for sentiment classification during training, and the results of the comparison experiments on the Semeval-2014 laptop and restaurant datasets and the ACL-14 Twitter social comment dataset show that the WGAT model has significantly improved accuracy and F1 values compared to other baseline models, validating its effectiveness in aspect-level sentiment analysis tasks.
Dissertations / Theses on the topic "Network Dependency Analysis"
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Fu, Yi. "Differential Dependency Network and Data Integration for Detecting Network Rewiring and Biomarkers." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/96634.
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Rapid advances in high-throughput molecular profiling techniques enabled large-scale genomics, transcriptomics, and proteomics-based biomedical studies, generating an enormous amount of multi-omics data. Processing and summarizing multi-omics data, modeling interactions among biomolecules, and detecting condition-specific dysregulation using multi-omics data are some of the most important yet challenging analytics tasks.
In the case of detecting somatic DNA copy number aberrations using bulk tumor samples in cancer research, normal cell contamination becomes one significant confounding factor that weakens the power regardless of whichever methods used for detection. To address this problem, we propose a computational approach – BACOM 2.0 to more accurately estimate normal cell fraction and accordingly reconstruct DNA copy number signals in cancer cells. Specifically, by introducing allele-specific absolute normalization, BACOM 2.0 can accurately detect deletion types and aneuploidy in cancer cells directly from DNA copy number data.
Genes work through complex networks to support cellular processes. Dysregulated genes can cause structural changes in biological networks, also known as network rewiring. Genes with a large number of rewired edges are more likely to be associated with functional alteration leading phenotype transitions, and hence are potential biomarkers in diseases such as cancers. Differential dependency network (DDN) method was proposed to detect such network rewiring and biomarkers.
However, the existing DDN method and software tool has two major drawbacks. Firstly, in imbalanced sample groups, DDN suffers from systematic bias and produces false positive differential dependencies. Secondly, the computational time of the block coordinate descent algorithm in DDN increases rapidly with the number of involved samples and molecular entities. To address the imbalanced sample group problem, we propose a sample-scale-wide normalized formulation to correct systematic bias and design a simulation study for testing the performance. To address high computational complexity, we propose several strategies to accelerate DDN learning, including two reformulated algorithms for block-wise coefficient updating in the DDN optimization problem. Specifically, one strategy on discarding predictors and one strategy on accelerating parallel computing. More importantly, experimental results show that new DDN learning speed with combined accelerating strategies is hundreds of times faster than that of the original method on medium-sized data.
We applied the DDN method on several biomedical datasets of omics data and detected significant phenotype-specific network rewiring. With a random-graph-based detection strategy, we discovered the hub node defined biomarkers that helped to generate or validate several novel scientific hypotheses in collaborative research projects. For example, the hub genes detected by the DDN methods in proteomics data from artery samples are significantly enriched in the citric acid cycle pathway that plays a critical role in the development of atherosclerosis.
To detect intra-omics and inter-omics network rewirings, we propose a method called multiDDN that uses a multi-layer signaling model to integrate multi-omics data. We adapt the block coordinate descent algorithm to solve the multiDDN optimization problem with accelerating strategies. The simulation study shows that, compared with the DDN method on single omics, the multiDDN method has considerable advantage on higher accuracy of detecting network rewiring. We applied the multiDDN method on the real multi-omics data from CPTAC ovarian cancer dataset, and detected multiple hub genes associated with histone protein deacetylation and were previously reported in independent ovarian cancer data analysis.<br>Doctor of Philosophy<br>We witnessed the start of the human genome project decades ago and stepped into the era of omics since then. Omics are comprehensive approaches for analyzing genome-wide biomolecular profiles. The rapid development of high-throughput technologies enables us to produce an enormous amount of omics data such as genomics, transcriptomics, and proteomics data, which makes researchers swim in a sea of omics information that once never imagined. Yet, the era of omics brings new challenges to us: to process the huge volumes of data, to summarize the data, to reveal the interactions between entities, to link various types of omics data, and to discover mechanisms hidden behind omics data.
In processing omics data, one factor that weakens the strengths of follow up data analysis is sample impurity. We call impure tumor samples contaminated by normal cells as heterogeneous samples. The genomic signals measured from heterogeneous samples are a mixture of signals from both tumor cells and normal cells. To correct the mixed signals and get true signals from pure tumor cells, we propose a computational approach called BACOM 2.0 to estimate normal cell fraction and corrected genomics signals accordingly. By introducing a novel normalization method that identifies the neutral component in mixed signals of genomic copy number data, BACOM 2.0 could accurately detect genes' deletion types and abnormal chromosome numbers in tumor cells.
In cells, genes connect to other genes and form complex biological networks to perform their functions. Dysregulated genes can cause structural change in biological networks, also known as network rewiring. In a biological network with network rewiring events, a large quantity of network rewiring linking to a single hub gene suggests concentrated gene dysregulation. This hub gene has more impact on the network and hence is more likely to associate with the functional change of the network, which ultimately leads to abnormal phenotypes such as cancer diseases. Therefore, the hub genes linked with network rewiring are potential indicators of disease status or known as biomarkers. Differential dependency network (DDN) method was proposed to detect network rewiring events and biomarkers from omics data.
However, the DDN method still has a few drawbacks. Firstly, for two groups of data with unequal sample sizes, DDN consistently detects false targets of network rewiring. The permutation test, which uses the same method on randomly shuffled samples is supposed to distinguish the true targets from random effects, however, is also suffered from the same reason and could let pass those false targets. We propose a new formulation that corrects the mistakes brought by unequal group size and design a simulation study to test the new formulation's correctness. Secondly, the time used for computing in solving DDN problems is unbearably long when processing omics data with a large number of samples scale or a large number of genes. We propose several strategies to increase DDN's computation speed, including three redesigned formulas for efficiently updating the results, one rule to preselect predictor variables, and one accelerating skill of utilizing multiple CPU cores simultaneously. In the timing test, the DDN method with increased computing speed is much faster than the original method.
To detect network rewirings within the same omics data or between different types of omics, we propose a method called multiDDN that uses an integrated model to process multiple types of omics data. We solve the new problem by adapting the block coordinate descending algorithm. The test on simulated data shows multiDDN is better than single omics DDN.
We applied DDN or multiDDN method on several datasets of omics data and detected significant network rewiring associated with diseases. We detected hub nodes from the network rewiring events. These hub genes as potential biomarkers help us to ask new meaningful questions in related researches.
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Gotthard, Arvid, and Zino Kader. "Vulnerability Analysis of the Dependency Network of JavaScript and Typescript Projects on GitHub." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301841.
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The ecosystem of JavaScript (JS) and TypeScript (TS) packages is heavily reliant on Open Source Software (OSS) provided without any guarantees. This poses inherent risks to dependents, one of which being stagnation of their dependencies. We investigated developer contribution data as well as the structure of the dependency network of GitHub- hosted JS and TS projects in order to learn whether the ecosystem is vulnerable to project stagnation. The data was partly acquired from available aggregated data sources, in conjunction with additional data collected using a highly optimized web- scraper. The produced dependency network was shown to be highly connected with many cycles, which rendered the entire network highly dependent on small projects with a high risk of stagnation. Furthermore, projects tended to have a skewed developer contribution frequency, on average 89.9% of the projects commits were found to be authored by the top 1% of contributors.<br>Ekosystemet av JS och TS moduler är starkt beroende av öppen mjukvara som distribueras utan garantier. Detta introducerar risker för beroende mjukvara, varav en risk är stagnering av använda moduler. Vi studerade data på bidragande utvecklare samt strukturen på beroendenätverket av JS- och TS- projekt hostade på GitHub för att ta reda på om stagnation av moduler gör ekosystemet sårbart. Datan samlades in från öppet tillgängliga datakällor, samt med en optimerad web- scraper. Nätverket visade sig vara väldigt sammanhängande och inkluderade många cykler, vilket ledde till att hela nätverket var väldigt beroende av små moduler med en hög risk för stagnering. Vidare visade det sig att fördelningen av utvecklares bidrag till projekt var väldigt skev, i snitt stod de topp1% största bidragarna till ett projekt för 89.9% av commits.
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Arnesson, Malin. "A business network analysis of the Botswana tourism industry : – Putting local ground operators relationships with foreign tour operators in the centre." Thesis, Linnéuniversitetet, Institutionen för marknadsföring (MF), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-35900.
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Purpose: This study aims to (1) describe and analyse the local ground operator’s role in the network of the Botswana tourism industry, (2) to investigate what factors characterize and influence their relationships with foreign tour operators, as well as (3) to provide implications for improving these relations. Methodology: This study has been conducted as a case study, where the empirical data was collected during a two months field trip to Botswana. A qualitative research method, and a deductive approach has been used. Five semi-structured interviews were conducted with ground operators to get their perspective of their own situation, and one with a person representing a governmental organisation. Most data was collected through the interviews, but participating observation also served as a source of information. The validity and reliability of the study has been enhanced by defining core concepts, interviewing several respondents, using an interview guide and recording most of the interviews. Results: Local ground operators play are crucial role in the network of Botswana’s tourism industry, as they are engaged in a high number of business relationships, are bringing the services at the destination together, and are connecting them to the tourists. They play a role as both customers and suppliers. Their relationship to tour operators abroad consists of a lot of resource ties, activity links as well as some actor bonds. Trust is found to be a very essential part of the relationship, and they perceive themselves as being dependent on the tour operators. Culture is not identified as a major influencing factor. As for managerial recommendations, a better communication in expectations and an increased adaptation of products and processes, to create customer dependence, are among the results advised.
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Kurita, Shuhei. "Neural Approaches for Syntactic and Semantic Analysis." Kyoto University, 2019. http://hdl.handle.net/2433/242436.
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Andersson, Karl. "Mapping out dependencies in network components in critical infrastructure." Thesis, Linköpings universitet, Programvara och system, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143981.
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Companies that operate with critical infrastructure face a growing threat from cyber-attacks while at the same time the development in the business is rapidly moving towards a higher level of digitalization. A common type of system in critical infrastructure is supervisory control and data acquisition systems, these systems have properties that can affect their security and will therefore serve as the basis for this thesis work. To stay protected despite systems changes, companies need to make risk assessments in order to analyze how changes will affect the overall system. One thing that is important to focus on is dependencies within the system, this means that not only interaction among computers and networks are concerned but instead a more holistic view of the system need to be considered. This thesis aims to aid the process of a future risk assessment by providing a methodology to be used as a preparatory step before a risk assessment by describing the current situation of the system. This is done by evaluating two system modeling approaches, and also by proposing a number of perspectives that each provides different kind of information about the system’s dependencies. These perspectives are then evaluated by creating system models and dependency graphs, and discussing the outcomes with experts in a utility company to find out their applicability. According to the experts, the proposed perspectives have promising properties that can be useful in future risk assessments as well as in other scenarios. Moreover, the evaluated modeling approaches got positive comments during evaluation and are considered to serve their purpose.
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Namoobe, Choolwe Chisuta. "An analysis of accessibility to grocery stores by walking and cycling. : A comparative study of Ålidhem, Tomtebo, Carlshem and Sofiehem neighbourhoods in Umeå using ArcGIS Network Dataset." Thesis, Umeå universitet, Kulturgeografi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-151871.
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Accessibility is a gateway to accessing opportunities or service sites, i.e., grocery shopping, healthcare and jobs and it has been used as a proxy to measure inequalities. The equity, in travel time or distance, to accessing of these services is vital to ensure high accessibility hence providing equality. Population increase leading to urban sprawl affects accessibility in the absence of prudent residential spatial planning and transport systems and infrastructure. Urban sprawl creates a distance between populations and centres of services such as grocery stores. This promotes the growth of car-dependency to accessing these services that are not easily reachable by non-motorised transport of walking and biking.This thesis measures accessibility to grocery stores by comparing the travel time of populations in the four neighbourhoods, of Ålidhem, Tomtebo, Carlshem and Sofiehem, by walking and biking. The measuring is done by using the ArcGIS Network Analyst to create and build a single-modal network dataset of pedestrian and bike lanes in the study area. Accessibility to grocery stores is measured using the service areas created based on the grocery store location and it highlights all the accessible streets from the centre within a time impedance. Afterwards, the populations are aggregated in each service area of each neighbourhood to compare the time difference of accessibility to a grocery store.With distance decay effects, the walking time distance restricts accessibility to grocery stores to a limited population in the neighbourhoods. The bike time distance, however, increased the residents in the study area who can access the grocery stores. The new location for a Coop grocery store provides the highest accessibility to grocery stores compared to the Ålidhem Centre grocery store.From the results of the analysis, the bike travel is the most equitable hence equal mode of active transport for all the four neighbourhoods.<br><p>The best time to build walk and cycle paths was 20 years ago. The second best time is now! This publication is my first and very close to my heart as it shows how land use and transport are related to reduce travel times and also promote more non-motorised transport usage. </p>
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Coufal, Zdeněk. "Korelace dat na vstupu a výstupu sítě Tor." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-235412.
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Communication in public networks based on the IP protocol is not really anonymous because it is possible to determine the source and destination IP address of each packet. Users who want to be anonymous are forced to use anonymization networks, such as Tor. In case such a user is target of lawful interception, it presents a problem for those systems because they only see that the user communicated with anonymization network and have a suspicion that the data stream at the output of anonymization network belong to the same user. The aim of this master thesis was to design a correlation method to determine the dependence of the data stream at the input and the output of the Tor network. The proposed method analysis network traffic and compares characteristics of data streams extracted from metadata, such as time of occurence and the size of packets. This method specializes in correlating data flows of protocol HTTP, specifically web server responses. It was tested on real data from the Tor network and successfully recognized dependency of data flows.
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Liu, Jian. "Fractal Network Traffic Analysis with Applications." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11477.
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Today, the Internet is growing exponentially, with traffic statistics that mathematically exhibit fractal characteristics: self-similarity and long-range dependence. With these properties, data traffic shows high peak-to-average bandwidth ratios and causes networks inefficient. These problems make it difficult to predict, quantify, and control data traffic. In this thesis, two analytical methods are used to study fractal network traffic. They are second-order self-similarity analysis and multifractal analysis.
First, self-similarity is an adaptability of traffic in networks. Many factors are involved in creating this characteristic. A new view of this self-similar traffic structure related to multi-layer network protocols is provided. This view is an improvement over the theory used in most current literature.
Second, the scaling region for traffic self-similarity is divided into two timescale regimes: short-range dependence (SRD) and long-range dependence (LRD). Experimental results show that the network transmission delay separates the two scaling regions. This gives us a physical source of the periodicity in the observed traffic. Also, bandwidth, TCP window size, and packet size have impacts on SRD. The statistical heavy-tailedness (Pareto shape parameter) affects the structure of LRD. In addition, a formula to estimate traffic burstiness is derived from the self-similarity property.
Furthermore, studies with multifractal analysis have shown the following results. At large timescales, increasing bandwidth does not improve throughput. The two factors affecting traffic throughput are network delay and TCP window size. On the other hand, more simultaneous connections smooth traffic, which could result in an improvement of network efficiency. At small timescales, in order to improve network efficiency, we need to control bandwidth, TCP window size, and network delay to reduce traffic burstiness. In general, network traffic processes have a Hlder exponent a ranging between 0.7 and 1.3. Their statistics differ from Poisson processes.
From traffic analysis, a notion of the efficient bandwidth, EB, is derived. Above that bandwidth, traffic appears bursty and cannot be reduced by multiplexing. But, below it, traffic is congested. An important finding is that the relationship between the bandwidth and the transfer delay is nonlinear.
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Chin, Ke-Tsai Edward. "Networks of queues with state-dependent flows." Diss., Georgia Institute of Technology, 1986. http://hdl.handle.net/1853/25419.
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Roxbo, Daniel. "A Detailed Analysis of Semantic Dependency Parsing with Deep Neural Networks." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-156831.
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The use of Long Short Term Memory (LSTM) networks continues to yield better results in natural language processing tasks. One area which recently has seen significant improvements is semantic dependency parsing, where the current state-of-the-art model uses a multilayer LSTM combined with an attention-based scoring function to predict the dependencies. In this thesis the state of the art model is first replicated and then extended to include features based on syntactical trees, which was found to be useful in a similar model. In addition, the effect of part-of-speech tags is studied. The replicated model achieves a labeled F1 score of 93.6 on the in-domain data and 89.2 on the out-of-domain data on the DM dataset, which shows that the model is indeed replicable. Using multiple features extracted from syntactic gold standard trees of the DELPH-IN Derivation Tree (DT) type increased the labeled scores to 97.1 and 94.1 respectively, while the use of predicted trees of the Stanford Basic (SB) type did not improve the results at all. The usefulness of part-of-speech tags was found to be diminished in the presence of other features.
Books on the topic "Network Dependency Analysis"
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Coatta, Terry James. Queueing networks with state-dependent service rates. University of Toronto, Dept. of Computer Science, 1987.
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Iammarino, Simona, and Philip McCann. Network Geographies and Geographical Networks: Co-dependence and Co-evolution of Multinational Enterprises and Space. Edited by Gordon L. Clark, Maryann P. Feldman, Meric S. Gertler, and Dariusz Wójcik. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198755609.013.48.
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The main analytical and multidisciplinary frameworks adopted for understanding the multinational enterprise (MNE) have tended to be largely non-spatial and non-geographical in nature. Although there have been some recent developments incorporating geography into the analysis of the of MNE studies the longstanding and widespread absence of geography in MNE studies leads to analytical problems. In particular, in the investigation of MNE operations and their interactions with different economic actors and contexts, the use of typical dichotomies, such as home versus host, horizontal versus vertical integration, and determinant versus impact, today prove to be much less effective or relevant than might previously have been the case. More specifically, the fundamental geographical and institutional re-orderings associated with modern globalization mean that nowadays we see increasingly co-dependent and co-evolutionary corporate and geographical networks. Understanding these is essential in order to understand the new international division of labour.
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Rhodes, R. A. W. Policy Networks. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198786108.003.0003.
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This chapter reviews literature on policy networks examining descriptive, theoretical, and prescriptive accounts. It identifies three descriptive uses—policy networks as: interest intermediation, interorganizational analysis, and governance. It identifies two theories about policy networks: power-dependence and rational choice. It reviews three approaches to reforming and managing networks: instrumental, interactive, and institutional. It then discusses the central debates and challenges about comparing networks, explaining change, and managing the institutional void. Finally, the chapter identifies four main problems: the mix of governing structures; the problem of many hands; the holy grail of coordination; and steering not rowing. Policy network analysis has become one more locus for the endless debates about how we know what we know in the social sciences.
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Snijders, Tom A. B., and Mark Pickup. Stochastic Actor Oriented Models for Network Dynamics. Edited by Jennifer Nicoll Victor, Alexander H. Montgomery, and Mark Lubell. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.10.
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Stochastic Actor Oriented Models for Network Dynamics are used for the statistical analysis of longitudinal network data collected as a panel. The probability model defines an unobserved stochastic process of tie changes, where social actors add new ties or drop existing ties in response to the current network structure; the panel observations are snapshots of the resulting changing network. The statistical analysis is based on computer simulations of this process, which provides a great deal of flexibility in representing data constraints and dependence structures. In this Chapter we begin by defining the basic model. We then explicate a new model for nondirected ties, including several options for the specification of how pairs of actors coordinate tie changes. Next, we describe coevolution models. These can be used to model the dynamics of several interdependent sets of variables, such as the analysis of panel data on a network and the behavior of the actors in the network, or panel data on two or more networks. We finish by discussing the differences between Stochastic Actor Oriented Models and some other longitudinal network models. A major distinguishing feature is the treatment of time, which allows straightforward application of the model to panel data with different time lags between waves. We provide a variety of applications in political science throughout.
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Choi, Wonjoon. Network flow models of building evacuation problems with flow- dependent arc capacities. 1987.
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Rhodes, R. A. W. Policy Networks and Policy-making in the European Union. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198786108.003.0004.
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Policy networks travel well and help us to understand EU policy-making. The chapter reviews the literature up to 1996 and identifies the main objections to using the concept to study the EU: explanation, level of analysis, institutions, boundaries, and policy. The chapter discusses the limits to policy networks and the conditions under which they work. The factors sustaining EU policy networks include: the national style of policy-making, degree of resource/power dependence, characteristics of the policy area, stage of the policy process, degree of aggregation, and functional representation. The Afterword assesses the record and concludes that policy networks became part of the conceptual vocabulary of studies of EU policy.
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Theiner, Georg, and Nikolaus Fogle. The “Ontological Complicity” of Habitus and Field. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198801764.003.0012.
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This chapter approaches the work of the French sociologist, Pierre Bourdieu, from the point of view of embodied, extended, and distributed cognition. The concepts that form Bourdieu’s central dyad, habitus and field, are remarkably consonant with externalist views. Habitus is a form of knowledge that is not only embodied but fundamentally environment-dependent, and field is a distributed network of cognitively active positions that serves not only as a repository of social knowledge, but also as an external template for individual schemes of perception and action. The aim of this chapter’s comparative analysis is not to merely show that Bourdieu’s concepts are compatible with cognitive and epistemological externalism. They further demonstrate that the resources of Bourdieu’s theoretical framework can prove particularly useful for developing externalist accounts of culture and society—two areas that are significantly underexplored within mainstream debates in analytic philosophy.
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Koch, Christof. Biophysics of Computation. Oxford University Press, 1998. http://dx.doi.org/10.1093/oso/9780195104912.001.0001.
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Neural network research often builds on the fiction that neurons are simple linear threshold units, completely neglecting the highly dynamic and complex nature of synapses, dendrites, and voltage-dependent ionic currents. Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can be encoded in the voltage across the membrane, in the intracellular calcium concentration, or in the timing of individual spikes. Key topics covered include the linear cable equation; cable theory as applied to passive dendritic trees and dendritic spines; chemical and electrical synapses and how to treat them from a computational point of view; nonlinear interactions of synaptic input in passive and active dendritic trees; the Hodgkin-Huxley model of action potential generation and propagation; phase space analysis; linking stochastic ionic channels to membrane-dependent currents; calcium and potassium currents and their role in information processing; the role of diffusion, buffering and binding of calcium, and other messenger systems in information processing and storage; short- and long-term models of synaptic plasticity; simplified models of single cells; stochastic aspects of neuronal firing; the nature of the neuronal code; and unconventional models of sub-cellular computation. Biophysics of Computation: Information Processing in Single Neurons serves as an ideal text for advanced undergraduate and graduate courses in cellular biophysics, computational neuroscience, and neural networks, and will appeal to students and professionals in neuroscience, electrical and computer engineering, and physics.
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Christopherson, Susan. Outside Regional Paths: Constructing an Economic Geography of Energy Transitions. Edited by Gordon L. Clark, Maryann P. Feldman, Meric S. Gertler, and Dariusz Wójcik. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198755609.013.52.
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Moving beyond theories of socio-technical adaptation, a new economic geography of energy transitions is developing that contributes to a deeper understanding of adaptation and change in energy systems. This new geography of energy transitions draws on concepts in evolutionary economic geography but moves beyond regional analysis to recognize the nation state as a critical venue for strategic action by firms. The dependence on the nation state for access to the resource; financing of exploration and production; favourable regulatory oversight; and the infrastructure to transport the commodity to profitable markets, make it the essential venue for strategic action. Drawing on the US case of shale gas and oil extraction, this chapter argues that, despite the emergence of global production networks in the oil and gas industry, national-scale governance remains central to understanding energy transitions.
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Strang, Veronica. Re-Imagined Communities. Edited by Ken Conca and Erika Weinthal. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199335084.013.4.
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Focusing on water as a connective material flow, this chapter reconsiders notions of community, agency, and identity from the perspective of contemporary debates on ecological ethics and relationality. By articulating the fluid relationships between humans, nonhumans, and the material world, these debates critique dominant conceptual assumptions about Nature and Culture as separate domains. Such assumptions continue to underpin water policy and management, casting ecosystems—and their dependent species—as the subjects of human action, with generally poor outcomes for their well-being. The chapter draws on actor-network theory, philosophical ideas about ethics, and analyses of materiality to propose a re-imagined model of “community” that reintegrates the human and nonhuman, and opens up the potential for more reciprocal—and thus more sustainable—human‒environmental relationships. In doing so, it proposes a new kind of “participatory” framework for water policy development.
Book chapters on the topic "Network Dependency Analysis"
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Kochut, Andrzej, and Gautam Kar. "Managing Virtual Storage Systems: An Approach Using Dependency Analysis." In Integrated Network Management VIII. Springer US, 2003. http://dx.doi.org/10.1007/978-0-387-35674-7_58.
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Farber, Steven, Antonio Páez, and Erik Volz. "Topology, Dependency Tests and Estimation Bias in Network Autoregressive Models." In Progress in Spatial Analysis. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03326-1_3.
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Zhang, Bai, Huai Li, Robert Clarke, Leena Hilakivi-Clarke, and Yue Wang. "Differential Dependency Network Analysis to Identify Topological Changes in Biological Networks." In Medical Biostatistics for Complex Diseases. Wiley-VCH Verlag GmbH & Co. KGaA, 2010. http://dx.doi.org/10.1002/9783527630332.ch10.
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Lange, Mona, and Ralf Möller. "Time Series Data Mining for Network Service Dependency Analysis." In International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47364-2_57.
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Murali, T. M., Matthew D. Dyer, David Badger, Brett M. Tyler, and Michael G. Katze. "Network-Based Prediction and Analysis of HIV Dependency Factors." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29627-7_18.
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Agosta, Giovanni, Alessandro Barenghi, Gerardo Pelosi, and Michele Scandale. "Enhancing Passive Side-Channel Attack Resilience through Schedulability Analysis of Data-Dependency Graphs." In Network and System Security. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38631-2_58.
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Tian, Fengzhan, Shengfeng Tian, Jian Yu, and Houkuan Huang. "An Improved Bayesian Network Learning Algorithm Based on Dependency Analysis." In Computational Intelligence and Security. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596448_5.
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He, Xinyu, Yujie Tang, Xue Han, and Yonggong Ren. "Biomedical Event Detection Based on Dependency Analysis and Graph Convolution Network." In Communications in Computer and Information Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9864-7_13.
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Chung, Ai Wern, and Markus D. Schirmer. "Network Dependency Index Stratified Subnetwork Analysis of Functional Connectomes: An Application to Autism." In Connectomics in NeuroImaging. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32391-2_13.
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Mao, Lei, Jianxia Chen, Shi Dong, et al. "A Novel Interaction Convolutional Network Based on Dependency Trees for Aspect-Level Sentiment Analysis." In Neural Information Processing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8082-6_30.
Full textConference papers on the topic "Network Dependency Analysis"
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Xiong, Shiyong, Hongtao Wang, Jun Shen, and Yanghui Xiang. "Syntactic and Semantic Dependency Graph Convolutional Network for Aspect-Based Sentiment Analysis." In 2024 6th International Conference on Frontier Technologies of Information and Computer (ICFTIC). IEEE, 2024. https://doi.org/10.1109/icftic64248.2024.10913075.
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Wu, Yanhong. "Intelligent Dependency Cracking Path Based on Technology Acceptance Model (TAM) and Convolutional Neural Network (CNN) Analysis Model." In 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2024. https://doi.org/10.1109/icmnwc63764.2024.10872019.
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Siddiqua, Ayesha, and H. C. Nagaraj. "Aspect based Sentiment Analysis via Dependency Parsing and Structured Dependency Representation." In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN). IEEE, 2025. https://doi.org/10.1109/iciscn64258.2025.10934195.
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Qin, Mengyuan, Haitao Li, and Xinrong Yang. "Attractor Analysis of Conjunctive Boolean Networks via Dependency Graph." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10661518.
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Sebastian, Rejeena, R. Ahmad, J. Lafrenière-Greig, X. Ropagnol, and François Blanchard. "Feature-Dependent Accuracy in Classification Models for Ultrafast THz Spectroscopy using Frequency Selective Surfaces." In Photonic Networks and Devices. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/networks.2024.now3h.6.
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This work analyzes data from a novel setup for ultrafast selective multispectral Terahertz (THz) spectroscopy using Frequency Selective Surface (FSS). A comparative evaluation of the performance of four different classification models is conducted and a feature-accuracy mapping is done for optimization.
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Liu, Shengyu, Xichao Wang, Yan Ci, Yawen Li, Yunfeng Cao, and Wangwang Yu. "Analysis of System Capability Generation Based on Functional Dependency Networks." In 2024 10th International Conference on Systems and Informatics (ICSAI). IEEE, 2024. https://doi.org/10.1109/icsai65059.2024.10893734.
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Speyer, Gil, Juan Rodriguez, Tomas Bencomo, and Seungchan Kim. "GPU-Accelerated Differential Dependency Network Analysis." In 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP). IEEE, 2018. http://dx.doi.org/10.1109/pdp2018.2018.00072.
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Cole, Reggie. "Data Dependency Network Analysis in SoS." In 2017 12th System of Systems Engineering Conference (SoSE). IEEE, 2017. http://dx.doi.org/10.1109/sysose.2017.7994931.
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Zimmermann, Thomas, and Nachiappan Nagappan. "Predicting defects using network analysis on dependency graphs." In the 13th international conference. ACM Press, 2008. http://dx.doi.org/10.1145/1368088.1368161.
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Li, Zhidong, Wu Yang, Wei Wang, and Dapeng Man. "Network Security Risk Assessment Based on Service Dependency Analysis." In 2010 Fifth International Conference on Internet Computing for Science and Engineering (ICICSE). IEEE, 2010. http://dx.doi.org/10.1109/icicse.2010.38.
Full textReports on the topic "Network Dependency Analysis"
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Bonnett, Michaela, Meaghan Kennedy, Odiraa Okala, and Teri Garstka. Precision Public Health: Empowering Communities with Hyperlocal Data for Targeted Interventions and Improved Outcomes. Orange Sparkle Ball, 2024. http://dx.doi.org/10.61152/sktq6431.
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Background Precision public health is an effective strategy for reaching the last mile in service delivery, but is frequently hampered by its dependence on unattainable data standards and the non-transferability of the solutions designed. This paper proposes a five-part system involving 1) dynamic data governance, 2) hyperlocal community data, 3) data synthesis and analysis, 4) the design and implementation of precision interventions, and 5) correlation between community data and traditional outcome data. Recent studies of community network data have found the connectedness of communities to be positively correlated with community social and environmental outcomes. Taking advantage of hyperlocal community data is therefore a promising approach to improve community outcomes by characterizing and optimizing for greater connectivity. Methods Collection and governance of hyper-local data that is community-owned can be accomplished through such transferable systems as IRIS, a community-led referral network originally designed for multi-sector social and healthcare organizations. Using this data, communities can identify precise areas of intervention through descriptive and network analysis techniques, and design a responsive, community-led intervention. Immersive Innovation Labs, an applied learning approach, is an effective methodology for the adaptive design of innovative precision interventions. This combination of approaches can empower communities and public health professionals. Conclusion The COVID-19 pandemic revealed the impact of chronic understaffing and skills gaps, particularly at the local level. This paper aims to broaden the definition of precision public health as a response, beyond the traditional application that is dependent on big, non-contextual data sources. Reframing precision public health to a methodology dependent on community-owned, ongoing data collection allows the design of hyper-local solutions while shifting the burden of scalability to data collection technology. While challenges in implementation remain, precision is necessary to make public health and communities more responsive and effective in delivering equitable health outcomes and reaching the last mile.
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Al Hosain, Nourah, and Alma Alhussaini. Evaluating Access to Riyadh’s Planned Public Transport System Using Geospatial Analysis. King Abdullah Petroleum Studies and Research Center, 2021. http://dx.doi.org/10.30573/ks--2021-dp10.
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The King Abdulaziz Project for Public Transport in Riyadh city is one of the world’s largest urban transit systems being developed. The project aims to meet the demands of the city’s growing urban population while reducing traffic congestion, heavy private car dependence and air pollution. The performance of any public transport system largely depends on its accessibility. Therefore, this study evaluates the populations’ access to Riyadh’s public transport stations using network analysis tools based on geographic information systems.
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Banner, Katharine, Eliot Liucci, Kelly McCaffrey, and Bradley Strickland. Statistical evaluation of potential redundancies in Everglades National Park's network of marsh hydro-stations. National Park Service, 2025. https://doi.org/10.36967/2308015.
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This report presents a purely statistical clustering approach to assess redundancies within the network of marsh hydro-stations based on their observed stage (water elevation) data recorded between January 1, 2000 (beginning of Comprehensive Everglades Restoration Plan, CERP) and December 31, 2020. Using only a statistical analysis to determine whether two marsh hydro-stations might be considered redundant or not is dependent on the various choices made during the analysis, which we thoroughly explain in Section 4. We summarize general patterns of similarities among regional cluster-results and all-region results in Subsection 1.1 and similarities within each region’s results in Subsection 1.2. The ultimate decision for whether stations should no longer be maintained should be based on best professional judgement and domain knowledge provided by the Data Sciences Branch and others at South Florida Natural Resources Center (SFNRC).
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Peterson, Brian, J. Beeco, Davyd Betchkal, et al. Exploring spatial patterns of overflights at Denali National Park and Preserve. National Park Service, 2025. https://doi.org/10.36967/2307459.
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This study explored spatial patterns of overflights at Denali National Park and Preserve. Data were collected at Old Town Cantwell, Healy Radio Repeater, and K?esugi Ken Ranger Station. In total, overflights were analyzed from September 18th, 2021 to October 6th, 2022 (384 total days; 56 days of missing data) and May 25th, 2023 to September 13th, 2023 (112 total days; 0 days of missing data) using Automatic Dependent Surveillance-Broadcast (ADS-B) data. Phase 1 of the analysis focused on all overflights and revealed the transportation network of overflights above DENA. Phase 2 of analysis focused on low-level overflights that fly at or below 21,000 feet above mean sea level (MSL) and flew within 10 miles of the DENA boundary and found that the majority of waypoints were below 12,000 feet MSL. Phase 3 of analysis removed all overflights that were government flights, major airlines, and survey flights. The remaining flights were low-level overflights predominantly air tours. Kernel density analysis was conducted using waypoints segmented into 500 feet above ground level (AGL) altitude intervals. The altitude interval with the highest density of overflights was ?0?500 feet AGL?. This information can be used for planning and management purposes and this study serves as a resource for future research that intends to use more advanced analytics.
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Nechaev, V., Володимир Миколайович Соловйов, and A. Nagibas. Complex economic systems structural organization modelling. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1118.
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One of the well-known results of the theory of management is the fact, that multi-stage hierarchical organization of management is unstable. Hence, the ideas expressed in a number of works by Don Tapscott on advantages of network organization of businesses over vertically integrated ones is clear. While studying the basic tendencies of business organization in the conditions of globalization, computerization and internetization of the society and the results of the financial activities of the well-known companies, the authors arrive at the conclusion, that such companies, as IBM, Boeing, Mercedes-Benz and some others companies have not been engaged in their traditional business for a long time. Their partner networks performs this function instead of them. The companies themselves perform the function of system integrators. The Tapscott’s idea finds its confirmation within the framework of a new powerful direction of the development of the modern interdisciplinary science – the theory of the complex networks (CN) [2]. CN-s are multifractal objects, the loss of multifractality being the indicator of the system transition from more complex state into more simple state. We tested the multifractal properties of the data using the wavelet transform modulus maxima approach in order to analyze scaling properties of our company. Comparative analysis of the singularity spectrumf(®), namely, the difference between maximum and minimum values of ® (∆ = ®max ¡ ®min) shows that IBM company is considerably more fractal in comparison with Apple Computer. Really, for it the value of ∆ is equal to 0.3, while for the vertically integrated company Apple it only makes 0.06 – 5 times less. The comparison of other companies shows that this dependence is of general character. Taking into consideration the fact that network organization of business has become dominant in the last 5-10 years, we carried out research for the selected companies in the earliest possible period of time which was determined by the availability of data in the Internet, or by historically later beginning of stock trade of computer companies. A singularity spectrum of the first group of companies turned out to be considerably narrower, or shifted toward the smaller values of ® in the pre-network period. The latter means that dynamic series were antipersistant. That is, these companies‘ management was rigidly controlled while the impact of market mechanisms was minimized. In the second group of companies if even the situation did changed it did not change for the better. In addition, we discuss applications to the construction of portfolios of stock that have a stable ratio of risk to return.
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Parker, Michael, Alex Stott, Brian Quinn, Bruce Elder, Tate Meehan, and Sally Shoop. Joint Chilean and US mobility testing in extreme environments. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42362.
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Vehicle mobility in cold and challenging terrains is of interest to both the US and Chilean Armies. Mobility in winter conditions is highly vehicle dependent with autonomous vehicles experiencing additional challenges over manned vehicles. They lack the ability to make informed decisions based on what they are “seeing” and instead need to rely on input from sensors on the vehicle, or from Unmanned Aerial Systems (UAS) or satellite data collections. This work focuses on onboard vehicle Controller Area Network (CAN) Bus sensors, driver input sensors, and some externally mounted sensors to assist with terrain identification and overall vehicle mobility. Analysis of winter vehicle/sensor data collected in collaboration with the Chilean Army in Lonquimay, Chile during July and August 2019 will be discussed in this report.
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Baete, Christophe, and Keith Parker. PR405-213601-R04 Validation of Digital Twins for Monitoring, Optimization, and Compliance of CP Systems. Pipeline Research Council International, Inc. (PRCI), 2023. http://dx.doi.org/10.55274/r0012254.
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The digital twin used in this research is a mechanistic (or deterministic) computer model that represents the cathodic protection behavior of a pipeline network. The model is calibrated based on field data such that it mimics the real-world behavior of the pipeline. The objective of this research is to validate the accuracy of the digital twin model on three industry cases. First, the sensitivity of the independent variables (soil, coating and cathodic protection properties) that are the inputs of the digital twin was investigated during a literature study, a lab and field-based modeling on a theoretical pipeline network. A statistical analysis with the multi-variate adaptive regression spline method was performed to identify the most influencing independent variables on the cathodic protection modeling. Secondly, the accuracy of the digital twin model was validated on three real-world pipeline cases with a different degree of complexity. The digital twin models were calibrated based on the available data without any additional field testing. In two out of three cases an accuracy of approximately 90% was obtained between the simulated and measured pipe-to-soil ON and OFF potentials. Digital twin models with sufficient accuracy are used to make assessments on the cathodic protection effectiveness and risk of DC stray current interference. It supports systemic improvements to CP monitoring with reduced dependence on field collected operational data - which is about preventing corrosion. A sound digital twin model is used for endorsing integrity programs and ultimately for compliance reporting to the regulator. Related webinar.
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Johansen, Richard, Alan Katzenmeyer, Kaytee Pokrzywinski, and Molly Reif. A review of sensor-based approaches for monitoring rapid response treatments of cyanoHABs. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47261.
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Water quality sensors are dynamic and vary greatly both in terms of utility and data acquisition. Data collection can range from single-parameter and one-dimensional to highly complex multiparameter spatiotemporal. Likewise, the analytical and statistical approaches range from relatively simple (e.g., linear regression) to more complex (e.g., artificial neural networks). Therefore, the decision to implement a particular water quality monitoring strategy is dependent upon many factors and varies widely. The purpose of this review was to document the current scientific literature to identify and compile approaches for water quality monitoring as well as statistical methodologies required to analyze and visualize highly diverse spatiotemporal water quality data. The literature review identified two broad categories: (1) sensor-based approaches for monitoring rapid response treatments of cyanobacterial harmful algal blooms (cyanoHABs), and (2) analytical tools and techniques to analyze complex high resolution spatial and temporal water quality data. The ultimate goal of this review is to provide the current state of the science as an array of scalable approaches, spanning from simple and practical to complex and comprehensive, and thus, equipping the US Army Corps of Engineers (USACE) water quality managers with options for technology-analysis combinations that best fit their needs.
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McDonald, Jacob, Eric Starkey, and Wendy Wright. Wadeable stream suitability assessment for long-term monitoring: Kennesaw Mountain National Battlefield Park. National Park Service, 2018. https://doi.org/10.36967/2256953.
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The Southeast Coast Network (SECN) initiated a monitoring effort to assess habitat conditions in wadeable streams at national parks, recreation areas, battlefields, and monuments in Alabama, Georgia, and South Carolina (McDonald et al. 2018a). These parks include Chattahoochee River National Recreation Area, Kennesaw Mountain National Battlefield Park, Congaree National Park, Horseshoe Bend National Military Park, and Ocmulgee National Monument. The monitoring, implemented in 2016, focuses specifically on providing relevant data to assess the physical condition of Piedmont and upper Coastal Plain streams with respect to aquatic and riparian habitats and how these habitats may be changing over time. The habitat assessment methods outlined in the protocol rely on standard data collection methods and standard operating procedures currently in use by the U.S. Geological Survey, U.S. Environmental Protection Agency, and U.S. Forest Service that have been modified to better meet the needs of National Park Service (NPS) managers. The Southeast Coast Network’s Monitoring Wadeable Stream Habitat Conditions in Southeast Coast Network Parks protocol (McDonald et al. 2018a) was developed to begin a monitoring program that will provide insight into the status of, and trends in, stream and riparian habitat conditions. The number of reaches surveyed at each park is dependent on the spatial extent of the park and the total number of wadeable streams that are present within park boundaries. Regardless of the size of the park and the number of reaches that are to be monitored, selected reaches (1) are representative of the processes influencing the streams in each park; (2) can address current and anticipated management concerns; and (3) offer the most utility for future complementary studies. The purpose of this report is to document the stream suitability survey conducted at Kennesaw Mountain National Battlefield Park (KEMO). The purpose of this survey was to determine which streams should be chosen for long-term monitoring (McDonald et al. 2018a). On 7 February 2017, SECN staff met with Chief Ranger Anthony Winegar to determine which streams in the park are of management concern and are suitable for long-term monitoring. Stream segment suitability was based on whether the stream: (1) could be safely accessed; (2) is part of a single channel, wadeable stream system; and (3) is not greatly influenced by upstream impoundments. Of the ten stream segments identified by a GIS analysis, four stream segments (two each on Noses Creek and Ward Creek) were determined to be of potential management concern (Figure 1). After evaluating the stream segments in the field, three of the segments (two on Noses Creek and one on Ward Creek) were determined to be suitable for long-term monitoring. The remaining segment (Upper Ward Creek) was determined to not be suitable for long-term monitoring (Table 1). The upstream stream segment on Ward Creek was determined not to be suitable because this portion of Ward Creek has been highly modified by the installation of a sewage line. Upper Ward Creek is now a multi-channel quasi-wetland and the SECN wadeable stream monitoring protocol was not developed to monitor multi-channel streams (McDonald et al. 2018a). Following an analysis of the basin characteristics of the three suitable stream segments and in consultation with park staff, one segment on Noses Creek and one segment on Ward Creek were chosen for long-term monitoring (Table 2). The two stream segments chosen provide comparisons between similar streams and will help determine if the upstream trails or bridges on these streams are having a downstream influence. The following report provides an overview of the methods used to assess the suitability of each stream segment and provides a description of how the monitored reaches within each stream segment will be compared in future reports.
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Barg, Rivka, Erich Grotewold, and Yechiam Salts. Regulation of Tomato Fruit Development by Interacting MYB Proteins. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7592647.bard.
Full textAbstract:
Background to the topic: Early tomato fruit development is executed via extensive cell divisions followed by cell expansion concomitantly with endoreduplication. The signals involved in activating the different modes of growth during fruit development are still inadequately understood. Addressing this developmental process, we identified SlFSM1 as a gene expressed specifically during the cell-division dependent stages of fruit development. SlFSM1 is the founder of a class of small plant specific proteins containing a divergent SANT/MYB domain (Barg et al 2005). Before initiating this project, we found that low ectopic over-expression (OEX) of SlFSM1 leads to a significant decrease in the final size of the cells in mature leaves and fruits, and the outer pericarp is substantially narrower, suggesting a role in determining cell size and shape. We also found the interacting partners of the Arabidopsis homologs of FSM1 (two, belonging to the same family), and cloned their tomato single homolog, which we named SlFSB1 (Fruit SANT/MYB–Binding1). SlFSB1 is a novel plant specific single MYB-like protein, which function was unknown. The present project aimed at elucidating the function and mode of action of these two single MYB proteins in regulating tomato fruit development. The specific objectives were: 1. Functional analysis of SlFSM1 and its interacting protein SlFSB1 in relation to fruit development. 2. Identification of the SlFSM1 and/or SlFSB1 cellular targets. The plan of work included: 1) Detailed phenotypic, histological and cellular analyses of plants ectopically expressing FSM1, and plants either ectopically over-expressing or silenced for FSB1. 2) Extensive SELEX analysis, which did not reveal any specific DNA target of SlFSM1 binding, hence the originally offered ChIP analysis was omitted. 3) Genome-wide transcriptional impact of gain- and loss- of SlFSM1 and SlFSB1 function by Affymetrix microarray analyses. This part is still in progress and therefore results are not reported, 4) Search for additional candidate partners of SlFSB1 revealed SlMYBI to be an alternative partner of FSB1, and 5) Study of the physical basis of the interaction between SlFSM1 and SlFSB1 and between FSB1 and MYBI. Major conclusions, solutions, achievements: We established that FSM1 negatively affects cell expansion, particularly of those cells with the highest potential to expand, such as the ones residing inner to the vascular bundles in the fruit pericarp. On the other hand, FSB1 which is expressed throughout fruit development acts as a positive regulator of cell expansion. It was also established that besides interacting with FSM1, FSB1 interacts also with the transcription factor MYBI, and that the formation of the FSB1-MYBI complex is competed by FSM1, which recognizes in FSB1 the same region as MYBI does. Based on these findings a model was developed explaining the role of this novel network of the three different MYB containing proteins FSM1/FSB1/MYBI in the control of tomato cell expansion, particularly during fruit development. In short, during early stages of fruit development (Phase II), the formation of the FSM1-FSB1 complex serves to restrict the expansion of the cells with the greatest expansion potential, those non-dividing cells residing in the inner mesocarp layers of the pericarp. Alternatively, during growth phase III, after transcription of FSM1 sharply declines, FSB1, possibly through complexing with the transcription factor MYBI serves as a positive regulator of the differential cell expansion which drives fruit enlargement during this phase. Additionally, a novel mechanism was revealed by which competing MYB-MYB interactions could participate in the control of gene expression. Implications, both scientific and agricultural: The demonstrated role of the FSM1/FSB1/MYBI complex in controlling differential cell growth in the developing tomato fruit highlights potential exploitations of these genes for improving fruit quality characteristics. Modulation of expression of these genes or their paralogs in other organs could serve to modify leaf and canopy architecture in various crops.