Relevant bibliographies by topics / Multilayer ego networks

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Academic literature on the topic 'Multilayer ego networks'

Author: Grafiati
Published: 10 March 2023

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Journal articles on the topic "Multilayer ego networks"

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Tamil Selvi P., Kishore Balasubramaniam, Vidhya S., Jayapandian N., Ramya K., Poongodi M., Mounir Hamdi, and Godwin Brown Tunze. "Social Network User Profiling With Multilayer Semantic Modeling Using Ego Network." International Journal of Information Technology and Web Engineering 17, no. 1 (January 1, 2022): 1–14. http://dx.doi.org/10.4018/ijitwe.304049.

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Social and information networks undermine the real relationship between the individuals (ego) and the friends (alters) they are connected with on social media. The structure of individual network is highlighted by the ego network. Egocentric approach is popular due to its focus on individuals, groups, or communities. Size, structure, and composition directly impact the ego networks. Moreover, analysis includes strength of ego – alter ties degree and strength of ties. Degree gives the first overview of network. Social support in the network is explored with the “gap” between the degree and average strength. These outcomes firmly propose that, regardless of whether the approaches to convey and to keep up social connections are evolving because of the dispersion of online social networks, the way individuals sort out their social connections appears to remain unaltered. As online social networks evolve, they help in receiving more diverse information.
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Parsaei, H., M. Faraz, and S. M. J. Mortazavi. "A Multilayer Perceptron Neural Network–Based Model for Predicting Subjective Health Symptoms in People Living in the Vicinity of Mobile Phone Base Stations." Ecopsychology 9, no. 2 (June 2017): 99–105. http://dx.doi.org/10.1089/eco.2017.0011.

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Lian, Fuxin, and Georg Northoff. "The Lost Neural Hierarchy of the Autistic Self—Locked-Out of the Mental Self and Its Default-Mode Network." Brain Sciences 11, no. 5 (April 29, 2021): 574. http://dx.doi.org/10.3390/brainsci11050574.

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Autism spectrum disorder (ASD) is characterized by a fundamental change in self-awareness including seemingly paradoxical features like increased ego-centeredness and weakened self-referentiality. What is the neural basis of this so-called “self-paradox”? Conducting a meta-analytic review of fMRI rest and task studies, we show that ASD exhibits consistent hypofunction in anterior and posterior midline regions of the default-mode network (DMN) in both rest and task with decreased self–non-self differentiation. Relying on a multilayered nested hierarchical model of self, as recently established (Qin et al. 2020), we propose that ASD subjects cannot access the most upper layer of their self, the DMN-based mental self—they are locked-out of their own DMN and its mental self. This, in turn, results in strong weakening of their self-referentiality with decreases in both self-awareness and self–other distinction. Moreover, this blocks the extension of non-DMN cortical and subcortical regions at the lower layers of the physical self to the DMN-based upper layer of the mental self, including self–other distinction. The ASD subjects remain stuck and restricted to their intero- and exteroceptive selves as manifested in a relative increase in ego-centeredness (as compared to self-referentiality). This amounts to what we describe as “Hierarchical Model of Autistic Self” (HAS), which, characterizing the autistic self in hierarchical and spatiotemporal terms, aligns well with and extends current theories of ASD including predictive coding and weak central coherence.
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Lafetá, Bruno Oliveira, Reynaldo Campos Santana, Gilciano Saraiva Nogueira, Tamires Mousslech Andrade Penido, and Diego Dos Santos Vieira. "Ecophysiology modeling by artificial neural networks for different spacings in eucalypt." Comunicata Scientiae 9, no. 3 (November 4, 2018): 438–48. http://dx.doi.org/10.14295/cs.v9i3.2741.

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Growth and production models are widely used to predict yields and support forestry decisions. Artificial Neural Networks (ANN) are computational models that simulate the brain and nervous system human functions, with a memory capable of establishing mathematical relationships between independent variables to estimate the dependent variables. This work aimed to evaluate the efficiency of eucalypt biomass modeling under different spacings using Multilayer Perceptron networks, trained through the backpropagation algorithm. The experiment was installed in randomized block, and the effect of five planting spacings was studied in three blocks: T1 – 3.0 x 0.5 m; T2 – 3.0 x 1.0 m; T3 – 3.0 x 1.5 m; T4 – 3.0 x 2.0 m e T5 – 3.0 x 3.0 m. A continuous forest inventory was carried out at the ages of 48, 61, 73, 85 and 101 months. The leaf area, leaf perimeter and specific leaf area were measured at 101 months in one sample tree per experimental unit. Two thousand ANN were trained, using all inventoried trees, to estimate the eco-physiological attributes and the prognosis of the wood biomass. The artificial neural networks modeling was adequate to estimate eucalypt wood biomass, according to age and under different spacings, using the diameter-at-breast-height and leaf perimeter as predictor variables.
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Park, Sang-Jun, Woo-Joong Kim, Byeong-Su Kang, Sung-Hyun Jang, Yeong-Jun Choi, and Young-Sun Hong. "Development of a Fault-Diagnosis System through the Power Conversion Module of an Electric Vehicle Fast Charger." Energies 15, no. 14 (July 11, 2022): 5056. http://dx.doi.org/10.3390/en15145056.

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The supply of electric vehicles (EVs), charging infrastructure, and the demand for chargers are rapidly increasing owing to global low-carbon and eco-friendly policies. As the maintenance of charging infrastructure varies depending on the manufacturer, fault detection and maintenance cannot be conducted promptly. Consequently, user inconvenience increases and becomes an obstacle to EV distribution. Recognizing charger failure after occurrence is a management method that is not economically effective in terms of follow-up. In this study, a data collection system was developed to diagnose EV fast-charger failure remotely in advance. The power module failure-prediction and management system consists of an AC sensor, DC sensor, temperature and humidity sensor, communication board, and data processing device. Furthermore, it was installed inside the fast charger. Four AC inputs, four DC outputs, and temperature and humidity data were collected for 12 months. Using the collected data, the power conversion efficiency was calculated and the power module status was diagnosed. In addition, a multilayer perceptron neural network was used as an algorithm for training the classification model. Charging patterns according to normal and failure were trained and verified. Based on results, the pre-failure diagnosis system demonstrated an accuracy of 97.2%.
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Song, Yang, Jun Zhao, Krzysztof Adam Ostrowski, Muhammad Faisal Javed, Ayaz Ahmad, Muhammad Ijaz Khan, Fahid Aslam, and Roman Kinasz. "Prediction of Compressive Strength of Fly-Ash-Based Concrete Using Ensemble and Non-Ensemble Supervised Machine-Learning Approaches." Applied Sciences 12, no. 1 (December 30, 2021): 361. http://dx.doi.org/10.3390/app12010361.

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The utilization of waste material, such as fly ash, in the concrete industry will provide a valuable alternative solution for creating an eco-friendly environment. However, experimental work is time-consuming; employing soft machine learning techniques can accelerate the process of forecasting the strength properties of concrete. Ensemble machine learning modeling using Python Jupyter Notebook was employed in the forecasting of compressive strength (CS) of high-performance concrete. Multilayer perceptron neuron network (MLPNN) and decision tree (DT) were used as individual learning which then ensembled with bagging and boosting to provide strong correlations. Random forest (RF) and gradient boosting regression (GBR) were also used for prediction. A total of 471 data points with input parameters (e.g., cement, fine aggregate, coarse aggregate, superplasticizer, water, days, and fly ash), and an output parameter of compressive strength (CS), were retrieved to train and test the individual learners. Cross-validation with K-fold and statistical error (i.e., MAE, MSE, RMSE, and RMSLE) analysis was applied to check the accuracy of all models. All models showed the best correlation with an ensemble model rather than an individual one. DT with AdaBoost and random forest gave a strong correlation of R2 = 0.89 with fewer errors. Cross-validation results revealed a good response with an error of less than 10 MPa. Thus, ensemble modeling not only trains the data by employing several weak learners but also produces a robust correlation that can then be used to model and predict the mechanical performance of concrete.
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Cheng, Jinyu, Ji Zhang, Zhongdao Wu, and Xiaoqiang Sun. "Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19." Briefings in Bioinformatics, December 21, 2020. http://dx.doi.org/10.1093/bib/bbaa327.

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Abstract Inferring how gene expression in a cell is influenced by cellular microenvironment is of great importance yet challenging. In this study, we present a single-cell RNA-sequencing data based multilayer network method (scMLnet) that models not only functional intercellular communications but also intracellular gene regulatory networks (https://github.com/SunXQlab/scMLnet). scMLnet was applied to a scRNA-seq dataset of COVID-19 patients to decipher the microenvironmental regulation of expression of SARS-CoV-2 receptor ACE2 that has been reported to be correlated with inflammatory cytokines and COVID-19 severity. The predicted elevation of ACE2 by extracellular cytokines EGF, IFN-γ or TNF-α were experimentally validated in human lung cells and the related signaling pathway were verified to be significantly activated during SARS-COV-2 infection. Our study provided a new approach to uncover inter-/intra-cellular signaling mechanisms of gene expression and revealed microenvironmental regulators of ACE2 expression, which may facilitate designing anti-cytokine therapies or targeted therapies for controlling COVID-19 infection. In addition, we summarized and compared different methods of scRNA-seq based inter-/intra-cellular signaling network inference for facilitating new methodology development and applications.
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Stoka, Veronika, and Vito Turk. "A structural network associated with the kallikrein-kinin and renin-angiotensin systems." Biological Chemistry 391, no. 4 (April 1, 2010). http://dx.doi.org/10.1515/bc.2010.046.

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Abstract The kallikrein-kinin and renin-angiotensin (KKS-RAS) systems represent two highly regulated proteolytic systems that are involved in several physiological and pathological processes. Although their protein-protein interactions can be studied using experimental approaches, it is difficult to differentiate between direct physical interactions and functional associations, which do not involve direct atomic contacts between macromolecules. This information can be obtained from an atomic-resolution characterization of the protein interfaces. As a result of this, various three-dimensional-based protein-protein interaction databases have become available. To gain insight into the multilayered interaction of the KKS-RAS systems, we present a protein network that is built up on three-dimensional domain-domain interactions. The essential domains that link these systems are as follows: Cystatin, Peptidase_C1, Thyroglobulin_1, Insulin, CIMR (Cation-independent mannose-6-phosphate receptor repeat), fn2 (Fibronectin type II domain), fn1 (Fibronectin type I domain), EGF, Trypsin, and Serpin. We found that the CIMR domain is located at the core of the network, thus connecting both systems. From the latter, all domain interactors up to level 4 were retrieved, thus displaying a more comprehensive representation of the KKS-RAS structural network.
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Santos, Catarina P., Eleonora Lapi, Jaime Martínez de Villarreal, Laura Álvaro-Espinosa, Asunción Fernández-Barral, Antonio Barbáchano, Orlando Domínguez, et al. "Urothelial organoids originating from Cd49fhigh mouse stem cells display Notch-dependent differentiation capacity." Nature Communications 10, no. 1 (September 27, 2019). http://dx.doi.org/10.1038/s41467-019-12307-1.

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Abstract Understanding urothelial stem cell biology and differentiation has been limited by the lack of methods for their unlimited propagation. Here, we establish mouse urothelial organoids that can be maintained uninterruptedly for >1 year. Organoid growth is dependent on EGF and Wnt activators. High CD49f/ITGA6 expression features a subpopulation of organoid-forming cells expressing basal markers. Upon differentiation, multilayered organoids undergo reduced proliferation, decreased cell layer number, urothelial program activation, and acquisition of barrier function. Pharmacological modulation of PPARγ and EGFR promotes differentiation. RNA sequencing highlighted genesets enriched in proliferative organoids (i.e. ribosome) and transcriptional networks involved in differentiation, including expression of Wnt ligands and Notch components. Single-cell RNA sequencing (scRNA-Seq) analysis of the organoids revealed five clusters with distinct gene expression profiles. Together with the use of γ-secretase inhibitors, scRNA-Seq confirms that Notch signaling is required for differentiation. Urothelial organoids provide a powerful tool to study cell regeneration and differentiation.
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Dissertations / Theses on the topic "Multilayer ego networks"

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Toprak, Mustafa. "Big Data Analytics in Online Social Networks to Characterize and Support Smart User Communities." Doctoral thesis, 2021. http://hdl.handle.net/2158/1245288.

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Advancements in Information and Communication Technologies affect all aspects of our lives, such as how we communicate with each other, how we establish social relationships, and even how we organize our professional interactions. This transformation in our daily lives also changes how we engage with the communities we are a member of. These communities are the groups where we meet other people with similar interests. We call these communities "smart" if their members use ICTs to transform their circumstances in a significant way. Recent studies show the importance of understanding the behavior of the smart community members to meet the needs of local communities in terms of services and resources. One of the ways to analyze the behavioral patterns of an individual is to focus on its social relationships. The collection of all the relationships of an individual constitutes their personal network. In this thesis, we exploit the personal networks of online community members based on a well-established framework from evolutionary anthropology, Dunbar's "ego network model". This model stems from the so-called "social brain hypothesis", which postulates that we are limited on how many relationships we can maintain due to the signal-processing capacity of our brains. These relationships have not the same importance for us, and are organized into five concentric "circles" with a decreasing intimacy moving outwards. The thesis consists of three main parts. In the first part, we focus on a specific community on Twitter (journalists from 17 countries), and we study their ego networks. We find that the same behavioral patterns observed for offline social networks also exist for these online community members, with only minimal differences across the countries. In the second part, we propose to exploit the intimacy levels of the personal networks of online community members (video gamers on Twitter, in our case study) to predict their future relationships. In this part, we show that, in the vast majority of cases, leveraging information on the social circles provides significant improvements in the prediction performance. We also validate these findings on generic Twitter users without any community information. In the third part, we bridge the gap between the literature on ego networks and that on multilayer (multicommunity) networks by introducing the concept of multilayer ego networks. Our goal is to assess whether multilayer ego networks feature the same structural regularities observed in single-layers ego networks and to investigate the role that the different layers play in how people interact with each other. Leveraging a Reddit dataset, we show that multilayer ego networks are self-similar and that people accommodate multiple layers by adapting the outermost social circles without significantly affecting the innermost ones.
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Conference papers on the topic "Multilayer ego networks"

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Wagner, Kelvin, and Demetri Psaltis. "Multilayer Optical Learning Networks." In OE LASE'87 and EO Imaging Symp (January 1987, Los Angeles), edited by Raymond Arrathoon. SPIE, 1987. http://dx.doi.org/10.1117/12.939913.

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Collings, N., A. R. Pourzand, and R. Völkel. "The construction of a programmable multilayer analogue neural network using space invariant interconnects." In Optical Computing. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/optcomp.1995.omc19.

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The use of a multilayer neural network is indicated in those cases of pattern classification where the input has a relatively low spatial complexity, eg 16 x 16 pixels. Such an input size arises in the post-segmentation stage of handwritten character recognition, or more generally after a pre-processing stage on more complex input scenes. Since the pre-processing is likely to be optical, eg Fourier or wavelet transform, it is of interest to consider the construction of an optical neural network where the training might be slow, due to the speed of the interface of the programmable weight matrices, but the classification stage would proceed at rates superior to electronics. This involves the use of stand-alone analog optical device for the intermediate layer of neural thresholding elements (hidden layer) in between the two layers of interconnects. The critical aspects of such an approach are the engineering of the programmable interconnect, the characteristics of the hidden layer optical device, the question of optical subtraction, and the use of discretization techniques to avoid the deleterious consequences of analog noise. The first aspect will be discussed in this summary and the other aspects will be more fully reported at the conference. The optical design of the system was presented previously [1], and this report will concentrate on the practical results.
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