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Artículos de revistas sobre el tema "Multi-layer networks"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 17 de febrero de 2022

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1

Chan, Vincent W. S. "Multi-Layer Network Research: Optical Networks". IEEE Communications Magazine 58, n.º 9 (septiembre de 2020): 4. http://dx.doi.org/10.1109/mcom.2020.9214375.

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2

Puri, Sanjeev y S. P. Tripathi. "Adaptive Scalable Cross Layer Framework for Multi-hop Wireless Sensor Networks". International Journal of Engineering and Technology 3, n.º 3 (2011): 327–33. http://dx.doi.org/10.7763/ijet.2011.v3.247.

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3

Guo, Yi Min y Ya Jun Guo. "A Multi-Layer Trust Management in Open Networks". Applied Mechanics and Materials 34-35 (octubre de 2010): 915–19. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.915.

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Open networks are always changing and unpredictable. The new trust management mechanisms will be needed in dynamic networks environment to ensure the open networks. A new trust management is proposed in this paper which trust is divided into two layers: network connection trust layer and application trust layer. Network connection trust layer is the foundation. If there is no network connection trust layer, there is no application trust layer. The trust relationship between each peer layer includes the basic trust and experience trust. Experience trust value is evaluated by fuzzy theory and a new connect operator and merge operator is presented to fuzzily evaluate the recommend trust value. Result shows that this trust architecture is suitable to establish trust relationship among principals and make a new secure solution in open network environment.
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4

Fodor, Péter, Gábor Enyedi, Gábor Rétvári y Tibor Cinkler. "Layer-preference policies in multi-layer GMPLS networks". Photonic Network Communications 18, n.º 3 (25 de febrero de 2009): 300–313. http://dx.doi.org/10.1007/s11107-009-0193-y.

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5

Khawaja, Faiza Riaz, Jinfang Sheng, Bin Wang y Yumna Memon. "Uncovering Hidden Community Structure in Multi-Layer Networks". Applied Sciences 11, n.º 6 (23 de marzo de 2021): 2857. http://dx.doi.org/10.3390/app11062857.

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Community detection, also known as graph clustering, in multi-layer networks has been extensively studied in the literature. The goal of community detection is to partition vertices in a network into densely connected components so called communities. Networks contain a set of strong, dominant communities, which may interfere with the detection of weak, natural community structure. When most of the members of the weak communities also belong to stronger communities, they are extremely hard to be uncovered. We call the weak communities the hidden or disguised community structure. In this paper, we present a method to uncover weak communities in a network by weakening the strength of the dominant structure. With the aim to detect the weak communities, through experiments, we observe real-world networks to answer the question of whether real-world networks have hidden community structure or not. Results of the hidden community detection (HCD) method showed the great variation in the number of communities detected in multiple layers when compared with the results of other community detection methods.
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6

Jakushokas, Renatas y Eby G. Friedman. "Multi-Layer Interdigitated Power Distribution Networks". IEEE Transactions on Very Large Scale Integration (VLSI) Systems 19, n.º 5 (mayo de 2011): 774–86. http://dx.doi.org/10.1109/tvlsi.2010.2043453.

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7

Kuipers, Fernando y Freek Dijkstra. "Path selection in multi-layer networks". Computer Communications 32, n.º 1 (enero de 2009): 78–85. http://dx.doi.org/10.1016/j.comcom.2008.09.026.

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8

Yang, Kai y Xiaodong Wang. "Cross-layer network planning for multi-radio multi-channel cognitive wireless networks". IEEE Transactions on Communications 56, n.º 10 (octubre de 2008): 1705–14. http://dx.doi.org/10.1109/tcomm.2008.4641901.

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9

Ma, Cun, Qirui Yang, Xiaoqun Wu y Jun-an Lu. "Cluster synchronization: From single-layer to multi-layer networks". Chaos: An Interdisciplinary Journal of Nonlinear Science 29, n.º 12 (diciembre de 2019): 123120. http://dx.doi.org/10.1063/1.5122699.

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10

Ban, Jung-Chao y Chih-Hung Chang. "The layer effect on multi-layer cellular neural networks". Applied Mathematics Letters 26, n.º 7 (julio de 2013): 706–9. http://dx.doi.org/10.1016/j.aml.2013.01.013.

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11

Alimi, Isiaka Ajewale. "Effective Multi-Layer Security for Campus Network". Journal of Communications Technology, Electronics and Computer Science 2 (21 de noviembre de 2015): 6. http://dx.doi.org/10.22385/jctecs.v2i0.18.

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The development in different communication systems as well as multimedia applications and services leads to high rate of Internet usage. However, transmission of information over such networks can be compromised and security breaches such as virus, denial of service, unauthorized access, and theft of proprietary information which may have devastating impact on the system may occur if adequate security measures are not employed. Consequently, building viable, effective, and safe network is one of the main technical challenges of information transmission in campus networks. Furthermore, it has been observed that, network threats and attacks exist from the lower layers of network traffic to the application layer; therefore, this paper proposes an effective multi-layer firewall system for augmenting the functionalities of other network security technologies due to the fact that, irrespective of the type of access control being employed, attacks are still bound to occur. The effectiveness of the proposed network architecture is demonstrated using Cisco Packet Tracer. The simulation results show that, implementation of the proposed topology is viable and offers reasonable degree of security at different network layers.
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12

Javarone, Marco Alberto. "Competitive dynamics of lexical innovations in multi-layer networks". International Journal of Modern Physics C 25, n.º 10 (11 de septiembre de 2014): 1450048. http://dx.doi.org/10.1142/s012918311450048x.

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We study the introduction of lexical innovations into a community of language users. Lexical innovations, i.e. new term added to people's vocabulary, plays an important role in the process of language evolution. Nowadays, information is spread through a variety of networks, including, among others, online and offline social networks and the World Wide Web. The entire system, comprising networks of different nature, can be represented as a multi-layer network. In this context, lexical innovations diffusion occurs in a peculiar fashion. In particular, a lexical innovation can undergo three different processes: its original meaning is accepted; its meaning can be changed or misunderstood (e.g. when not properly explained), hence more than one meaning can emerge in the population. Lastly, in the case of a loan word, it can be translated into the population language (i.e. defining a new lexical innovation or using a synonym) or into a dialect spoken by part of the population. Therefore, lexical innovations cannot be considered simply as information. We develop a model for analyzing this scenario using a multi-layer network comprising a social network and a media network. The latter represents the set of all information systems of a society, e.g. television, the World Wide Web and radio. Furthermore, we identify temporal directed edges between the nodes of these two networks. In particular, at each time-step, nodes of the media network can be connected to randomly chosen nodes of the social network and vice versa. In doing so, information spreads through the whole system and people can share a lexical innovation with their neighbors or, in the event they work as reporters, by using media nodes. Lastly, we use the concept of "linguistic sign" to model lexical innovations, showing its fundamental role in the study of these dynamics. Many numerical simulations have been performed to analyze the proposed model and its outcomes.
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13

Ning, Zhao Long, Yu Huai Peng y Lei Guo. "Performance Comparison for Routing Protocols in Multi-Radio Multi-Channel Multi-Hop Wireless Networks". Advanced Materials Research 433-440 (enero de 2012): 5107–12. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.5107.

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In this paper, we consider a TDMA-based wireless network and address two major issues, fading and interference, existing in wireless networks since fading will lead to low quality links and interference may lead to unfair channel allocation. Because channel allocation selects the proper channel for each logical link and routing determines through which logical links the packets should be forwarded, there is strong interaction between MAC layer and network layer. In this paper, we seek for the performance trend with more channels allocated and further compare the performances of Bellman-Ford and AODV routing protocols based on the Packet Deliver Rate (PDR) to MAC layer in multi-radio multi-channel multi-hop wireless networks. Numerical results are given and discussions as well as insights into performance aspects for multi-radio multi-channel deployment are provided.
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14

Mittal, Ruchi y M. P. S. Bhatia. "Classifying the Influential Individuals in Multi-Layer Social Networks". International Journal of Electronics, Communications, and Measurement Engineering 8, n.º 1 (enero de 2019): 21–32. http://dx.doi.org/10.4018/ijecme.2019010102.

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Nowadays, social media is one of the popular modes of interaction and information diffusion. It is commonly found that the main source of information diffusion is done by some entities and such entities are also called as influencers. An influencer is an entity or individual who has the ability to influence others because of his/her relationship or connection with his/her audience. In this article, we propose a methodology to classify influencers from multi-layer social networks. A multi-layer social network is the same as a single layer social network depict that it includes multiple properties of a node and modeled them into multiple layers. The proposed methodology is a fusion of machine learning techniques (SVM, neural networks and so on) with centrality measures. We demonstrate the proposed algorithm on some real-life networks to validate the effectiveness of the approach in multi-layer systems.
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15

Paolucci, Francesco. "Network Service Chaining Using Segment Routing in Multi-Layer Networks". Journal of Optical Communications and Networking 10, n.º 6 (30 de abril de 2018): 582. http://dx.doi.org/10.1364/jocn.10.000582.

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16

Du, Wen-Bo, Xing-Lian Zhou, Oriol Lordan, Zhen Wang, Chen Zhao y Yan-Bo Zhu. "Analysis of the Chinese Airline Network as multi-layer networks". Transportation Research Part E: Logistics and Transportation Review 89 (mayo de 2016): 108–16. http://dx.doi.org/10.1016/j.tre.2016.03.009.

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17

Thottan, Marina, Catello Di Martino, Young-Jin Kim, Gary Atkinson, Nakjung Choi, Nishok Mohanasamy, Lalita Jagadeesan, Veena Mendiratta, Jesse E. Simsarian y Bartek Kozicki. "The network OS: Carrier-grade SDN control of multi-domain, multi-layer networks". Bell Labs Technical Journal 24 (diciembre de 2019): 1–26. http://dx.doi.org/10.15325/bltj.2018.2856598.

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18

Peng, Xing Zhao, Bi Yue Li y Hong Yao. "A Cascading Invulnerability Analysis for Multi-Layered Networks". Advanced Materials Research 846-847 (noviembre de 2013): 853–57. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.853.

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A cascading failure model for multi-layered networks is established using the Coupled Map Lattices (CML) method, the invulnerability of multi-layered network under random attacks and intentional attacks is investigated. The simulation results show that compared with isolated networks, multi-layered networks are more fragile and dont exhibit the invulnerability to suppress cascading failures under random attacks. Furthermore, we find that decreasing the inter-layer coupling strength or enhancing the inner-layer coupling strength can significantly improve the invulnerability of the multi-layered networks to resist cascading failures.
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19

Subotin, M., W. Marsh, J. McMichael, J. J. Fung y I. Dvorchik. "Performance of Multi-Layer Feedforward Neural Networks to Predict Liver Transplantation Outcome". Methods of Information in Medicine 35, n.º 01 (enero de 1996): 12–18. http://dx.doi.org/10.1055/s-0038-1634637.

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AbstractA novel multisolutional clustering and quantization (MCO) algorithm has been developed that provides a flexible way to preprocess data. It was tested whether it would impact the neural network’s performance favorably and whether the employment of the proposed algorithm would enable neural networks to handle missing data. This was assessed by comparing the performance of neural networks using a well-documented data set to predict outcome following liver transplantation. This new approach to data preprocessing leads to a statistically significant improvement in network performance when compared to simple linear scaling. The obtained results also showed that coding missing data as zeroes in combination with the MCO algorithm, leads to a significant improvement in neural network performance on a data set containing missing values in 59.4% of cases when compared to replacement of missing values with either series means or medians.
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20

Scherer, Magdalena. "Multi-layer neural networks for sales forecasting". Journal of Applied Mathematics and Computational Mechanics 17, n.º 1 (marzo de 2018): 61–68. http://dx.doi.org/10.17512/jamcm.2018.1.06.

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21

Ban, Jung-Chao y Chih-Hung Chang. "Hausdorff Dimension of Multi-Layer Neural Networks". Advances in Pure Mathematics 03, n.º 09 (2013): 9–14. http://dx.doi.org/10.4236/apm.2013.39a1002.

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22

Ban, Jung-Chao y Chih-Hung Chang. "Diamond in multi-layer cellular neural networks". Applied Mathematics and Computation 222 (octubre de 2013): 1–12. http://dx.doi.org/10.1016/j.amc.2013.07.010.

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23

YAMAGUCHI, MAKOTO. "Are multi-layer backpropagation networks catastrophically amnesic?" Scandinavian Journal of Psychology 45, n.º 5 (noviembre de 2004): 357–61. http://dx.doi.org/10.1111/j.1467-9450.2004.00417.x.

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24

Kaddoura, Omar, Juan J. Sanchez-Sanchez, Inmaculada Serrano y Raquel Barco. "Swapped Sectors Detection on Multi-Layer Networks". IEEE Communications Letters 22, n.º 11 (noviembre de 2018): 2342–45. http://dx.doi.org/10.1109/lcomm.2018.2867846.

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25

Hackl, Jürgen y Bryan T. Adey. "Modelling multi-layer spatially embedded random networks". Journal of Complex Networks 7, n.º 2 (20 de agosto de 2018): 254–80. http://dx.doi.org/10.1093/comnet/cny019.

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26

Du, Changfeng, Jinlong Ma y Dongwen Zhang. "Traffic Resource Allocation for Multi-Layer Networks". IEEE Access 8 (2020): 132134–43. http://dx.doi.org/10.1109/access.2020.3009975.

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27

BRAUSE, RÜDIGER W. "SELF-ORGANIZED LEARNING IN MULTI-LAYER NETWORKS". International Journal on Artificial Intelligence Tools 04, n.º 04 (diciembre de 1995): 433–51. http://dx.doi.org/10.1142/s0218213095000218.

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We present a framework for the self-organized formation of high level learning by a statistical preprocessing of features. The paper focuses first on the formation of the features in the context of layers of feature processing units as a kind of resource-restricted associative multiresolution learning We clame that such an architecture must reach maturity by basic statistical proportions, optimizing the information processing capabilities of each layer. The final symbolic output is learned by pure association of features of different levels and kind of sensorial input. Finally, we also show that common error-correction learning for motor skills can be accomplished also by non-specific associative learning.
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28

Shahrivar, Ebrahim Moradi y Shreyas Sundaram. "The Strategic Formation of Multi-Layer Networks". IEEE Transactions on Network Science and Engineering 2, n.º 4 (1 de octubre de 2015): 164–78. http://dx.doi.org/10.1109/tnse.2015.2500162.

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29

Iqbal, Farabi, Jeroen van der Ham y Fernando Kuipers. "Technology-aware multi-domain multi-layer routing". Computer Communications 62 (mayo de 2015): 85–96. http://dx.doi.org/10.1016/j.comcom.2015.01.010.

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30

YEN, GARY y HAIMING LU. "HIERARCHICAL GENETIC ALGORITHM FOR NEAR-OPTIMAL FEEDFORWARD NEURAL NETWORK DESIGN". International Journal of Neural Systems 12, n.º 01 (febrero de 2002): 31–43. http://dx.doi.org/10.1142/s0129065702001023.

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In this paper, we propose a genetic algorithm based design procedure for a multi-layer feed-forward neural network. A hierarchical genetic algorithm is used to evolve both the neural network's topology and weighting parameters. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies, including a feasibility check highlighted in literature. A multi-objective cost function is used herein to optimize the performance and topology of the evolved neural network simultaneously. In the prediction of Mackey–Glass chaotic time series, the networks designed by the proposed approach prove to be competitive, or even superior, to traditional learning algorithms for the multi-layer Perceptron networks and radial-basis function networks. Based upon the chosen cost function, a linear weight combination decision-making approach has been applied to derive an approximated Pareto-optimal solution set. Therefore, designing a set of neural networks can be considered as solving a two-objective optimization problem.
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31

Redwan, Renas M. "Neural networks and Sigmoid Activation Function in Multi-Layer Networks". Qubahan Academic Journal 1, n.º 2 (14 de noviembre de 2020): 29–43. http://dx.doi.org/10.48161/qaj.v1n2a11.

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Back propagation neural networks are known for computing the problems that cannot easily be computed (huge datasets analysis or training) in artificial neural networks. The main idea of this paper is to implement XOR logic gate by ANNs using back propagation neural networks for back propagation of errors, and sigmoid activation function. This neural networks to map non-linear threshold gate. The non-linear used to classify binary inputs ( ) and passing it through hidden layer for computing and ( ), after computing errors by ( ) the weights and thetas ( ) are changing according to errors. Sigmoid activation function is = and Derivation of sigmoid is = . The sig(x) and Dsig(x) is between 1 to 0.
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32

Pourhabibi, Tahereh, Kok-Leong Ong, Yee Ling Boo y Booi H. Kam. "Detecting covert communities in multi-layer networks: A network embedding approach". Future Generation Computer Systems 124 (noviembre de 2021): 467–79. http://dx.doi.org/10.1016/j.future.2021.06.027.

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33

BEN-OTHMAN, JALEL, LYNDA MOKDAD y SOUHEILA BOUAM. "AMCLM: ADAPTIVE MULTI-SERVICES CROSS-LAYER MAC PROTOCOL FOR IEEE 802.11 NETWORKS". Journal of Interconnection Networks 10, n.º 04 (diciembre de 2009): 283–301. http://dx.doi.org/10.1142/s0219265909002583.

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In wireless networks, the radio link vulnerability attributed to effects such as noise, interference, free-space loss, shadowing and multipath fading, must be considered. MAC protocols developed for these networks do not take into account these perturbations. It was shown, in the literature, that 802.11 suffers from what is called 'the 802.11 anomaly'. This anomaly concerns two aspects: all nodes throughput, in a 802.11 network, falls to the worst one of all nodes and the bandwidth will be divided by the number of the mobile nodes of the network. In order to improve the quality of service of a BSS (Basic Service Set) and to solve 802.11 anomaly, Cross-layer approaches are developed. These approaches are especially based on information given by the Physical layer. In this study we propose a new cross-layer scheme: AMCLM (Adaptive Multi-services Cross-Layer MAC). The goal of this protocol is to improve the Quality-of-Service (QoS) of Mobile Nodes (MNs) connected in a BSS by a temporary disassociation of the ones for which the SNR (Signal to Noise Ratio) is under a defined threshold. In this way, the network's throughput is improved. Our approach aims to improve global networks QoS by unselfishness decisions of nodes. In order to show the benefit of our method, a performance evaluation of this protocol has been made. We have built the discrete Markov Chain associated to the behavior of AMCLM protocol to analyze the throughput of mobile nodes connected to the BSS.
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34

Farzinvash, Leili y Mehdi Dehghan. "A cross-layer approach for multi-layer multicast routing in multi-channel multi-radio wireless mesh networks". International Journal of Ad Hoc and Ubiquitous Computing 21, n.º 1 (2016): 26. http://dx.doi.org/10.1504/ijahuc.2016.074387.

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35

Markus, E. D., O. U. Okereke y John T. Agee. "Predicting Telephone Traffic Congestion Using Multi Layer Feedforward Neural Networks". Advanced Materials Research 367 (octubre de 2011): 191–98. http://dx.doi.org/10.4028/www.scientific.net/amr.367.191.

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Predicting congestion in a telephone network has become part of an efficient network planning operation. The excellent capability of neural network (NN) to learn complex nonlinear systems makes it suitable for identifying the relationship between traffic congestion and the variables responsible for its occurrence in a time-varying traffic situation. This paper presents an artificial NN model for predicting traffic congestion in a telephone network. The design strategy uses a multilayered feedforward NN with backpropagation algorithm to model the telephone traffic situation. Matlab was used as a platform for all simulations. Regression analysis between predicted traffic congestion volumes and corresponding actual volumes gave a correlation coefficient of 87% which clearly shows the utility and effectiveness of Neural Networks in traffic prediction and control.
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36

Nortje, Wimpie D., Johann E. W. Holm, Gerhard P. Hancke, Imre J. Rudas y Laszlo Horvath. "Results of Bias-variance Tests on Multi-layer Perceptron Neural Networks". Journal of Advanced Computational Intelligence and Intelligent Informatics 5, n.º 5 (20 de septiembre de 2001): 300–305. http://dx.doi.org/10.20965/jaciii.2001.p0300.

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Training neural networks involves selection of a set of network parameters, or weights, on account of fitting a non-linear model to data. Due to the bias in the training data and small computational errors, the neural networks’ opinions are biased. Some improvement is possible when multiple networks are used to do the classification. This approach is similar to taking the average of a number of biased opinions in order to remove some of the bias that resulted from training. Bayesian networks are effective in removing some of the bias associated with training, but Bayesian techniques are tedious in terms of computational time. It is for this reason that alternatives to Bayesian networks are investigated.
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37

Wang, Yuefeng y Ibrahim Matta. "Multi-layer Virtual Transport Network management". Computer Communications 130 (octubre de 2018): 38–49. http://dx.doi.org/10.1016/j.comcom.2018.08.011.

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38

Nguyen, Tan Loc y Yonggwan Won. "Sleep snoring detection using multi-layer neural networks". Bio-Medical Materials and Engineering 26, s1 (17 de agosto de 2015): S1749—S1755. http://dx.doi.org/10.3233/bme-151475.

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39

Ban, Jung-Chao y Chih-Hung Chang. "The learning problem of multi-layer neural networks". Neural Networks 46 (octubre de 2013): 116–23. http://dx.doi.org/10.1016/j.neunet.2013.05.006.

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40

Ban, Jung-Chao y Chih-Hung Chang. "Realization problem of multi-layer cellular neural networks". Neural Networks 70 (octubre de 2015): 9–17. http://dx.doi.org/10.1016/j.neunet.2015.06.003.

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41

Coury, Denis V. y Mário Oleskovicz. "Multi-layer neural networks applied to distance relaying". International Journal of Electrical Power & Energy Systems 20, n.º 8 (noviembre de 1998): 539–42. http://dx.doi.org/10.1016/s0142-0615(98)00018-0.

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42

Oselio, Brandon, Alex Kulesza y Alfred O. Hero. "Multi-Layer Graph Analysis for Dynamic Social Networks". IEEE Journal of Selected Topics in Signal Processing 8, n.º 4 (agosto de 2014): 514–23. http://dx.doi.org/10.1109/jstsp.2014.2328312.

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43

Sabella, Roberto y Xipeng Xiao. "Guest Editorial Traffic Engineering for Multi-Layer Networks". IEEE Journal on Selected Areas in Communications 25, n.º 5 (junio de 2007): 865–67. http://dx.doi.org/10.1109/jsac.2007.070601.

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44

SATO, K. i. y H. HASEGAWA. "Prospects and Challenges of Multi-Layer Optical Networks". IEICE Transactions on Communications E90-B, n.º 8 (1 de agosto de 2007): 1890–902. http://dx.doi.org/10.1093/ietcom/e90-b.8.1890.

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45

TATIPAMULA, M., E. OKI, I. INOUE, K. SHIOMOTO y Z. ALI. "Framework for PCE Based Multi-Layer Service Networks". IEICE Transactions on Communications E90-B, n.º 8 (1 de agosto de 2007): 1903–11. http://dx.doi.org/10.1093/ietcom/e90-b.8.1903.

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46

Gerstel, Ori, Clarence Filsfils, Thomas Telkamp, Matthias Gunkel, Martin Horneffer, Victor Lopez y Arturo Mayoral. "Multi-layer capacity planning for IP-optical networks". IEEE Communications Magazine 52, n.º 1 (enero de 2014): 44–51. http://dx.doi.org/10.1109/mcom.2014.6710063.

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47

Penoff, Brad, Humaira Kamal, Alan Wagner, Mike Tsai, Karol Mroz y Janardhan Iyengar. "Employing transport layer multi-railing in cluster networks". Journal of Parallel and Distributed Computing 70, n.º 3 (marzo de 2010): 259–69. http://dx.doi.org/10.1016/j.jpdc.2009.11.005.

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48

Svozil, Daniel, Vladimír Kvasnicka y Jir̂í Pospichal. "Introduction to multi-layer feed-forward neural networks". Chemometrics and Intelligent Laboratory Systems 39, n.º 1 (noviembre de 1997): 43–62. http://dx.doi.org/10.1016/s0169-7439(97)00061-0.

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Mittal, Ruchi y M. P. S. Bhatia. "Discovering bottlenecks entities in multi-layer social networks". Journal of Discrete Mathematical Sciences and Cryptography 22, n.º 2 (17 de febrero de 2019): 241–52. http://dx.doi.org/10.1080/09720529.2019.1582870.

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Coupechoux, Marceau, Bruno Baynat, Thierry Lestable, Vinod Kumar y Christian Bonnet. "Improving the MAC Layer of Multi-Hop Networks". Wireless Personal Communications 29, n.º 1/2 (abril de 2004): 71–100. http://dx.doi.org/10.1023/b:wire.0000037571.13880.0f.

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