Journal articles: 'Behaviour network'

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Turčaník, Michal. "Network User Behaviour Analysis by Machine Learning Methods." Information & Security: An International Journal 50 (2021): 66–78. http://dx.doi.org/10.11610/isij.5014.

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DRNOVSEK, MATEJA, OTMAR ZORN, and MARJANA MARTINCIC. "RESPONSIBLE ENTREPRENEURS: THE NETWORK EFFECTS." Journal of Enterprising Culture 16, no. 03 (September 2008): 209–31. http://dx.doi.org/10.1142/s0218495808000168.

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By drawing from the social network literature, this research analyses relationships between strong and weak ties and network's benefits for personal success of entrepreneurs. The empirical analysis is context-based within a specific network where such multiplex ties are embedded. In particular, networks of entrepreneurs who adhere to socially responsible behaviour through activities in the Rotary International Organisation are examined. Prior research into entrepreneurship has shown the varying benefits of strong and weak ties for personal success. The aim of this study is to develop a conceptual model of strong and weak ties related benefits and their contribution to personal success to empirically test it within a specific network context. We identified the Rotary International Organisation as such a network. The findings of this study indicate that engaging in socially responsible behaviours through organised attempts, such as the Rotary International network, brings significant benefits for the personal success of entrepreneurs. The implications of our empirical analysis indicate that the socially responsible behaviour of entrepreneurs can be motivated through network enhanced joint values such as adherence to high ethical standards in doing business and advancement of an international fellowship of business and professional people.

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BADHAM, JENNIFER, FRANK KEE, and RUTH F. HUNTER. "Simulating network intervention strategies: Implications for adoption of behaviour." Network Science 6, no. 2 (May 16, 2018): 265–80. http://dx.doi.org/10.1017/nws.2018.4.

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AbstractThis study uses simulation over real and artificial networks to compare the eventual adoption outcomes of network interventions, operationalized as idealized contagion processes with different sets of seeds. While the performance depends on the details of both the network and behaviour adoption mechanisms, interventions with seeds that are central to the network are more effective than random selection in the majority of simulations, with faster or more complete adoption throughout the network. These results provide additional theoretical justification for utilizing relevant network information in the design of public health behavior interventions.

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Ssali, Sarah, Glenn Wagner, Christopher Tumwine, Annette Nannungi, and Harold Green. "HIV Clients as Agents for Prevention: A Social Network Solution." AIDS Research and Treatment 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/815823.

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HIV prevention efforts to date have not explored the potential for persons living with HIV to act as change agents for prevention behaviour in their social networks. Using egocentric social network analysis, this study examined the prevalence and social network correlates of prevention advocacy behaviours (discussing HIV in general; encouraging abstinence or condom use, HIV testing, and seeking HIV care) enacted by 39 HIV clients in Uganda. Participants engaged in each prevention advocacy behaviour with roughly 50–70% of the members in their network. The strongest determinant of engaging in prevention advocacy with more of one’s network members was having a greater proportion of network members who knew one’s HIV seropositive status, as this was associated with three of the four advocacy behaviours. These findings highlight the potential for PLHA to be key change agents for HIV prevention within their networks and the importance of HIV disclosure in facilitating prevention advocacy.

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Hunt, P. J. "Pathological behaviour in loss networks." Journal of Applied Probability 32, no. 2 (June 1995): 519–33. http://dx.doi.org/10.2307/3215305.

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Hunt and Kurtz [9] consider a loss network as the number of circuits and the offered traffics become large. They prove a functional law of large numbers for such a network and illustrate their results with some simple examples. In this paper we apply their results to slightly more complicated examples to illustrate other, and sometimes surprising, behaviour of the loss networks in heavy traffic. The networks we consider operate under somewhat unusual routing rules but this is to enable us to produce the behaviour in networks with only a few links. In larger, real-world networks it is likely that much more ‘natural' and intuitively appealing routing rules could produce similar undesirable behaviour.

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Hunt, P. J. "Pathological behaviour in loss networks." Journal of Applied Probability 32, no. 02 (June 1995): 519–33. http://dx.doi.org/10.1017/s0021900200102955.

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Hunt and Kurtz [9] consider a loss network as the number of circuits and the offered traffics become large. They prove a functional law of large numbers for such a network and illustrate their results with some simple examples. In this paper we apply their results to slightly more complicated examples to illustrate other, and sometimes surprising, behaviour of the loss networks in heavy traffic. The networks we consider operate under somewhat unusual routing rules but this is to enable us to produce the behaviour in networks with only a few links. In larger, real-world networks it is likely that much more ‘natural' and intuitively appealing routing rules could produce similar undesirable behaviour.

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Ceni, Andrea, Peter Ashwin, and Lorenzo Livi. "Interpreting Recurrent Neural Networks Behaviour via Excitable Network Attractors." Cognitive Computation 12, no. 2 (March 23, 2019): 330–56. http://dx.doi.org/10.1007/s12559-019-09634-2.

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Zungeru, Adamu Murtala, Li-Minn Ang, and Kah Phooi Seng. "Termite-Hill." International Journal of Swarm Intelligence Research 3, no. 4 (October 2012): 1–22. http://dx.doi.org/10.4018/jsir.2012100101.

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Termites present a good natural metaphor to evolutionary computation. While each individual’s computational power is small compared to more evolved species, it is the power of their colonies that inspires communication engineers. This paper presents a study of artificial termites in sensor networks for the purpose of solving its’ routing problem. The behaviours of each of the termites in their colony allow their simulation in a restricted environment. The simulating behaviour demonstrates how the termites make use of an autocatalytic behaviour to collectively find a solution for a posed problem in reasonable time. The derived algorithm termed Termite-hill demonstrates the principle of termites’ behavior to routing problem solving in the real applications of sensor networks. The performance of the algorithm was tested on static and dynamic sink scenarios. The results as compared with other routing algorithms and with varying network density show that Termite-hill is scalable and improved on network energy consumption with a control over best-effort-service.

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Santos, Carmen Rodrguez. "CONSUMER BEHAVIOUR ERASMUS NETWORK - COBEREN." International Journal of Sales, Retailing and Marketing 1, no. 4 (January 1, 2012): 61–75. http://dx.doi.org/10.5848/apbj.2012.0037.

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Copello, Alex, Jim Orford, Ray Hodgson, Gillian Tober, and Clive Barrett. "Social behaviour and network therapy." Addictive Behaviors 27, no. 3 (May 2002): 345–66. http://dx.doi.org/10.1016/s0306-4603(01)00176-9.

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Surjo, David, and Saskia S. Arndt. "The Mutant Mouse Behaviour network." Physiology & Behavior 73, no. 5 (August 2001): 691–94. http://dx.doi.org/10.1016/s0031-9384(01)00526-1.

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Manlove, Kezia, Christina Aiello, Pratha Sah, Bree Cummins, Peter J. Hudson, and Paul C. Cross. "The ecology of movement and behaviour: a saturated tripartite network for describing animal contacts." Proceedings of the Royal Society B: Biological Sciences 285, no. 1887 (September 19, 2018): 20180670. http://dx.doi.org/10.1098/rspb.2018.0670.

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Ecologists regularly use animal contact networks to describe interactions underlying pathogen transmission, gene flow, and information transfer. However, empirical descriptions of contact often overlook some features of individual movement, and decisions about what kind of network to use in a particular setting are commonly ad hoc . Here, we relate individual movement trajectories to contact networks through a tripartite network model of individual, space, and time nodes. Most networks used in animal contact studies (e.g. individual association networks, home range overlap networks, and spatial networks) are simplifications of this tripartite model. The tripartite structure can incorporate a broad suite of alternative ecological metrics like home range sizes and patch occupancy patterns into inferences about contact network metrics such as modularity and degree distribution. We demonstrate the model's utility with two simulation studies using alternative forms of ecological data to constrain the tripartite network's structure and inform expectations about the harder-to-measure metrics related to contact.

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Poulin, Robert. "Modification of host social networks by manipulative parasites." Behaviour 155, no. 7-9 (2018): 671–88. http://dx.doi.org/10.1163/1568539x-00003456.

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Abstract Social network models provide a powerful tool to estimate infection risk for individual hosts and track parasite transmission through host populations. Here, bringing together concepts from social network theory, animal personality, and parasite manipulation of host behaviour, I argue that not only are social networks shaping parasite transmission, but parasites in turn shape social networks through their effects on the behaviour of infected individuals. Firstly, I review five general categories of behaviour (mating behaviour, aggressiveness, activity levels, spatial distribution, and group formation) that are closely tied to social networks, and provide evidence that parasites can affect all of them. Secondly, I describe scenarios in which behaviour-altering parasites can modify either the role or position of individual hosts within their social network, or various structural properties (e.g., connectance, modularity) of the entire network. Experimental approaches allowing comparisons of social networks pre- versus post-infection are a promising avenue to explore the feedback loop between social networks and parasite infections.

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Curras-Perez, Rafael, Carla Ruiz-Mafe, and Silvia Sanz-Blas. "Determinants of user behaviour and recommendation in social networks." Industrial Management & Data Systems 114, no. 9 (October 7, 2014): 1477–98. http://dx.doi.org/10.1108/imds-07-2014-0219.

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Purpose – The purpose of this paper is to analyse the determinants of social network use behaviour using Theory of Planned Behaviour (TPB) and Uses and Gratifications Theory. Design/methodology/approach – A quantitative study was carried out with data gathered by personal interview using a structured questionnaire. Relationships proposed in the theoretical model were estimated through structural equation models. In total, 811 users of social networks over 14 and residents in Spain were part of the sample. Findings – Results indicate that use attitude and the subjective norm, both TPB variables, directly and significantly influence social network loyalty. Thus, network loyalty can be achieved if the individual shows a favourable attitude towards use and receives positive feed-back (comments, opinions, etc.) from friends, acquaintances and other individuals in their environment. Perceived control has no influence on social network loyalty. Finally, this research highlights the importance of socialisation and entertainment as antecedents of social network attitude. Research limitations/implications – Social network use has been analysed globally and database includes users with very different profiles (e.g. Facebook and Tuenti) what may skew the results. Practical implications – Individuals access social networks to establish social links with friends/family/acquaintances, seeking entertainment and fun, but they are never used to be fashionable or up-to-date. Given the importance of social relations, companies should motivate participation elements in their social networks profiles. Originality/value – The paper presents a model that explains the formation of loyal social network customers, integrating TPB and Uses and Gratifications Theory to enable understanding of the role of these networks in individuals’ lives.

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Perna, Andrea, and Tanya Latty. "Animal transportation networks." Journal of The Royal Society Interface 11, no. 100 (November 6, 2014): 20140334. http://dx.doi.org/10.1098/rsif.2014.0334.

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Many group-living animals construct transportation networks of trails, galleries and burrows by modifying the environment to facilitate faster, safer or more efficient movement. Animal transportation networks can have direct influences on the fitness of individuals, whereas the shape and structure of transportation networks can influence community dynamics by facilitating contacts between different individuals and species. In this review, we discuss three key areas in the study of animal transportation networks: the topological properties of networks, network morphogenesis and growth, and the behaviour of network users. We present a brief primer on elements of network theory, and then discuss the different ways in which animal groups deal with the fundamental trade-off between the competing network properties of travel efficiency, robustness and infrastructure cost. We consider how the behaviour of network users can impact network efficiency, and call for studies that integrate both network topology and user behaviour. We finish with a prospectus for future research.

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Staar, Henning, Monique Janneck, Frederik Metzger, Stefan Berwing, and Thomas Armbrüster. "Does the Playing Field Determine the Game?" International Journal of Sociotechnology and Knowledge Development 5, no. 3 (July 2013): 32–50. http://dx.doi.org/10.4018/ijskd.2013070103.

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Virtual networks are often characterized as having less formal and hierarchical structures compared to traditional intra-organizational contexts. Instead, informal actions and so-called political behaviour of individual members play an important role regarding power and decision-making. In the present paper the authors investigate the relation between structural characteristics of virtual networks and informal influence tactics of network members. Results show that there are indeed numerous relations between the ‘playing field’ provided by the network structure and the behaviour of individual network players. Surprisingly, an increase of formalization, e.g. through the use of agreements, fixed principles and rules or even contracts, did not constrain informal political behavior. On the contrary, higher formalization was comprehensively associated with higher use of micro-political tactics. In addition, the use of all tested influence tactics showed to be positively related to network outcomes in terms of product, process and service innovation on a moderate level. Overall, these first insights into the interplay between network structures and informal influence behavior suggest that the approach to restrict micro-politics through stronger structuring might not only be ineffective but possibly misconceives the potential of micro-politics for collective goals in virtual collaborations. Despite the fact that this was an exploratory study, the results strongly argue for a deeper investigation on how virtual networks could benefit from micro-political actions.

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Blaszczyk, Andreas, Reto Flückiger, Thomas Müller, and Carl-Olof Olsson. "Convergence behaviour of coupled pressure and thermal networks." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 33, no. 4 (July 1, 2014): 1233–50. http://dx.doi.org/10.1108/compel-12-2012-0378.

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Purpose – The purpose of this paper is to present a method for thermal computations of power devices based on a coupling between thermal and pressure networks. The concept of the coupling as well as the solution procedure is explained. The included examples demonstrate that the new method can be efficiently used for design of transformers and other power devices. Design/methodology/approach – The bidirectional propagation of temperature signal is introduced to the pressure network, which enables control of the power flow and a close coupling to the thermal network. The solution method is based on automatic splitting of the network definition (netlist) into two separate networks and iteratively solving the model using the Newton-Raphson approach as well as the adaptive relaxation enhanced by the direction change control. Findings – The proposed approach offers reliable convergence behaviour even for models with unknown direction of the fluid flow (bidirectional flows). The accuracy is sufficient for engineering applications and comparable with the computational fluid dynamics method. The computation times in the range of milliseconds and seconds are attractive for using the method in engineering design tools. Originality/value – The new method can be considered as a foundation for a consistent network modelling system of arbitrary thermodynamic problems including fluid flow. Such a modelling system can be used directly by device designers since the complexity of thermodynamic formulations is encapsulated in predefined network elements while the numerical solution is based on a standard network description and solvers (Spice).

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Goold, Conor, Judit Vas, Christine Olsen, and Ruth C. Newberry. "Using network analysis to study behavioural phenotypes: an example using domestic dogs." Royal Society Open Science 3, no. 10 (October 2016): 160268. http://dx.doi.org/10.1098/rsos.160268.

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Phenotypic integration describes the complex interrelationships between organismal traits, traditionally focusing on morphology. Recently, research has sought to represent behavioural phenotypes as composed of quasi-independent latent traits. Concurrently, psychologists have opposed latent variable interpretations of human behaviour, proposing instead a network perspective envisaging interrelationships between behaviours as emerging from causal dependencies. Network analysis could also be applied to understand integrated behavioural phenotypes in animals. Here, we assimilate this cross-disciplinary progression of ideas by demonstrating the use of network analysis on survey data collected on behavioural and motivational characteristics of police patrol and detection dogs ( Canis lupus familiaris ). Networks of conditional independence relationships illustrated a number of functional connections between descriptors, which varied between dog types. The most central descriptors denoted desirable characteristics in both patrol and detection dog networks, with ‘Playful’ being widely correlated and possessing mediating relationships between descriptors. Bootstrap analyses revealed the stability of network results. We discuss the results in relation to previous research on dog personality, and benefits of using network analysis to study behavioural phenotypes. We conclude that a network perspective offers widespread opportunities for advancing the understanding of phenotypic integration in animal behaviour.

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KO, C. C., W. C. WONG, and T. T. TJHUNG. "Dynamic behaviour of CSMA/CD network." International Journal of Electronics 73, no. 3 (September 1992): 485–95. http://dx.doi.org/10.1080/00207219208925684.

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Boldrini, Chiara, Marco Conti, and Andrea Passarella. "Autonomic behaviour of opportunistic network routing." International Journal of Autonomous and Adaptive Communications Systems 1, no. 1 (2008): 122. http://dx.doi.org/10.1504/ijaacs.2008.019203.

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Romeo, Luis M., and Raquel Gareta. "Neural network for evaluating boiler behaviour." Applied Thermal Engineering 26, no. 14-15 (October 2006): 1530–36. http://dx.doi.org/10.1016/j.applthermaleng.2005.12.006.

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Brinda, K. V., Saraswathi Vishveshwara, and Smitha Vishveshwara. "Random network behaviour of protein structures." Mol. BioSyst. 6, no. 2 (2010): 391–98. http://dx.doi.org/10.1039/b903019k.

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Youssef, Ahmed, and Ahmed Emam. "Network Intrusion Detection Using Data Mining and Network Behaviour Analysis." International Journal of Computer Science and Information Technology 3, no. 6 (December 31, 2011): 87–98. http://dx.doi.org/10.5121/ijcsit.2011.3607.

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Badham, Jennifer, Frank Kee, and Ruth F. Hunter. "Network structure influence on simulated network interventions for behaviour change." Social Networks 64 (January 2021): 55–62. http://dx.doi.org/10.1016/j.socnet.2020.08.003.

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Bednarik, Peter, Katrin Fehl, and Dirk Semmann. "Costs for switching partners reduce network dynamics but not cooperative behaviour." Proceedings of the Royal Society B: Biological Sciences 281, no. 1792 (October 7, 2014): 20141661. http://dx.doi.org/10.1098/rspb.2014.1661.

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Social networks represent the structuring of interactions between group members. Above all, many interactions are profoundly cooperative in humans and other animals. In accordance with this natural observation, theoretical work demonstrates that certain network structures favour the evolution of cooperation. Yet, recent experimental evidence suggests that static networks do not enhance cooperative behaviour in humans. By contrast, dynamic networks do foster cooperation. However, costs associated with dynamism such as time or resource investments in finding and establishing new partnerships have been neglected so far. Here, we show that human participants are much less likely to break links when costs arise for building new links. Especially, when costs were high, the network was nearly static. Surprisingly, cooperation levels in Prisoner's Dilemma games were not affected by reduced dynamism in social networks. We conclude that the mere potential to quit collaborations is sufficient in humans to reach high levels of cooperative behaviour. Effects of self-structuring processes or assortment on the network played a minor role: participants simply adjusted their cooperative behaviour in response to the threats of losing a partner or of being expelled.

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Hunt, P. J. "Asymptotic Behaviour of an Integrated Video-Data Network." Probability in the Engineering and Informational Sciences 5, no. 4 (October 1991): 429–47. http://dx.doi.org/10.1017/s0269964800002217.

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We consider a communication network that can support both wideband video calls and narrowband data traffic. First we consider a single link and prove a weak convergence result to justify a piecewise-deterministic Markov process approximation to the system. We then generalize this approximation to allow priorities and more than one link. This second approximation is a generalization of the Erlang fixed-point approximation for loss networks and is justified via a diverse routing limit theorem.

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Partzsch, Johannes, Christian Mayr, Massimiliano Giulioni, Marko Noack, Stefan Hänzsche, Stefan Scholze, Sebastian Höppner, Paolo Del Giudice, and Rene Schüffny. "Mean Field Approach for Configuring Population Dynamics on a Biohybrid Neuromorphic System." Journal of Signal Processing Systems 92, no. 11 (June 27, 2020): 1303–21. http://dx.doi.org/10.1007/s11265-020-01556-9.

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Abstract Real-time coupling of cell cultures to neuromorphic circuits necessitates a neuromorphic network that replicates biological behaviour both on a per-neuron and on a population basis, with a network size comparable to the culture. We present a large neuromorphic system composed of 9 chips, with overall 2880 neurons and 144M conductance-based synapses. As they are realized in a robust switched-capacitor fashion, individual neurons and synapses can be configured to replicate with high fidelity a wide range of biologically realistic behaviour. In contrast to other exploration/heuristics-based approaches, we employ a theory-guided mesoscopic approach to configure the overall network to a range of bursting behaviours, thus replicating the statistics of our targeted in-vitro network. The mesoscopic approach has implications beyond our proposed biohybrid, as it allows a targeted exploration of the behavioural space, which is a non-trivial task especially in large, recurrent networks.

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AL MARZOOQ, SADIQAH, ALVARO ORTIZ-LUGO, and BENJAMIN L. VAUGHAN. "Mathematical model of biofilm-mediated pathogen persistence in a water distribution network with time-constant flows." European Journal of Applied Mathematics 29, no. 6 (June 6, 2018): 991–1019. http://dx.doi.org/10.1017/s0956792518000281.

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In industrialized nations, potable water is often provided through sophisticated water distribution systems. If pathogenic bacteria are introduced into the water distribution network, the presence of a biofilm can lead to biofilm-assisted retention of the pathogens, affecting the potability of the water. To study the dynamics of planktonic and biofilm-bound pathogens within the large network of pipes in a water distribution system, we develop a network model governing the concentration of introduced pathogens within the bulk fluid and the biofilms lining the pipes. Under time-constant flow regimes within the network, we prove that the long-time behaviour of the entire network is dependent on the Lyapunov exponents for each connection in the network when viewed in isolation and the network connectivity. An efficient algorithm is developed for predicting the long-time behaviour of the pathogen population within large networks using the network's topological ordering. The algorithm's predictions are validated using numerical simulations of the full non-linear system on a range of water distribution network sizes.

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Choi, Kanghae, Hokyoung Ryu, and Jieun Kim. "Deep Residual Networks for User Authentication via Hand-Object Manipulations." Sensors 21, no. 9 (April 23, 2021): 2981. http://dx.doi.org/10.3390/s21092981.

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With the ubiquity of wearable devices, various behavioural biometrics have been exploited for continuous user authentication during daily activities. However, biometric authentication using complex hand behaviours have not been sufficiently investigated. This paper presents an implicit and continuous user authentication model based on hand-object manipulation behaviour, using a finger-and hand-mounted inertial measurement unit (IMU)-based system and state-of-the-art deep learning models. We employed three convolutional neural network (CNN)-based deep residual networks (ResNets) with multiple depths (i.e., 50, 101, and 152 layers) and two recurrent neural network (RNN)-based long short-term memory (LSTMs): simple and bidirectional. To increase ecological validity, data collection of hand-object manipulation behaviours was based on three different age groups and simple and complex daily object manipulation scenarios. As a result, both the ResNets and LSTMs models acceptably identified users’ hand behaviour patterns, with the best average accuracy of 96.31% and F1-score of 88.08%. Specifically, in the simple hand behaviour authentication scenarios, more layers in residual networks tended to show better performance without showing conventional degradation problems (the ResNet-152 > ResNet-101 > ResNet-50). In a complex hand behaviour scenario, the ResNet models outperformed user authentication compared to the LSTMs. The 152-layered ResNet and bidirectional LSTM showed an average false rejection rate of 8.34% and 16.67% and an equal error rate of 1.62% and 9.95%, respectively.

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Chang, Teng-Wen, Hsin-Yi Huang, Chung-Wen Hung, Sambit Datta, and Terrance McMinn. "A Network Sensor Fusion Approach for a Behaviour-Based Smart Energy Environment for Co-Making Spaces." Sensors 20, no. 19 (September 25, 2020): 5507. http://dx.doi.org/10.3390/s20195507.

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User behaviour and choice is a significant parameter in the consumption patterns of energy in the built environment. This paper introduces a behavior-based approach for developing smart energy applications. With the rapid development of wireless sensor networks and the Internet of Things (IoT), human-computer interfaces can be created through the mapping of user experiences. These applications can provide users with dynamic feedback on their energy consumption patterns in their built environment. The paper describes a “Sensible Energy System” (SENS) that is based on user experience design methods with sensor network technology. Through SENS, solar energy simulation is combined with device consumption data in order to achieve an IoT network to facilitate the interaction between user behaviors and electricity consumption. The interaction between users and devices through SENS can not only optimize power consumption, but also provide consumers with additional choice and dynamic decision making regarding their consumption. This article provides an (1) understanding and analysis of users’ spatial interaction, explains the (2) planning of the new smart environment design and user experiences, discusses (3) designing a suitable Wireless sensor network (WSN) agent and energy connection, describes (4) the information that has been collected, and (5) incorporates a rooftop solar potential simulation for predicting energy outputs into the sensor network model.

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Matic, A., L. Börjesson, G. Ruocco, C. Masciovecchio, A. Mermet, F. Sette, and R. Verbeni. "Contrasting behaviour of acoustic modes in network and non-network glasses." Europhysics Letters (EPL) 54, no. 1 (April 2001): 77–83. http://dx.doi.org/10.1209/epl/i2001-00231-y.

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Rosnan, Herwina, and Nuraisyah Chua Abdullah. "International Transportation Network Companies: Behaviour of drivers and consumers." Environment-Behaviour Proceedings Journal 3, no. 8 (June 28, 2018): 167. http://dx.doi.org/10.21834/e-bpj.v3i8.1409.

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The growth of Transportation Network Companies (TNCs) in Malaysia is an evidence that their e-hailing services are well-accepted by consumers. The emergent of TNCs which is termed as industry’s “disruptive innovation” has changed the traditional way of commuting. This paper seeks to investigate how the recent business environment affects the quality of life and the behaviour of the drivers and consumers at large. The study employs a qualitative content analysis and data was collected from secondary sources. Behaviours of drivers and consumers which contributes to the change of the industry is critical for the policy-maker to respond and introduce appropriate policy.eISSN: 2398-4287 © 2018. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.DOI: https://doi.org/10.21834/e-bpj.v3i8.1409

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Chen, Zhaobo, Gangzhu Qiao, and Jianchao Zeng. "Study on the Relationship between Worker States and Unsafe Behaviours in Coal Mine Accidents Based on a Bayesian Networks Model." Sustainability 11, no. 18 (September 13, 2019): 5021. http://dx.doi.org/10.3390/su11185021.

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Unsafe behaviours, such as violations of rules and procedures, are commonly identified as important causal factors in coal mine accidents. Meanwhile, a recurring conclusion of accident investigations is that worker states, such as mental fatigue, illness, physiological fatigue, etc., are important contributory factors to unsafe behaviour. In this article, we seek to provide a quantitative analysis on the relationship between the worker state and unsafe behaviours in coal mine accidents, based on a case study drawn from Chinese practice. Using Bayesian networks (BN), a graphical structure of the network was designed with the help of three experts from a coal mine safety bureau. In particular, we propose a verbal versus numerical fuzzy probability assessment method to elicit the conditional probability of the Bayesian network. The junction tree algorithm is further employed to accomplish this analysis. According to the BN established by expert knowledge, the results show that when the worker is in a poor state, the most vulnerable unsafe behaviour is violation, followed by decision-making error. Furthermore, insufficient experience may be the most significant contributory factor to unsafe behaviour, and poor fitness for duty may be the principal state that causes unsafe behaviours.

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Bansal, Shweta, Bryan T. Grenfell, and Lauren Ancel Meyers. "When individual behaviour matters: homogeneous and network models in epidemiology." Journal of The Royal Society Interface 4, no. 16 (July 19, 2007): 879–91. http://dx.doi.org/10.1098/rsif.2007.1100.

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Heterogeneity in host contact patterns profoundly shapes population-level disease dynamics. Many epidemiological models make simplifying assumptions about the patterns of disease-causing interactions among hosts. In particular, homogeneous-mixing models assume that all hosts have identical rates of disease-causing contacts. In recent years, several network-based approaches have been developed to explicitly model heterogeneity in host contact patterns. Here, we use a network perspective to quantify the extent to which real populations depart from the homogeneous-mixing assumption, in terms of both the underlying network structure and the resulting epidemiological dynamics. We find that human contact patterns are indeed more heterogeneous than assumed by homogeneous-mixing models, but are not as variable as some have speculated. We then evaluate a variety of methodologies for incorporating contact heterogeneity, including network-based models and several modifications to the simple SIR compartmental model. We conclude that the homogeneous-mixing compartmental model is appropriate when host populations are nearly homogeneous, and can be modified effectively for a few classes of non-homogeneous networks. In general, however, network models are more intuitive and accurate for predicting disease spread through heterogeneous host populations.

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De Bartolo, S. G., S. Gabriele, and R. Gaudio. "Multifractal behaviour of river networks." Hydrology and Earth System Sciences 4, no. 1 (March 31, 2000): 105–12. http://dx.doi.org/10.5194/hess-4-105-2000.

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Abstract. A numerical multifractal analysis was performed for five river networks extracted from Calabrian natural basins represented on 1:25000 topographic sheets. The spectrum of generalised fractal dimensions, D(q), and the sequence of mass exponents, τ(q), were obtained using an efficient generalised box-counting algorithm. The multi-fractal spectrum, f(α), was deduced with a Legendre transform. Results show that the nature of the river networks analysed is multifractal, with support dimensions, D(0), ranging between 1.76 and 1.89. The importance of the specific number of digitised points is underlined, in order to accurately define, the geometry of river networks through a direct generalised box-counting measure that is not influenced by their topology. The algorithm was also applied to a square portion of the Trionto river network to investigate border effects. Results confirm the multifractal behaviour, but with D(0) = 2. Finally, some open mathematical problems related to the assessment of the box-counting dimension are discussed. Keywords: River networks; measures; multifractal spectrum

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Rao, Sally, and Lorelle Frazer. "Australian Franchisor Websites: Moving towards Network Behaviour." Journal of Theoretical and Applied Electronic Commerce Research 1, no. 1 (April 1, 2006): 56–64. http://dx.doi.org/10.3390/jtaer1010006.

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Australia is one of the leading nations using the internet in both business-to-consumer (B2C) and business-tobusiness (B2B) settings (ABS 2004). However, little academic attention has been paid to the adoption of internetbased technology in the Australian franchising sector. Thus this paper addresses the problem of how Australian franchise organisations use internet-based technologies in their franchise systems. This research examines the websites of a sample of 202 Australian franchisors to determine their function as a communication and marketing tool within the franchise network. The aim is to document the web activities undertaken by Australian franchisors to provide an exploratory and descriptive snapshot that can be used as a platform for theoretical efforts The analysis reveals that adoption of internet technologies is still in its infancy in Australian franchise systems with the major uses of franchisor websites being providing information to consumers and as a means of attracting potential franchisees. There is evidence that franchise organisations are moving towards network behaviour through the internet.

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I. Obuhuma, James, Henry O. Okoyo, and Sylvester O. McOyowo. "Driver Behaviour Profiling Using Dynamic Bayesian Network." International Journal of Modern Education and Computer Science 10, no. 7 (July 8, 2018): 50–59. http://dx.doi.org/10.5815/ijmecs.2018.07.05.

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Wang Hong-Xia and He Chen. "Dynamical behaviour of a cellular neural network." Acta Physica Sinica 52, no. 10 (2003): 2409. http://dx.doi.org/10.7498/aps.52.2409.

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Vergari, Cecilia. "Herd Behaviour in Adoption of Network Technologies." Journal of Economics 86, no. 2 (November 2005): 161–82. http://dx.doi.org/10.1007/s00712-005-0153-7.

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Field, Tony, Uli Harder, and Peter Harrison. "Network traffic behaviour in switched Ethernet systems." Performance Evaluation 58, no. 2-3 (November 2004): 243–60. http://dx.doi.org/10.1016/j.peva.2004.07.017.

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Richardson, C. J., A. Mbanefo, R. Aboofazeli, M. J. Lawrence, and D. J. Barlow. "Neural network prediction of microemulsion phase behaviour." European Journal of Pharmaceutical Sciences 4 (September 1996): S139. http://dx.doi.org/10.1016/s0928-0987(97)86412-3.

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Fan, Huiling. "Predicting Manager’s Behaviour with BP Neural Network." Journal of Physics: Conference Series 1168 (February 2019): 032032. http://dx.doi.org/10.1088/1742-6596/1168/3/032032.

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Chen, Ruohan, and Xiaohong Chen. "Formation and simulation of network aggregation behaviour." International Journal of Wireless and Mobile Computing 19, no. 3 (2020): 302. http://dx.doi.org/10.1504/ijwmc.2020.10033272.

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Chen, Xiaohong, and Ruohan Chen. "Formation and simulation of network aggregation behaviour." International Journal of Wireless and Mobile Computing 19, no. 3 (2020): 302. http://dx.doi.org/10.1504/ijwmc.2020.111218.

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Xu, Kuai, Feng Wang, Supratik Bhattacharyya, and Zhi Li Zhang. "Real-time behaviour profiling for network monitoring." International Journal of Internet Protocol Technology 5, no. 1/2 (2010): 65. http://dx.doi.org/10.1504/ijipt.2010.032616.

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Benham, Anthony, Huw Read, and Iain Sutherland. "Network Attack Analysis and the Behaviour Engine." International Journal of Computing and Network Technology 1, no. 2 (May 1, 2013): 103–16. http://dx.doi.org/10.12785/ijcnt/010202.

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Lawniczak, A. T., and X. Tang. "Network traffic behaviour near phase transition point." European Physical Journal B 50, no. 1-2 (March 2006): 231–36. http://dx.doi.org/10.1140/epjb/e2006-00141-x.

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Katsikas, Sokratis, Thomas Spyrou, Dimitris Gritzalis, and John Darzentas. "Model for network behaviour under viral attack." Computer Communications 19, no. 2 (February 1996): 124–32. http://dx.doi.org/10.1016/0140-3664(95)01033-5.

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Vakulenko, S., and M. Zimin. "An Analytically Tractable Model of Large Network." International Journal of Nanotechnology and Molecular Computation 2, no. 1 (January 2010): 1–12. http://dx.doi.org/10.4018/jnmc.2010010101.

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This paper considers specially organized networks of large size. They can serve as models of computer communication systems, economical systems, neural and genetic networks. The topology of this network is simple and the analysis of the network behaviour is an analytically tractable task, while computer simulations are difficult. The authors show that such networks generate any structurally stable attractors in particular chaotic and periodic. They can simulate all Turing machines, that is, perform any computations. In noisy cases, the reliability of such network is exponentially high as a function of network size and has a maximum for an optimal network size.

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Bachmann, Ivana, Javier Bustos-Jiménez, and Benjamin Bustos. "A Survey on Frameworks Used for Robustness Analysis on Interdependent Networks." Complexity 2020 (April 29, 2020): 1–17. http://dx.doi.org/10.1155/2020/2363514.

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The analysis of network robustness tackles the problem of studying how a complex network behaves under adverse scenarios, such as failures or attacks. In particular, the analysis of interdependent networks’ robustness focuses on the specific case of the robustness of interacting networks and their emerging behaviors. This survey systematically reviews literature of frameworks that analyze the robustness of interdependent networks published between 2005 and 2017. This review shows that there exists a broad range of interdependent network models, robustness metrics, and studies that can be used to understand the behaviour of different systems under failure or attack. Regarding models, we found that there is a focus on systems where a node in one layer interacts with exactly one node at another layer. In studies, we observed a focus on the network percolation. While among the metrics, we observed a focus on measures that count network elements. Finally, for the networks used to test the frameworks, we found that the focus was on synthetic models, rather than analysis of real network systems. This review suggests opportunities in network research, such as the study of robustness on interdependent networks with multiple interactions and/or spatially embedded networks, and the use of interdependent network models in realistic network scenarios.

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Journal articles on the topic 'Behaviour network'

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