Journal articles on the topic 'Models of self-similar and multifractal traffic'

We consider the problem of modeling self-similar bursty packet traffic using chaotic maps. In particular, we introduce a family of maps to overcome the difficulties of describing the self-similar statistics of the packet traffic as a function of the maps' parameters. The packet traffic that can be modeled with these maps varies from Poisson to bursty traffic with different decay tails in the ON and OFF regions. In contrast with Fractional Brownian Models, these maps have a multifractal spectrum so they can generate packet traffic with the same mean, variance and self-similar parameter but different higher order statistics.

This paper addresses the question of whether self-similar processes are sufficient to model packet network traffic, or whether a broader class of multifractal processes is needed. By using the absolute moments of aggregate traffic measurements, we conclude that measured local-area network (LAN) and wide-area network (WAN) traffic traces, with the sample means subtracted, are well modeled by random processes that are either exactly or asymptotically self-similar.

The problem of load balancing in intrusion detection systems is considered in this paper. The analysis of existing problems of load balancing and modern methods of their solution are carried out. Types of intrusion detection systems and their description are given. A description of the intrusion detection system, its location, and the functioning of its elements in the computer system are provided. Comparative analysis of load balancing methods based on packet inspection and service time calculation is performed. An analysis of the causes of load imbalance in the intrusion detection system elements and the effects of load imbalance is also presented. A model of a network intrusion detection system based on packet signature analysis is presented. This paper describes the multifractal properties of traffic. Based on the analysis of intrusion detection systems, multifractal traffic properties and load balancing problem, the method of balancing is proposed, which is based on the funcsioning of the intrusion detection system elements and analysis of multifractal properties of incoming traffic. The proposed method takes into account the time of deep packet inspection required to compare a packet with signatures, which is calculated based on the calculation of the information flow multifractality degree. Load balancing rules are generated by the estimated average time of deep packet inspection and traffic multifractal parameters. This paper presents the simulation results of the proposed load balancing method compared to the standard method. It is shown that the load balancing method proposed in this paper provides for a uniform load distribution at the intrusion detection system elements. This allows for high speed and accuracy of intrusion detection with high-quality multifractal load balancing.

Markov queueing models are a powerful tool to evaluate the performance of computer networks and have been used in telecommunication studies for over 100 years. To apply them to the evaluation of the modern Internet, we should not only adapt them to the contemporary network structures but also include a description of the complex stochastic patterns (self-similarity and long-range dependance) of transmitted flows. We examine the features of two Markov models of an almost self-similar process, keeping in mind the modeling of Internet traffic. We have found that the obtained results are comparable with those achieved using a well-known generator of self-similar traffic.

This paper is devoted to simulations the networks with self-similar traffic. The self-similarity in the stochastic process is identified by calculation of the Herst parameter value. Based on the results, received from the experimental research of network performance, we may conclude that the observed traffic in real-time mode is self-similar by its nature. Given results may be used for the further investigation of network traffic and work on the existing models of network traffic (particularly for new networks concepts like IoT, WSN, BYOD etc) from viewpoint of its cybersecurity. Furthermore, the adequacy of the description of real is achieved by complexifying the models, combining several models and integration of new parameters. Accordingly, for more complex models, there are higher computing abilities needed or longer time for the generation of traffic realization..

The article examines self-similar properties of real telecommunications network traffic data over a wide range of time scales. These self-similar properties are very different from the properties of traditional models based on Poisson and Markov-modulated Poisson processes. Simulation with stochastic and long range dependent traffic source models is performed, and the algorithms for buffer overflow simulation for finite buffer single server model under self-similar traffic load SSM/M/1/B are explained. The algorithms for modeling fixedlength sequence generators that are used to simulate self-similar behavior of wireless IP network traffic are developed and applied. Numerical examples are provided, and simulation results are analyzed.

Simulation modelling is carried out, which allows adequate describing the traffic of multiservice networks with the commutation of packets with the characteristic of burstiness. One of the most effective methods for studying the traffic of telecommunications systems is computer simulation modelling. By using the theory of queuing systems (QS), computer simulation modelling of packet flows (traffic) in modern multi-service networks is performed as a random self-similar process. Distribution laws such as exponential, Poisson and normal-logarithmic distributions, Pareto and Weibull distributions have been considered. The distribution of time intervals between arrivals of packages and the service duration of service of packages at different system loads has been studied. The research results show that the distribution function of time intervals between packet arrivals and the service duration of packages is in good agreement with the Pareto and Weibull distributions, but in most cases the Pareto distribution prevails. The queuing systems with the queues M/Pa/1 and Pa/M/1 has been studied, and the fractality of the intervals of requests arriving have been compared by the properties of the estimates of the system load and the service duration. It has been found out that in the system Pa/M/1, with the parameter of the form a> 2, the fractality of the intervals of requests arriving does not affect the average waiting time and load factor. However, when 𝑎≤2, as in the M/Pa/1 system, both considered statistical estimates differ. The application of adequate mathematical models of traffic allows to correctly assess the characteristics of the quality of service (QoS) of the network.

Advanced Models and Algorithms for Self-Similar IP Network Traffic Simulation and Performance Analysis The paper examines self-similar (or fractal) properties of real communication network traffic data over a wide range of time scales. These self-similar properties are very different from the properties of traditional models based on Poisson and Markov-modulated Poisson processes. Advanced fractal models of sequentional generators and fixed-length sequence generators, and efficient algorithms that are used to simulate self-similar behavior of IP network traffic data are developed and applied. Numerical examples are provided; and simulation results are obtained and analyzed.

Abstract. This paper shows how modern ideas of scaling can be used to model topography with various morphologies and also to accurately characterize topography over wide ranges of scales. Our argument is divided in two parts. We first survey the main topographic models and show that they are based on convolutions of basic structures (singularities) with noises. Focusing on models with large numbers of degrees of freedom (fractional Brownian motion (fBm), fractional Levy motion (fLm), multifractal fractionally integrated flux (FIF) model), we show that they are distinguished by the type of underlying noise. In addition, realistic models require anisotropic singularities; we show how to generalize the basic isotropic (self-similar) models to anisotropic ones. Using numerical simulations, we display the subtle interplay between statistics, singularity structure and resulting topographic morphology. We show how the existence of anisotropic singularities with highly variable statistics can lead to unwarranted conclusions about scale breaking. We then analyze topographic transects from four Digital Elevation Models (DEMs) which collectively span scales from planetary down to 50 cm (4 orders of magnitude larger than in previous studies) and contain more than 2×108 pixels (a hundred times more data than in previous studies). We use power spectra and multiscaling analysis tools to study the global properties of topography. We show that the isotropic scaling for moments of order ≤2 holds to within ±45% down to scales ≈40 m. We also show that the multifractal FIF is easily compatible with the data, while the monofractal fBm and fLm are not. We estimate the universal parameters (α, C1) characterizing the underlying FIF noise to be (1.79, 0.12), where α is the degree of multifractality (0≤α≤2, 0 means monofractal) and C1 is the degree of sparseness of the surface (0≤C1, 0 means space filling). In the same way, we investigate the variation of multifractal parameters between continents, oceans and continental margins. Our analyses show that no significant variation is found for (α, C1) and that the third parameter H, which is a degree of smoothing (higher H means smoother), is variable: our estimates are H=0.46, 0.66, 0.77 for bathymetry, continents and continental margins. An application we developped here is to use (α, C1) values to correct standard spectra of DEMs for multifractal resolution effects.

In the recent study of network traffic, it is shown that the traffic flow presents both periodic and self-similar characteristics. Due to these two features, the short-term forecast of network traffic cannot be accurately fit in either autoregressive integrated moving average (ARIMA) models which is suitable for linear behavior, or chaotic models which is corresponding to self-similarity characteristic. In this paper, our methodology suggests that by using wavelet multiresolution analysis, we can obtain a joint short-term network traffic prediction method and get a more precise forecast result as compared to using either ARIMA models or chaotic models. We also run simulations to show the improvement of prediction accuracy of our proposed approach.

The impacts of self-similar traffic on network performance has received much attention recently, however the effects of control network on self-similar traffic networks has not been fully investigated yet. Therefore, the objective of this paper is to to understand self-similarity on physical grounds in a realistic network environment. This understanding is important when developing efficient and integrated network frameworks within which end-to-end QoS guarantees are fully supported. We first introduce the techniques that we use to generate self-similar network traffic .Then we compare the performance of an Ethernet segment run with heavy-tail traffic and with exponential traffic. Our study results show that self-similar traffic, compared with traditional short-range dependent models, requires longer queues and thus larger buffers in the control network design.

The self-similarity properties of the considered traffic were checked on different time scales obtained on the available daily traffic data. An estimate of the tail severity of the distribution self-similar traffic was obtained by constructing a regression line for the additional distribution function on a logarithmic scale. The self-similarity parameter value, determined by the severity of the distribution “tail”, made it possible to confirm the assumption of traffic self-similarity. A review of models simulating real network traffic with a self-similar structure was made. Implemented tools for generating artificial traffic in accordance with the considered models. Made comparison of artificial network traffic generators according to the least squares method criterion for approximating the artificial traffic point values by the approximation function of traffic. Qualitative assessments traffic generators in the form of the software implementation complexity were taken into account, which, however, can be a subjective assessment. Comparative characteristics allow you to choose some generators that most faithfully simulate real network traffic. The proposed sequence of methods to study the network traffic properties is necessary to understand its nature and to develop appropriate models that simulate real network traffic.

Mixed multifractal analysis for functions studies the Hölder pointwise behavior of more than one single function. For a vector [Formula: see text] of [Formula: see text] functions, with [Formula: see text], we are interested in the mixed Hölder spectrum, which is the Hausdorff dimension of the set of points for which each function [Formula: see text] has exactly a given value [Formula: see text] of pointwise Hölder regularity. We will conjecture a formula which relates the mixed Hölder spectrum to some mixed averaged wavelet quantities of [Formula: see text]. We will prove an upper bound valid for any vector of uniform Hölder functions. Then we will prove the validity of the conjecture for self-similar vectors of functions, quasi-self-similar vectors and their superpositions. These functions are written as the superposition of similar structures at different scales, reminiscent of some possible modelization of turbulence or cascade models. Their expressions look also like wavelet decompositions.

Traffic streams, sources as well as aggregated traffic flows, often exhibit long-range-dependent (LRD) properties. This paper presents the theoretical foundations to justify that the behavior of traffic in a high-speed computer network can be modeled from a self-similar perspective by limiting its scope of analysis to the network layer, since the most relevant properties of self-similar processes are consistent for use in the formulation of traffic models when performing this specific task.

This special issue gathers together a number of recent papers on fractal geometry and its applications to the modeling of flow and transport in porous media. The aim is to provide a systematic approach for analyzing the statics and dynamics of fluids in fractal porous media by means of theory, modeling and experimentation. The topics covered include lacunarity analyses of multifractal and natural grayscale patterns, random packing's of self-similar pore/particle size distributions, Darcian and non-Darcian hydraulic flows, diffusion within fractals, models for the permeability and thermal conductivity of fractal porous media and hydrophobicity and surface erosion properties of fractal structures.

Fracture is ubiquitous in a crystalline material. Inspired by the observed phenomenological similarities between the spatial profile of a fractured surface and velocities in hydrodynamic turbulence, we set up a hydrodynamic description for the dynamics of fracture surface propagation mode I or opening fracture front. We consider several related continuum hydrodynamic models and use them to extract the similarities between the profile of a fractured surface and velocities in hydrodynamic turbulence. We conclude that a fractured surface should be generically self-similar with an underlying multifractal behaviour. This article is part of the theme issue ‘Statistical physics of fracture and earthquakes’.

The lack of effectiveness of the use of models of self-similar processes to the analysis of queues telecommunications systems is presented. The evolution of the flow models managed by Markovs chain is considered. The specifics of the use of Markovs flows as models of telecommunications traffic systems are considered. Models of single-channel queueing systems with input flows that have an arbitrary correlation are presented. Generalizations of the Khinchin-Pollaczek formula are given for these systems. The perspective of the application of interval methods developed by the author for queue analysis in queueing systems with correlated input flows is shown. It is suggested to use the group Poisson extraordinary flow as a model of telecommunication traffic. Interval characteristics of the given flows are reviewed and the prospects of their application are shown. The issues of multiplexing these flows during processing in queueing systems are considered. It is demonstrated that the resulting flow is also a group Poisson flow when summing up several group Poisson flows. The conclusions are confirmed by the simulation modeling results. The examples show the validity of such models to the characteristics of real video traffic flows.

Most models of earthquakes attempt to reproduce the observed scaling laws of seismic events: the Gutenberg-Richter frequency magnitude distribution, but not the Omori law for aftershocks and the multifractal distribution of hypocenters location. Many of these models are based on the idea of Self-Organized Criticality (SOC). These are dynamic systems which organize themselves into a transitional state and can reproduce the Gutenberg-Richter distribution, but generally do not reproduce the space-time distribution. Here, we suggest a model based on a fractal geometry: the two sides of a fault are modeled by means of a fractal surface. As a first step, one of them is slipped of a random amount with periodic boundary conditions, then new contact points between the surfaces are found. The area surrounded by these points is assumed to be proportional to the area of the earthquake. The size distribution of events is in good agreement with the observed Gutenberg-Richter law and the local fluctuations of the b value are explained in terms of variations of the fractal dimension of the surface. Also the multifractal distribution of earthquakes in space is well-reproduced with global properties not depending on the fractal dimension of the surface. However, we are not able to obtain something similar to the Omori law simply because we do not control the time evolution of the model.

Self-similar process with long-range dependence (LRD), that is, fractional Gaussian noise (fGn) with LRD is a widely used model of Internet traffic. It is indexed by its Hurst parameterHfGnthat linearly relates to its fractal dimensionDfGn. Note that, on the one hand, the fractal dimensionDof traffic measures local self-similarity. On the other hand, LRD is a global property of traffic, which is characterized by its Hurst parameterH. However, by using fGn, both the self-similarity and the LRD of traffic are measured byHfGn. Therefore, there is a limitation for fGn to accurately model traffic. Recently, the generalized Cauchy (GC) process was introduced to model traffic with the flexibility to separately measure the fractal dimensionDGCand the Hurst parameterHGCof traffic. However, there is a fundamental problem whether or not there exists the generality that the GC model is more conformable with real traffic than single parameter models, such as fGn,irrelevant of traffic traces used in experimental verification. The solution to that problem remains unknown but is desired for model evaluation in traffic theory or for model selection against specific issues, such as queuing analysis relating to the autocorrelation function (ACF) of arrival traffic. The key contribution of this paper is our solution to that fundamental problem (see Theorem 3.17) with the following features in analysis. (i) Set-valued analysis of the traffic of the fGn type. (ii) Set-valued analysis of the traffic of the GC type. (iii) Revealing the generality previously mentioned by comparing metrics of the traffic of the fGn type to that of the GC type.

Introduction. The association between perceived built environmental attributes and hypertension among adults has received little attention in an African context. We investigated the association between the perceived built environment and prevalent hypertension in adult South Africans. Method. A cross-sectional study was conducted using 2008-2009 Prospective Urban Rural Epidemiology data among South African (n=671) adults aged ≥35 years. Perceived built environment was assessed using the neighborhood environment walkability scale questionnaire. Prevalent hypertension was defined as previously diagnosed by a physician, screen-detected hypertension as ≥140/90 mmHg, and a combination of both as any hypertension. Logistic regressions were applied for analyses. Results. In crude logistic regressions, self-reported hypertension was associated with land use mix-diversity, street connectivity, infrastructure for walking/cycling, aesthetics, traffic, and crime. In adjusted model, land use mix-diversity was significantly associated with self-reported hypertension. In similar multivariable models, the direction and magnitude of the effects were mostly similar to the outcomes of “screen-detected hypertension” which was further predicted by perceived lack of safety from traffic. Conclusion. Perceived built environment attributes were significantly associated with hypertension. This has relevance to population-based approaches to hypertension prevention and control.

Internet traffic surveillance is gaining importance in today’s digital world. Lots of international agencies are putting in efforts to monitor the network around their countries to see suspicious activities and illegal or illegitimate transmission of messages. India, being a center of attraction for terrorist activities, is also working towards the development of such surveillance systems. NETRA or Network Traffic Analysis is one such effort being taken by the Indian Government to filter suspicious keywords from messages in the network. But is it good enough to be used at the highest level for security analysis or does the system design needs to be improved as compared to other similar systems around the world; this question is answered through this study. The comparison of NETRA is done against Dish Fire, Prism, and Echelon. The design of the NETRA scheme and implementation level analysis of the system shows few weaknesses like limited memory options, limited channels for monitoring, pre-set filters, ignoring big data demands, security concerns, social values breach and ignoring ethical issues. These can be covered through alternate options which can improve the existing system. The Inclusion of self-similarity models, Self-Configuring Network Monitoring, and smart monitoring through early intrusion detections can be embedded in the architecture of existing surveillance system to give it more depth and make it more robust.

Objective: To generate a simulation model of communication network aggregation node for the study of impact of structural, functional and load aggregation node parameters on characteristics of aggregate communications network traffic, which will make it possible to set adequate requirements for aggregation node if the given level of service quality will be observed for all categories of network traffic. Methods: The laws of waiting lines’ theory, graph theory, probability theory, fractal geometry, as well as a simulation modeling device were applied. Results: On the basis of simulation modeling, models of traffic generators were designed, forming video traffic, voice traffic and data traffic, as well as, considering self-similar nature of traffic and combinations of ON/OFF duration periods of different types of load coming in an aggregation node. A simulation model of an aggregation node was designed in “Extended GPSS World editor” programme, the former takes into account structural-functional parameters of a real aggregation node. Histograms of dependences of aggregation traffic characteristics (transmission frame and jitter delays) were obtained from parameters of an aggregation node. Practical importance: On the basis of the developed simulation model of an aggregation node, the values of transmission frame and jitter delays of an aggregation node are calculated, considering real load, structural and functional parameters of a simulated aggregation node. When applying the obtained calculated data, the adequacy of decision-making when planning and designing an aggregation node is increasing, in case the requirements on the level of service quality are fulfilled, as well as the adequacy of selecting an optimal type of an aggregation node from those offered by producer companies from a price/quality point of view.

Òhe functional architecture of the subsystem picture messaging-IMS (Internet Protocol Multimedia Subsystem) for multimedia session management for multiservice NGN/IMS networks that determine the interaction systems and NGN protocols are investigated. The basic element of the IMS core network architecture are session control function CSCF (Call/Session Control Function) which is implemented on the SIP-server (Session Initiation Protocol) using the protocol. A block diagram of the functioning of the traffic service model of multimedia services in the NGN/IMS network when establishing sessions that use the network elements of the user data server HSS (Home Subscriber Server), CSCF control system core, signaling and media gateways (SGW & MGW, Signaling Gateway & Media Gateway). The characteristics of the effectiveness of the IMS and found that in the NGN / IMS multimedia services network in real time significantly alter the traffic characteristics as a service, and efficiency, which requires new models and approaches to assess the main indicators of quality of the service Triple Play. Based on the analysis of the quality NGN/IMS networks in the provision multimedia services, the mathematical model for evaluating the quality of services. This model takes into account the properties of self-similar random process with Hurst index and describes the quality of functioning of NGN/IMS architectural concept. Based on the model studied when performing voice services with the ability to activate multimedia applications, video telephony, multimedia traffic, Triple Play services. According to the study of mathematical models of NGN / IMS networks using SIP-servers analytical expressions to assess the indicators of a common set of operating characteristics of IP-based networks, which take into account the recommendations of ITU-T Y.1540 and Y.2000 and determined the average waiting time in the queue IMS system in the provision of multimedia services with the required parameters, providing a guaranteed quality of service QoS (Quality of Service). According to the latest recommendations of the ITU-T and based on SMO with queues can be defined by five network characteristics that are indicative of the effectiveness of multi-service NGN/IMS networks and an important indicator of QoS.

Cyberattacks against the elements of technological data transmission networks represent a rather significant threat of disrupting the management of regional electric power complexes. Therefore, evaluating the functioning quality of data transmission networks in the context of cyberattacks is an important task that helps to make the right decisions on the telecommunication support of electric power systems. The known models and methods for solving this problem have limited application areas determined by the admissible packet distribution laws. The paper proposes a new method for evaluating the quality of the functioning of data transmission networks, based on modeling the process of functioning of data transmission networks in the form of a stochastic network. The proposed method removes restrictions on the form of the initial distributions and makes the assumptions about the exponential distribution of the expected time and packet servicing in modern technological data transmission networks unnecessary. The method gives the possibility to evaluate the quality of the network functioning in the context of cyberattacks for stationary Poisson transmission and self-similar traffic, represented by Pareto and Weibul flows models. The obtained evaluation results are in good agreement with the data represented in previously published papers.

The article is devoted to the development of methods infolohichnoho modeling and simulation polierhatychnoho documentary describing the test bearing properties nanomaterials that determine the 33 endurance and technological resources mixtures structural components pavement layers in terms the variety effects to cover non-stationary environment and traffic. Formalized mathematical description models and methods necessary for the system CM-PD as hardware and software testing technology polierhatychnoho structure formation nanomaterials under technical and technological orders endurance performance evaluation and resource material layers work surfaces for safe movement traffic forecasting. The peculiarities the modes interaction and the language simulation ergatic modeling for the search rational technologies creation road materials are substantiated. The proposed analytical tools aimed at forming the structure silicon-carbon polymers and composites with expected force-moment and mass-energy physicochemical properties to counteract the effects heterogeneous factors non-stationary environment. The method infological modeling processes for providing conditions for the purposeful between atomic and molecular interactions in the limited local spatial volumes is developed. Formalized parametrization of mutual functions distribution concentrations, pressure and temperatures, which jointly accelerates the stage thermodynamic self-organization and controlledly form the domain-granular structures substances artificial structural materials (SCM) road cover. The information analytical tools the KM-PD system provide the reliability the estimates endurance interval, mechanical strength, integrated resource, obtained by the methods simulation the MSC and in general, KSSSDO. Mixtures substances withstand similar effects from environmental factors and respond equally to microphase spatial separation compatible component components. The maximum levels energy efficiency ordered load bearing coverings are the achievement for the operation the transport and road complex with non-stationary flows moving objects on the surfaces the future ITS infrastructure provided by the agreed levels multiple heterogeneous interactions. KEY WORDS: THERMODYNAMIC SYNTHESIS, FORMATION OF NANOSTRUCTURES, DOCUMENTARIES DECISION, INFORMATION TECHNOLOGY, INFOLOHICHNE MODELING, CARRYING LAYERS TRANSPORT INTERACTION CRITERION SIMILARITY SECURITY RESOURCE SPACE SELF-ORGANIZATION.

The article is devoted to the development of methods infolohichnoho modeling and simulation polierhatychnoho documentary describing the test bearing properties nanomaterials that determine the endurance and technological resources mixtures structural components pavement layers in terms the variety effects to cover non-stationary environment and traffic. Formalized mathematical description models and methods necessary for the system CM-PD as hardware and software testing technology polierhatychnoho structure formation nanomaterials under technical and technological orders endurance performance evaluation and resource material layers work surfaces for safe movement traffic forecasting. The peculiarities the modes interaction and the language simulation ergatic modeling for the search rational technologies creation road materials are substantiated. The proposed analytical tools aimed at forming the structure silicon-carbon polymers and composites with expected force-moment and mass-energy physicochemical properties to counteract the effects heterogeneous factors non-stationary environment. The method infological modeling processes for providing conditions for the purposeful between atomic and molecular interactions in the limited local spatial volumes is developed. Formalized parametrization of mutual functions distribution concentrations, pressure and temperatures, which jointly accelerates the stage thermodynamic self-organization and controlledly form the domain-granular structures substances artificial structural materials (SCM) road cover. The information analytical tools the KM-PD system provide the reliability the estimates endurance interval, mechanical strength, integrated resource, obtained by the methods simulation the MSC and in general, KSSSDO. Mixtures substances withstand similar effects from environmental factors and respond equally to microphase spatial separation compatible component components. The maximum levels energy efficiency ordered load bearing coverings are the achievement for the operation the transport and road complex with non-stationary flows moving objects on the surfaces the future ITS infrastructure provided by the agreed levels multiple heterogeneous interactions. KEY WORDS: THERMODYNAMIC SYNTHESIS, FORMATION OF NANOSTRUCTURES, DOCUMENTARIES DECISION, INFORMATION TECHNOLOGY, INFOLOHICHNE MODELING, CARRYING LAYERS TRANSPORT INTERACTION CRITERION SIMILARITY SECURITY RESOURCE SPACE SELF-ORGANIZATION.

Summary Objectives: According to results from the epidemiological literature, it can be expected that the prevalence odds ratio (POR) and the prevalence ratio (PR) differ with increasing disease prevalence. We illustrate different concepts to calculate these effect measures in cross-sectional studies and discuss their advantages and weaknesses, using actual data from the ISAAC Phase III cross-sectional survey in Münster, Germany. Methods: We analyzed data on the association between self-reported traffic density and wheeze and asthma by means of the POR, obtained from a logistic regression, and the PR, which was estimated from a log-linear binomial model and from different variants of a Poisson regression. Results: The analysis based on the less frequent disease, i.e. asthma with an overall prevalence of 7.8%, yielded similar results for all estimates. When wheezing with a prevalence of 17.5% was analyzed, the POR produced the highest estimates with the widest confidence intervals. While the point estimates were similar in the log-binomial model and Poisson regression, the latter showed wider confidence intervals. When we calculated the Poisson regression with robust variances, confidence intervals narrowed. Conclusions: Since cross-sectional studies often deal with frequent diseases, we encourage analyzing cross-sectional data based on log-linear binomial models, which is the ‘natural method’ for estimating prevalence ratios. If algorithms fail to converge, a useful alternative is to define appropriate starting values or, if models still do not converge, to calculate a Poisson regression with robust estimates to control for overestimation of errors in the binomial data.

Abstract The purpose of this study is to develop a mechanism to account for the effects of a distributed denial of service attack on a switching node, as well as to assess the quality of its functioning under destructive effects. Relevance stems from the possibility of disruption of regional economic complex management due to the impact on the elements of the technological network of data transmission attacker type “distributed denial of service”. Based on the mathematical apparatus of GERT-networks the authors propose an approach to assess the quality of switching nodes. The essence lies in the representation of the data flow service node switching network data transmission under attack by an intruder in the form of a stochastic network, setting the type of partial distributions, the definition of the equivalent function, followed by determining the distribution function delay time packets. The model proposed by the authors allows to evaluate the quality of switching nodes operation under the conditions of “distributed denial of service” intruder attacks, both when transmitting stationary Poisson and self-similar traffic, represented by the Weibull and Pareto flow models. The results obtained are in good agreement with the data given in previously published works. The model allows to analyze and develop directions to improve the quality of functioning of switching nodes of technological network of data transmission under conditions of destructive information impact of the intruder.

Abstract. 1 Facts about the Workshop This workshop was convened on November 13-15 1995 by E. Falgarone and D. Schertzer within the framework of the Groupe de Recherche Mecanique des Fluides Geophysiques et Astrophysiques (GdR MFGA, Research Group of Geophysical and Astrophysical Fluid Mechanics) of Centre National de la Recherche Scientifique (CNRS, (French) National Center for Scientific Research). This Research Group is chaired by A. Babiano and the meeting was held at Ecole Normale Superieure, Paris, by courtesy of its Director E. Guyon. More than sixty attendees participated to this workshop, they came from a large number of institutions and countries from Europe, Canada and USA. There were twenty-five oral presentations as well as a dozen posters. A copy of the corresponding book of abstracts can be requested to the conveners. The theme of this meeting is somewhat related to the series of Nonlinear Variability in Geophysics conferences (NVAG1, Montreal, Aug. 1986; NVAG2, Paris, June 1988; NVAG3, Cargese (Corsica), September, 1993), as well as seven consecutive annual sessions at EGS general assemblies and two consecutive spring AGU meeting sessions devoted to similar topics. One may note that NVAG3 was a joint American Geophysical Union Chapman and European Geophysical Society Richardson Memorial conference, the first topical conference jointly sponsored by the two organizations. The corresponding proceedings were published in a special NPG issue (Nonlinear Processes in Geophysics 1, 2/3, 1994). In comparison with these previous meetings, MFGA-IDT2 is at the same time specialized to fluid turbulence and its intermittency, and an extension to the fields of astrophysics. Let us add that Nonlinear Processes in Geophysics was readily chosen as the appropriate journal for publication of these proceedings since this journal was founded in order to develop interdisciplinary fundamental research and corresponding innovative nonlinear methodologies in Geophysics. It had an appropriate editorial structure, in particular a large number of editors covering a wide range of methodologies, expertises and schools. At least two of its sections (Scaling and Multifractals, Turbulence and Diffusion) were directly related to the topics of the workshop, in any case contributors were invited to choose their editor freely. 2 Goals of the Workshop The objective of this meeting was to enhance the confrontation between turbulence theories and empirical data from geophysics and astrophysics fluids with very high Reynolds numbers. The importance of these data seems to have often been underestimated for the evaluation of theories of fully developed turbulence, presumably due to the fact that turbulence does not appear as pure as in laboratory experiments. However, they have the great advantage of giving access not only to very high Reynolds numbers (e.g. 1012 for atmospheric data), but also to very large data sets. It was intended to: (i) provide an overview of the diversity of potentially available data, as well as the necessary theoretical and statistical developments for a better use of these data (e.g. treatment of anisotropy, role of processes which induce other nonlinearities such as thermal instability, effect of magnetic field and compressibility ... ), (ii) evaluate the means of discriminating between different theories (e.g. multifractal intermittency models) or to better appreciate the relevance of different notions (e.g. Self-Organized Criticality) or phenomenology (e.g. filaments, structures), (iii) emphasise the different obstacles, such as the ubiquity of catastrophic events, which could be overcome in the various concerned disciplines, thanks to theoretical advances achieved. 3 Outlines of the Workshop During the two days of the workshop, the series of presentations covered many manifestations of turbulence in geophysics, including: oceans, troposphere, stratosphere, very high atmosphere, solar wind, giant planets, interstellar clouds... up to the very large scale of the Universe. The presentations and the round table at the end of the workshop pointed out the following: - the necessity of this type of confrontation which makes intervene numerical simulations, laboratory experiments, phenomenology as well as a very large diversity of geophysical and astrophysical data, - presumably a relative need for new geophysical data, whereas there have been recent astrophysical experiments which yield interesting data and exciting questions; - the need to develop a closer intercomparison between various intermittency models (in particular Log-Poisson /Log Levy models). Two main questions were underlined, in particular during the round table: - the behaviour of the extremes of intermittency, in particular the question of divergence or convergence of the highest statistical moments (equivalently, do the probability distributions have algebraic or more rapid falloffs?); - the extension of scaling ranges; in other words do we need to divide geophysics and astrophysics in many small (nearly) isotropic subranges or is it sufficient to use anisotropic scaling notions over wider ranges? 4 The contributions in this special issue Recalling that some of the most useful insights into the nature of turbulence in fluids have come from observations of geophysical flows, Van Atta gives a review of the impacts of geophysical turbulence data into theories. His paper starts from Taylor's inference of the nearly isotropy of atmospheric turbulence and the corresponding elegant theoretical developments by von Karman of the theory of isotropic turbulence, up to underline the fact that the observed extremely large intermittency in geophysical turbulence also raised new fundamental questions for turbulence theory. The paper discusses the potential contribution to theoretical development from the available or currently being made geophysical turbulence measurements, as well as from some recent laboratory measurements and direct numerical simulations of stably stratified turbulent shear flows. Seuront et al. consider scaling and multiscaling properties of scalar fields (temperature and phytoplankton concentration) advected by oceanic turbulence in both Eulerian and Lagrangian frameworks. Despite the apparent complexity linked to a multifractal background, temperature and fluorescence (i.e. phytoplankton biomass surrogate) fields are expressed over a wide range of scale by only three universal multifractal parameters, H, \\alpha and C_l. On scales smaller than the characteristic scale of the ship, sampling is rather Eulerian. On larger scales, the drifting platform being advected by turbulent motions, sampling may be rather considered as Lagrangian. Observed Eulerian and Lagrangian universal multifractal properties of the physical and biological fields are discussed. Whereas theoretical models provide different scaling laws for fluid and MHD turbulent flows, no attempt has been done up to now to experimentally support evidence for these differences. Carbone et al. use measurements from the solar wind turbulence and from turbulence in ordinary fluid flows, in order to assess these differences. They show that the so-called Extended Self-Similarity (ESS) is evident in the solar wind turbulence up to a certain scale. Furthermore, up to a given order of the velocity structure functions, the scaling laws of MHD and fluids flows axe experimentally indistinguishable. However, differences can be observed for higher orders and the authors speculate on their origin. Dudok de Wit and Krasnosel'skikh present analysis of strong plasma turbulence in the vicinity of the Earth's bow shock with the help of magnetometer data from the AMPTE UKS satellite. They demonstrate that there is a departure from Gaussianity which could be a signature of multifractality. However, they point out that the complexity of plasma turbulence precludes a more quantitative understanding. Finally, the authors emphasise the fact that the duration of records prevents to obtain any reliable estimate of structure functions beyond the fourth order. Sylos Labini and Pietronero discuss the problem of galaxy correlations. They conclude from all the recently available three dimensional catalogues that the distribution of galaxies and clusters is fractal with dimension D ~ 2 up to the present observational limits without any tendency towards homogenization. This result is discussed in contrast to angular data analysis. Furthermore, they point out that the galaxy-cluster mismatch disappears when considering a multifractal distribution of matter. They emphasise that a new picture emerges which changes the standard ideas about the properties of the universe and requires a corresponding change in the related theoretical concepts. Chilla et al. investigate with the help of a laboratory experiment the possible influence of the presence of a large scale structure on the intermittency of small scale structures. They study a flow between coaxial co-rotating disks generating a strong axial vortex over a turbulent background. They show that the cascade process is preserved although strongly modified and they discuss the relevance of parameters developed for the description of intermittency in homogeneous turbulence to evaluate this modification.

OBJECTIVES/SPECIFIC AIMS: To access obesity risk perceptions, knowledge and behaviors of Hispanic taxi cab drivers and develop a better understanding of the factors that influence health outcomes in this population. METHODS/STUDY POPULATION: Focus groups were conducted at NYC H+H/Lincoln, where subjects were screened and recruited from taxi bases with the help of the local Federation of Taxi Drivers. This was done by utilizing flyers, messages through taxi-base radios, and referrals from livery cab drivers. Approval from the local Institutional Review Board was obtained. The research investigators, developed a structured focus group procedural protocol of open-ended interview questions related to cardiovascular disease. Participants for the focus groups were older than 18 years old and working as livery cab drivers in NYC for at least 6 months. Three focus groups were held with informed consent obtained from each participant in their primary language before the start of each session. After completion of the focus group, participants received a gift voucher for attending the approximately 1-hour session. Focus groups were moderated by trained research staff members at Lincoln. Three main categories of questions were organized based on perception, knowledge, and behavior. Participants were questioned on topics about obesity, CVD and diabetes knowledge; knowledge about etiology, risk perception, possible prevention and interventions. Responses were recorded using audiotapes and transcribed verbatim. If participants did not elaborate on the initial question, a probing question was asked to clarify. The transcript was translated from Spanish by trained bilingual staff and analyzed using standard qualitative techniques with open code method. Four research investigators read the transcript separately and formulated concepts, which were then categorized and formulated into dominant themes. These themes were then compared and analyzed with a group consensus to ensure representative data. Once recurring themes emerged and the saturation point was reached, the study concluded, after enrolling 25 participants. The Health Believe Model (HBM) was employed to understand and explain the perceptions and behaviors of taxi drivers. HBM is one of the most widely recognized models and is used to understand, predict and modify health behavior. HBM helps to identify perception of risks of unhealthy behavior, barriers for having healthy behavior, actions taken by patients to stay healthy, self-efficacy and commitment to goals [12]. RESULTS/ANTICIPATED RESULTS: Of the 25 Hispanic livery cab drivers, 92% were male. The majority of taxi drivers that participated in the study were immigrants (96%), with a mean age of 53 years (ranged 21–69), and 92%, were spoke Spanish. In total, 52% participants identified themselves as Hispanic, 20% White, 4% Black, and 20% did not identify their race. Mean body mass index (BMI) was 31 (22.8–38.7) kg/m2. In all, 56% were obese and another 40% were overweight. From this sample, 50% had been diagnosed with hypertension and 27% were living with diabetes. In all, 64% had a high school education or higher. Answers provided by the taxi drivers to focus group questions were recorded, reviewed and divided into 8 dominant themes based on concepts that emerged from the focus groups discussions. (a) Focus group study findings: Themes recorded during the focus group discussions, include poor diet, sedentary lifestyle, comorbidities/risk factors, stress, health not being a priority, discipline, education, and intervention. Participants shared their opinions in regards to these themes with minimal differences, making an emphasis on the fact that the nature of their profession was the root cause. Of the themes, the top 3 dominant themes include poor diet, sedentary/lifestyle and comorbidities/risk factors. (1) Diet: The theme “Poor diet” evolved from 151 related concepts that were described by participants. All 25 participants perceived their diet as bad due to eating high-fat meals associated with the cultural food and restaurant chains with lower food prices and ease of car parking. Drivers also reported that they did not have enough time to eat healthy foods based on their long working hours. They say: “comemos muy tarde por que preferimos montar un pasajero” … stating that they preferred to pick up passengers and delay their meals. However, they consider poor diet as the most decisive factor in their increased risk for obesity, diabetes, and hypertension. (2) Life Style: The theme “Sedentary lifestyle” was derived from 147 similar concepts described by participants. They believe that physical inactivity is another leading risk factor for obesity, diabetes, and CVD. The demands of the profession force them to drive more than 10 hours per day. They understand the importance of daily exercise but they admit that at the end of the workday they are too tired to exercise or “stop working” to participate in exercise as this means less money. They also understand that family history of obesity in addition to poor diet increases their risk of obesity, diabetes, and cardiovascular risks. (3) Comorbidity: The theme “Comorbidities” developed from 143 concepts grouped together. Taxi-drivers perceived that obesity complications directly affects many vital organs, such as the kidneys, the heart, and vasculature. Participants perceive obesity as important risk factor for high blood sugar and cholesterol levels. Taxi drivers see an association between their health condition and their work as a taxi driver. However, taxi-drivers reported that they are more concerned about the economic well-being of their families than themselves. Taxi-drivers begin to intervene in their own health only when more serious health conditions related to obesity, diabetes, and hypertension developed. (4) Work Stress: The theme “Stress/other risk factors” was derived from 141 concepts. Taxi-drivers perceive their profession with lack of organization and high-stress levels as one of the leading risk factors contributing to obesity, diabetes, and cardiovascular disease. They also attribute a combination of stressful lifestyle, poor diet, lack of exercise, consumption of alcohol and cigarettes as determining factors in developing negative health outcomes. “One participant says; Tenemos el paquete completo” … we have the entire package. (5) Health as a priority: The theme “Health is not a priority” was derived from 120 concepts based on the cab drivers’ responses. Taxi drivers prioritize their work while their health takes a back seat. They work long shifts as they feel the pressures of financial responsibilities of their family. They admitted lack of intentions to change their behavior and they consider themselves as “hard headed.” Drivers changed their behavior only when serious health conditions develop that require professional medical attention. Taxi drivers explain that the lack of time as being a big factor in pursuing preventative care. (6) Personal Discipline: The theme “Discipline” evolved from 80 concepts derived from the driver’s transcripts. Taxi drivers are aware of their lack of organizational skills in general, especially when it comes to the balance between work and a healthy lifestyle. Taxi drivers recognize that not being disciplined results in the development of their obesity and chronic health conditions. Drivers admit that they do not have a fixed schedule, with no direct supervision, and cannot find the time to go to the doctor or change their behavior. (7) Health Education: The theme “Education” was derived from79 concepts noted from the focus group discussion. Taxi drivers know that their lack of health education is affecting them. With little understanding about the severity of the disease process it is difficult to take proactive measures. They are interested in the development of programs that will educate them about obesity, diabetes and CVD prevention. They want to attend programs that can educate them about prevention of obesity, diabetes, and CVD prevention with strong focus on healthy eating. They understand that this would increase their ability to change their unhealthy behavior. (8) Health interventions: The last major theme “Intervention” was derived out of 71 concepts. When asked about possible interventions that might help them towards healthy behaviors, taxi drivers think that the use of technology as a means of education is very effective. They understand the most direct route to reach them is by cellphone, email, and social media such as Facebook. They also feel that it would be good to use this type of communication to not only to inform them about health issues, but to also educate them directly. (b) Application of Health Behavior Model: We employed the HBM, one of the most utilized and easy to understand health models (18, 20–22) to explain the knowledge, perception, and health behaviors of our study participants. The HBM consist of 6 posits: (1) risk susceptibility, (2) risk severity, (3) benefits of action, and (4) barriers to action, (5) self-efficacy, and (6) cues to action [23]. According to the HBM, people’s beliefs about their risk and their perception of the benefits of taking action to avoid it, influence their readiness to take action [15, 21–22, 24]. Using the HBM, health behavior can be modified positively if the 6 posits are perceived by the person [23]. According to the results of our study, taxi drivers that participated in our study, do not perceive the severity of their risk. Participants admitted that they go to the doctor and start paying attention to their health condition only when they get seriously sick. Another posit of the HBM, understanding benefit of actions, is also not perceived by taxi drivers. Participants understand that they should be involved in physical activity, but do not pursue physical activity. They stated that they are too busy and tired to exercise daily without realizing the benefits of having a healthy life style. Findings from the focus groups also demonstrate that taxi drivers do not possess self-efficacy, as they are not confident that they are able to change their own health behavior. They openly admitted to having poor discipline, lack of organizational skills, and lack of time management skills. But, they expressed their wish to get information about time management, healthy snacks, places where they can get affordable and healthy food, learn more about different physical activities, and places where they can exercise. The sixth posit of the HBM model is the cues for action which should trigger the action to change behavior. Cues such as physical pain or illness in them or family members of cab drivers, trigger a visit to the physician’s office. Cab drivers were open to receiving educational material provided by physicians or health information provided on TV/cellphone about disease prevention. DISCUSSION/SIGNIFICANCE OF IMPACT: Obesity is steadily on the increase in the US population and has become a major public health concern [1–3]. Latinos are at the higher risk of heart diseases such as obesity, hypertension compared to other ethnical groups [3, 13]. There is a higher prevalence of obesity among particular occupational groups with cab drivers having one of the highest obesity prevalence among all professions [5, 7–9, 13]. Obesity risks therefore seem to affect NYC cab drivers who are of Latino background more than others. Surveys conducted in different countries in Asia, Europe, and Africa reported that taxi, truck, and bus show that drivers are at a higher risk of developing obesity, diabetes, and hypertension [5, 8–11]. This study is the first to evaluate the knowledge, perception, and behaviors of NYC Latino taxi cab drivers with respect to obesity. The study uncovers factors and barriers that contribute to their behavior, and identify possible ways that can modify their behavior and decrease their chances of developing obesity. The study results demonstrated that Latino immigrant taxi drivers perceive themselves at a high risk for obesity development. As the result of discussions with focus groups, the eight dominant themes were identified. Participants perceive their risk susceptibility and understand that working as a driver is a sedentary occupation with lack of physical activity significantly contributing to obesity development. Additionally, taxi drivers report that their unhealthy diet is a major factor that contributes to their weight gain. Taxi drivers perceive their poor diet as the result of the food they consume being high in fat content. Due to financial constraints and their cultural diet requirements, they feel limited to unhealthy food options. They acknowledge the risk that poor diet contributes to obesity, high cholesterol, obesity development. Participants also expressed that work stress is another important factor. Busy traffic, lack of organization, financial stress to support their families-push them to work prolonged hours. Participants also admitted that in their leisure time, they use alcohol, smoke cigarettes, and watch TV, instead of going to the gym, because they feel too tired to exercise. Taxi drivers perceive their barriers as a lack of education and knowledge about healthy food choices, places where they can buy healthy affordable snacks, information about physical activities, stress management skills, and organizational skills. Other perceived barriers that prevent them from leading healthy lifestyle include lack of discipline, lack of time for physical activity, economic uncertainty, financial responsibility and the perception that the wellbeing of their families is more important than themselves and their health. HBM is a widely used model that helps to identify perception of risks of unhealthy behavior, barriers to healthy behavior, actions taken by patients to stay healthy, self-efficacy, and commitment to goals. Based on the Glasgow theory, the core of health behavior models is the identification of the barriers and self-efficacy [25]. Our study is unique as it involves using the HBM to explain the basis of taxi cab drivers’ behavior. Results of our research study showed that our participants perceived barriers very well. However, lack of self-efficacy, lack of perceiving benefits of action, lack of cues to action, and lack of understanding the risk of disease severity explain why taxi drivers have greater risk for obesity among occupations, and are not ready to embrace health behavior modification. This qualitative study shows us where the window of opportunity for intervention lies, how we can intervene and modify the health behavior of the at-risk NYC Latino cab driver population. By Glasgow theory, self-efficacy is an important factor in behavior modification models [25]. If the barriers that are perceived by participants as too high, and self-efficacy is low, one can intervene by improving self-efficacy. Bandura has offered ways to increase patients’ self-efficacy by using three strategies: (a) setting small, incremental, and achievable goals; (b) using formalized behavioral contracting to establish goals and specify rewards; and (c) monitoring and reinforcement, including patient self-monitoring by keeping records [20]. We can also improve perception of the benefits of action by providing cues to action namely education during the office visits, by providing reading materials, and the use of modern technology (emails, interactive Web sites, apps, etc.). A study was conducted in South Asia, encouraging taxi drivers to exercise through the use of pedometers [7]. This study provides an example of ways to motivate taxi drivers, improve their self-efficacy, overcome barriers, and provide cues to action. As one of the theories that can explain and help in behavioral modification, the Health Belief model includes the impact of the environment and elements of social learning. Using this model, we were able to differentiate and identify the factors that influence their behavior that need to be addressed by health care workers and public health representatives to improve obesity related risks among inner city taxi cab drivers in NYC.

<p><strong>Abstract.</strong> Within large engineering consultancies, multi-disciplinary projects are common, incorporating large volumes of varied and volatile high-dimensional data, usually geospatial in nature, and often uncertain. <strong>Environmental Impact Assessments</strong> (EIAs), legally embedded in engineering practice as an EU directive, use a combination of data types, sourced from numerous disciplines. These provide information for stakeholders, including planners, local authorities, and the public, in projects which affect neighbourhoods, districts, and landscapes. Contemporary geovisualisations can incorporate and help use of the outputs from a variety of analysis and models, enhancing decision-making processes inherent in all engineering projects.</p><p> Treating geovisualisations as a formal communication tool allows for improved map and graphical understanding, reducing the loss of information within the communication system (MacEachren, 1995). Therefore, it is important to consider geovisualisation as a critical element of the whole information retrieval and decision-making process. Decisions within the EIA process tend to assess the likely significant environmental effects arising from proposed development, in a systematic, data informed system, allowing for a reduction in risk of damage to the environment (Town and Country Planning Regulations, 2011).</p><p> Much of the data generated for EIA is processed through models and analysis, meaning there are margins of error and uncertainty present in the data. Traditional cartographic methods can be effectively utilised in the <strong>representation of such uncertainty</strong> (MacEachren, 1992). Incorporating uncertainty in a geovisualisation allows the viewer to interpret information about the data, rather than just observe location and attributes.</p><p> This study considers four main themes: cognition (decision making), visualisation, risk and EIA. Although discrete, these all contribute to decision making in an engineering consultancy (Figure 1). A major aim is to understand how users interact with spatial data, in terms of considering the role of uncertainty in decision making within industrial practice, and assess methods of visualising such uncertainty. This requires investigation of user interaction with such visual, graphical and map-based representations.</p><p> <strong>Eye tracking using</strong> the <i>Tobii X2-30</i> will be the main method used to evaluate user interaction. Such experimental testing reflects vision as the strongest of the senses in humans, revealing both physiological eye movements and also reflecting a subconscious representation of the user's internal thoughts (Çöltekin et al., 2010). Unlike some user interaction testing methods, eye movement recordings do not rely on self-reporting, and can thus be considered objective, as well as being used to enhance traditional performance tests (Goldberg and Kotval, 1999). Eye tracking, plus the recording of keyboard and mouse clicks and recording of screen images, will be monitored to understand the user’s subliminal interaction with the data.</p><p> For this focussed and discipline-specific interaction with spatial data, <strong>user knowledge</strong> will be assessed and recorded: this important factor has been recognised through many study results (e.g. Kobus et al., 2001; Hope and Hunter, 2007) which suggest that the knowledge of users has a major impact on their decision-making abilities and outcomes. The user knowledge and experience, recorded in an individual personal profile and examined in conjunction with the environmental and engineering decisions made, will be collected through an <i>Esri Survey123</i> online document prior to the main interaction testing.</p><p> <strong>Geovisualisations</strong> will be displayed in an online interface, accessible in the users’ ‘typical’ working environment, through their standard desktop device. Avoiding novel and unique interactions (e.g. virtual reality headsets, immersive decision theatres) which may reveal interesting but irreproducible results, the regular day-to-day environment for those engineers who are charged with planning and implementing major infrastructure and environmental projects is used, giving a realistic impression of their working practices, and triggering decisions to be made which are similar to those usually made by the user. There will be no artificial environmental control during the study, with users able to pan, zoom and navigate the interface, switch on and off available layers, undertake analysis to create and incorporate added-value, interrogate data through the map interface, add their own data and develop scenarios, and in all senses interact freely with the geovisualisation (exemplified in Figure 2). Importantly, opportunities to assess data uncertainty, both implicitly and explicitly, will be incorporated. Each test should take no more than 20 minutes, with users being asked to make decisions based on the data available, the task required, and their own interpretation of the scenario.</p><p> Three <strong>case studies</strong> will be presented to the user, covering typical arenas where decision making and EIA within an engineering consultancy are interacting: a rural study relates to flood risk, an urban example addresses traffic risk near a school, and an environmentally sensitive area must consider environmental risk from the construction of a new cable route.</p><p> Once data is collected from between 30 and 40 participants, it will be analysed through established areas of interest (eAOI), used to categorise the points of gaze, fixation time, fixation count and saccades through <i>Tobii Pro Studio</i>. Capture of associated screen and keyboard interactions will also be incorporated, with statistical tests being used to establish the significance in the results.</p><p> It is hoped that the results from this study will help assist engineering consultancies, allowing for collaboration between teams, and streamlining of decision making, resulting in better practice. By discussing and considering the four discrete themes of cognition, risk, visualisation and environmental impact assessments, the research is intended to develop a rounded and informed approach for <strong>identifying issues and establishing best practice</strong> for common industry tasks.</p>

Un modelo multifractal simplificado para flujos de tráfico autosimilares A simplified multifractal model for self-similar traffic flows Ginno Millán Universidad Católica del Norte, Larrondo 1281, Coquimbo, Chile DOI: https://doi.org/10.33017/RevECIPeru2014.0003/ Resumen Este artículo propone un nuevo modelo multifractal, con el ánimo de proveer una posible explicación al fenómeno de localidad que aparece en la estimación del exponente de Hurst en series temporales estacionarias de segundo orden, representativas de los flujos de tráfico autosimilares en las actuales redes de computadoras de alta velocidad. Analíticamente se demuestra que este fenómeno se presenta cuando los flujos se componen de diversos tipos de tráficos con diferentes exponentes de Hurst. Descriptores: Autosimilitud, exponente de Hurst (H), fenómeno de localidad, multifractales. Abstract This paper proposes a new multifractal model, with the aim to provide a possible explanation to the locality phenomena to appear in the estimation of Hurst exponent in stationary second order temporal series, representing the self-similar traffic flows in high-speed computer networks. Analytically it is shown that this phenomenon occurs if the network flow consists of several components whit different Hurst exponents. Keywords: Self-similarity, Hurst exponent (H), locality phenomena, multifractals.

The article considers the issues of statistical modeling of traffic in telecommunication networks with packet switching. The simulation results are used in the development of network technical condition management systems, in particular, diagnostics, troubleshooting and network configuration management. The peculiarities of congestion control of separate network segments are emphasized. With improper analysis the overload condition can be mistaken for equipment failure. Therefore, control and elimination of congestion is a statistical task. The concept of end-to-end network diagnostics is considered. This concept provides for effective assessment of the quality of functioning of all network components taking into account their interrelationships. The main issues are the interaction of equipment, inefficient configuration, improper network organization and user operation. Methods of traffic statistical control characteristics based on perforated and marker bucket algorithms are analyzed. A feature of these algorithms is the formation of a strict output stream at a rate that does not depend on the non-uniformity of the input stream. The possibility of improving the token bucket algorithm by adapting to changes in the statistical characteristics of traffic is shown. To solve this problem, statistical mathematical models of network traffic are built. Data traffic circulating in telecommunication networks by packet switching has self-similar (fractal) properties. The self-similar process retains its properties when considered at different time scales (invariance to scale changes). The degree of statistical stability of the process with multiple scaling is determined by the Hirst parameter (the self-similarity parameter). Graphs of statistical characteristics of low-speed and high-speed data traffic are obtained. Their comparative analysis is carried out.

Biometrics In conjunction with the new development of the Internet of Things (IoT), augmented reality (AR) systems are evolving to visualize 3D virtual models of the real world into an intelligent and interactive virtual reality environment that facilitates physical identification of objects and defines their specifications efficiently. The integration between AR and IoT in a complementary way helps identify network-related items' specifications and interact with the Internet of Things more efficiently. An identity is a dedicated, publicly known attribute or set of names for an individual device. Typically, identifiers operate within a specific area or network, making it difficult to identify things globally. This paper explores the use of Augmented Reality (AR) Technology for identifying devices and displaying relevant information about the device to the user. Based on the developed model network, the developed system of identification of IoT devices was tested. Also, the traffic generated by the AR device when generating requests to the organization server was investigated. According to the test results, the system is undemanding to the main network indicators. The system-generated traffic is self-similar. The test results show that the server software can solve the problems of identifying IoT devices through interaction with augmented reality devices.

Dhaka, one of the fastest-growing megacities in the world, faces severe traffic congestion leading to a loss of 3.2 million business hours per day. While peak-spreading policies hold the promise to reduce the traffic congestion levels, the absence of comprehensive data sources makes it extremely challenging to develop econometric models of departure time choices for Dhaka. This motivates this paper, which develops advanced discrete choice models of departure time choice of car commuters using secondary data sources and quantifies how level-of-service attributes (e.g., travel time), socio-demographic characteristics (e.g., type of job, income, etc.), and situational constraints (e.g., schedule delay) affect their choices. The trip diary data of commuters making home-to-work and work-to-home trips by personal car/ride-hailing services (957 and 934 respectively) have been used in this regard. Given the discrepancy between the stated travel times and those extracted using the Google Directions API, a sub-model is developed first to derive more reliable estimates of travel time throughout the day. A mixed multinomial logit model and a simple multinomial logit model are developed for outbound and return trip, respectively, to capture the heterogeneity associated with different departure time choice of car commuters. Estimation results indicate that the choices are significantly affected by travel time, schedule delay, and socio-demographic factors. The influence of type of job on preferred departure time (PDT) has been estimated using two different distributions of PDT for office employees and self-employed people (Johnson’s SB distribution and truncated normal respectively). The proposed framework could be useful in other developing countries with similar data issues.

The main stages of the historical development of telecommunication networks are shown. It is noted that today the most common concepts are NGN (Next Generation Network), supporting the functionality of Triple-Play Services (triple services – voice, video and data transmission), as well as IMS (IP Multimedia Subsystem), which combines cellular technology and landline communications. They are able to provide a wide range of services: basic, additional, intelligent. In addition, IMS is able to provide mobile services. The IMS architecture layers are presented – transport layer, control layer; layer of service and application servers. Particular attention is paid to the layer of service and application servers. Based on the analysis of ITU recommendations, the main telecommunication services quality indexes are determined, namely, the time of service provision, the length of the queue that the service request falls into to wait for service on the server, and the probability of refusing to provide services. Based on the proposed mathematical models of the NGN intelligent superstructure, analytical expressions are presented for calculating the quality indexes of intelligent services in NGN for one type of service and for K types of services, taking into account the self-similarity of traffic, and without taking into account. To evaluate the quality of services in IMS, it is proposed to use the approaches of queuing theory and tensor analysis. Tensor analysis offers a mathematical apparatus for transforming coordinate systems, considering IMS as a set of geometric objects whose projections are different in different coordinate systems, but the physical properties of the objects themselves do not change. Expressions are proposed that make it possible to calculate the following QoS indexes in IMS: average queue length, average residence time of an application in the system for all types of services in IMS. It is considered that the incoming traffic is the simplest. Further development of the work is to take into account the self-similar nature of traffic, which is created by the flow of requests for services that come to IMS, as well as the limitations of the server buffer, which will allow a more accurate assessment of the quality of services in IMS.

Abstract Background Airport personnel are at risk of occupational exposure to jet engine emissions, which similarly to diesel exhaust emissions include volatile organic compounds and particulate matter consisting of an inorganic carbon core with associated polycyclic aromatic hydrocarbons, and metals. Diesel exhaust is classified as carcinogenic and the particulate fraction has in itself been linked to several adverse health effects including cancer. Method In this review, we summarize the available scientific literature covering human health effects of exposure to airport emissions, both in occupational settings and for residents living close to airports. We also report the findings from the limited scientific mechanistic studies of jet engine emissions in animal and cell models. Results Jet engine emissions contain large amounts of nano-sized particles, which are particularly prone to reach the lower airways upon inhalation. Size of particles and emission levels depend on type of aircraft, engine conditions, and fuel type, as well as on operation modes. Exposure to jet engine emissions is reported to be associated with biomarkers of exposure as well as biomarkers of effect among airport personnel, especially in ground-support functions. Proximity to running jet engines or to the airport as such for residential areas is associated with increased exposure and with increased risk of disease, increased hospital admissions and self-reported lung symptoms. Conclusion We conclude that though the literature is scarce and with low consistency in methods and measured biomarkers, there is evidence that jet engine emissions have physicochemical properties similar to diesel exhaust particles, and that exposure to jet engine emissions is associated with similar adverse health effects as exposure to diesel exhaust particles and other traffic emissions.

Computer vision is one of the fields of computer science that is one of the most powerful and persuasive types of artificial intelligence. It is similar to the human vision system, as it enables computers to recognize and process objects in pictures and videos in the same way as humans do. Computer vision technology has rapidly evolved in many fields and contributed to solving many problems, as computer vision contributed to self-driving cars, and cars were able to understand their surroundings. The cameras record video from different angles around the car, then a computer vision system gets images from the video, and then processes the images in real-time to find roadside ends, detect other cars, and read traffic lights, pedestrians, and objects. Computer vision also contributed to facial recognition; this technology enables computers to match images of people’s faces to their identities. which these algorithms detect facial features in images and then compare them with databases. Computer vision also play important role in Healthcare, in which algorithms can help automate tasks such as detecting Breast cancer, finding symptoms in x-ray, cancerous moles in skin images, and MRI scans. Computer vision also contributed to many fields such as image classification, object discovery, motion recognition, subject tracking, and medicine. The rapid development of artificial intelligence is making machine learning more important in his field of research. Use algorithms to find out every bit of data and predict the outcome. This has become an important key to unlocking the door to AI. If we had looked to deep learning concept, we find deep learning is a subset of machine learning, algorithms inspired by structure and function of the human brain called artificial neural networks, learn from large amounts of data. Deep learning algorithm perform a task repeatedly, each time tweak it a little to improve the outcome. So, the development of computer vision was due to deep learning. Now we'll take a tour around the convolution neural networks, let us say that convolutional neural networks are one of the most powerful supervised deep learning models (abbreviated as CNN or ConvNet). This name ;convolutional ; is a token from a mathematical linear operation between matrixes called convolution. CNN structure can be used in a variety of real-world problems including, computer vision, image recognition, natural language processing (NLP), anomaly detection, video analysis, drug discovery, recommender systems, health risk assessment, and time-series forecasting. If we look at convolutional neural networks, we see that CNN are similar to normal neural networks, the only difference between CNN and ANN is that CNNs are used in the field of pattern recognition within images mainly. This allows us to encode the features of an image into the structure, making the network more suitable for image-focused tasks, with reducing the parameters required to set-up the model. One of the advantages of CNN that it has an excellent performance in machine learning problems. So, we will use CNN as a classifier for image classification. So, the objective of this paper is that we will talk in detail about image classification in the following sections.