Aggregate Flows Research Articles

Delay Bound Analysis of Networks based on Flow Aggregation

Delay Bound Analysis of Networks based on Flow Aggregation

Platelet activation during chronic hepatitis B infection exacerbates liver inflammation and promotes fibrosis.

Recurrent hepatitis activity during chronic hepatitis B virus infection results in fibrosis and even hepatocellular carcinoma. It is still unclear what causes acute exacerbation. As platelets have recently been identified as a significant role in inflammation, we here investigated the role of platelets in mediating liver damage in patients with chronic hepatitis B virus infection. Platelet aggregation testing and flow cytometry were carried out to evaluate platelet activation status in 121 patients chronically infected with hepatitis B across different phases of the condition. The correlation between platelet aggregation rate and liver inflammation or liver fibrosis index was evaluated. To investigate the genesis of platelet activation, several serum cytokines were also assessed by MILLIPLEX microsphere-based multiplex cytokine assay. Active hepatitis patients showed a higher aggregation rate than others. Levels of CD62p, a marker of platelet activation, were also increased in this group of patients. Positive correlations between platelet aggregation rate and liver inflammation or liver fibrosis were also noted, indicating a significant role of platelet in the progression of liver disease. The level of tumor necrosis factor-alpha, which is known to trigger platelet activation, was markedly higher in the active hepatitis group (P < .005). Based on the findings in our study, platelet activation plays a vital role in the progression of chronic hepatitis B virus infection. Antiplatelet therapy may provide a new means of hepatitis B infection treatment.

Terrorist attacks and investor risk preference: Evidence from mutual fund flows

Using a comprehensive list of terrorist attacks over three decades, we find that aggregate investor risk aversion inversely relates to terrorist activity in the United States. A one standard deviation increase in the number of attacks each month leads to a $75.09 million drop in aggregate flows to equity funds and a $56.81 million increase to government bond funds. Tests on alternative channels further suggest that the shift in aggregate risk aversion is driven mainly by an emotional shock rather than changes in wealth or the outside environment. We also investigate possible alternate explanations for reduced flows to risky assets. Our evidence is consistent with a fear-induced increase in aggregate risk aversion.

Enhanced Rough K-Means Based Flow Aggregation for QoS Mapping in Heterogeneous Network Environments

Flow aggregation is capable of reducing the burden on core routing and provide the scalability of network. Among the state-of-the-art methods of QoS (Quality of Service) mapping, the existing schemes lack effective means to guarantee the QoS of Internet flows in variable network environment. To tackle this problem, in this paper, we design a dynamic flow aggregation method that uses an enhanced rough ${k}$ -means algorithm (ERKM) to properly aggregate network flows and perform flexible QoS mapping across different networks. More importantly, it is able to adjust the allocation of flows according to the degree of membership in ever-changing Internet environment, and control the distance threshold parameter th to make the algorithm more flexible. The experimental results suggest that the proposed method outperforms other methods in terms of QoS support in the high-load and ever-changing networks. Moreover, it is validated that the proposed method can ensure the consistency and flexibility of QoS class mapping.

Social and economic flows across multimodal transportation networks in the Greater Tokyo Area

We model the flow of human capital and resources across multimodal transportation networks throughout the Greater Tokyo Area. Our transportation networks include trains, buses, and roads integrated with a walking network among a geographically grounded hexagonal grid and connecting nodes of different modes. The hexagonal grid holds data on both the working population and number of jobs from which we built probability distributions for the origins and destinations of commuting trips. Using both the network simplex method and stochastically generated origin-destination trips we estimate the population flows necessary to satisfy this demand. Rather than micro-simulations of actual commuting patterns, congestion, or route planning, our approach aims to uncover patterns in the aggregate flow of human resources to and from economic opportunities. We describe the details of the socioeconomic data, network generation, and the results of our exploratory analysis, then discuss the implications of these findings for transportation usage and future work.

MPLS-based reduction of flow table entries in SDN switches supporting multipath transmission

In this paper, the problem of resource utilisation improvement in software-defined networking (SDN) is addressed. The need for resource optimisation is understood here be twofold. First, bandwidth in links should be saved when congestion appears. Second, the internal resources represented by table entries of SDN switches should be minimised to ensure fast processing and flexibility. Here, both types of resources are optimised with a new mechanism for flow aggregation. The mechanism is accompanied with a multipath transmission supporting reaction when network conditions change.The proposed mechanism uses classical MPLS labelling, which enables flow aggregation together with multipath transmission; therefore, neither involves any definition of new protocols nor requires the application of legacy signalling protocols. Only simple yet powerful modifications of the existing solutions assured by flexibility of the OpenFlow protocol are necessary. Furthermore, the proposed solution can be incrementally deployed in legacy networks.The aggregation results in a low number of flow entries in core switches in comparison to legacy OpenFlow operation. The simulations show that the number of flow entries in core switches can be reduced by as much as 93%, while the overall network traffic is increased by around 171%. This type of scalability improvement of flow processing is obtained as a result of the introduction of a centrally managed MPLS label distribution performed by an SDN controller. Moreover, the proposed method of multipath transmission improves network resource utilisation. Additionally, independently of the traffic pattern, the proposed approach significantly reduces the communication overhead between the controller and the switches.

Review of Emergy Analysis and Life Cycle Assessment: Coupling Development Perspective

Two methods of natural ecosystem assessment—emergy analysis (EMA) and life cycle assessment (LCA)—are reviewed in this paper. Their advantages, disadvantages, and application areas are summarized, and the similarities and differences between these two evaluation methods are analyzed respectively. Their research progress is also sorted out. The study finds that EMA and LCA share common attributes in evaluation processes and research fields, but they focus on different aspects of macrocosms and microcosms. The assessment of system sustainability is valued by both EMA and LCA, but the former has unique advantages in natural system input analysis, and the latter is more convincing in assessing environmental loading capacity. If the system boundaries of the two methods are expanded, in other words, factors such as ecosystem services, labor, and infrastructure construction are integrated into the upstream of the target system, and environmental impact is further analyzed using LCA in the downstream of the system, the two approaches would complete each other. The quantified results would be more objective. Therefore, these two theories have the necessity of coupling development. After reviewing recent coupling application cases, the results show that LCA and EMA have commonality in the upstream of the target system (mainly in inventory database construction), while the environmental impact assessment methods are different in the downstream. So the overall coupling analysis method is not formed. The current paper gives rational suggestions on the coupling development of the two systems in terms of the aggregate emergy flow table, the indicator system construction and indicator evaluation methods. In addition, it is necessary to introduce sensitivity analysis and uncertainty analysis in order to improve the reliability of assessment results. At present, the research on the coupling development of the two theories is in rapid development stage, but there are still many problems that need further exploration.

Micro Particles Aggregation and Motion in a Tornado-like Flow

Micro Particles Aggregation and Motion in a Tornado-like Flow

TF-MIDAS: A Transfer Function Based Mixed-Frequency Model

This paper tackles the mixed-frequency modeling problem from a new perspective. Instead of drawing upon the common distributed lag polynomial model, we use a transfer function representation to develop a new type of models, named TF-MIDAS. We derive the theoretical TF-MIDAS implied by the high-frequency VARMA family models for two common aggregation schemes, flow and stock. This exact correspondence leads to potential gains in terms of now-casting and forecasting performance against the current alternatives. The estimation of the model proposed is also addressed via its state space equivalent form. A Monte Carlo simulation exercise confirms that TF-MIDAS beats U-MIDAS models (its natural competitor) in terms of out-of-sample now-casting performance for several data generating high-frequency processes.

MECHANISMS OF ANALYSIS OF AGGREGATE FINANCIAL FLOWS FOR GROUPS AND INDIVIDUAL OF ECONOMIC ENTERPRISES

The article examines the mechanisms of analysis of aggregate financial flows of economic entities. Analysis of the formation and movement of financial flows showed that, despite the widespread use of financial indicators in the analysis, today there is no integrated assessment of the state of financial flows of a group of economic entities that form the commodity-production subsystem (СPS). The authors propose to analyze the formation of financial flows using the structuring of financial flows and reflect their state within the СPS. This approach, as well as a number of financial indicators, takes into account data that assess flows by type of activity in their ratio. At the stage of formation of the financial system of СPS, the relationship between the change in the results of the functioning of СPS-forming business entity and the efficiency of financial flows is established through a block of indicators. First, absolute, then relative, and then get integrated indicators. The calculation of relative indicators is based on the comparison of the obtained values with the normative ones, which are determined by a vector that reflects the directions (positive or negative) of changes in financial flows, based on a specific stage of development. The assessment of the formation of financial flows of СPS-forming economic entities is able to show the main sources of funding and identify the centres of consumption of finance within the СPS. An autocorrelation model and an adaptive Brown model were used for prediction. In the first step, the characteristic regression method determines the best regression function - the most appropriate for the available values of financial flows for certain periods of time. The second step calculates the regression parameters using the least squares method. It is concluded that the constructed model is consistent with the experimental data. The presented developments reflect the developed and proposed algorithm for analyzing the formation of financial flows of СPS-forming economic entities, which differs from existing approaches using not only financial indicators but also a block of indicators that characterize the ratio of financial flows. This allows to increase the objectivity of the assessment of financial flows in the process of economic system development

Investigation of Driver Route Choice Behaviour using Bluetooth Data

Many local authorities use small-scale transport models to manage their transportation networks. These may assume drivers’ behaviour to be rational in choosing the fastest route, and thus all drivers behave the same given an origin and destination, leading to simplified aggregate flow models, fitted to anonymous traffic flow measurements. Recent price falls in traffic sensors, data storage, and compute power now enable Data Science to empirically test such assumptions, by using per-driver data to infer route selection from sensor observations and compare with optimal route selection. A methodology is presented using per-driver data to analyse driver route choice behaviour in transportation networks. Traffic flows on multiple measurable routes for origin-destination pairs are compared based on the length of each route. A driver rationality index is defined by considering the shortest physical route between an origin-destination pair. The proposed method is intended to aid calibration of parameters used in traffic assignment models e.g. weights in generalized cost formulations or dispersion within stochastic user equilibrium models. The method is demonstrated using raw sensor datasets collected through Bluetooth sensors in the area of Chesterfield, Derbyshire, UK. The results for this region show that routes with a significant difference in lengths of their paths have the majority (71%) of drivers using the optimal path but as the difference in length decreases, the probability of optimal route choice decreases (27%). The methodology can be used for extended research considering the impact on route choice of other factors including travel time and road specific conditions.

Sign Optimization Model for Rail Transit: A Big Data Approach

The subway station has a large passenger flow and strong mobility, and subway-oriented signs have become the necessary core support for the subway system. The paper first takes the complaint data of Beijing Metro as the research object and uses Word2vec methods to effectively prove the phenomenon of subway-oriented identification. Next, it builds a MIP model for the subway-oriented identification system based on the optimization theory and analyzes the model. As an example, Dongzhimen Station of the subway has optimized the spatial layout of its guide signs. Dongzhimen station is an important transportation hub, there are some sign problem, such as long transfer distance, narrow transfer space, complicated traffic conditions, and obvious passenger flow aggregation. The amount of induced information in the area is the objective function, and an optimization model for the optimization of signs is established to help scientifically find the optimal placement of guide signs from multiple candidate positions. The model can theoretically reduce the guidance errors caused by the guidance marks, and will be verified by real cases in practical applications, thereby verifying the effectiveness of the model. The research results can provide reference and reference for subway passenger flow guidance.

A New Symbolic-Based Flow Aggregation and Disaggregation Modular Approach for Tree-shaped Networks

This paper presents the representation and modeling of real-life tree-shaped natural and man-made networks. It is shown that the tree-shaped networks could be composed of two entities of different functionalities that can operate separately or jointly. The first entity is the feet/head aggregation networks, while the second entity is the head/feet disaggregation networks. Each entity is represented with the same symbolic-based modular model expressions. Moreover, it is illustrated that the aggregation entity network can be mapped through a mirroring type process to an analogous disaggregation entity network and vice versa. The suggested technique is demonstrated by an application of the modeling of 20-nodes real-life tree-shaped irrigation network. The paper also addresses simultaneously the analogy between natural plant tree morphology and natural/man-made operational network of both the aggregation or disaggregation types. It is highlighted that such analogy with the natural tree system could help in future schematizing of stages of operational networks expansion in the most efficient way as learnt from nature and in building advanced generations of operational networks. Furthermore, it is pointed out that the new approach has unlimited scope of real-life applications in engineering/technology such as electric generation, water basins, sewage, agriculture drainage, highway transportation..etc. networks for the aggregation entity, and electric distribution, irrigation, oil, gas, potable water, roads transport,..etc. networks for the disaggregation entity. In all respects, the paper has succeeded within the area of tree-shaped networks in crossing the boundaries between the Science of Botany and Engineering/technology (and vice versa) and to create new common areas of important shared interests of great benefits to these disciplines and the science world as a whole. Finally, the new notion of crossing boundaries between sciences can also be extended to and among many other sciences themselves dealing with tree-shaped systems.

The Joint Optimization of Online Traffic Matrix Measurement and Traffic Engineering For Software-Defined Networks

Software-Defined Networking (SDN) provides programmable, flexible and fine-grained traffic control capability, which paves the way for realizing dynamic and high-performance traffic measurement and traffic engineering. In the SDN paradigm, the traffic forwarding and measurement strategies are realized through flow tables stored in the Tenantry Content Addressable Memories (TCAM) of SDN switches. However, the number of TCAM entries in SDN switches is limited. In this paper, we aim to jointly optimize the Traffic Matrix Measurement (TMM) and Traffic Engineering (TE) process under the TCAM capacity and flow aggregation constraints in software-defined networks. We first formulate the joint optimization problem as a Mixed Integer Linear Programming (MILP) model. Then to get an initial traffic matrix for the joint optimization problem, we propose a simple flow rule generation strategy named Maximum Load Rule First (MLRF) to efficiently generate feasible flow rules, which are used to provide direct measurements for the traffic matrix measurement problem. At last, to solve the joint optimization efficiently, we propose two efficient heuristic algorithms named Traffic Matrix Measurement First (TMMF) and Traffic Engineering First (TEF), respectively. TMMF and TEF can generate feasible flow rules for realizing TMM and TE strategies. Our evaluations on real network topologies and traffic traces verify that by jointly optimizing the TMM and TE strategies, both TMMF and TEF can significantly improve TMM accuracy and TE objective (i.e., load balancing) with limited TCAM resource.

METHOD OF CONGESTION MANAGEMENT AND BALANCED RESOURCE ALLOCATION ON MPLS-TE TELECOMMUNICATION NETWORK ROUTERS

The method of congestion management and balanced resource allocation in telecommunication networks operating on the basis of technology of multi-protocol label switching with support of the concept of Traffic Engineering is considered. The essence of the method is the coordinated solution of the problems of aggregation of packet flows and their distribution according to the queues formed on the router interface, as well as the balanced allocation of interface bandwidth in accordance with the principles of Traffic Engineering queues. These technological tasks were reduced to linear programming optimization problems. The proposed method allows increasing the efficiency of distribution and use of network resources while simplifying the computational complexity of the calculations.

Tensor-Based Rule-Space Management System in SDN

This paper presents a tensor-based rule-space management (TERM) system for improving the available capacity of switches in software defined networking (SDN). Limited storage capacity of switches is a key challenge in SDN as the switches use ternary content addressable memories having very low capacity. Low rule storage capacity eventually leads to the high number of Packet-In messages and control plane overloading. The challenge is to design a dynamic scheme to store a large number of heterogeneous flow-rules in SDN switches and reduce the number of Packet-In messages. To address this problem, we apply the concept of tensor decomposition in order to aggregate flow-rules. In addition, we employ a rule caching mechanism for better throughput. Simulation results show the efficiency of TERM in terms of reduction in the number of Packet-In messages. TERM reduces the Packet-In message count by ${{57.78\%}}$ than the flow aggregation approach proposed in the existing literature.

What Determines Capital Flow Dynamics for Russia

The paper studies the impact of external supply or “push” factors (global economic and financial indicators, oil prices, sanctions etc.) and internal demand or “pull” factors (actual and expected GDP growth rates, institutional indicators, openness indicators etc.) on private sector capital flow components (foreign direct, portfolio and other investments) and capital flow aggregates (gross capital inflow and outflow, net capital inflow) in the Russian case, based on regressions utilizing 1994–2018 quarterly data. Among the external factors, the VIX volatility index, the real effective exchange rate of the U.S. dollar and the sanction intensity index prove to be consistently significant. The impact of sanction shocks is found to weaken over time, probably reflecting adaptation to sanctions. Among the internal factors, only the expected GDP growth rates prove significant. In contrast to existing literature on emerging markets as a whole, no significant impact of the interest rate and GDP growth differentials (with respect to advanced economies) is established. It is demonstrated that the push factors account for the larger share of explained variance of gross capital inflow and outflow as well as net capital inflow. In addition, capital flow volatility indicators are analyzed, with positive correlations found for the VIX index, the U.S. interest rates, the oil price volatility, and the financial account openness index.

Price of Anarchy in Networks with Heterogeneous Latency Functions

We address the performance of selfish network routing in multicommodity flows where the latency or delay function on edges is dependent on the flow of individual commodities, rather than on the aggregate flow. An application of this study is the analysis of a network with differentiated traffic, that is, in transportation networks where there are multiple types of traffic and in networks where traffic is prioritized according to type classification. We consider the inefficiency of equilibrium in this model and provide price of anarchy bounds for networks with k (types of) commodities, where each link is associated with heterogeneous polynomial delays, that is, commodity i on edge e faces delay specified by a multivariate polynomial dependent on the individual flow of each commodity on the edge. We consider both atomic and nonatomic flows and show bounds on the price of anarchy that depend on the relative impact of each type of traffic on the edge delay when the delay functions are polynomials of degree θ, for example, [Formula: see text]. The price of anarchy is unbounded for arbitrary polynomials. For networks with decomposable delay functions where the delay is the same for all commodities using the edge, we show improved bounds on the price of anarchy, for both nonatomic and atomic flows. The results illustrate that the inefficiency of selfish routing worsens in the case of heterogeneous delays compared with the standard delay functions that do not consider type differentiation.

Hierarchical organization of urban mobility and its connection with city livability

The recent trend of rapid urbanization makes it imperative to understand urban characteristics such as infrastructure, population distribution, jobs, and services that play a key role in urban livability and sustainability. A healthy debate exists on what constitutes optimal structure regarding livability in cities, interpolating, for instance, between mono- and poly-centric organization. Here anonymous and aggregated flows generated from three hundred million users, opted-in to Location History, are used to extract global Intra-urban trips. We develop a metric that allows us to classify cities and to establish a connection between mobility organization and key urban indicators. We demonstrate that cities with strong hierarchical mobility structure display an extensive use of public transport, higher levels of walkability, lower pollutant emissions per capita and better health indicators. Our framework outperforms previous metrics, is highly scalable and can be deployed with little cost, even in areas without resources for traditional data collection.

A Simple and Efficient Way to Save Energy in Multihop Wireless Networks with Flow Aggregation

Today, whether in industry, research, or civil applications, there are many incentives to reduce the energy footprint of automated systems. In multihop wireless networks, the main objective in that regard is usually to maximize the lifetime of the network by distributing the load over all nodes. In this paper, we improve a solution that aggregates flows to optimize the number of nodes that can be turned off. We introduce interference awareness in a routing metric designed to aggregate flows to avoid overloading the network and to preserve the quality of service required by the flows. This way, it becomes possible to integrate this metric into classical shortest path routing algorithms that do not consider interference. We also show that flow aggregation and overall energy consumption are equivalent problems.