11,829 research outputs found
Navigation of brain networks
Understanding the mechanisms of neural communication in large-scale brain
networks remains a major goal in neuroscience. We investigated whether
navigation is a parsimonious routing model for connectomics. Navigating a
network involves progressing to the next node that is closest in distance to a
desired destination. We developed a measure to quantify navigation efficiency
and found that connectomes in a range of mammalian species (human, mouse and
macaque) can be successfully navigated with near-optimal efficiency (>80% of
optimal efficiency for typical connection densities). Rewiring network topology
or repositioning network nodes resulted in 45%-60% reductions in navigation
performance. Specifically, we found that brain networks cannot be progressively
rewired (randomized or clusterized) to result in topologies with significantly
improved navigation performance. Navigation was also found to: i) promote a
resource-efficient distribution of the information traffic load, potentially
relieving communication bottlenecks; and, ii) explain significant variation in
functional connectivity. Unlike prevalently studied communication strategies in
connectomics, navigation does not mandate biologically unrealistic assumptions
about global knowledge of network topology. We conclude that the wiring and
spatial embedding of brain networks is conducive to effective decentralized
communication. Graph-theoretic studies of the connectome should consider
measures of network efficiency and centrality that are consistent with
decentralized models of neural communication
Simulation of the Long-Term Effects of Decentralized and Adaptive Investments in Cross-Agency Interoperable and Standard IT Systems
Governments have come under increasing pressure to promote horizontal flows of information across agencies, but investment in cross-agency interoperable and standard systems have been minimally made since it seems to require government agencies to give up the autonomies in managing own systems and its outcomes may be subject to many external and interaction risks. By producing an agent-based model using 'Blanche' software, this study provides policy-makers with a simulation-based demonstration illustrating how government agencies can autonomously and interactively build, standardize, and operate interoperable IT systems in a decentralized environment. This simulation designs an illustrative body of 20 federal agencies and their missions. A multiplicative production function is adopted to model the interdependent effects of heterogeneous systems on joint mission capabilities, and six social network drivers (similarity, reciprocity, centrality, mission priority, interdependencies, and transitivity) are assumed to jointly determine inter-agency system utilization. This exercise simulates five policy alternatives derived from joint implementation of three policy levers (IT investment portfolio, standardization, and inter-agency operation). The simulation results show that modest investments in standard systems improve interoperability remarkably, but that a wide range of untargeted interoperability with lagging operational capabilities improves mission capability less remarkably. Nonetheless, exploratory modeling against the varying parameters for technology, interdependency, and social capital demonstrates that the wide range of untargeted interoperability responds better to uncertain future states and hence reduces the variances of joint mission capabilities. In sum, decentralized and adaptive investments in interoperable and standard systems can enhance joint mission capabilities substantially and robustly without requiring radical changes toward centralized IT management.Public IT Investment, Interoperability, Standardization, Social Network, Agent-Based Modeling, Exploratory Modeling
Incentive Mechanisms for Internet Congestion Management: Fixed-Budget Rebate versus Time-of-Day Pricing
Mobile data traffic has been steadily rising in the past years. This has
generated a significant interest in the deployment of incentive mechanisms to
reduce peak-time congestion. Typically, the design of these mechanisms requires
information about user demand and sensitivity to prices. Such information is
naturally imperfect. In this paper, we propose a \emph{fixed-budget rebate
mechanism} that gives each user a reward proportional to his percentage
contribution to the aggregate reduction in peak time demand. For comparison, we
also study a time-of-day pricing mechanism that gives each user a fixed reward
per unit reduction of his peak-time demand. To evaluate the two mechanisms, we
introduce a game-theoretic model that captures the \emph{public good} nature of
decongestion. For each mechanism, we demonstrate that the socially optimal
level of decongestion is achievable for a specific choice of the mechanism's
parameter. We then investigate how imperfect information about user demand
affects the mechanisms' effectiveness. From our results, the fixed-budget
rebate pricing is more robust when the users' sensitivity to congestion is
"sufficiently" convex. This feature of the fixed-budget rebate mechanism is
attractive for many situations of interest and is driven by its closed-loop
property, i.e., the unit reward decreases as the peak-time demand decreases.Comment: To appear in IEEE/ACM Transactions on Networkin
Agent-based transportation planning compared with scheduling heuristics
Here we consider the problem of dynamically assigning vehicles to transportation orders that have di¤erent time windows and should be handled in real time. We introduce a new agent-based system for the planning and scheduling of these transportation networks. Intelligent vehicle agents schedule their own routes. They interact with job agents, who strive for minimum transportation costs, using a Vickrey auction for each incoming order. We use simulation to compare the on-time delivery percentage and the vehicle utilization of an agent-based planning system to a traditional system based on OR heuristics (look-ahead rules, serial scheduling). Numerical experiments show that a properly designed multi-agent system may perform as good as or even better than traditional methods
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
A Dynamic Incentive Mechanism for Transmission Expansion in Electricity Networks: Theory, Modeling, and Application
We propose a price-cap mechanism for electricity-transmission expansion based on redefining transmission output in terms of financial transmission rights. Our mechanism applies the incentive-regulation logic of rebalancing a two-part tariff. First, we test this mechanism in a three-node network. We show that the mechanism intertemporally promotes an investment pattern that relieves congestion, increases welfare, augments the Transco´s profits, and induces convergence of prices to marginal costs. We then apply the mechanism to a grid of northwestern Europe and show a gradual convergence toward a common-price benchmark, an increase in total capacity, and convergence toward the welfare optimum.Electricity transmission expansion, incentive regulation
Introduction to the Special Issue on Sustainable Solutions for the Intelligent Transportation Systems
The intelligent transportation systems improve the transportation system’s operational efficiency and enhance its safety and reliability by high-tech means such as information technology, control technology, and computer technology. In recent years, sustainable development has become an important topic in intelligent transportation’s development, including new infrastructure and energy distribution, new energy vehicles and new transportation systems, and the development of low-carbon and intelligent transportation equipment. New energy vehicles’ development is a significant part of green transportation, and its automation performance improvement is vital for smart transportation.
The development of intelligent transportation and green, low-carbon, and intelligent transportation equipment needs to be promoted, a significant feature of transportation development in the future. For intelligent infrastructure and energy
distribution facilities, the electricity for popular electric vehicles and renewable energy, such as nuclear power and hydrogen
power, should be considered
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