Determining the change of Brillouin frequency shift by using the similarity matching method

We have proposed a similarity matching method (SMM) to obtain the change of Brillouin frequency shift (BFS), in which the change of BFS can be determined from the frequency difference between detecting spectrum and selected reference spectrum by comparing their similarity. We have also compared three similarity measures in the simulation, which has shown that the correlation coefficient is more accurate to determine the change of BFS. Compared with the other methods of determining the change of BFS, the SMM is more suitable for complex Brillouin spectrum profiles. More precise result and much faster processing speed have been verified in our simulation and experiments. The experimental results have shown that the measurement uncertainty of the BFS has been improved to 0.72 MHz by using the SMM, which is almost one-third of that by using the curve fitting method, and the speed of deriving the BFS change by the SMM is 120 times faster than that by the curve fitting method.

Estimation of average annual daily traffic (AADT) using geographically weighted regression (GWR) method and geographic information system (GIS)

Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^

Numerical investigation on the heat transfer enhancement using micro/nano phase-change particulate flow

The introduction of phase change material fluid and nanofluid in micro-channel heat sink design can significantly increase the cooling capacity of the heat sink because of the unique features of these two kinds of fluids. To better assist the design of a high performance micro-channel heat sink using phase change fluid and nanofluid, the heat transfer enhancement mechanism behind the flow with such fluids must be completely understood. ^ A detailed parametric study is conducted to further investigate the heat transfer enhancement of the phase change material particle suspension flow, by using the two-phase non-thermal-equilibrium model developed by Hao and Tao (2004). The parametric study is conducted under normal conditions with Reynolds numbers of Re = 90–600 and phase change material particle concentrations of ϵp ≤ 0.25, as well as extreme conditions of very low Reynolds numbers (Re < 50) and high phase change material particle concentration (ϵp = 50%–70%) slurry flow. By using the two newly-defined parameters, named effectiveness factor ϵeff and performance index PI, respectively, it is found that there exists an optimal relation between the channel design parameters L and D, particle volume fraction ϵp, Reynolds number Re, and the wall heat flux qw. The influence of the particle volume fraction ϵp, particle size dp, and the particle viscosity μ p, to the phase change material suspension flow, are investigated and discussed. The model was validated by available experimental data. The conclusions will assist designers in making their decisions that relate to the design or selection of a micro-pump suitable for micro or mini scale heat transfer devices. ^ To understand the heat transfer enhancement mechanism of the nanofluid flow from the particle level, the lattice Boltzmann method is used because of its mesoscopic feature and its many numerical advantages. By using a two-component lattice Boltzmann model, the heat transfer enhancement of the nanofluid is analyzed, through incorporating the different forces acting on the nanoparticles to the two-component lattice Boltzmann model. It is found that the nanofluid has better heat transfer enhancement at low Reynolds numbers, and the Brownian motion effect of the nanoparticles will be weakened by the increase of flow speed. ^

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

Hybrid approaches to estimating freeway travel times using point traffic detector data

The accurate and reliable estimation of travel time based on point detector data is needed to support Intelligent Transportation System (ITS) applications. It has been found that the quality of travel time estimation is a function of the method used in the estimation and varies for different traffic conditions. In this study, two hybrid on-line travel time estimation models, and their corresponding off-line methods, were developed to achieve better estimation performance under various traffic conditions, including recurrent congestion and incidents. The first model combines the Mid-Point method, which is a speed-based method, with a traffic flow-based method. The second model integrates two speed-based methods: the Mid-Point method and the Minimum Speed method. In both models, the switch between travel time estimation methods is based on the congestion level and queue status automatically identified by clustering analysis. During incident conditions with rapidly changing queue lengths, shock wave analysis-based refinements are applied for on-line estimation to capture the fast queue propagation and recovery. Travel time estimates obtained from existing speed-based methods, traffic flow-based methods, and the models developed were tested using both simulation and real-world data. The results indicate that all tested methods performed at an acceptable level during periods of low congestion. However, their performances vary with an increase in congestion. Comparisons with other estimation methods also show that the developed hybrid models perform well in all cases. Further comparisons between the on-line and off-line travel time estimation methods reveal that off-line methods perform significantly better only during fast-changing congested conditions, such as during incidents. The impacts of major influential factors on the performance of travel time estimation, including data preprocessing procedures, detector errors, detector spacing, frequency of travel time updates to traveler information devices, travel time link length, and posted travel time range, were investigated in this study. The results show that these factors have more significant impacts on the estimation accuracy and reliability under congested conditions than during uncongested conditions. For the incident conditions, the estimation quality improves with the use of a short rolling period for data smoothing, more accurate detector data, and frequent travel time updates.

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

Numerical Investigation on the Heat Transfer Enhancement Using Micro/Nano Phase-Change Particulate Flow

The introduction of phase change material fluid and nanofluid in micro-channel heat sink design can significantly increase the cooling capacity of the heat sink because of the unique features of these two kinds of fluids. To better assist the design of a high performance micro-channel heat sink using phase change fluid and nanofluid, the heat transfer enhancement mechanism behind the flow with such fluids must be completely understood. A detailed parametric study is conducted to further investigate the heat transfer enhancement of the phase change material particle suspension flow, by using the two-phase non-thermal-equilibrium model developed by Hao and Tao (2004). The parametric study is conducted under normal conditions with Reynolds numbers of Re=600-900 and phase change material particle concentrations ¡Ü0.25 , as well as extreme conditions of very low Reynolds numbers (Re < 50) and high phase change material particle concentration (0.5-0.7) slurry flow. By using the two newly-defined parameters, named effectiveness factor and performance index, respectively, it is found that there exists an optimal relation between the channel design parameters, particle volume fraction, Reynolds number, and the wall heat flux. The influence of the particle volume fraction, particle size, and the particle viscosity, to the phase change material suspension flow, are investigated and discussed. The model was validated by available experimental data. The conclusions will assist designers in making their decisions that relate to the design or selection of a micro-pump suitable for micro or mini scale heat transfer devices. To understand the heat transfer enhancement mechanism of the nanofluid flow from the particle level, the lattice Boltzmann method is used because of its mesoscopic feature and its many numerical advantages. By using a two-component lattice Boltzmann model, the heat transfer enhancement of the nanofluid is analyzed, through incorporating the different forces acting on the nanoparticles to the two-component lattice Boltzmann model. It is found that the nanofluid has better heat transfer enhancement at low Reynolds numbers, and the Brownian motion effect of the nanoparticles will be weakened by the increase of flow speed.

Seawater carbonate chemistry and sperm swimming speed of the polychaete Galeolaria caespitosa in lab experiment

The rapidity of ocean acidification intensifies selection pressure for resilient phenotypes, particularly during sensitive early life stages. The scope for selection is greater in species with greater within-species variation in responses to changing environments, thus enhancing the potential for adaptation. We investigated among-male variation in sperm swimming responses (percent motility and swimming speeds) of the serpulid polychaete Galeolaria caespitosa to near- (delta pH 0.3) and far-future ocean acidification (delta pH 0.5). Responses of sperm swimming to acidification varied significantly among males and were overall negative. Robust sperm swimming behavior under near-future ocean acidification in some males may ameliorate climate change impacts, if traits associated with robustness are heritable, and thereby enhance the potential for adaptation to far-future conditions. Reduced sperm swimming in the majority of male G. caespitosa may decrease their fertilization success in a high CO2 future ocean. Resultant changes in offspring production could affect recruitment success and population fitness downstream.

Seawater carbonate chemistry and sperm swimming speed, fertilization success of the Australasian sea urchin Heliocidaris erythrogramma in lab experiment

Background: Climate change will lead to intense selection on many organisms, particularly during susceptible early life stages. To date, most studies on the likely biotic effects of climate change have focused on the mean responses of pooled groups of animals. Consequently, the extent to which inter-individual variation mediates different selection responses has not been tested. Investigating this variation is important, since some individuals may be preadapted to future climate scenarios. Methodology/Principal Findings: We examined the effect of CO2-induced pH changes ("ocean acidification") in sperm swimming behaviour on the fertilization success of the Australasian sea urchin Heliocidaris erythrogramma, focusing on the responses of separate individuals and pairs. Acidification significantly decreased the proportion of motile sperm but had no effect on sperm swimming speed. Subsequent fertilization experiments showed strong inter-individual variation in responses to ocean acidification, ranging from a 44% decrease to a 14% increase in fertilization success. This was partly explained by the significant relationship between decreases in percent sperm motility and fertilization success at delta pH = 0.3, but not at delta pH = 0.5. Conclusions and Significance: The effects of ocean acidification on reproductive success varied markedly between individuals. Our results suggest that some individuals will exhibit enhanced fertilization success in acidified oceans, supporting the concept of 'winners' and 'losers' of climate change at an individual level. If these differences are heritable it is likely that ocean acidification will lead to selection against susceptible phenotypes as well as to rapid fixation of alleles that allow reproduction under more acidic conditions. This selection may ameliorate the biotic effects of climate change if taxa have sufficient extant genetic variation upon which selection can act.

Cruise Speed Reduction for Ground Delay Programs: A Case Study for San Francisco International Airport Arrivals

Ground Delay Programs (GDP) are sometimes cancelled before their initial planned duration and for this reason aircraft are delayed when it is no longer needed. Recovering this delay usually leads to extra fuel consumption, since the aircraft will typically depart after having absorbed on ground their assigned delay and, therefore, they will need to cruise at more fuel consuming speeds. Past research has proposed speed reduction strategy aiming at splitting the GDP-assigned delay between ground and airborne delay, while using the same fuel as in nominal conditions. Being airborne earlier, an aircraft can speed up to nominal cruise speed and recover part of the GDP delay without incurring extra fuel consumption if the GDP is cancelled earlier than planned. In this paper, all GDP initiatives that occurred in San Francisco International Airport during 2006 are studied and characterised by a K-means algorithm into three different clusters. The centroids for these three clusters have been used to simulate three different GDPs at the airport by using a realistic set of inbound traffic and the Future Air Traffic Management Concepts Evaluation Tool (FACET). The amount of delay that can be recovered using this cruise speed reduction technique, as a function of the GDP cancellation time, has been computed and compared with the delay recovered with the current concept of operations. Simulations have been conducted in calm wind situation and without considering a radius of exemption. Results indicate that when aircraft depart early and fly at the slower speed they can recover additional delays, compared to current operations where all delays are absorbed prior to take-off, in the event the GDP cancels early. There is a variability of extra delay recovered, being more significant, in relative terms, for those GDPs with a relatively low amount of demand exceeding the airport capacity.

Effect of wind on operating cost based cruise speed reduction for delay absorption

En route speed reduction can be used for air traffic flow management (ATFM), e.g., delaying aircraft while airborne or realizing metering at an arrival fix. In previous publications, the authors identified the flight conditions that maximize the airborne delay without incurring extra fuel consumption with respect to the nominal (not delayed) flight. In this paper, the effect of wind on this strategy is studied, and the sensitivity to wind forecast errors is also assessed. A case study done in Chicago O’Hare airport (ORD) is presented, showing that wind has a significant effect on the airborne delay that can be realized and that, in some cases, even tailwinds might lead to an increase in the maximum amount of airborne delay. The values of airborne delay are representative enough to suggest that this speed reduction technique might be useful in a real operational scenario. Moreover, the speed reduction strategy is more robust than nominal operations against fuel consumption in the presence of wind forecast uncertainties.

Climate change-related regime shifts have altered spatial synchrony of plankton dynamics in the North Sea

During the 1980s, the North Sea plankton community underwent a well-documented ecosystem regime shift, including both spatial changes (northward species range shifts) and temporal changes (increases in the total abundances of warmer water species). This regime shift has been attributed to climate change. Plankton provide a link between climate and higher trophic-level organisms, which can forage on large spatial and temporal scales. It is therefore important to understand not only whether climate change affects purely spatial or temporal aspects of plankton dynamics, but also whether it affects spatiotemporal aspects such as metapopulation synchrony. If plankton synchrony is altered, higher trophic-level feeding patterns may be modified. A second motivation for investigating changes in synchrony is that the possibility of such alterations has been examined for few organisms, in spite of the fact that synchrony is ubiquitous and of major importance in ecology. This study uses correlation coefficients and spectral analysis to investigate whether synchrony changed between the periods 1959–1980 and 1989–2010. Twenty-three plankton taxa, sea surface temperature (SST), and wind speed were examined. Results revealed that synchrony in SST and plankton was altered. Changes were idiosyncratic, and were not explained by changes in abundance. Changes in the synchrony of Calanus helgolandicus and Para-pseudocalanus spp appeared to be driven by changes in SST synchrony. This study is one of few to document alterations of synchrony and climate-change impacts on synchrony. We discuss why climate-change impacts on synchrony may well be more common and consequential than previously recognized.

Climate change-related regime shifts have altered spatial synchrony of plankton dynamics in the North Sea

During the 1980s, the North Sea plankton community underwent a well-documented ecosystem regime shift, including both spatial changes (northward species range shifts) and temporal changes (increases in the total abundances of warmer water species). This regime shift has been attributed to climate change. Plankton provide a link between climate and higher trophic-level organisms, which can forage on large spatial and temporal scales. It is therefore important to understand not only whether climate change affects purely spatial or temporal aspects of plankton dynamics, but also whether it affects spatiotemporal aspects such as metapopulation synchrony. If plankton synchrony is altered, higher trophic-level feeding patterns may be modified. A second motivation for investigating changes in synchrony is that the possibility of such alterations has been examined for few organisms, in spite of the fact that synchrony is ubiquitous and of major importance in ecology. This study uses correlation coefficients and spectral analysis to investigate whether synchrony changed between the periods 1959–1980 and 1989–2010. Twenty-three plankton taxa, sea surface temperature (SST), and wind speed were examined. Results revealed that synchrony in SST and plankton was altered. Changes were idiosyncratic, and were not explained by changes in abundance. Changes in the synchrony of Calanus helgolandicus and Para-pseudocalanus spp appeared to be driven by changes in SST synchrony. This study is one of few to document alterations of synchrony and climate-change impacts on synchrony. We discuss why climate-change impacts on synchrony may well be more common and consequential than previously recognized.

Feature Study on a Programmable Network Traffic Classifier

Monitoring and tracking of IP traffic flows are essential for network services (i.e. packet forwarding). Packet header lookup is the main part of flow identification by determining the predefined matching action for each incoming flow. In this paper, an improved header lookup and flow rule update solution is investigated. A detailed study of several well-known lookup algorithms reveals that searching individual packet header field and combining the results achieve high lookup speed and flexibility. The proposed hybrid lookup architecture is comprised of various lookup algorithms, which are selected based on the user applications and system requirements.

Investigation of the Effect of Speed on the Dynamic Impact Factor for Bridges with Different Entrance Conditions, 2016

The dynamic interaction of vehicles and bridges results in live loads being induced into bridges that are greater than the vehicle’s static weight. To limit this dynamic effect, the Iowa Department of Transportation (DOT) currently requires that permitted trucks slow to five miles per hour and span the roadway centerline when crossing bridges. However, this practice has other negative consequences such as the potential for crashes, impracticality for bridges with high traffic volumes, and higher fuel consumption. The main objective of this work was to provide information and guidance on the allowable speeds for permitted vehicles and loads on bridges .A field test program was implemented on five bridges (i.e., two steel girder bridges, two pre-stressed concrete girder bridges, and one concrete slab bridge) to investigate the dynamic response of bridges due to vehicle loadings. The important factors taken into account during the field tests included vehicle speed, entrance conditions, vehicle characteristics (i.e., empty dump truck, full dump truck, and semi-truck), and bridge geometric characteristics (i.e., long span and short span). Three entrance conditions were used: As-is and also Level 1 and Level 2, which simulated rough entrance conditions with a fabricated ramp placed 10 feet from the joint between the bridge end and approach slab and directly next to the joint, respectively. The researchers analyzed and utilized the field data to derive the dynamic impact factors (DIFs) for all gauges installed on each bridge under the different loading scenarios.