An integrated modeling approach to assess the impact on road freight transport demand of allowing longer and heavier vehicles (LHVs) in Spain

The assessment on introducing Longer and Heavier Vehicles (LHVs) on the road freight transport demand is performed in this paper by applying an integrated modeling approach composed of a Random Utility-Based Multiregional Input-Output model (RUBMRIO) and a road transport network model. The approach strongly supports the concept that changes in transport costs derived from the LHVs allowance as well as the economic structure of regions have both direct and indirect effects on the road freight transport system. In addition, we estimate the magnitude and extent of demand changes in the road freight transportation system by using the commodity-based structure of the approach to identify the effect on traffic flows and on pollutant emissions over the whole network of Spain by considering a sensitivity analysis of the main parameters which determine the share of Heavy-Goods Vehicles (HGVs) and LHVs. The results show that the introduction of LHVs will strengthen the competitiveness of the road haulage sector by reducing costs, emissions, and the total freight vehicles required.

Online Learning-based Robust Visual Tracking for Autonomous Landing of Unmanned Aerial Vehicles

Autonomous landing is a challenging and important technology for both military and civilian applications of Unmanned Aerial Vehicles (UAVs). In this paper, we present a novel online adaptive visual tracking algorithm for UAVs to land on an arbitrary field (that can be used as the helipad) autonomously at real-time frame rates of more than twenty frames per second. The integration of low-dimensional subspace representation method, online incremental learning approach and hierarchical tracking strategy allows the autolanding task to overcome the problems generated by the challenging situations such as significant appearance change, variant surrounding illumination, partial helipad occlusion, rapid pose variation, onboard mechanical vibration (no video stabilization), low computational capacity and delayed information communication between UAV and Ground Control Station (GCS). The tracking performance of this presented algorithm is evaluated with aerial images from real autolanding flights using manually- labelled ground truth database. The evaluation results show that this new algorithm is highly robust to track the helipad and accurate enough for closing the vision-based control loop.

Robust Real-time Vision-based Aircraft Tracking From Unmanned Aerial Vehicles.

Aircraft tracking plays a key and important role in the Sense-and-Avoid system of Unmanned Aerial Vehicles (UAVs). This paper presents a novel robust visual tracking algorithm for UAVs in the midair to track an arbitrary aircraft at real-time frame rates, together with a unique evaluation system. This visual algorithm mainly consists of adaptive discriminative visual tracking method, Multiple-Instance (MI) learning approach, Multiple-Classifier (MC) voting mechanism and Multiple-Resolution (MR) representation strategy, that is called Adaptive M3 tracker, i.e. AM3. In this tracker, the importance of test sample has been integrated to improve the tracking stability, accuracy and real-time performances. The experimental results show that this algorithm is more robust, efficient and accurate against the existing state-of-art trackers, overcoming the problems generated by the challenging situations such as obvious appearance change, variant surrounding illumination, partial aircraft occlusion, blur motion, rapid pose variation and onboard mechanical vibration, low computation capacity and delayed information communication between UAVs and Ground Station (GS). To our best knowledge, this is the first work to present this tracker for solving online learning and tracking freewill aircraft/intruder in the UAVs.

A General Purpose Configurable Navigation Controller for Micro Aerial Multirotor Vehicles

In this paper, we consider the problem of autonomous navigation of multirotor platforms in GPS-denied environments. The focus of this work is on safe navigation based on unperfect odometry measurements, such as on-board optical flow measurements. The multirotor platform is modeled as a flying object with specific kinematic constraints that must be taken into account in order to obtain successful results. A navigation controller is proposed featuring a set of configurable parameters that allow, for instance, to have a configuration setup for fast trajectory following, and another to soften the control laws and make the vehicle navigation more precise and slow whenever necessary. The proposed controller has been successfully implemented in two different multirotor platforms with similar sensoring capabilities showing the openness and tolerance of the approach. This research is focused around the Computer Vision Group's objective of applying multirotor vehicles to civilian service applications. The presented work was implemented to compete in the International Micro Air Vehicle Conference and Flight Competition IMAV 2012, gaining two awards: the Special Award on "Best Automatic Performance - IMAV 2012" and the second overall prize in the participating category "Indoor Flight Dynamics - Rotary Wing MAV". Most of the code related to the present work is available as two open-source projects hosted in GitHub.

Online Learning-based Robust Visual Tracking for Autonomous Landing of Unmanned Aerial Vehicles

Autonomous landing is a challenging and important technology for both military and civilian applications of Unmanned Aerial Vehicles (UAVs). In this paper, we present a novel online adaptive visual tracking algorithm for UAVs to land on an arbitrary field (that can be used as the helipad) autonomously at real-time frame rates of more than twenty frames per second. The integration of low-dimensional subspace representation method, online incremental learning approach and hierarchical tracking strategy allows the autolanding task to overcome the problems generated by the challenging situations such as significant appearance change, variant surrounding illumination, partial helipad occlusion, rapid pose variation, onboard mechanical vibration (no video stabilization), low computational capacity and delayed information communication between UAV and Ground Control Station (GCS). The tracking performance of this presented algorithm is evaluated with aerial images from real autolanding flights using manually- labelled ground truth database. The evaluation results show that this new algorithm is highly robust to track the helipad and accurate enough for closing the vision-based control loop.

Real-Time Adaptive Multi-Classifier Multi-Resolution Visual Tracking Framework for Unmanned Aerial Vehicles

This paper presents a novel robust visual tracking framework, based on discriminative method, for Unmanned Aerial Vehicles (UAVs) to track an arbitrary 2D/3D target at real-time frame rates, that is called the Adaptive Multi-Classifier Multi-Resolution (AMCMR) framework. In this framework, adaptive Multiple Classifiers (MC) are updated in the (k-1)th frame-based Multiple Resolutions (MR) structure with compressed positive and negative samples, and then applied them in the kth frame-based Multiple Resolutions (MR) structure to detect the current target. The sample importance has been integrated into this framework to improve the tracking stability and accuracy. The performance of this framework was evaluated with the Ground Truth (GT) in different types of public image databases and real flight-based aerial image datasets firstly, then the framework has been applied in the UAV to inspect the Offshore Floating Platform (OFP). The evaluation and application results show that this framework is more robust, efficient and accurate against the existing state-of-art trackers, overcoming the problems generated by the challenging situations such as obvious appearance change, variant illumination, partial/full target occlusion, blur motion, rapid pose variation and onboard mechanical vibration, among others. To our best knowledge, this is the first work to present this framework for solving the online learning and tracking freewill 2D/3D target problems, and applied it in the UAVs.

Using the Cross-Entropy Method for Control Optimization: A Case Study of See-and-Avoid on Unmanned Aerial Vehicles

This paper presents an adaptation of the Cross-Entropy (CE) method to optimize fuzzy logic controllers. The CE is a recently developed optimization method based on a general Monte-Carlo approach to combinatorial and continuous multi-extremal optimization and importance sampling. This work shows the application of this optimization method to optimize the inputs gains, the location and size of the different membership functions' sets of each variable, as well as the weight of each rule from the rule's base of a fuzzy logic controller (FLC). The control system approach presented in this work was designed to command the orientation of an unmanned aerial vehicle (UAV) to modify its trajectory for avoiding collisions. An onboard looking forward camera was used to sense the environment of the UAV. The information extracted by the image processing algorithm is the only input of the fuzzy control approach to avoid the collision with a predefined object. Real tests with a quadrotor have been done to corroborate the improved behavior of the optimized controllers at different stages of the optimization process.

Analysis of fire behaviour simulations over Spain with WRF-FIRE

Wildland fire spread and behaviour are complex phenomena owing to both the number of involved physico-chemical factors, and the non-linear relationship between variables. Spain is plagued by forest and brush fires every summer, when the extremely dry weather sets in along with high temperatures. The use of fire behaviour models requires the availability of high resolution environmental and fuel data; in the absence of real data, errors on the simulated fire spread can be compounded to affect the spatial and temporal accuracy of predicted data. The effect of input values on the accuracy of WRF-FIRE simulations was evaluated to assess the capabilities of the new system for wildland fire in accurately forecasting fire behaviour. The results confirm that the use of accurate meteorological data and a custom fuel moisture content model is crucial to obtain precise simulations of fire behaviour.

Determinación del riesgo de incendio en una instalación de secado de alperujo = Determination of the risk of fire in an olive-oil mill waste dryer

For the energy valorization of alperujo, residue of the olive oil two phases extraction process, it is necessary to perform a drying process to reduce moisture content from over 60% to less than 10%. In order to reduce primary energy consumption and get an economic return, usually in this kind of drying facilities Gas Turbine CHP is used as a heat source. There have been recently in Spain some fires in this kind of GT-CHP facilities, which have caused high material losses. In some of these fires it has been suggested that the fire was caused by the output of incandescent alperujo in the flue gasesof the drying system. Therefore, the aim of this study is to determine experimentally and analytically under which operational conditions a process of alperujo self-ignition in the drying process can begin, and determine the actual fire hazard in this type of TG-CHP system. For analytical study, the temperature and initial composition of the combustion gases of the Gas Turbine at the entrance of the drying process was calculated and the gas equilibrium conditions reached in contact with the biomass were calculated and, therefore, the temperature of the biomass during the drying process. Moreover, the layer and dust ignition temperature of alperujo has been experimentally determined, according to EN 50281-2-1: 2000. With these results, the operating conditions of the drying process, in which there are real risk of auto-ignition of alperujo have been established.Para la valorización energética del alperujo, residuo del proceso de extracción en dos fases del aceite de oliva, es necesario realizar un proceso de secado para reducir su contenido de humedad de más del 60% al 10% m/m en b.h. Con el fin de reducir el consumo de energía primaria y obtener una rentabilidad económica, normalmente en este tipo de instalaciones de secado se usa la cogeneración con turbina de gas (TG) como fuente de calor. En España en los últimos años han ocurrido algunos casos de incendio en este tipo de instalaciones de cogeneración, que han supuesto pérdidas materiales muy elevadas. Por esta razón, el objetivo de este trabajo es determinar analítica y experimentalmente las condiciones operativas del secadero bajo las cuales podría comenzar un proceso de autoinflamación del alperujo y determinar el riesgo real de incendio en este tipo de instalaciones. Para el estudio analítico, se ha planteado y validado el modelo matemático que permite calcular la temperatura y la composición de los gases de combustión a la entrada y a la salida del secadero, en función de las curvas características de la TG, de las condiciones atmosféricas, del caudal y del grado de humedad de la biomasa tratada. El modelo permite además calcular la temperatura de bulbo húmedo, que es la máxima temperatura que podría alcanzar la biomasa durante el proceso de secado y determinar la cantidad de biomasa que se puede secar completamente en función del caudal y de las condiciones de entrada de los gases de combustión. Con estos resultados y la temperatura mínima de autoinflamación del alperujo determinada experimentalmente siguiendo la norma EN 50281- 2-1:2000, se demuestra que en un proceso de secado de alperujo en condiciones normales de operación no existe riesgo de autoencendido que pueda dar origen a un incendio.

Heat shock fusion proteins as vehicles for antigen delivery into the major histocompatibility complex class I presentation pathway

Mice immunized with heat shock proteins (hsps) isolated from mouse tumor cells (donor cells) produce CD8 cytotoxic T lymphocytes (CTL) that recognize donor cell peptides in association with the major histocompatibility complex (MHC) class I proteins of the responding mouse. The CTL are induced apparently because peptides noncovalently associated with the isolated hsp molecules can enter the MHC class I antigen processing pathway of professional antigen-presenting cells. Using a recombinant heat shock fusion protein with a large fragment of ovalbumin covalently linked to mycobacterial hsp70, we show here that when the soluble fusion protein was injected without adjuvant into H-2b mice, CTL were produced that recognized an ovalbumin-derived peptide, SIINFEKL, in association with Kb. The peptide is known to arise from natural processing of ovalbumin in H-2b mouse cells, and CTL from the ovalbumin-hsp70-immunized mice and a highly effective CTL clone (4G3) raised against ovalbumin-expressing EL4 tumor cells (EG7-OVA) were equally effective in terms of the concentration of SIINFEKL required for half-maximal lysis in a CTL assay. The mice were also protected against lethal challenge with ovalbumin-expressing melanoma tumor cells. Because large protein fragments or whole proteins serving as fusion partners can be cleaved into short peptides in the MHC class I processing pathway, hsp fusion proteins of the type described here are promising candidates for vaccines aimed at eliciting CD8 CTL in populations of MHC-disparate individuals.

Social evolution in a new environment: the case of introduced fire ants.

The inadvertent introduction of the fire ant Solenopsis invicta to the United States from South America provides the opportunity to study recent social evolution by comparing social organization in native and introduced populations. We report that several important elements of social organization in multiple-queen nests differ consistently and dramatically between ants in Argentina and the United States. Colonies in Argentina contain relatively few queens and they are close relatives, whereas colonies in the United States contain high numbers of unrelated queens. A corollary of these differences is that workers in the native populations are significantly related to the new queens that they rear in contrast to the zero relatedness between workers and new queens in the introduced populations. The observed differences in queen number and relatedness signal a shift in the breeding biology of the introduced ants that is predicted on the basis of the high population densities in the new range. An additional difference in social organization that we observed, greater proportions of permanently unmated queens in introduced than in native populations, is predicted from the loss of alleles at the sex-determining locus and consequent skewing of operational sex ratios in the colonizing ants. Thus, significant recent social evolution in fire ants is consistent with theoretical expectations based on the altered ecology and population genetics of the introduced populations.

Rapid local synchronization of action potentials: toward computation with coupled integrate-and-fire neurons.

The collective behavior of interconnected spiking nerve cells is investigated. It is shown that a variety of model systems exhibit the same short-time behavior and rapidly converge to (approximately) periodic firing patterns with locally synchronized action potentials. The dynamics of one model can be described by a downhill motion on an abstract energy landscape. Since an energy landscape makes it possible to understand and program computation done by an attractor network, the results will extend our understanding of collective computation from models based on a firing-rate description to biologically more realistic systems with integrate-and-fire neurons.