Multivariate Quantile Impulse Response Functions

A reduced form multivariate quantile autoregressive model is developed to study heterogeneity in the effects of macroeconomic shocks. This framework is used for forecasting and for constructing quantile impulse response functions that explore dynamic heterogeneity in the response of endogenous variables to different shocks. The methodology allows evaluating different quantile paths, defined as the dynamic effects for a fix collection of quantile indexes. The model is applied to study monetary shocks in a three‐variable macroeconomic model (output gap, inflation, Fed Funds rate) for the USA for the period 1980q1–2010q1.     

Reinforcement of polypropylene with lignocellulose nanofibrils and compatibilization with biobased polymers

Freeze‐dried and milled lignocellulose nanofibrils (LCNF) were used to reinforce polypropylene (PP) nanocomposites. The LCNF, containing up to 9% lignin, was obtained from residual Empty Palm Fruit Bunch (EPFB) fibers. Soy protein isolate (SPI) and hydroxypropyl cellulose (HPC) were tested as coupling agents as well as maleic anhydride grafted polypropylene (MAPP), which was used as a reference. A good level of dispersion of LCNF in the PP matrix while mechanical testing and thermal analyses indicated an improvement of the thermo‐mechanical behavior of the nanocomposites was revealed upon loading of the lignin‐containing nanofibrils. The tensile modulus of PP was increased by 15% upon the addition of 1% LCNF with SPI as a compatibilizer. Likewise, the thermal stability of the composites was most markedly enhanced. Overall, LCNF and SPI, two important bioresources, are introduced here for the development of novel and cost‐effective PP‐based composites. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43854.     

Daily living activity recognition based on statistical feature quality group selection

The benefits arising from proactive conduct and subject-specialized healthcare have driven e-health and e-monitoring into the forefront of research, in which the recognition of motion, postures and physical exercise is one of the main subjects. We propose here a multidisciplinary method for the recognition of physical activity with the emphasis on feature extraction and selection processes, which are considered to be the most critical stages in identifying the main unknown activity discriminant elements. Efficient feature selection processes are particularly necessary when dealing with huge training datasets in a multidimensional space, where conventional feature selection procedures based on wrapper methods or ‘branch and bound’ are highly expensive in computational terms. We propose an alternative filter method using a feature quality group ranking via a couple of two statistical criteria. Satisfactory results are achieved in both laboratory and semi-naturalistic activity living datasets for real problems using several classification models, thus proving that any body sensor location can be suitable to define a simple one-feature-based recognition system, with particularly remarkable accuracy and applicability in the case of the wrist.     

FAO’s AVHRR-based Agricultural Stress Index System (ASIS) for global drought monitoring

Agricultural production is highly dependent on climate variability in many parts of the world. In particular, drought may severely reduce crop yields, potentially affecting food availability at local, regional, and global scales. The Food and Agriculture Organization of the United Nations (FAO) operates the Global Early Warning System (GIEWS), which monitors global food supply and demand. One of the key challenges is to obtain synoptic information on a recurrent and timely basis about drought-affected agricultural zones. This is needed to quickly identify areas requiring immediate attention. The Agricultural Stress Index System (ASIS), based on imagery from the Advanced Very High Resolution Radiometer (AVHRR) sensors on board the National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational Satellite (METOP) satellites, was specifically developed to meet this need. The system is based on a methodology developed by Rojas, Vrieling, and Rembold over the African continent. This approach has been modified and adapted to the global scale to produce an agricultural stress index (ASI) representing, per administrative unit, the percentage of cropland (or pasture) areas affected by drought over the growing season. The vegetation health index (VHI), based on normalized difference vegetation index (NDVI) and temperature anomalies, is used as a drought indicator. A fused time series of AVHRR data from METOP and NOAA was used to produce a consistent time series of VHI at 1 km resolution. Global phenology maps, indicating the number of growing seasons and their start and end dates, were derived from a multi-annual image set of SPOT-Vegetation (1999–2011). The VHI time series and phenology maps were then combined to produce the ASI for the years 1984 to the present. This allowed evaluation of the suitability of the ASIS to identify drought using historical reports and ancillary data. As a result of this analysis, ASIS was positively evaluated to support the FAO early warning system.     

Accelerating network applications by distributed interfaces on heterogeneous multiprocessor architectures

Hosts with several, possibly heterogeneous and/or multicore, processors provide new challenges and opportunities to accelerate applications with high communications bandwidth requirements. Many opportunities to scale these network applications with the increase in the link bandwidths are related to the exploitation of the available parallelism provided by the presence of several processing cores in the servers, not only for computing the workload of the user application but also for decreasing the overhead associated to the network interface and the system software.     

Evolutionary authoring tool for adaptive hypermedia with multimodal navigation

This paper discusses the importance of user adaptation and software evolution in hypermedia applications, and reviews some of the most relevant approaches to both fields. The paper describes a model that has been conceived for the development, maintenance and navigation of adaptive hypermedia systems. This model, called SEMantic, systemic and evolutionary model to develop HyPermedia systems (SEM-HP), includes an incremental design process, a layered architecture and an authoring tool integrated with a navigation tool. SEM-HP architecture is composed of four subsystems, which allow the separation of aspects related to knowledge representation, presentation, navigation and user adaptation. In addition, SEM-HP has a higher layer, which acts as a meta-system and allows a consistent evolution of the elements defined in each of the four subsystems, as well as their automatic co-evolution. Regarding user interaction, four alternative ways of navigating hypermedia information are supported. Finally, the paper shows the main results of two case studies carried out with the authoring and navigation tool, JSEM-HP, at the University of Granada, Spain.     

Linear quadratic gaussian optimization approach for signal-to-noise ratio constrained control over network

The research area of control over networks has attracted great interest in recent years. Inserted in this research area is the study of control feedback limitations imposed by the presence of a communication channel. In this paper we analyze the fundamental limitations in control feedback stabilizability imposed by a class of Signal-to-Noise Ratio (SNR) constrained communication channels. We solve the SNR constrained control over network problem as a linear quadratic Gaussian (LQG) optimization with loop transfer recovery (LTR). If the communication channel is located on the feedback path then the LTR is said to be performed at the output. Vice versa, if the communication channel is on the control path, then the recovery is said to be performed at the input. In the present paper we address both cases, namely the LQG optimization with LTR at the output and the LQG optimization with LTR at the input to solve an LTI SNR constrained problem. We then explore the link between these two solutions.