Magnetic properties and sand composition at DSDP Site 87-582 and Hole 87-583D

At Site 582, DSDP Leg 87, turbidites about 560 m thick were recovered from the floor of the Nankai Trough. A turbidite bed is typically composed of three subdivisions: a lower graded sand unit, an upper massive silt unit, and an uppermost Chondrites burrowed silt unit. The turbidites intercalate with bluish gray hemipelagic mud which apparently accumulated below the calcite compensation depth. In order to investigate the nature and provenance of the turbidites, we studied the grain orientation, based on magnetic fabric measurements and thin-section grain counting, and grain size, using a photo-extinction settling tube and detrital modal analysis. The following results were obtained: (1) grain orientation analysis indicates that the turbidity current transport parallels the trench axis, predominantly from the northeast; (2) Nankai Trough turbidites generally decrease in grain size to the southwest; (3) turbidite sands include skeletal remains indicative of fresh-water and shallow-marine environments; and (4) turbidites contain abundant volcanic components, and their composition is analogous to the sediments of the Fuji River-Suruga Bay area. Considering other evidence, such as physiography and geometry of trench fill, we conclude that the turbidites of Site 582 as well as Site 583 were derived predominantly from the mouth of Fuji River and were transported through the Suruga Trough to the Nankai Trough, a distance of some 700 km. This turbidite transport system has tectonic implications: (1) the filling of the Nankai Trough is the direct consequence of the Izu collision in Pliocene- Pleistocene times; (2) the accretion of trench fill at the trench inner slope observed in the Nankai Trough is controlled by collision tectonics; and (3) each event of turbidite deposition may be related to a Tokai mega-earthquake.

PM2.5, PM10, and PM10-associated elements in the air in Bamako, Mali (2012-2013)

Saharan dust incursions and particulates emitted from human activities degrade air quality throughout West Africa, especially in the rapidly expanding urban centers in the region. Particulate matter (PM) that can be inhaled is strongly associated with increased incidence of and mortality from cardiovascular and respiratory diseases and cancer. Air samples collected in the capital of a Saharan-Sahelian country (Bamako, Mali) between September 2012 - July 2013 were found to contain inhalable PM concentrations that exceeded World Health Organization (WHO) and US Environmental Protection Agency (USEPA) PM2.5 and PM10 24-h limits 58 - 98% of days and European Union (EU) PM10 24-h limit 98% of days. Mean concentrations were 1.2-to-4.5 fold greater than existing limits. Inhalable PM was enriched in transition metals, known to produce reactive oxygen species and initiate the inflammatory reaction, and other potentially bioactive and biotoxic metals/metalloids. Eroded mineral dust composed the bulk of inhalable PM, whereas most enriched metals/metalloids were likely emitted from oil combustion, biomass burning, refuse incineration, vehicle traffic, and mining activities. Human exposure to inhalable PM and associated metals/metalloids over 24-h was estimated. The findings indicate that inhalable PM in the Sahara-Sahel region may present a threat to human health, especially in urban areas with greater inhalable PM and transition metal exposure.

Solar radiation over and under sea ice during POLARSTERN cruise PS92 (TRANSSIZ) in 2015

Measurements of solar radiation over and under sea ice have been performed on various stations in the Arctic Ocean during the Polarstern cruise PS92 (TRANSSIZ) between 19 May and 30 June 2015. All radiation measurements have been performed with Ramses spectral radiometers (Trios, Rastede, Germany). All data are given in full spectral resolution interpolated to 1.0 nm, and integrated over the entire wavelength range (broadband, total: 320 to 950 nm). Two sensors were mounted on a Remotely Operated Vehicle (ROV) and one radiometer was installed on the sea ice for surface reference measurements (solar irradiance). On the ROV, one irradiance sensor (cos-collector) for energy budget calculations and one radiance sensor (9° opening angle) to obtain high resolution spatial variability were installed. Along with the radiation measurements, ROV positions were obtained from acoustic USBL-positioning and all parameters of vehicle depth, distance to the ice and attitude recorded. All times are given in UTC.

Comparison and Assessment of Two Emission inventories for the Madrid Region

Emission inventories are databases that aim to describe the polluting activities that occur across a certain geographic domain. According to the spatial scale, the availability of information will vary as well as the applied assumptions, which will strongly influence its quality, accuracy and representativeness. This study compared and contrasted two emission inventories describing the Greater Madrid Region (GMR) under an air quality simulation approach. The chosen inventories were the National Emissions Inventory (NEI) and the Regional Emissions Inventory of the Greater Madrid Region (REI). Both of them were used to feed air quality simulations with the CMAQ modelling system, and the results were compared with observations from the air quality monitoring network in the modelled domain. Through the application of statistical tools, the analysis of emissions at cell level and cell – expansion procedures, it was observed that the National Inventory showed better results for describing on – road traffic activities and agriculture, SNAP07 and SNAP10. The accurate description of activities, the good characterization of the vehicle fleet and the correct use of traffic emission factors were the main causes of such a good correlation. On the other hand, the Regional Inventory showed better descriptions for non – industrial combustion (SNAP02) and industrial activities (SNAP03). It incorporated realistic emission factors, a reasonable fuel mix and it drew upon local information sources to describe these activities, while NEI relied on surrogation and national datasets which leaded to a poorer representation. Off – road transportation (SNAP08) was similarly described by both inventories, while the rest of the SNAP activities showed a marginal contribution to the overall emissions.

Categorical Missing Data Imputation Using Fuzzy Neural Networks with Numerical and Categorical Inputs

There are many situations where input feature vectors are incomplete and methods to tackle the problem have been studied for a long time. A commonly used procedure is to replace each missing value with an imputation. This paper presents a method to perform categorical missing data imputation from numerical and categorical variables. The imputations are based on Simpson’s fuzzy min-max neural networks where the input variables for learning and classification are just numerical. The proposed method extends the input to categorical variables by introducing new fuzzy sets, a new operation and a new architecture. The procedure is tested and compared with others using opinion poll data.

A new feature extraction method for signal classification applied to cat spinal cord signals

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods.

Air pollution data classification by SOM Neural Network

Over the last ten years, Salamanca has been considered among the most polluted cities in México. This paper presents a Self-Organizing Maps (SOM) Neural Network application to classify pollution data and automatize the air pollution level determination for Sulphur Dioxide (SO2) in Salamanca. Meteorological parameters are well known to be important factors contributing to air quality estimation and prediction. In order to observe the behavior and clarify the influence of wind parameters on the SO2 concentrations a SOM Neural Network have been implemented along a year. The main advantages of the SOM is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. The results show a significative correlation between pollutant concentrations and some environmental variables.

Non-destructive optical characterization of fruits in the red and near infrared.

Increasing attention is being paid to the possible development of non-invasive tests for the assessment of the quality of Fruits. We propose a novel non-destructive method for the measurement of the internal optical properties of fruits and vegetables by means of lime-resolved reflectance spectroscopy in the visible and NIR range. A Fully automated instrumentation for time-resolved reflectance measurements was developed. It is based on mode-locked laser sources and electronics for time-correlated single photon counting, and provides a time-resolution of 120-160 ps. The system was used to probe the optical properties of several species and varieties of Fruits and vegetables in the red and NIR range (650-1000 nm). In most Fruits, the absorption line shape is dominated by the absorption peak of water, centred around 970 nm. Generally, the absorption spectra also show the spectral features typical of chlorophyll, with maximum at 675 nm. In particular, for what concerns apples, variations in peak intensity are observed depending on the variety, the degree of ripeness as well as the position on the apple. For all the species and varieties considered, the transport scattering coefficient decreases progressively upon increasing the wavelength.

Flexible low-cost system to test batteries and ultracapacitors for electric and hybrid vehicles in real working conditions

Batteries and ultracapacitors for hybrid and electric vehicles must satisfy very demanding working conditions that are not usual in other applications. In this sense, specific tests must be performed in order to draw accurate conclusions about their behaviour. To do so, new advanced test benches are needed. These platforms must allow the study of a wide variety of energy storage systems under conditions similar to the real ones. In this paper, a flexible, low-cost and highly customizable system is presented. This system allows batteries and ultracapacitors to be tested in many and varied ways, effectively emulating the working conditions that they face in an electric vehicle. The platform was specifically designed to study energy storage systems for electric and hybrid vehicles, meaning that it is suitable to test different systems in many different working conditions, including real driving cycles. This flexibility is achieved keeping the cost of the platform low, which makes the proposed test bench a feasible alternative for the industry. As an example of the functionality of the platform, a test consisting of a 17-minute ARTEMIS urban cycle with a NiMH battery pack is presented.

Energy Storage Systems for Electric Vehicles: Performance Comparison based on a Simple Equivalent Circuit and Experimental Tests

The decision to select the most suitable type of energy storage system for an electric vehicle is always difficult, since many conditionings must be taken into account. Sometimes, this study can be made by means of complex mathematical models which represent the behavior of a battery, ultracapacitor or some other devices. However, these models are usually too dependent on parameters that are not easily available, which usually results in nonrealistic results. Besides, the more accurate the model, the more specific it needs to be, which becomes an issue when comparing systems of different nature. This paper proposes a practical methodology to compare different energy storage technologies. This is done by means of a linear approach of an equivalent circuit based on laboratory tests. Via these tests, the internal resistance and the self-discharge rate are evaluated, making it possible to compare different energy storage systems regardless their technology. Rather simple testing equipment is sufficient to give a comparative idea of the differences between each system, concerning issues such as efficiency, heating and self-discharge, when operating under a certain scenario. The proposed methodology is applied to four energy storage systems of different nature for the sake of illustration.

HOG-Like gradient-based descriptor for visual vehicle detection

One of the main challenges for intelligent vehicles is the capability of detecting other vehicles in their environment, which constitute the main source of accidents. Specifically, many methods have been proposed in the literature for video-based vehicle detection. Most of them perform supervised classification using some appearance-related feature, in particular, symmetry has been extensively utilized. However, an in-depth analysis of the classification power of this feature is missing. As a first contribution of this paper, a thorough study of the classification performance of symmetry is presented within a Bayesian decision framework. This study reveals that the performance of symmetry-based classification is very limited. Therefore, as a second contribution, a new gradient-based descriptor is proposed for vehicle detection. This descriptor exploits the known rectangular structure of vehicle rears within a Histogram of Gradients (HOG)-based framework. Experiments show that the proposed descriptor outperforms largely symmetry as a feature for vehicle verification, achieving classification rates over 90%.

A General Purpose Configurable Controller for Indoors and Outdoors GPS-Denied Navigation for Multirotor Unmanned Aerial Vehicles

This research on odometry based GPS-denied navigation on multirotor Unmanned Aerial Vehicles is focused among the interactions between the odometry sensors and the navigation controller. More precisely, we present a controller architecture that allows to specify a speed specified flight envelope where the quality of the odometry measurements is guaranteed. The controller utilizes a simple point mass kinematic model, described by a set of configurable parameters, to generate a complying speed plan. For experimental testing, we have used down-facing camera optical-flow as odometry measurement. This work is a continuation of prior research to outdoors environments using an AR Drone 2.0 vehicle, as it provides reliable optical flow on a wide range of flying conditions and floor textures. Our experiments show that the architecture is realiable for outdoors flight on altitudes lower than 9 m. A prior version of our code was utilized to compete in the International Micro Air Vehicle Conference and Flight Competition IMAV 2012. The code will be released as an open-source ROS stack hosted on GitHub.