Directional imbalance of freight rates : evidence from Japanese inter-prefectural data

By analyzing a comprehensive dataset on transport transactions in Japan, we describe a directional imbalance in freight rates by transport mode and examine its potential sources, such as economies of density and directionally imbalanced transport flow. There are certain numbers of observed links which show asymmetric transport costs. Instrumental variable analysis is used to show that economies of density account for deviation from symmetric freight rates between prefectures. Our results show that a 10% increase in outbound transport flow relative to inbound transport flow leads to a 2.1% decrease in outbound freight rate relative to inbound freight rate.

Measuring the costs of FTA utilization : evidence from transaction-level import data of Thailand

In this study, we measure the utilization costs of free trade agreement (FTA) tariff schemes. To do that, we use shipment-level customs data on Thai imports, which identify not only firms, source country, and commodity but also tariff schemes. We propose several measures as a proxy for FTA utilization costs. The example includes the minimum amount of firm-level savings on tariff payments, i.e., trade values under FTA schemes multiplied by the tariff margin, in all transactions. Consequently, the median costs for FTA utilization in 2008, for example, are estimated to be approximately US$2,000 for exports from China, US$300 for exports from Australia, and US$1,000 for exports from Japan. We also found that FTA utilization costs differ by rule of origin and industry.

Analysis of Thematic Classified Aerial Images Trough Multispectral and LIDAR Data

The application of thematic maps obtained through the classification of remote images needs the obtained products with an optimal accuracy. The registered images from the airplanes display a very satisfactory spatial resolution, but the classical methods of thematic classification not always give better results than when the registered data from satellite are used. In order to improve these results of classification, in this work, the LIDAR sensor data from first return (Light Detection And Ranging) registered simultaneously with the spectral sensor data from airborne are jointly used. The final results of the thematic classification of the scene object of study have been obtained, quantified and discussed with and without LIDAR data, after applying different methods: Maximum Likehood Classification, Support Vector Machine with four different functions kernel and Isodata clustering algorithm (ML, SVM-L, SVM-P, SVM-RBF, SVM-S, Isodata). The best results are obtained for SVM with Sigmoide kernel. These allow the correlation with others different physical parameters with great interest like Manning hydraulic coefficient, for their incorporation in a GIS and their application in hydraulic modeling.

Impact of nuclear data uncertainties in the Phase-1B Benchmark

Accurate control over the spent nuclear fuel content is essential for its safe and optimized transportation, storage and management. Consequently, the reactivity of spent fuel and its isotopic content must be accurately determined. Nowadays, to predict isotopic evolution throughout irradiation and decay periods is not a problem thanks to the development of powerful codes and methodologies. In order to have a realistic confidence level in the prediction of spent fuel isotopic content, it is desirable to determine how uncertainties in the basic nuclear data affect isotopic prediction calculations by quantifying their associated uncertainties

Floating car data augmentation based on infrastructure sensors and neural networks

The development of new-generation intelligent vehicle technologies will lead to a better level of road safety and CO2 emission reductions. However, the weak point of all these systems is their need for comprehensive and reliable data. For traffic data acquisition, two sources are currently available: 1) infrastructure sensors and 2) floating vehicles. The former consists of a set of fixed point detectors installed in the roads, and the latter consists of the use of mobile probe vehicles as mobile sensors. However, both systems still have some deficiencies. The infrastructure sensors retrieve information fromstatic points of the road, which are spaced, in some cases, kilometers apart. This means that the picture of the actual traffic situation is not a real one. This deficiency is corrected by floating cars, which retrieve dynamic information on the traffic situation. Unfortunately, the number of floating data vehicles currently available is too small and insufficient to give a complete picture of the road traffic. In this paper, we present a floating car data (FCD) augmentation system that combines information fromfloating data vehicles and infrastructure sensors, and that, by using neural networks, is capable of incrementing the amount of FCD with virtual information. This system has been implemented and tested on actual roads, and the results show little difference between the data supplied by the floating vehicles and the virtual vehicles.

StreamCloud: An elastic and scalable data streaming system

Many applications in several domains such as telecommunications, network security, large scale sensor networks, require online processing of continuous data lows. They produce very high loads that requires aggregating the processing capacity of many nodes. Current Stream Processing Engines do not scale with the input load due to single-node bottlenecks. Additionally, they are based on static con?gurations that lead to either under or over-provisioning. In this paper, we present StreamCloud, a scalable and elastic stream processing engine for processing large data stream volumes. StreamCloud uses a novel parallelization technique that splits queries into subqueries that are allocated to independent sets of nodes in a way that minimizes the distribution overhead. Its elastic protocols exhibit low intrusiveness, enabling effective adjustment of resources to the incoming load. Elasticity is combined with dynamic load balancing to minimize the computational resources used. The paper presents the system design, implementation and a thorough evaluation of the scalability and elasticity of the fully implemented system.

Sea Bottom Classification by Means of Bathymetric LIDAR Data

Light Detection and Ranging (LIDAR) provides high horizontal and vertical resolution of spatial data located in point cloud images, and is increasingly being used in a number of applications and disciplines, which have concentrated on the exploit and manipulation of the data using mainly its three dimensional nature. Bathymetric LIDAR systems and data are mainly focused to map depths in shallow and clear waters with a high degree of accuracy. Additionally, the backscattering produced by the different materials distributed over the bottom surface causes that the returned intensity signal contains important information about the reflection properties of these materials. Processing conveniently these values using a Simplified Radiative Transfer Model, allows the identification of different sea bottom types. This paper presents an original method for the classification of sea bottom by means of information processing extracted from the images generated through LIDAR data. The results are validated using a vector database containing benthic information derived by marine surveys.

Financial fragility, trade credit and contagion effects during the crisis: a spatial econometric approach to firm-level data

The number of distressed manufacturing firms increased sharply during recessionary phase 2009-13. Financial indebtness traditionally plays a key role in assessing firm solvency but contagion effects that originate from the supply chain are usually neglected in literature. Firm interconnections, captured via the trade credit channel, represent a primary vehicle of individual shocks’ propagation, especially during an economic downturn, when liquidity tensions arise. A representative sample of 11,920 Italian manufacturing firms is considered to model a two-step econometric design, where chain reactions in terms of trade credit accumulation (i.e. default of payments to suppliers) are primarily analyzed by resorting to a spatial autoregressive approach (SAR). Spatial interactions are modeled based on a unique dataset of firm-to-firm transactions registered before the outbreak of the crisis. The second step in instead a binary outcome model where trade credit chains are considered together with data on the bank-firm relationship to assess determinants of distress likelihoods in 2009-13. Results show that outstanding trade debt is affected by the liquidity position of a firm and by positive spatial effects. Trade credit chain reactions are found to exert, in turn, a positive impact on distress likelihoods during the crisis. The latter effect is comparable in magnitude to the one exerted by individual financial rigidity, and stresses the importance to include complex interactions between firms in the analysis of the solvency behavior.

A data driven study of relationships between relief and farmland abandonment in a Mediterranean region

The environmental, cultural and socio-economic causes and consequences of farmland abandonment are issues of increasing concern for researchers and policy makers. In previous studies, we proposed a new methodology for selecting the driving factors in farmland abandonment processes. Using Data Mining and GIS, it is possible to select those variables which are more significantly related to abandonment. The aim of this study is to investigate the application of the above mentioned methodology for finding relationships between relief and farmland abandonment in a Mediterranean region (SE Spain).We have taken into account up to 28 different variables in a single analysis, some of them commonly considered in land use change studies (slope, altitude, TWI, etc), but also other novel variables have been evaluated (sky view factor, terrain view factor, etc). The variable selection process provides results in line with the previous knowledge of the study area, describing some processes that are region specific (e.g. abandonment versus intensification of the agricultural activities). The European INSPIRE Directive (2007/2/EC) establishes that the digital elevation models for land surfaces should be available in all member countries, this means that the research described in this work can be extrapolated to any European country to determine whether these variables (slope, altitude, etc) are important in the process of abandonment.

Explaining transactions in time banks in economic crisis

This article studies the services exchanged in a particular Spanish time bank. Using data from users and transactions, we analyse the users’ profile as well as the determinants of providing and receiving different services. Our results show that the representative user is a Spanish female, not married, middle aged, highly educated and unemployed. We also find differences in the personal characteristics driving the supply and demand of services.

Efficient Mitigation of Data and Control Flow Errors in Microprocessors

The use of microprocessor-based systems is gaining importance in application domains where safety is a must. For this reason, there is a growing concern about the mitigation of SEU and SET effects. This paper presents a new hybrid technique aimed to protect both the data and the control-flow of embedded applications running on microprocessors. On one hand, the approach is based on software redundancy techniques for correcting errors produced in the data. On the other hand, control-flow errors can be detected by reusing the on-chip debug interface, existing in most modern microprocessors. Experimental results show an important increase in the system reliability even superior to two orders of magnitude, in terms of mitigation of both SEUs and SETs. Furthermore, the overheads incurred by our technique can be perfectly assumable in low-cost systems.

Novas tendências, o big data: deteção de fraudes e transações no comércio local: SAF-T com tecnologia big data

Dissertação apresentada à Escola Superior de Tecnologia do Instituto Politécnico de Castelo Branco para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Desenvolvimento de Software e Sistemas Interactivos, realizada sob a orientação científica da categoria profissional do orientador Doutor Eurico Ribeiro Lopes, do Instituto Politécnico de Castelo Branco.