Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity

Accurate cropland information is of paramount importance for crop monitoring. This study compares five existing cropland mapping methodologies over five contrasting Joint Experiment for Crop Assessment and Monitoring (JECAM) sites of medium to large average field size using the time series of 7-day 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) mean composites (red and near-infrared channels). Different strategies were devised to assess the accuracy of the classification methods: confusion matrices and derived accuracy indicators with and without equalizing class proportions, assessing the pairwise difference error rates and accounting for the spatial resolution bias. The robustness of the accuracy with respect to a reduction of the quantity of calibration data available was also assessed by a bootstrap approach in which the amount of training data was systematically reduced. Methods reached overall accuracies ranging from 85% to 95%, which demonstrates the ability of 250 m imagery to resolve fields down to 20 ha. Despite significantly different error rates, the site effect was found to persistently dominate the method effect. This was confirmed even after removing the share of the classification due to the spatial resolution of the satellite data (from 10% to 30%). This underlines the effect of other agrosystems characteristics such as cloudiness, crop diversity, and calendar on the ability to perform accurately. All methods have potential for large area cropland mapping as they provided accurate results with 20% of the calibration data, e.g. 2% of the study area in Ukraine. To better address the global cropland diversity, results advocate movement towards a set of cropland classification methods that could be applied regionally according to their respective performance in specific landscapes.     

Learning a subclass of k-quasi-Horn formulas with membership queries

Boolean formulas have been widely studied in the field of learning theory. We focus on the model of learning with queries, and study a restriction of the class of k-quasi-Horn formulas, that is, conjunctive normal form formulas where the number of unnegated literals per clause is at most k. This class is known to be as hard to learn as the general class CNF of conjunctive normal form formulas. By imposing some constraints, we define a fragment of this logic that can be learned using only membership queries. Also we prove that none of these constraints makes by itself the class learnable.     

Robust quantification of the exposure to operational risk: Bringing economic sense to economic capital

Operational risk is commonly analyzed in terms of the distribution of aggregate yearly losses. Risk measures can then be computed as statistics of this distribution that focus on the region of extreme losses. Assuming independence among the operational risk events and between the likelihood that they occur and their magnitude, separate models are made for the frequency and for the severity of the losses. These are then combined to estimate the distribution of aggregate losses. While the detailed form of the frequency distribution does not significantly affect the risk analysis, the choice of model for the severity often has a significant impact on operational risk measures. For heavy-tailed distributions these measures are dominated by extreme losses, whose probability cannot be reliably extrapolated from the available data. With limited empirical evidence, it is difficult to distinguish among alternative models that produce very different values of the risk measures. Furthermore, the estimates obtained can be unstable and overly sensitive to the presence or absence of single extreme events. Setting a bound on the maximum amount that can be lost in a single event reduces the dependence on the distributional assumptions and improves the robustness and stability of the risk measures, while preserving their sensitivity to changes in the risk profile. This bound should be determined by expert assessment on the basis of economic arguments and validated by the regulator, so that it can be used as a control parameter in the risk analysis.     

A direct calculation of moments of the sample variance

A systematic method to deal with the interrelations of systems with multi-index quantities (random variables) is proposed. The method differs of the well-known Polykays. An application of the theoretical results here presented is the calculation of the moments of the sample variance for general populations in a direct way. The main advantage of the proposed methodology is that no conversion formulae and other complicated Polykays rules are needed. However, the proposed method is compatible with Polykays philosophy and conversion formulae and multiplication rules can be derived by using the theoretical results of this work. For practical purposes, two algorithms for the calculation of the moments of the sample variance are proposed.     

Numerical analysis of a DAR IMPATT diode

The analysis and optimization of the n ⁺ pvnp ⁺ avalanche diode structure that includes two avalanche regions have been realized on basis of the nonlinear model and special optimization procedure. The admittance and energy characteristics of the DAR diode were analyzed in very wide frequency band from 30 up to 360 GHz. Output power level was optimized for the second frequency band near the 220 GHz.     

Engineering Aspects of the Integration of Chemical and Biological Oxidation: Simple Mechanistic Models for the Oxidation Treatment

Oxidation processes can oxidize biorecalcitrant compounds into biodegradable intermediates, which in turn can be treated less expensively by a subsequent biological process. To design such a two-step (chemical+biological) process to treat poorly characterized wastewaters, it is useful to model the time evolution of characteristic global variables, chemical oxygen demand (COD) and biochemical oxygen demand (BOD), in order to develop a useful treatment strategy based upon these classical variables. We consider two simple model reaction networks, requiring three- and five-rate constants, respectively. The first model, proposed recently, involves conversion of a nonbiodegradable species, C, into a single biodegradable intermediate S. Here, biodegradable compounds are immediate kinetic products of oxidation. In general, it is not probable that a single recalcitrant compound undergoes a single-step reaction to CO2. However, when working with complex undefined wastewaters streams, single-step reactions may be used to simplify. The second new model corresponds to a lag time in BOD formation due to the necessity of multiple partial oxidations to reach a first biodegradable intermediate. The second network includes two intermediates, I and S, which are, respectively, nonbiodegradable and biodegradable. We then compare model behavior with an unfortunately sparse literature on BOD and COD values versus time in chemical reactors, and demonstrate the convenience of the first model, and the occasional necessity of the second, which reflects the presence of early intermediates which are nonbiodegradable.     

Autonomous indoor ultrasonic positioning system based on a low-cost conditioning circuit

This work presents a low cost 3-D location system based on ultrasonics and implemented with low-cost FPGAs. The mobile nodes of the system use distance estimation to several anchor points in order to trilaterate their positions with an accuracy of few centimeters. The ultrasonic transducers are handled with an ad hoc conditioning circuit based on instrumental amplifiers which provides high amplification keeping low noise. The proposed system is autonomous so there is no need of an external PC or other devices. A prototype of the system has been attached to a mobile robot to check the viability of the location system in a real scenario.     

Acceptance of Corporate Blogs for Collaboration and Knowledge Sharing

ABSTRACT

This study investigates employees’ acceptance of corporate Web 2.0 applications for collaboration and knowledge sharing. Results show that altruism, managerial support, and perceived usefulness are good predictors of the intention to use corporate blogs as tools for collaboration and knowledge sharing. The study further explores differences between employees from companies with low and high adoption rates, and uses prediction-oriented segmentation to unveil two different paths to adoption of Web 2.0 collaborative tools in companies.