Soil Moisture Measurements: Comparison of Instrumentation Performances

It has long been recognized that reliable, robust, and automated instrumentation for the measurement of soil moisture content can be extremely useful, if not essential, in hydrological, environmental, and agricultural applications. A number of automated techniques for point measurement of soil water content have been developed to operational level over the past few decades. While each of those techniques has been individually calibrated by the gravimetric method, typically under laboratory conditions, there have been few studies that made a direct comparison between the various techniques, particularly under field conditions. This paper compares ECH2O probes, EC-5 (both sensors based on capacitance measurements, developed by Decagon Devices) and time domain reflectometer sensors (CS616 Campbell Scientific Water Content Reflectometer), with gravimetric data and with each other, under field conditions. Data were collected during two field experiments characterized by different soils and a wide range of soil moistures, resulting from irrigation/drying cycle. Results show that all the tested probes give acceptable results after being calibrated in the field. The capacitive sensors can be used in each type of soil with the same calibration equation, independently from depth, with root mean square error (RMSE) ranging between 2.5 and 3.6%. Time Domain Reflectometry probes showed a dependence on depth but a lower RMSE (1.6%).     

Applying Web usage mining for personalizing hyperlinks in Web-based adaptive educational systems

Nowadays, the application of Web mining techniques in e-learning and Web-based adaptive educational systems is increasing exponentially. In this paper, we propose an advanced architecture for a personalization system to facilitate Web mining. A specific Web mining tool is developed and a recommender engine is integrated into the AHA! system in order to help the instructor to carry out the whole Web mining process. Our objective is to be able to recommend to a student the most appropriate links/Web pages within the AHA! system to visit next. Several experiments are carried out with real data provided by Eindhoven University of Technology students in order to test both the architecture proposed and the algorithms used. Finally, we have also described the meaning of several recommendations, starting from the rules discovered by the Web mining algorithms.     

Packed column supercritical fluid chromatography/mass spectrometry for high-throughput analysis. Part 2

A supercritical fluid chromatograph was previously interfaced to a mass spectrometer (SFC/MS) and the system evaluated for applications requiring high sample throughput using negative-mode atmospheric-pressure chemical ionization (APCI) (Ventura et al. Anal. Chem. 1999, 71, 2410-2416). This report extends the previous work demonstrating the effectiveness of SFC/MS, using positive ion APCI for the analysis of compounds with a wide range of polarities. Substituting SFC/MS for LC/MS results in substantial time saving, increased chromatographic efficiency, and more precise quantitation of sample mixtures. Flow injection analysis (FIA) also benefits from our SFC/MS system. A broader range of solvents is compatible with the SFC mobile phase compared with LC/MS, and solutes elute more rapidly from the SFC/MS system, reducing sample carryover and cycle time. Our instrumental setup also allows for facile conversion between LC/MS and SFC/MS modes of operation.     

Statistical smoothing of neuronal data

The purpose of smoothing (filtering) neuronal data is to improve the estimation of the instantaneous firing rate. In some applications, scientific interest centres on functions of the instantaneous firing rate, such as the time at which the maximal firing rate occurs or the rate of increase of firing rate over some experimentally relevant period. In others, the instantaneous firing rate is needed for probability-based calculations. In this paper we point to the very substantial gains in statistical efficiency from smoothing methods compared to using the peristimulus-time histogram (PSTH), and we also demonstrate a new method of adaptive smoothing known as Bayesian adaptive regression splines (DiMatteo 1, Genovese C R and Kass R E 2001 Biometrika 88 1055-71). We briefly review additional applications of smoothing with non-Poisson processes and in the joint PSTH for a pair of neurons.     

Construction of Time-Relaxed c-Broadcast Networks

C-broadcasting is an information dissemination task where a message, originated at any node in a network, is transmitted to all other nodes with the restriction that each node having the message can transmit it to almost c neighbors simultaneously. If the transmission time of the message is set to be one time unit, a minimal c-broadcast network (c-MBN) is a communication network in which c-broadcasting from any node can be accomplished in log _c+1 n time units, where n is the number of nodes and log _c+1 n is the fastest possible broadcast time. If networks are sparse, additional time units may be required to perform c-broadcasting. A time-relaxed c-broadcast network, denoted as (t,c)-RBN, is a network where c-broadcasting from any node can be completed in log _c+1 n+t time units. In this paper, a network compounding algorithm is proposed to construct large sparse (t,c)-RBNs by linking multiple copies of a time-relaxed network of small size using the structure of another time-relaxed network. Computational results are presented to show the effectiveness of the proposed algorithm.     

Effect of pH on the precipitation of hydroxyapatite on silica gels

Silica-HAp composites have been produced with particle size ranging from several nm to few μm, through control of the pH of the solution, which also controls morphology. A calcium ions reservoir has been made available as a Ca/EDTA soluble complex allowing the production of HAp at low temperature and short periods of time (hours). The presence of silica seems to promote the formation of HAp under these conditions. Electronic Publication     

Valuing credit default swap in a non-homogeneous semi-Markovian rating based model

In this paper, we use a discrete time non-homogeneous semi-Markov model for the rating evolution of the credit quality of a firm C (see D’Amico, Janssen, and Manca Proceedings of the II international workshop in applied probablity, 2004a) and we determine the credit default swap spread for a contract between two parties, A and B that, respectively, sell and buy a protection about the failure of the firm C. We work both in the case of deterministic and stochastic recovery rate. We also highlight the link between credit risk and reliability theory.