La Monogamie, ou l'Unité dans le mariage, ouvrage dans lequel on entreprend d'établir... l'exacte... conformité des trois loix, de la nature, de Moïse et de Jésus-Christ sur ce sujet, par M. de Prémontval. Dédié aux dames par son épouse.... Tome 1

Comprend : Épître dédicatoire

Nouvelle méhode d'étude en nature des relations spatiales et sociales chez Crocidura russula (Mammalia, Soricidae)

Une méthode de marquage radioactif simultané de deux musaraignes est décrite. Comme instruments, 2 compteurs a scintillation portatifs et un dispositif d'enregistrement automatique à 20 sondes ont été utilisés. Les musaraignes ont été respectivement marquées avec un filament de 100 µ Ci et 600 µ Ci. Avant l'enregistrement simultané il faut déterminer pour chaque individu le domaine vital et l'emplacement des nids et calibrer les instruments. Cette technique est appliquée à une population de Crocidura russula. Elle permet d'étudier les relations spatio-temporelles des individus durant leur activité et repos (occupation commune d'un nid).

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen

Life events in conversion disorder: Role of timing and nature of events

Aims: To compare the frequency of life events in the year preceding illness onset in a series of Conversion Disorder (CD) patients, with those of a matched control group and to characterize the nature of those events in terms of "escape" potential. Traditional models of CD hypothesise that relevant stressful experiences are "converted" into physical symptoms to relieve psychological pressure, and that the resultant disability allows "escape" from the stressor, providing some advantage to the individual. Methods: The Life Events and Difficulties Schedule (LEDS) is a validated semi-structured interview designed to minimise recall and interviewer bias through rigorous assessment and independent rating of events. An additional "escape" rating was developed. Results: In the year preceding onset in 25 CD patients (mean age 38.9 years ± 8) and a similar matched period in 13 controls (mean age 36.2 years ± 10), no significant difference was found in the proportion of subjects having ≥ 1 severe event (CD 64%, controls 38%; p=0.2). In the last month preceding onset, a higher number of patients experienced ≥1 severe events than controls (52% vs 15%, odds ratio 5.95 (CI: 1.09-32.57)). Patients were twice as much more likely to have a severe escape events than controls, in the month preceding onset (44% vs 7%, odds ratio 9.43 (CI: 1.06-84.04). Conclusion: Preliminary data from this ongoing study suggest that the time frame (preceding month) and the nature ("escape") of the events may play an important role in identifying key events related to CD onset.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.