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Cet ouvrage se veut, avant tout, être un outil de travail qui s'attelle à décrire les grands rouages d'une matière complexe qui permet de priver une personne de sa liberté. Ce livre permet à ceux qui ne sont pas familiers de ce sujet, et d'abord aux étudiants, aux jeunes avocats et magistrats, de trouver un accès commode aux principes qui gouvernent la matière. Ils en apprécieront le caractère méthodique, systématique et didactique. Aux juristes plus spécialisés, il offre la possibilité de nourrir leur recherche et leur réflexion par le traitement actualisé des problèmes posés.

Preventive detention --- Détention provisoire --- BPB1101 --- Voorlopige hechtenis --- Ebooks --- Détention provisoire --- Belgium --- Criminal justice [Administration of ] --- Warrants (Law) --- pirmstiesas apcietinājums --- Untersuchungshaft --- rannsakningshäkte --- vazba --- tutkintavankeus --- varetægtsarrest --- väzba pred začatím procesu --- ndalim përpara gjykimit --- eelvangistus --- areszt tymczasowy --- voorlopige hechtenis --- detention before trial --- задържане преди съдебен процес --- detenzione preventiva --- előzetes letartóztatás --- detenção provisória --- pritvor --- kardomasis sulaikymas --- pripor --- προσωρινή κράτηση --- притвор --- detenție preventivă --- reclusión provisional --- краткотраен притвор --- привремен притвор --- домашен притвор --- мерка притвор --- carcerazione preventiva --- истражен притвор --- vizsgálati fogság --- E-books --- coinneáil roimh thriail --- Belgique --- Procédure pénale --- Procédure pénale -- Belgique

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Par une recherche exploratoire, le TFE portera sur les différentes victimisations subies par les seniors (à partir de 65 ans) concernant le vol par ruse. Il reprendra le modus opérandi de cette qualification "par ruse" notamment en s'attachant soit au vol par ruse dans les habitations soit dans une zone publique . 

Il sera alors nécessaire de s'attacher aux différents plans de prévention qui touchent ce public a première vue vulnérable mais également l'impact qu'à pu avoir cet acte qui affecte d'un point de vue intime et peut peut être entrainer une réorganisation des habitudes de vie.

Vol par ruse, séniors, habitudes de vie, victimisation --- Droit, criminologie & sciences politiques > Criminologie

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Abstract Introduction: Age estimation is an integral part of forensic science. It is a scientific process that assess the chronological age of a person (deceased or alive) through dental and skeletal indicators. For dental age estimation in children, developing permanent teeth are commonly used. As, third molars are also in initial development in children, it is expected that combined information from both developing seven permanent teeth and third molar will provide better age prediction performances. Aim: To evaluate if third molar development is providing an additional age prediction information in children with permanent teeth (4-16years). Materials and methods: 1818 panoramic radiographs from South African black population was used. Two staging techniques were used. Demirjian’s et al (A-H) [6] staging technique for all developing seven mandibular permanent teeth and modified Kohler et al [14] was used to evaluate all available third molars. A weighted analysis of variance (ANOVA) and Bayes’ rule were statistically applied. Comparison was quantified by MAE, RMSE and mean width and coverage of the prediction interval. Inter and intra reliability was checked using Cohen’s Kappa test by restaging 10% of the subjects randomly from the sample. Results: D+K age prediction model showed less RMSE (1.182) in comparison with D (1.95) and K (1.60) model in males and 1.38 (RMSE) in case of females. Significance difference of 0.58 (p<.0001) when compared with MAE D+K with D and 0.36 (p<.0001) when compared with K model separately in males and 0.48 (p<.0001) between MAE D and D+K and 0.35 when compared MAE K with D+K in females was found. D+K model gave maximum coverage in prediction than D and K models separately for males and females. Conclusion: D+K model was providing better age prediction information in children (4-16years) than D and K models separately.

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Deze master thesis onderzocht of de forensisch odontoloog risico heeft voor het ontwikkelen van post-traumatische stress. Een enquête werd opgesteld en gestuurd naar forensisch odontologen die internationaal actief zijn. Deze enquête bevatte verschillende vragen gaande over symptomen van post-traumatische stress. Als hoofdresultaat vonden we dat de internationale forensisch odontoloog geen risico vertoonde voor het ontwikkelen van post-traumatische stress. Een ander belangrijk resultaat was dat 85% van de participanten geloofde dat training tegen post-traumatische stress geïncorporeerd moest worden in de training forensich odontologen

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The biological age of a person is critical in many legal settings related to sports and forensics. A typical scenario is to determine the age of athletes, unaccompanied fugitives claiming to be minors or a corpse. This demands for a method to determine the biological age of a person by doing relevant tests. In this regard, third molar-based age estimation looks a promising approach as the third molar shows distinct patterns of growth corresponding to the biological age of the person. The current methodology of age estimation from third molars involves radiology expertise to manually categorize the panoramic X-ray images to the development stage and later use statistical models to predict the biological age. This process is time-consuming and cumbersome. The goal of the project is to automate the age assessment process from dental radiograph images. The focus of the thesis is on exploring the advancements in deep learning for dental age estimation with an emphasis on regression and ordinal regression methods. The Dataset for the experiments consists of 2214 dental radiograph images distributed over age ranges of 8-24 years. Five-fold cross-validation was used to evaluate the models. A deep learning model with, Densenet201 as the convolutional neural network, achieved the best results with the ordinal regression method. The corresponding Mean Absolute Error (MAE) was 1.15 years.