Multi-dimensional approach to bullying

RÉSUMÉ : Le bullying est un type de comportement agressif qu'un élève (ou plusieurs) fait subir à un autre et qui se manifeste par des agressions verbales, physiques et/ou psychologiques. Les caractéristiques du bullying sont la répétitivité d'actions négatives sur le long terme et une relation de pouvoir asymétrique. Pour la victime, ce type de comportement peut avoir des conséquences graves telles qu'échec scolaire, dépression, troubles alimentaires, ou idées suicidaires. De plus, les auteurs de bullying commettent plus de comportements déviants au sein de l'école ou à l'extérieur de cette dernière. La mise en place d'actions ciblées auprès des auteurs de bullying pourrait donc non seulement prévenir une victimisation, mais aussi réduire les actes de délinquance en général. Hormis quelques études locales ou cantonales, aucune recherche nationale auprès d'adolescents n'existait dans le domaine. Ce travail propose de combler cette lacune afin d'obtenir une compréhension suffisante du phénomène qui permet de donner des pistes pour définir des mesures de prévention appropriées. Afin d'appréhender la problématique du bullying dans les écoles secondaires suisses, deux sondages de délinquance juvénile autoreportée ont été effectués. Le premier a eu lieu entre 2003 et 2005 dans le canton de Vaud auprès de plus de 4500 écoliers. Le second a été administré en 2006 dans toute la Suisse et environ 3600 jeunes y ont participé. Les jeunes ont répondu au sondage soit en classe (questionnaire papier) soit en salle d'informatique (questionnaire en ligne). Les jeunes ayant répondu avoir sérieusement harcelé un autre élève est d'environ 7% dans le canton de Vaud et de 4% dans l'échantillon national. Les analyses statistiques ont permis tout d'abord de sélectionner les variables les plus fortement liées au bullying. Les résultats montrent que les jeunes avec un bas niveau d'autocontrôle et ayant une attitude positive envers la violence sont plus susceptibles de commettre des actes de bullying. L'importance des variables environnementales a aussi été démontrée: plus le jeune est supervisé et encadré par des adultes, plus les autorités (école, voisinage) jouent leur rôle de contrôle social en faisant respecter les règles et en intervenant de manière impartiale, moins le jeune risque de commettre des actes de bullying. De plus, l'utilisation d'analyses multiniveaux a permis de montrer l'existence d'effets de l'école sur le bullying. En particulier, le taux de bullying dans une école donnée augmente lorsque les avis des jeunes divergent par rapport à leur perception du climat scolaire. Un autre constat que l'on peut mettre en évidence est que la réaction des enseignants lors de bagarres a une influence différente sur le taux de bullying en fonction de l'établissement scolaire. ABSTRACT : Bullying is the intentional, repetitive or persistent hurting of one pupil by another (or several), where the relationship involves an imbalance of power. Bullying is a type of aggressive behaviour and the act can be verbal, physical and/or psychological. The consequences on the victims are serious: school failure, depressive symptomatology, eating disorders, or suicidal ideation. Moreover, the authors of bullying display more delinquent behaviour within or outside the school. Thus, preventive programmes targeting bullying could not only prevent victimisation, but also reduce delinquency in general. Very little data concerning bullying had been collected in Switzerland and, except some local or cantonal studies, no national research among teenagers existed in the field. This work intends to fill the gap in order to provide sufficient understanding of the phenomenon and to suggest some tracks for defining appropriate measures of prevention. In order to understand the problems of bullying in Swiss secondary schools better, two surveys of self-reported juvenile delinquency were carried out. The first one took place between 2003 and 2005 in the canton Vaud among more than 4500 pupils, the second in 2006 across Switzerland with about 3600 youths taking part. The pupils answered to the survey either in the classroom (paper questionnaire) or in the computer room (online questionnaire). The youths that answered having seriously bullied another pupil are about 7% in canton Vaud and 4% in the national sample. Statistical analyses have selected the variables most strongly related to bullying. The results show that the youths with a low level of self-control and adopting a positive attitude towards violence are more likely to bully others. The importance of the environmental variables was also shown: the more that youth is supervised and monitored by adults, and the more the authorities (school, neighbourhood) play their role of social control by making the rules be respected through intervening in an impartial way, the less the youth bully. Moreover, the use of multilevel analyses permitted to show the existence of effects of the school on bullying. In particular, the rate of bullying in a given school increases when there is a wide variation among students of the same school in their perception of their school climate. Another important aspect concerns teachers' reactions when pupils fight: this variable does not influence the bullying rate to the same extent, and depends on the school.

Stable isotope compositions of mammoth teeth from Niederweningen, Switzerland: Implications for the Late Pleistocene climate, environment, and diet

Oxygen and carbon isotope compositions of well-preserved mammoth teeth from the Middle Wurmian (40-70 ka) peat layer of Niederweningen, the most important mammoth site in Switzerland, were analysed to reconstruct Late Pleistocene palaeoclimatic and palaeoenvironmental conditions. Drinking water (delta(18)O values of approximately -12.3 +/- 0.9 parts per thousand were calculated front oxygen isotope compositions of mammoth tooth enamel apatite using a species-specific calibration for modern elephants. These delta(18)O(H2O) values reflect the mean oxygen isotope composition of the palaeo-precipitation and are similar to those directly measured for fate Pleistocene groundwater from aquifers in northern Switzerland and southern Germany. Using a present-day delta(18)O(H2)o-precipitation-air temperature relation for Switzerland, a mean annual air temperature (MAT) of around 4.3 +/- 2.1 degrees C can be calculated for the Middle Wurmian at this site. This MAT is in good agreement with palaeotemperature estimates on the basis of Middle Wurmian groundwater recharge temperatures and beetle assemblages. Hence, the climatic conditions in this region were around 4 degrees C cooler during the Middle Wurmian interstadial phase, around 45-50ka BP, than they are today. During this period the mammoths from Niederweningen lived in an open tundra-like, C(3) plant-dominated environment as indicated by enamel (delta(13)C values of -11.5 +/- 0.3 parts per thousand and pollen and macroplant fossils found in the embedding peat. The low variability of enamel delta(13)C and delta(18)O values from different mammoth teeth reflects similar environmental conditions and supports a relatively small time frame for the fossil assemblage. (C) 2006 Elsevier Ltd and INQUA. All rights reserved.

Low-grade metamorphism of the Gets nappe (Western Alps)

The Gets nappe, a decollement cover nappe located at the top of the Prealps, is characterized by the occurrence of ophiolitic rocks. The metamorphic grade in the Gets nappe was determined using illite crystallinity and clay mineral assemblages. Samples from the same locality were analyzed to estimate variations in illite crystallinity values and in the parageneses of clay minerals, both in sedimentary elements of a breccia and in the embedding shaly flysch. For samples from one and the same locality, the range in illite crystallinity data between breccia elements and the shaly flysch is comparable to the variation between different shaly beds. Two S-N transects along the Gets nappe reveal the same metamorphic gradient, with the internal parts of the nappe being characterized by middle anchizonal metamorphism and the external parts showing diagenetic conditions. The metamorphic grade is higher within the Gets nappe than in its hangingwall (i.e. the Breche and Simme nappes), suggesting that the metamorphism in the Gets unit is transported. The timing and conditions of thrusting of the Gets Nappe onto the Br che and the Simme nappes is constrained by stratigraphic and metamorphic data.

Multicentre validation study of nucleic acids extraction from FFPE tissues.

In most pathology laboratories worldwide, formalin-fixed paraffin embedded (FFPE) samples are the only tissue specimens available for routine diagnostics. Although commercial kits for diagnostic molecular pathology testing are becoming available, most of the current diagnostic tests are laboratory-based assays. Thus, there is a need for standardized procedures in molecular pathology, starting from the extraction of nucleic acids. To evaluate the current methods for extracting nucleic acids from FFPE tissues, 13 European laboratories, participating to the European FP6 program IMPACTS (www.impactsnetwork.eu), isolated nucleic acids from four diagnostic FFPE tissues using their routine methods, followed by quality assessment. The DNA-extraction protocols ranged from homemade protocols to commercial kits. Except for one homemade protocol, the majority gave comparable results in terms of the quality of the extracted DNA measured by the ability to amplify differently sized control gene fragments by PCR. For array-applications or tests that require an accurately determined DNA-input, we recommend using silica based adsorption columns for DNA recovery. For RNA extractions, the best results were obtained using chromatography column based commercial kits, which resulted in the highest quantity and best assayable RNA. Quality testing using RT-PCR gave successful amplification of 200 bp-250 bp PCR products from most tested tissues. Modifications of the proteinase-K digestion time led to better results, even when commercial kits were applied. The results of the study emphasize the need for quality control of the nucleic acid extracts with standardised methods to prevent false negative results and to allow data comparison among different diagnostic laboratories.

Profiling of counterfeit medicines by vibrational spectroscopy

Counterfeit pharmaceutical products have become a widespread problem in the last decade. Various analytical techniques have been applied to discriminate between genuine and counterfeit products. Among these, Near-infrared (NIR) and Raman spectroscopy provided promising results.The present study offers a methodology allowing to provide more valuable information fororganisations engaged in the fight against counterfeiting of medicines.A database was established by analyzing counterfeits of a particular pharmaceutical product using Near-infrared (NIR) and Raman spectroscopy. Unsupervised chemometric techniques (i.e. principal component analysis - PCA and hierarchical cluster analysis - HCA) were implemented to identify the classes within the datasets. Gas Chromatography coupled to Mass Spectrometry (GC-MS) and Fourier Transform Infrared Spectroscopy (FT-IR) were used to determine the number of different chemical profiles within the counterfeits. A comparison with the classes established by NIR and Raman spectroscopy allowed to evaluate the discriminating power provided by these techniques. Supervised classifiers (i.e. k-Nearest Neighbors, Partial Least Squares Discriminant Analysis, Probabilistic Neural Networks and Counterpropagation Artificial Neural Networks) were applied on the acquired NIR and Raman spectra and the results were compared to the ones provided by the unsupervised classifiers.The retained strategy for routine applications, founded on the classes identified by NIR and Raman spectroscopy, uses a classification algorithm based on distance measures and Receiver Operating Characteristics (ROC) curves. The model is able to compare the spectrum of a new counterfeit with that of previously analyzed products and to determine if a new specimen belongs to one of the existing classes, consequently allowing to establish a link with other counterfeits of the database.

A receptor-like kinase mutant with absent endodermal diffusion barrier displays selective nutrient homeostasis defects.

The endodermis represents the main barrier to extracellular diffusion in plant roots, and it is central to current models of plant nutrient uptake. Despite this, little is known about the genes setting up this endodermal barrier. In this study, we report the identification and characterization of a strong barrier mutant, schengen3 (sgn3). We observe a surprising ability of the mutant to maintain nutrient homeostasis, but demonstrate a major defect in maintaining sufficient levels of the macronutrient potassium. We show that SGN3/GASSHO1 is a receptor-like kinase that is necessary for localizing CASPARIAN STRIP DOMAIN PROTEINS (CASPs)--major players of endodermal differentiation--into an uninterrupted, ring-like domain. SGN3 appears to localize into a broader band, embedding growing CASP microdomains. The discovery of SGN3 strongly advances our ability to interrogate mechanisms of plant nutrient homeostasis and provides a novel actor for localized microdomain formation at the endodermal plasma membrane.

The Smiling Project: development of a new program for gait and balance training

Introduction: The SMILING project, a multicentric project fundedby the European Union, aims to develop a new gait and balance trainingprogram to prevent falls in older persons. The program includes the"SMILING shoe", an innovative device that generates mechanical perturbationwhile walking by changing the soles' inclination. Induced perturbationschallenge subjects' balance and force them to react to avoidfalls. By training specifically the complex motor reactions used to maintainbalance when walking on irregular ground, the program will improvesubjects' ability to react in situation of unsteadiness and reduce theirrisk of falling. Methods: The program will be evaluated in a multicentric,cross-over randomized controlled trial. Overall, 112 subjects (aged≥65 years, ≥1 falls, POMA score 22-26/28) will be enrolled. Subjectswill be randomised in 2 groups: group A begin the training with active"SMILING shoes", group B with inactive dummy shoes. After 4 weeksof training, group A and B will exchange the shoes. Supervised trainingsessions (30 minutes twice a week for 8 weeks) include walkingtasks of progressive difficulties.To avoid a learning effect, "SMILINGshoes" perturbations will be generated in a non-linear and chaotic way.Gait performance, fear of falling, and acceptability of the program willbe assessed. Conclusion: The SMILING program is an innovative interventionfor falls prevention in older persons based on gait and balancetraining using chaotic perturbations. Because of the easy use of the"SMILING shoes", this program could be used in various settings, suchas geriatric clinics or at home.

Animal Tissue Response To Mid-Term Electronic Modular Artificial Sphincter Implantation

Introduction and objectives: The AMS 800TM is considered the gold standard for sphincter replacement. However, the one-ring design can erode the urethra and lead to severe complications. A mechanism that could alternatively compress successive segments of the urethra would limit such deleterious outcome. We report 12 weeks animal urethral tissue analysis following implantation of a new modular artificial sphincter. METHODS: The device is composed by three parts: the contractile unit, two rings and an integrated microprocessor. The contractile unit is made of Nitinol fibers. The rings are placed around the urethra to control the flow of urine by squeezing the urethra. They work in a sequential alternative mode and are controlled by a microprocessor connected to an external computer. The computer can reveal specific failure of device components. The device was impkanted in eight male sheep. The rings were positioned around the urethra and the control unit was placed 5cm away. The device was working twenty hours per day; it was open 10min. per hour to allow urination. The animals were sacrificed after 12 weeks. The urethra and the tissues surrounding the control unit were macroscopically and microscopically examined. Two transversal sections crossing the sphincter and two transversal sections crossing the urethra alone were obtained and stained with modified Paragon after resin embedding. Urethra was also embedded in paraffin. The first section was stained with safranin-hematoxylin-eosin, the second section was stained with Masson's Trichrome and the remaining eight sections were available for immunolabelling of the macrophages.Results: The chronic study went uneventful. No clinical infection or pain was observed. The computer registered no specific failure in ring function, Nitinol wires and tube connectors. At explantation, except for a slight grade of lymphocytes in two out of eight specimens, no urethral stricture or atrophy could be observed. Immunohistochemistry confirmed the absence of macrophages. Tissue structure and organization of the urethra with and without artificial sphincter were similar. No migration of the device was observed.Conclusions: The study clearly showed no tissue damage or inflammation of the urethra. Electronic design, preservation of urethral vascularisation and adjustability after implantation are the key ideas to improve the actual AUS. Further studies will be carried out to evaluate this potential.

Uncertainty Quantification with Support Vector Regression Prediction Models

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. The paper considers a data driven approach in modelling uncertainty in spatial predictions. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic features and describe stochastic variability and non-uniqueness of spatial properties. It is able to capture and preserve key spatial dependencies such as connectivity, which is often difficult to achieve with two-point geostatistical models. Semi-supervised SVR is designed to integrate various kinds of conditioning data and learn dependences from them. A stochastic semi-supervised SVR model is integrated into a Bayesian framework to quantify uncertainty with multiple models fitted to dynamic observations. The developed approach is illustrated with a reservoir case study. The resulting probabilistic production forecasts are described by uncertainty envelopes.

High-intensity intermittent training in hypoxia: a double-blinded, placebo-controlled field study in youth football players.

High-intensity intermittent training in hypoxia: A double-blinded, placebo-controlled field study in youth football players. J Strength Cond Res 29(1): 226-237, 2015-This study examined the effects of 5 weeks (∼60 minutes per training, 2 d·wk) of run-based high-intensity repeated-sprint ability (RSA) and explosive strength/agility/sprint training in either normobaric hypoxia repeated sprints in hypoxia (RSH; inspired oxygen fraction [FIO2] = 14.3%) or repeated sprints in normoxia (RSN; FIO2 = 21.0%) on physical performance in 16 highly trained, under-18 male footballers. For both RSH (n = 8) and RSN (n = 8) groups, lower-limb explosive power, sprinting (10-40 m) times, maximal aerobic speed, repeated-sprint (10 × 30 m, 30-s rest) and repeated-agility (RA) (6 × 20 m, 30-s rest) abilities were evaluated in normoxia before and after supervised training. Lower-limb explosive power (+6.5 ± 1.9% vs. +5.0 ± 7.6% for RSH and RSN, respectively; both p < 0.001) and performance during maximal sprinting increased (from -6.6 ± 2.2% vs. -4.3 ± 2.6% at 10 m to -1.7 ± 1.7% vs. -1.3 ± 2.3% at 40 m for RSH and RSN, respectively; p values ranging from <0.05 to <0.01) to a similar extent in RSH and RSN. Both groups improved best (-3.0 ± 1.7% vs. -2.3 ± 1.8%; both p ≤ 0.05) and mean (-3.2 ± 1.7%, p < 0.01 vs. -1.9 ± 2.6%, p ≤ 0.05 for RSH and RSN, respectively) repeated-sprint times, whereas sprint decrement did not change. Significant interactions effects (p ≤ 0.05) between condition and time were found for RA ability-related parameters with very likely greater gains (p ≤ 0.05) for RSH than RSN (initial sprint: 4.4 ± 1.9% vs. 2.0 ± 1.7% and cumulated times: 4.3 ± 0.6% vs. 2.4 ± 1.7%). Maximal aerobic speed remained unchanged throughout the protocol. In youth highly trained football players, the addition of 10 repeated-sprint training sessions performed in hypoxia vs. normoxia to their regular football practice over a 5-week in-season period was more efficient at enhancing RA ability (including direction changes), whereas it had no additional effect on improvements in lower-limb explosive power, maximal sprinting, and RSA performance.

Domain Adaptation in remote sensing: increasing the portability of land-cover classifiers

Among the types of remote sensing acquisitions, optical images are certainly one of the most widely relied upon data sources for Earth observation. They provide detailed measurements of the electromagnetic radiation reflected or emitted by each pixel in the scene. Through a process termed supervised land-cover classification, this allows to automatically yet accurately distinguish objects at the surface of our planet. In this respect, when producing a land-cover map of the surveyed area, the availability of training examples representative of each thematic class is crucial for the success of the classification procedure. However, in real applications, due to several constraints on the sample collection process, labeled pixels are usually scarce. When analyzing an image for which those key samples are unavailable, a viable solution consists in resorting to the ground truth data of other previously acquired images. This option is attractive but several factors such as atmospheric, ground and acquisition conditions can cause radiometric differences between the images, hindering therefore the transfer of knowledge from one image to another. The goal of this Thesis is to supply remote sensing image analysts with suitable processing techniques to ensure a robust portability of the classification models across different images. The ultimate purpose is to map the land-cover classes over large spatial and temporal extents with minimal ground information. To overcome, or simply quantify, the observed shifts in the statistical distribution of the spectra of the materials, we study four approaches issued from the field of machine learning. First, we propose a strategy to intelligently sample the image of interest to collect the labels only in correspondence of the most useful pixels. This iterative routine is based on a constant evaluation of the pertinence to the new image of the initial training data actually belonging to a different image. Second, an approach to reduce the radiometric differences among the images by projecting the respective pixels in a common new data space is presented. We analyze a kernel-based feature extraction framework suited for such problems, showing that, after this relative normalization, the cross-image generalization abilities of a classifier are highly increased. Third, we test a new data-driven measure of distance between probability distributions to assess the distortions caused by differences in the acquisition geometry affecting series of multi-angle images. Also, we gauge the portability of classification models through the sequences. In both exercises, the efficacy of classic physically- and statistically-based normalization methods is discussed. Finally, we explore a new family of approaches based on sparse representations of the samples to reciprocally convert the data space of two images. The projection function bridging the images allows a synthesis of new pixels with more similar characteristics ultimately facilitating the land-cover mapping across images.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

BIO-INSPIRED COMPUTATIONAL TECHNIQUES APPLIED TO THE CLUSTERING AND VISUALIZATION OF SPATIO-TEMPORAL GEOSPATIAL DATA

The coverage and volume of geo-referenced datasets are extensive and incessantly¦growing. The systematic capture of geo-referenced information generates large volumes¦of spatio-temporal data to be analyzed. Clustering and visualization play a key¦role in the exploratory data analysis and the extraction of knowledge embedded in¦these data. However, new challenges in visualization and clustering are posed when¦dealing with the special characteristics of this data. For instance, its complex structures,¦large quantity of samples, variables involved in a temporal context, high dimensionality¦and large variability in cluster shapes.¦The central aim of my thesis is to propose new algorithms and methodologies for¦clustering and visualization, in order to assist the knowledge extraction from spatiotemporal¦geo-referenced data, thus improving making decision processes.¦I present two original algorithms, one for clustering: the Fuzzy Growing Hierarchical¦Self-Organizing Networks (FGHSON), and the second for exploratory visual data analysis:¦the Tree-structured Self-organizing Maps Component Planes. In addition, I present¦methodologies that combined with FGHSON and the Tree-structured SOM Component¦Planes allow the integration of space and time seamlessly and simultaneously in¦order to extract knowledge embedded in a temporal context.¦The originality of the FGHSON lies in its capability to reflect the underlying structure¦of a dataset in a hierarchical fuzzy way. A hierarchical fuzzy representation of¦clusters is crucial when data include complex structures with large variability of cluster¦shapes, variances, densities and number of clusters. The most important characteristics¦of the FGHSON include: (1) It does not require an a-priori setup of the number¦of clusters. (2) The algorithm executes several self-organizing processes in parallel.¦Hence, when dealing with large datasets the processes can be distributed reducing the¦computational cost. (3) Only three parameters are necessary to set up the algorithm.¦In the case of the Tree-structured SOM Component Planes, the novelty of this algorithm¦lies in its ability to create a structure that allows the visual exploratory data analysis¦of large high-dimensional datasets. This algorithm creates a hierarchical structure¦of Self-Organizing Map Component Planes, arranging similar variables' projections in¦the same branches of the tree. Hence, similarities on variables' behavior can be easily¦detected (e.g. local correlations, maximal and minimal values and outliers).¦Both FGHSON and the Tree-structured SOM Component Planes were applied in¦several agroecological problems proving to be very efficient in the exploratory analysis¦and clustering of spatio-temporal datasets.¦In this thesis I also tested three soft competitive learning algorithms. Two of them¦well-known non supervised soft competitive algorithms, namely the Self-Organizing¦Maps (SOMs) and the Growing Hierarchical Self-Organizing Maps (GHSOMs); and the¦third was our original contribution, the FGHSON. Although the algorithms presented¦here have been used in several areas, to my knowledge there is not any work applying¦and comparing the performance of those techniques when dealing with spatiotemporal¦geospatial data, as it is presented in this thesis.¦I propose original methodologies to explore spatio-temporal geo-referenced datasets¦through time. Our approach uses time windows to capture temporal similarities and¦variations by using the FGHSON clustering algorithm. The developed methodologies¦are used in two case studies. In the first, the objective was to find similar agroecozones¦through time and in the second one it was to find similar environmental patterns¦shifted in time.¦Several results presented in this thesis have led to new contributions to agroecological¦knowledge, for instance, in sugar cane, and blackberry production.¦Finally, in the framework of this thesis we developed several software tools: (1)¦a Matlab toolbox that implements the FGHSON algorithm, and (2) a program called¦BIS (Bio-inspired Identification of Similar agroecozones) an interactive graphical user¦interface tool which integrates the FGHSON algorithm with Google Earth in order to¦show zones with similar agroecological characteristics.

Community Policing in Switzerland's Major Urban Areas: An Observational Study of the Implementation and Impact Using Geospatial Data Mining

La présente étude est à la fois une évaluation du processus de la mise en oeuvre et des impacts de la police de proximité dans les cinq plus grandes zones urbaines de Suisse - Bâle, Berne, Genève, Lausanne et Zurich. La police de proximité (community policing) est à la fois une philosophie et une stratégie organisationnelle qui favorise un partenariat renouvelé entre la police et les communautés locales dans le but de résoudre les problèmes relatifs à la sécurité et à l'ordre public. L'évaluation de processus a analysé des données relatives aux réformes internes de la police qui ont été obtenues par l'intermédiaire d'entretiens semi-structurés avec des administrateurs clés des cinq départements de police, ainsi que dans des documents écrits de la police et d'autres sources publiques. L'évaluation des impacts, quant à elle, s'est basée sur des variables contextuelles telles que des statistiques policières et des données de recensement, ainsi que sur des indicateurs d'impacts construit à partir des données du Swiss Crime Survey (SCS) relatives au sentiment d'insécurité, à la perception du désordre public et à la satisfaction de la population à l'égard de la police. Le SCS est un sondage régulier qui a permis d'interroger des habitants des cinq grandes zones urbaines à plusieurs reprises depuis le milieu des années 1980. L'évaluation de processus a abouti à un « Calendrier des activités » visant à créer des données de panel permettant de mesurer les progrès réalisés dans la mise en oeuvre de la police de proximité à l'aide d'une grille d'évaluation à six dimensions à des intervalles de cinq ans entre 1990 et 2010. L'évaluation des impacts, effectuée ex post facto, a utilisé un concept de recherche non-expérimental (observational design) dans le but d'analyser les impacts de différents modèles de police de proximité dans des zones comparables à travers les cinq villes étudiées. Les quartiers urbains, délimités par zone de code postal, ont ainsi été regroupés par l'intermédiaire d'une typologie réalisée à l'aide d'algorithmes d'apprentissage automatique (machine learning). Des algorithmes supervisés et non supervisés ont été utilisés sur les données à haute dimensionnalité relatives à la criminalité, à la structure socio-économique et démographique et au cadre bâti dans le but de regrouper les quartiers urbains les plus similaires dans des clusters. D'abord, les cartes auto-organisatrices (self-organizing maps) ont été utilisées dans le but de réduire la variance intra-cluster des variables contextuelles et de maximiser simultanément la variance inter-cluster des réponses au sondage. Ensuite, l'algorithme des forêts d'arbres décisionnels (random forests) a permis à la fois d'évaluer la pertinence de la typologie de quartier élaborée et de sélectionner les variables contextuelles clés afin de construire un modèle parcimonieux faisant un minimum d'erreurs de classification. Enfin, pour l'analyse des impacts, la méthode des appariements des coefficients de propension (propensity score matching) a été utilisée pour équilibrer les échantillons prétest-posttest en termes d'âge, de sexe et de niveau d'éducation des répondants au sein de chaque type de quartier ainsi identifié dans chacune des villes, avant d'effectuer un test statistique de la différence observée dans les indicateurs d'impacts. De plus, tous les résultats statistiquement significatifs ont été soumis à une analyse de sensibilité (sensitivity analysis) afin d'évaluer leur robustesse face à un biais potentiel dû à des covariables non observées. L'étude relève qu'au cours des quinze dernières années, les cinq services de police ont entamé des réformes majeures de leur organisation ainsi que de leurs stratégies opérationnelles et qu'ils ont noué des partenariats stratégiques afin de mettre en oeuvre la police de proximité. La typologie de quartier développée a abouti à une réduction de la variance intra-cluster des variables contextuelles et permet d'expliquer une partie significative de la variance inter-cluster des indicateurs d'impacts avant la mise en oeuvre du traitement. Ceci semble suggérer que les méthodes de géocomputation aident à équilibrer les covariables observées et donc à réduire les menaces relatives à la validité interne d'un concept de recherche non-expérimental. Enfin, l'analyse des impacts a révélé que le sentiment d'insécurité a diminué de manière significative pendant la période 2000-2005 dans les quartiers se trouvant à l'intérieur et autour des centres-villes de Berne et de Zurich. Ces améliorations sont assez robustes face à des biais dus à des covariables inobservées et covarient dans le temps et l'espace avec la mise en oeuvre de la police de proximité. L'hypothèse alternative envisageant que les diminutions observées dans le sentiment d'insécurité soient, partiellement, un résultat des interventions policières de proximité semble donc être aussi plausible que l'hypothèse nulle considérant l'absence absolue d'effet. Ceci, même si le concept de recherche non-expérimental mis en oeuvre ne peut pas complètement exclure la sélection et la régression à la moyenne comme explications alternatives. The current research project is both a process and impact evaluation of community policing in Switzerland's five major urban areas - Basel, Bern, Geneva, Lausanne, and Zurich. Community policing is both a philosophy and an organizational strategy that promotes a renewed partnership between the police and the community to solve problems of crime and disorder. The process evaluation data on police internal reforms were obtained through semi-structured interviews with key administrators from the five police departments as well as from police internal documents and additional public sources. The impact evaluation uses official crime records and census statistics as contextual variables as well as Swiss Crime Survey (SCS) data on fear of crime, perceptions of disorder, and public attitudes towards the police as outcome measures. The SCS is a standing survey instrument that has polled residents of the five urban areas repeatedly since the mid-1980s. The process evaluation produced a "Calendar of Action" to create panel data to measure community policing implementation progress over six evaluative dimensions in intervals of five years between 1990 and 2010. The impact evaluation, carried out ex post facto, uses an observational design that analyzes the impact of the different community policing models between matched comparison areas across the five cities. Using ZIP code districts as proxies for urban neighborhoods, geospatial data mining algorithms serve to develop a neighborhood typology in order to match the comparison areas. To this end, both unsupervised and supervised algorithms are used to analyze high-dimensional data on crime, the socio-economic and demographic structure, and the built environment in order to classify urban neighborhoods into clusters of similar type. In a first step, self-organizing maps serve as tools to develop a clustering algorithm that reduces the within-cluster variance in the contextual variables and simultaneously maximizes the between-cluster variance in survey responses. The random forests algorithm then serves to assess the appropriateness of the resulting neighborhood typology and to select the key contextual variables in order to build a parsimonious model that makes a minimum of classification errors. Finally, for the impact analysis, propensity score matching methods are used to match the survey respondents of the pretest and posttest samples on age, gender, and their level of education for each neighborhood type identified within each city, before conducting a statistical test of the observed difference in the outcome measures. Moreover, all significant results were subjected to a sensitivity analysis to assess the robustness of these findings in the face of potential bias due to some unobserved covariates. The study finds that over the last fifteen years, all five police departments have undertaken major reforms of their internal organization and operating strategies and forged strategic partnerships in order to implement community policing. The resulting neighborhood typology reduced the within-cluster variance of the contextual variables and accounted for a significant share of the between-cluster variance in the outcome measures prior to treatment, suggesting that geocomputational methods help to balance the observed covariates and hence to reduce threats to the internal validity of an observational design. Finally, the impact analysis revealed that fear of crime dropped significantly over the 2000-2005 period in the neighborhoods in and around the urban centers of Bern and Zurich. These improvements are fairly robust in the face of bias due to some unobserved covariate and covary temporally and spatially with the implementation of community policing. The alternative hypothesis that the observed reductions in fear of crime were at least in part a result of community policing interventions thus appears at least as plausible as the null hypothesis of absolutely no effect, even if the observational design cannot completely rule out selection and regression to the mean as alternative explanations.

Mult-class differentiation of cannabis seedlings in a forensic context

This article presents an experimental study about the classification ability of several classifiers for multi-classclassification of cannabis seedlings. As the cultivation of drug type cannabis is forbidden in Switzerland lawenforcement authorities regularly ask forensic laboratories to determinate the chemotype of a seized cannabisplant and then to conclude if the plantation is legal or not. This classification is mainly performed when theplant is mature as required by the EU official protocol and then the classification of cannabis seedlings is a timeconsuming and costly procedure. A previous study made by the authors has investigated this problematic [1]and showed that it is possible to differentiate between drug type (illegal) and fibre type (legal) cannabis at anearly stage of growth using gas chromatography interfaced with mass spectrometry (GC-MS) based on therelative proportions of eight major leaf compounds. The aims of the present work are on one hand to continueformer work and to optimize the methodology for the discrimination of drug- and fibre type cannabisdeveloped in the previous study and on the other hand to investigate the possibility to predict illegal cannabisvarieties. Seven classifiers for differentiating between cannabis seedlings are evaluated in this paper, namelyLinear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLS-DA), Nearest NeighbourClassification (NNC), Learning Vector Quantization (LVQ), Radial Basis Function Support Vector Machines(RBF SVMs), Random Forest (RF) and Artificial Neural Networks (ANN). The performance of each method wasassessed using the same analytical dataset that consists of 861 samples split into drug- and fibre type cannabiswith drug type cannabis being made up of 12 varieties (i.e. 12 classes). The results show that linear classifiersare not able to manage the distribution of classes in which some overlap areas exist for both classificationproblems. Unlike linear classifiers, NNC and RBF SVMs best differentiate cannabis samples both for 2-class and12-class classifications with average classification results up to 99% and 98%, respectively. Furthermore, RBFSVMs correctly classified into drug type cannabis the independent validation set, which consists of cannabisplants coming from police seizures. In forensic case work this study shows that the discrimination betweencannabis samples at an early stage of growth is possible with fairly high classification performance fordiscriminating between cannabis chemotypes or between drug type cannabis varieties.