Traducció d'un text tècnic sobre sistemes d'enllumenat: Erco. Einkaufswelten

L’objectiu del present treball és la traducció i l’anàlisi d’una publicació tècnica especialitzada sobre sistemes d’enllumenat, els quals han adquirit una rellevància notable, tant des del punt de vista arquitectònic de l’edifici, com des de la tecnologia de control lumínic que afavoreix l’estalvi energètic.

[Archives de la Parole]. , Special text in santali / [Sir George Grierson], éd. ; Linguistic survey of India ; Voix parlée, homme

[Traditions. Asie. Inde. Chotā Nāgpur]

[Archives de la Parole]. , Special text in mundari / [Sir George Grierson], éd. ; Linguistic survey of India ; Voix parlée, homme

[Traditions. Asie. Inde. Chotā Nāgpur]

[Archives de la Parole]. , Special text in Ho [i.e. hō] : (poésie) / [Sir George Grierson], éd. ; Linguistic survey of India ; Voix parlée, homme

[Traditions. Asie. Inde. Chotā Nāgpur]

Intégration de méthodes de data mining dans le renseignement criminel : analyse par des structures issues de la théorie des graphes dans le profilage des stupéfiants

Data mining can be defined as the extraction of previously unknown and potentially useful information from large datasets. The main principle is to devise computer programs that run through databases and automatically seek deterministic patterns. It is applied in different fields of application, e.g., remote sensing, biometry, speech recognition, but has seldom been applied to forensic case data. The intrinsic difficulty related to the use of such data lies in its heterogeneity, which comes from the many different sources of information. The aim of this study is to highlight potential uses of pattern recognition that would provide relevant results from a criminal intelligence point of view. The role of data mining within a global crime analysis methodology is to detect all types of structures in a dataset. Once filtered and interpreted, those structures can point to previously unseen criminal activities. The interpretation of patterns for intelligence purposes is the final stage of the process. It allows the researcher to validate the whole methodology and to refine each step if necessary. An application to cutting agents found in illicit drug seizures was performed. A combinatorial approach was done, using the presence and the absence of products. Methods coming from the graph theory field were used to extract patterns in data constituted by links between products and place and date of seizure. A data mining process completed using graphing techniques is called ``graph mining''. Patterns were detected that had to be interpreted and compared with preliminary knowledge to establish their relevancy. The illicit drug profiling process is actually an intelligence process that uses preliminary illicit drug classes to classify new samples. Methods proposed in this study could be used \textit{a priori} to compare structures from preliminary and post-detection patterns. This new knowledge of a repeated structure may provide valuable complementary information to profiling and become a source of intelligence.

Web Mining Mineração de dados na Web utilizando CMS

Este trabalho foi realizado no âmbito do regulamento dos cursos de graduação da Universidade Jean Piaget de Cabo Verde, procura realçar a importância da recolha de dados na Web nos dias de hoje. Também apresenta um CMS (Sistema de Gestão de Conteúdo) utilizado no desenvolvimento de Websites, mostrando que é possível obter dados que podem ser considerados úteis acerca do acesso e utilização dos mesmos, dotando-os de componentes desenvolvidos para estes sistemas.

The case for resequencing studies of Arabidopsis thaliana accessions: mining the dark matter of natural genetic variation.

Ultra-high-throughput sequencing (UHTS) techniques are evolving rapidly and may soon become an affordable and routine tool for sequencing plant DNA, even in smaller plant biology labs. Here we review recent insights into intraspecific genome variation gained from UHTS, which offers a glimpse of the rather unexpected levels of structural variability among Arabidopsis thaliana accessions. The challenges that will need to be addressed to efficiently assemble and exploit this information are also discussed.

Preliminary evaluation of acid mine drainage in Minas Gerais State, Brazil

Mining in the State of Minas Gerais-Brazil is one of the activities with the strongest impact on the environment, in spite of its economical importance. Amongst mining activities, acid drainage poses a serious environmental problem due to its widespread practice in gold-extracting areas. It originates from metal-sulfide oxidation, which causes water acidification, increasing the risk of toxic element mobilization and water resource pollution. This research aimed to evaluate the acid drainage problem in Minas Gerais State. The study began with a bibliographic survey at FEAM (Environment Foundation of Minas Gerais State) to identify mining sites where sulfides occur. Substrate samples were collected from these sites to determine AP (acidity potential) and NP (neutralization potential). The AP was evaluated by the procedure of the total sulfide content and by oxygen peroxide oxidation, followed by acidity titration. The NP was evaluated by the calcium carbonate equivalent. Petrographic thin sections were also mounted and described with a special view to sulfides and carbonates. Based on the chemical analysis, the acid-base accounting (ABA) was determined by the difference of AP and NP, and the acid drainage potential obtained by the ABA value and the total volume of material at each site. Results allowed the identification of substrates with potential to generate acid drainage in Minas Gerais state. Altogether these activities represent a potential to produce between 3.1 to 10.4 billions of m³ of water at pH 2 or 31.4 to 103.7 billions of m³ of water at pH 3. This, in turn, would imply in costs of US$ 7.8 to 25.9 millions to neutralize the acidity with commercial limestone. These figures are probably underestimated because some mines were not surveyed, whereas, in other cases, surface samples may not represent reality. A more reliable state-wide evaluation of the acid drainage potential would require further studies, including a larger number of samples. Such investigations should consider other mining operations beyond the scope of this study as well as the kinetics of the acid generation by simulated weathering procedures.

Growth of seedlings of pigeon pea (Cajanus cajan (l.) millsp), wand riverhemp (Sesbania virgata (cav.) pers.), and lead tree (Leucaena leucocephala (lam.) de wit) in an arsenic-contaminated soil

Phytoremediation strategies utilize plants to decontaminate or immobilize soil pollutants. Among soil pollutants, metalloid As is considered a primary concern as a toxic element to organisms. Arsenic concentrations in the soil result from anthropogenic activities such as: the use of pesticides (herbicides and fungicides); some fertilizers; Au, Pb, Cu and Ni mining; Fe and steel production; coal combustion; and as a bi-product during natural gas extraction. This study evaluated the potential of pigeon pea (Cajanus cajan), wand riverhemp (Sesbania virgata), and lead tree (Leucaena leucocephala) as phytoremediators of soils polluted by As. Soil samples were placed in plastic pots, incubated with different As doses (0; 50; 100 and 200 mg dm-3) and then sown with seeds of the three species. Thirty (pigeon pea) and 90 days after sowing, the plants were evaluated for height, collar diameter and dry matter of young, intermediate and basal leaves, stems and roots. Arsenic concentration was determined in different aged leaves, stems and roots to establish the translocation index (TI) between the plant root system and aerial plant components and the bioconcentration factors (BF). The evaluated species showed distinct characteristics regarding As tolerance, since the lead tree and wand riverhemp were significantly more tolerant than pigeon pea. The high As levels found in wand riverhemp roots suggest the existence of an efficient accumulation and compartmentalization mechanism in order to reduce As translocation to shoot tissues. Pigeon pea is a sensitive species and could serve as a potential bioindicator plant, whereas the other two species have potential for phytoremediation programs in As polluted areas. However, further studies are needed with longer exposure times in actual field conditions to reach definite conclusions on relative phytoremediation potentials.

Behavior of Eucalyptus grandis and E. cloeziana seedlings grown in arsenic-contaminated soil

Arsenic has been considered the most poisonous inorganic soil pollutant to living creatures. For this reason, the interest in phytoremediation species has been increasing in the last years. Particularly for the State of Minas Gerais, where areas of former mining activities are prone to the occurrence of acid drainage, the demand is great for suitable species to be used in the revegetation and "cleaning" of As-polluted areas. This study was carried out to evaluate the potential of seedlings of Eucalyptus grandis (Hill) Maiden and E. cloeziana F. Muell, for phytoremediation of As-polluted soils. Soil samples were incubated for a period of 15 days with different As (Na2HAsO4) doses (0, 50, 100, 200, and 400 mg dm-3). After 30 days of exposure the basal leaves of E. cloeziana plants exhibited purple spots with interveinal chlorosis, followed by necrosis and death of the apical bud at the 400 mg dm-3 dose. Increasing As doses in the soil reduced root and shoot dry matter, plant height and diameter in both species, although the reduction was more pronounced in E. cloeziana plants. In both species, As concentrations were highest in the root system; the highest root concentration was found in E. cloeziana plants (305.7 mg kg-1) resulting from a dose of 400 mg dm-3. The highest As accumulation was observed in E. grandis plants, which was confirmed as a species with potential for As phytoextraction, tending to accumulate As in the root system and stem.

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.