Quantificação da área ocupada pela guta-percha e pelo Resilon em canais achatados após termoplastificação

O presente trabalho teve por objetivo quantificar, comparativamente, a área de preenchimento de dois materiais obturadores sólidos, cones de guta-percha (GP) e cones de Resilon (R), no terço apical de incisivos inferiores humanos, ex vivo, obturados pela técnica da onda contínua de condensação. Os espécimes foram submetidos a um protocolo, desde a cirurgia de acesso até o final do preparo químico-mecânico e divididos aleatoriamente em dois grupos, de 21 dentes cada, de acordo com o material utilizado. Não foi utilizado cimento endodôntico em nenhuma das amostras. Após a obturação, as amostras foram seccionadas transversalmente em dois níveis, a 3 e a 5mm do ápice, e subdivididas em grupos de acordo com a altura de corte e do material obturador, sendo estabelecido: GP3 (guta-percha com corte a 3mm), GP5 (guta-percha com corte a 5mm), R3 (Resilon com corte a 3mm) e R5 (Resilon com corte a 5mm). Posteriormente, as amostras foram submetidas a um processo de lixamento e polimento e examinadas em microscópio óptico por reflexão com aumento de 50x a 100x. Para a análise e processamento digital das imagens, foi utilizado o sistema de imagens Axio Vision 4.6 para Windows, sendo obtidas as medidas para cada área observada em micrômetros (μm); uma da área da cavidade, e outra da área de material obturador. Foi aferido o grau de circularidade de cada amostra, por uma fórmula matemática utilizada automaticamente pelo programa, onde 1 (um) é considerado o círculo perfeito e, quanto mais achatado o canal, mais tendente a 0 (zero) nesta escala. Obteve-se a área do canal, a circularidade de 0 a 1, a área preenchida pelo material obturador e, a porcentagem da área de preenchimento do material obturador em relação à área do canal. Foi realizado o cruzamento dos grupos dois a dois pelo teste t de Student, sendo verificada diferença estatisticamente significante entre os grupos GP3 e R3, tendo o grupo R3 apresentado maior porcentagem de área do canal radicular preenchida pelo material obturador em suas amostras (p<0,05). Na relação da circularidade com a quantidade de preenchimento, com o teste de Correlação de Pearson, não foi observada forte correlação entre a forma final do canal (relação de circularidade) e a quantidade de preenchimento do canal radicular pelos materiais obturadores testados. Conclui-se que houve grande variação de preenchimento mínimo e máximo em todos os grupos testados e o Resilon apresentou maior porcentagem de preenchimento de área do canal radicular em suas amostras.

Avaliação Comparativa dos Scanners 3D Artec MHT e Cyberware WBX para aplicações em Antropometria e Ergonomia

A partir das dimensões dos indivíduos pode-se definir dimensionamentos adequados para os produtos e postos de trabalho, proporcionando segurança e conforto aos usuários. Com o avanço da tecnologia de digitalização de imagens (escaneamento) 3D, é possível tirar algumas medidas de maneira mais rápida e com a redução da presença do entrevistado durante o processo. No entanto, faltam estudos que avaliem estas tecnologias no Brasil, sendo necessária a realização de uma comparação das tecnologias e das respectivas precisões para que seu uso em pesquisas. Com o objetivo de oferecer métodos comparativos para escolha dos marcadores e equipamentos a serem utilizados em uma pesquisa antropométrica tridimensional da população brasileira, no presente estudo estão comparadas duas tecnologias de escaneamento: o sistema a laser WBX da empresa norte americana Cyberware e o sistema MHT da empresa russa Artec Group. O método para avaliação da precisão dimensional dos dados advindos desses equipamentos de digitalização de imagens 3D teve cinco etapas: Estudo dos processos de escaneamento; Escaneamento dos marcadores de pontos anatômicos; Escaneamento utilizando um corpo de prova cilíndrico; Escaneamento de um manequim; Escaneamento de um voluntário que teve seus pontos anatômicos marcados para a retirada de medidas. Foi feita uma comparação entre as medidas retiradas manualmente, por meio de antropômetro e virtualmente, com o auxílio do software de modelagem tridimensional Rhinoceros. Em relação aos resultados obtidos na avaliação do manequim e do voluntário, concluiu-se que a magnitude do erro absoluto é semelhante para ambos os scanners, e permanece constante independentemente das dimensões sob análise. As principais diferenças são em relação às funcionalidades dos equipamentos.

Construction site image retrieval based on material cluster recognition

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. This capability is a product of the technological breakthroughs in the area of image processing that has allowed for the development of a large number of digital imaging applications in all industries. In this paper, an automated and content based construction site image retrieval method is presented. This method is based on image retrieval techniques, and specifically those related with material and object identification and matches known material samples with material clusters within the image content. The results demonstrate the suitability of this method for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.

Identification of Materials from Construction Site Images Using Content Based Image Retrieval Techniques

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. Examples include surface cracks detection, assessment of fire-damaged mortar, fatigue evaluation of asphalt mixes, aggregate shape measurements, velocimentry, vehicles detection, pore size distribution in geotextiles, damage detection and others. This capability is a product of the technological breakthroughs in the area of Image and Video Processing that has allowed for the development of a large number of digital imaging applications in all industries ranging from the well established medical diagnostic tools (magnetic resonance imaging, spectroscopy and nuclear medical imaging) to image searching mechanisms (image matching, content based image retrieval). Content based image retrieval techniques can also assist in the automated recognition of materials in construction site images and thus enable the development of reliable methods for image classification and retrieval. The amount of original imaging information produced yearly in the construction industry during the last decade has experienced a tremendous growth. Digital cameras and image databases are gradually replacing traditional photography while owners demand complete site photograph logs and engineers store thousands of images for each project to use in a number of construction management tasks. However, construction companies tend to store images without following any standardized indexing protocols, thus making the manual searching and retrieval a tedious and time-consuming effort. Alternatively, material and object identification techniques can be used for the development of automated, content based, construction site image retrieval methodology. These methods can utilize automatic material or object based indexing to remove the user from the time-consuming and tedious manual classification process. In this paper, a novel material identification methodology is presented. This method utilizes content based image retrieval concepts to match known material samples with material clusters within the image content. The results demonstrate the suitability of this methodology for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.

Comparison of manual and user guided methodologies for the classification and retrieval of construction site images

The amount of original imaging information produced yearly during the last decade has experienced a tremendous growth in all industries due to the technological breakthroughs in digital imaging and electronic storage capabilities. This trend is affecting the construction industry as well, where digital cameras and image databases are gradually replacing traditional photography. Owners demand complete site photograph logs and engineers store thousands of images for each project to use in a number of construction management tasks like monitoring an activity's progress and keeping evidence of the "as built" in case any disputes arise. So far, retrieval methodologies are done manually with the user being responsible for imaging classification according to specific rules that serve a limited number of construction management tasks. New methods that, with the guidance of the user, can automatically classify and retrieve construction site images are being developed and promise to remove the heavy burden of manually indexing images. In this paper, both the existing methods and a novel image retrieval method developed by the authors for the classification and retrieval of construction site images are described and compared. Specifically a number of examples are deployed in order to present their advantages and limitations. The results from this comparison demonstrates that the content based image retrieval method developed by the authors can reduce the overall time spent for the classification and retrieval of construction images while providing the user with the flexibility to retrieve images according different classification schemes.

Shape Recognition of Linear Construction Entities from Construction Site Images

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. This capability is a product of the technological breakthroughs in the area of Image Processing that has allowed for the development of a large number of digital imaging applications in all industries. In this paper, an automated and content based shape recognition model is presented. This model was devised to enhance the recognition capabilities of our existing material based image retrieval model. The shape recognition model is based on clustering techniques, and specifically those related with material and object segmentation. The model detects the borders of each previously detected material depicted in the image, examines its linearity (length/width ratio) and detects its orientation (horizontal/vertical). The results emonstrate the suitability of this model for construction site image retrieval purposes and reveal the capability of existing clustering technologies to accurately identify the shape of a wealth of materials from construction site images.

Shape-Based Retrieval of Construction Site Photographs

Digital photographs of construction site activities are gradually replacing their traditional paper based counterparts. Existing digital imaging technologies in hardware and software make it easy for site engineers to take numerous photographs of “interesting” processes and activities on a daily basis. The resulting photographic data are evidence of the “as-built” project, and can therefore be used in a number of project life cycle tasks. However, the task of retrieving the relevant photographs needed in these tasks is often burdened by the sheer volume of photographs accumulating in project databases over time and the numerous objects present in each photograph. To solve this problem, the writers have recently developed a number of complementary techniques that can automatically classify and retrieve construction site images according to a variety of criteria (materials, time, date, location, etc.). This paper presents a novel complementary technique that can automatically identify linear (i.e., beam, column) and nonlinear (i.e., wall, slab) construction objects within the image content and use that information to enhance the performance of the writers’ existing construction site image retrieval approach.

Material-Based Construction Site Image Retrieval

The technological advancements in digital imaging, the widespread popularity of digital cameras, and the increasing demand by owners and contractors for detailed and complete site photograph logs have triggered an ever-increasing growth in the rate of construction image data collection, with thousands of images being stored for each project. However, the sheer volume of images and the difficulties in accurately and manually indexing them have generated a pressing need for methods that can index and retrieve images with minimal or no user intervention. This paper reports recent developments from research efforts in the indexing and retrieval of construction site images in architecture, engineering, construction, and facilities management image database systems. The limitations and benefits of the existing methodologies will be presented, as well as an explanation of the reasons for the development of a novel image retrieval approach that not only can recognize construction materials within the image content in order to index images, but also can be compatible with existing retrieval methods, enabling enhanced results.

Content-Based Search Engines for Construction Image Databases

Images represent a valuable source of information for the construction industry. Due to technological advancements in digital imaging, the increasing use of digital cameras is leading to an ever-increasing volume of images being stored in construction image databases and thus makes it hard for engineers to retrieve useful information from them. Content-Based Search Engines are tools that utilize the rich image content and apply pattern recognition methods in order to retrieve similar images. In this paper, we illustrate several project management tasks and show how Content-Based Search Engines can facilitate automatic retrieval, and indexing of construction images in image databases.

基于精细结构描述及数值试验的断续节理岩体力学参数智能选取

Evaluating the mechanical properties of rock masses is the base of rock engineering design and construction. It has great influence on the safety and cost of rock project. The recognition is inevitable consequence of new engineering activities in rock, including high-rise building, super bridge, complex underground installations, hydraulic project and etc. During the constructions, lots of engineering accidents happened, which bring great damage to people. According to the investigation, many failures are due to choosing improper mechanical properties. ‘Can’t give the proper properties’ becomes one of big problems for theoretic analysis and numerical simulation. Selecting the properties reasonably and effectively is very significant for the planning, design and construction of rock engineering works. A multiple method based on site investigation, theoretic analysis, model test, numerical test and back analysis by artificial neural network is conducted to determine and optimize the mechanical properties for engineering design. The following outcomes are obtained: (1) Mapping of the rock mass structure Detailed geological investigation is the soul of the fine structure description. Based on statistical window，geological sketch and digital photography，a new method for rock mass fine structure in-situ mapping is developed. It has already been taken into practice and received good comments in Baihetan Hydropower Station. (2) Theoretic analysis of rock mass containing intermittent joints The shear strength mechanisms of joint and rock bridge are analyzed respectively. And the multiple modes of failure on different stress condition are summarized and supplied. Then, through introducing deformation compatibility equation in normal direction, the direct shear strength formulation and compression shear strength formulation for coplanar intermittent joints, as well as compression shear strength formulation for ladderlike intermittent joints are deducted respectively. In order to apply the deducted formulation conveniently in the real projects, a relationship between these formulations and Mohr-Coulomb hypothesis is built up. (3) Model test of rock mass containing intermittent joints Model tests are adopted to study the mechanical mechanism of joints to rock masses. The failure modes of rock mass containing intermittent joints are summarized from the model test. Six typical failure modes are found in the test, and brittle failures are the main failure mode. The evolvement processes of shear stress, shear displacement, normal stress and normal displacement are monitored by using rigid servo test machine. And the deformation and failure character during the loading process is analyzed. According to the model test, the failure modes quite depend on the joint distribution, connectivity and stress states. According to the contrastive analysis of complete stress strain curve, different failure developing stages are found in the intact rock, across jointed rock mass and intermittent jointed rock mass. There are four typical stages in the stress strain curve of intact rock, namely shear contraction stage, linear elastic stage, failure stage and residual strength stage. There are three typical stages in the across jointed rock mass, namely linear elastic stage, transition zone and sliding failure stage. Correspondingly, five typical stages are found in the intermittent jointed rock mass, namely linear elastic stage, sliding of joint, steady growth of post-crack, joint coalescence failure, and residual strength. According to strength analysis, the failure envelopes of intact rock and across jointed rock mass are the upper bound and lower bound separately. The strength of intermittent jointed rock mass can be evaluated by reducing the bandwidth of the failure envelope with geo-mechanics analysis. (4) Numerical test of rock mass Two sets of methods, i.e. the distinct element method (DEC) based on in-situ geology mapping and the realistic failure process analysis (RFPA) based on high-definition digital imaging, are developed and introduced. The operation process and analysis results are demonstrated detailedly from the research on parameters of rock mass based on numerical test in the Jinping First Stage Hydropower Station and Baihetan Hydropower Station. By comparison，the advantages and disadvantages are discussed. Then the applicable fields are figured out respectively. (5) Intelligent evaluation based on artificial neural network (ANN) The characters of both ANN and parameter evaluation of rock mass are discussed and summarized. According to the investigations, ANN has a bright application future in the field of parameter evaluation of rock mass. Intelligent evaluation of mechanical parameters in the Jinping First Stage Hydropower Station is taken as an example to demonstrate the analysis process. The problems in five aspects, i. e. sample selection, network design, initial value selection, learning rate and expected error, are discussed detailedly.

Localization of Metal Electrodes in the Intact Rat Brain Using Registration of 3D Microcomputed Tomography Images to a Magnetic Resonance Histology Atlas.

Simultaneous neural recordings taken from multiple areas of the rodent brain are garnering growing interest due to the insight they can provide about spatially distributed neural circuitry. The promise of such recordings has inspired great progress in methods for surgically implanting large numbers of metal electrodes into intact rodent brains. However, methods for localizing the precise location of these electrodes have remained severely lacking. Traditional histological techniques that require slicing and staining of physical brain tissue are cumbersome, and become increasingly impractical as the number of implanted electrodes increases. Here we solve these problems by describing a method that registers 3-D computerized tomography (CT) images of intact rat brains implanted with metal electrode bundles to a Magnetic Resonance Imaging Histology (MRH) Atlas. Our method allows accurate visualization of each electrode bundle's trajectory and location without removing the electrodes from the brain or surgically implanting external markers. In addition, unlike physical brain slices, once the 3D images of the electrode bundles and the MRH atlas are registered, it is possible to verify electrode placements from many angles by "re-slicing" the images along different planes of view. Further, our method can be fully automated and easily scaled to applications with large numbers of specimens. Our digital imaging approach to efficiently localizing metal electrodes offers a substantial addition to currently available methods, which, in turn, may help accelerate the rate at which insights are gleaned from rodent network neuroscience.

Assessing the occupational risk of dust particles in the Kenyan tanning industry using rapid image processing and microscopy techniques

The purpose of this study was to investigate the occupational hazards within the tanning industry caused by contaminated dust. A qualitative assessment of the risk of human exposure to dust was made throughout a commercial Kenyan tannery. Using this information, high-risk points in the processing line were identified and dust sampling regimes developed. An optical set-up using microscopy and digital imaging techniques was used to determine dust particle numbers and size distributions. The results showed that chemical handling was the most hazardous (12 mg m(-3)). A Monte Carlo method was used to estimate the concentration of the dust in the air throughout the tannery during an 8 h working day. This showed that the high-risk area of the tannery was associated with mean concentrations of dust greater than the UK Statutory Instrument 2002 No. 2677. stipulated limits (exceeding 10 mg m(-3) (Inhalable dust limits) and 4 mg m(-3) (Respirable dust limits). This therefore has implications in terms of provision of personal protective equipment (PPE) to the tannery workers for the mitigation of occupational risk.

Chiapas a través de los viajeros del siglo XVII: aspectos narrativos e historia descriptiva en Thomas Gage y Antonio Vázquez de Espinosa

Chiapas representó en el siglo XVII una región donde confluían los mitos, temores y fascinaciones de colonos y europeos. Al ser visitada por Thomas Gage en su travesía hacia Guatemala, es descrita en su Nuevo reconocimiento de las Indias Occidentales de modo muy distinto cuando el narrador, como es este caso, registra sus vivencias personales e, incluso, pasionales. Sólo la historiografía moderna podría explicarnos particulares pasajes en que el viajero es abordado súbitamente por una realidad que pasa desapercibida a otros viajeros con un programa muy claro de supervisión y registro de datos, como es el caso de Antonio Vázquez de Espinosa en su Descripción de la Nueva España. Si el historiógrafo describe puntualmente flora, fauna y geografía, el narrador huele, paladea y recorre con nosotros el laberinto de la tierra chiapaneca. El paisaje a través de la persona, con todos los cabos sueltos y apariciones inexplicables para quien se interna en lo desconocido, cobran, a la luz de investigaciones recientes acerca del contexto sociohistórico de Chiapas, un sentido cabal que no sólo nos ilustra, sino que nos interna y se nos interna.

A New Fast Fractal Modeling Approach for the Detection of Microcalcifications in Mammograms

In this paper, a novel fast method for modeling mammograms by deterministic fractal coding approach to detect the presence of microcalcifications, which are early signs of breast cancer, is presented. The modeled mammogram obtained using fractal encoding method is visually similar to the original image containing microcalcifications, and therefore, when it is taken out from the original mammogram, the presence of microcalcifications can be enhanced. The limitation of fractal image modeling is the tremendous time required for encoding. In the present work, instead of searching for a matching domain in the entire domain pool of the image, three methods based on mean and variance, dynamic range of the image blocks, and mass center features are used. This reduced the encoding time by a factor of 3, 89, and 13, respectively, in the three methods with respect to the conventional fractal image coding method with quad tree partitioning. The mammograms obtained from The Mammographic Image Analysis Society database (ground truth available) gave a total detection score of 87.6%, 87.6%, 90.5%, and 87.6%, for the conventional and the proposed three methods, respectively.