Well-defined Geometric Shapes Research Articles

The Neotectonic Deformation of the Eastern Rif Foreland (Morocco): New Insights from Morphostructural Analysis

The Neotectonic Deformation of the Eastern Rif Foreland (Morocco): New Insights from Morphostructural Analysis

A geometry-independent moment correction method for the MPMS3 SQUID-based magnetometer

Modern superconducting quantum interference device (SQUID) magnetometers enable fully automated measurements with currently unmatched sensitivity. The measured moment values are, however, prone to deviations from their actual value due to geometric effects, namely sample size, shape, and radial offset. The current procedure to correct these geometric effects, for the magnetic property measurement 3 (MPMS3) magnetometer, is based on an available simulation tool, valid for both SQUID-VSM and DC-scan methods. Still, determining the correction factor requires samples with well-defined geometric shapes together with accurate sample dimensions and the usually difficult to determine radial offset. There is no established solution to correct geometry effects of irregularly shaped samples or unknown radial offsets. In this work, we find a systematic relation between the difference of SQUID-VSM and DC-scan measurements and their corresponding correction factors for the MPMS3. This relation follows a clear trend, independent of sample geometry and radial offset, for a given pair of DC-scan length and SQUID-VSM amplitude values. Exploiting this trend, a geometry-independent correction method is presented and validated by measurements of two different geometries of the same Al piece, two different radial offsets of a Ni square, and metallic Fe powder using a far from optimal sample mounting.

Transfer of Learning in the Convolutional Neural Networks on Classifying Geometric Shapes Based on Local or Global Invariants.

The convolutional neural networks (CNNs) are a powerful tool of image classification that has been widely adopted in applications of automated scene segmentation and identification. However, the mechanisms underlying CNN image classification remain to be elucidated. In this study, we developed a new approach to address this issue by investigating transfer of learning in representative CNNs (AlexNet, VGG, ResNet-101, and Inception-ResNet-v2) on classifying geometric shapes based on local/global features or invariants. While the local features are based on simple components, such as orientation of line segment or whether two lines are parallel, the global features are based on the whole object such as whether an object has a hole or whether an object is inside of another object. Six experiments were conducted to test two hypotheses on CNN shape classification. The first hypothesis is that transfer of learning based on local features is higher than transfer of learning based on global features. The second hypothesis is that the CNNs with more layers and advanced architectures have higher transfer of learning based global features. The first two experiments examined how the CNNs transferred learning of discriminating local features (square, rectangle, trapezoid, and parallelogram). The other four experiments examined how the CNNs transferred learning of discriminating global features (presence of a hole, connectivity, and inside/outside relationship). While the CNNs exhibited robust learning on classifying shapes, transfer of learning varied from task to task, and model to model. The results rejected both hypotheses. First, some CNNs exhibited lower transfer of learning based on local features than that based on global features. Second the advanced CNNs exhibited lower transfer of learning on global features than that of the earlier models. Among the tested geometric features, we found that learning of discriminating inside/outside relationship was the most difficult to be transferred, indicating an effective benchmark to develop future CNNs. In contrast to the “ImageNet” approach that employs natural images to train and analyze the CNNs, the results show proof of concept for the “ShapeNet” approach that employs well-defined geometric shapes to elucidate the strengths and limitations of the computation in CNN image classification. This “ShapeNet” approach will also provide insights into understanding visual information processing the primate visual systems.

Morphometrics of complex cell shapes: lobe contribution elliptic Fourier analysis (LOCO-EFA).

ABSTRACTQuantifying cell morphology is fundamental to the statistical study of cell populations, and can help unravel mechanisms underlying cell and tissue morphogenesis. Current methods, however, require extensive human intervention, are highly parameter sensitive, or produce metrics that are difficult to interpret biologically. We therefore developed a method, lobe contribution elliptical Fourier analysis (LOCO-EFA), which generates from digitalised two-dimensional cell outlines meaningful descriptors that can be directly matched to morphological features. This is shown by studying well-defined geometric shapes as well as actual biological cells from plant and animal tissues. LOCO-EFA provides a tool to phenotype efficiently and objectively populations of cells, here demonstrated by applying it to the complex shaped pavement cells of Arabidopsis thaliana wild-type and speechless leaves, and Drosophila amnioserosa cells. To validate our method's applicability to large populations, we analysed computer-generated tissues. By controlling in silico cell shape, we explored the potential impact of cell packing on individual cell shape, quantifying through LOCO-EFA deviations between the specified shape of single cells in isolation and the resultant shape when they interact within a confluent tissue.

Implementation of Vision-based Object Tracking Algorithms for Motor Skill Assessments

Assessment of upper extremity motor skills often involves object manipulation, drawing or writing using a pencil, or performing specific gestures. Traditional assessment of such skills usually requires a trained person to record the time and accuracy resulting in a process that can be labor intensive and costly. Automating the entire assessment process will potentially lower the cost, produce electronically recorded data, broaden the implementations, and provide additional assessment infor-mation. This paper presents a low-cost, versatile, and easy-to-use algorithm to automatically detect and track single or multiple well-defined geometric shapes or markers. It therefore can be applied to a wide range of assessment protocols that involve object manipulation or hand and arm gestures. The algorithm localizes the objects using color thresholding and morphological operations and then estimates their 3-dimensional pose. The utility of the algorithm is demonstrated by implementing it for automating the following five protocols: the sport of Cup Stacking, the Soda Pop Coordination test, the Wechsler Block Design test, the visual-motor integration test, and gesture recognition.

Polyethylene glycol-supported recyclable NC palladacycle catalyst for Heck cross-coupling reactions

Palladium nanoparticles with narrow size distribution were easily prepared by applying polyethylene glycol (PEG 1000 and 15000) and NC palladacycle in the absence of other chemical agents. The Pd-PEG catalysts were fully characterized by a variety of techniques including X-ray diffraction (XRD), transmission electron microscopy (TEM), UV–vis and Fourier transform infrared (FT-IR) spectroscopy. TEM micrographs of Pd/PEG 15000 show that palladium nanoparticles have mostly very well-defined geometrical shapes. Also Pd/PEG 15000 was relatively an efficient catalyst system for the Heck reaction, affording a diverse range of products in moderate to good yields.

Nanostructured columnar heterostructures of TiO2 and Cu2O enabled by a thin-film self-assembly approach: Potential for photovoltaics

Significant efforts are being devoted to the development of multifunctional thin-film heterostructures and nanostructured material architectures for components with novel applications of superconductivity, multiferroicity, solar photocatalysis and energy conversion. In particular, nanostructured assemblies with well-defined geometrical shapes have emerged as possible high efficiency and economically viable alternatives to planar photovoltaic thin-film architectures. By exploiting phase-separated self-assembly, here we present advances in a vertically oriented two-component system that offers potential for future development of nanostructured thin film solar cells. Through a single-step deposition by magnetron sputtering, we demonstrate growth of an epitaxial, composite film matrix formed as self-assembled, well ordered, phase segregated, and oriented nanopillars of n-type TiO2 and p-type Cu2O. The composite films were structurally characterized to atomic resolution by a variety of analytical tools, and evaluated for preliminary optical properties using absorption measurements. We find nearly atomically distinct TiO2–Cu2O interfaces (i.e., needed for possible active p–n junctions), and an absorption profile that captures a wide range of the solar spectrum extending from ultraviolet to visible wavelengths. This high-quality materials system could lead to photovoltaic devices that can be optimized for both incident light absorption and carrier collection.

Zinc oxide colloids with controlled size, shape, and structure

Highly dispersed uniform ZnO particles of different sizes and shapes were prepared by slowly adding zinc salt and sodium hydroxide solutions in parallel into aqueous solutions of Arabic gum. Except for the very early stages, the precipitated solids consisted of a well-defined zinc oxide phase. Depending on the experimental conditions, the size of the final polycrystalline particles formed by the aggregation of nanosize entities varied from 100 to 300 nm. The reaction temperature affected both the size of the nanosize precursors and their arrangement in the final particles. At ambient temperature the primary nanoparticles, approximately 10 nm in size, formed spherical aggregates, while at 600 °C they were much larger ( 44 nm ) and combined to form rather uniform hexagonal ZnO prisms. The aspect ratio and the internal structure of the latter could be altered by changing the nature of the zinc salt, the addition rate, and the initial concentration of the reactants. Based on the findings of the study a two-stage mechanism for the formation of uniform polycrystalline particles with well-defined geometric shapes is proposed.

Effects of Carinal Ridge Shapes on Lung Airstreams

Experimental tests cited herein have established that the deposits of inhaled particles may be highly concentrated at carinal ridges within lung bifurcations. Airway cells located at these sites will receive relatively massive doses of toxic substances and pharmacologic drugs. The deposition patterns, therefore, have immediate implications to risk assessment programs and aerosol therapy protocols. Herein, the software FIDAP was employed to study the effects of carinal ridge shapes upon fluid dynamics patterns. A series of well-defined geometric shapes (symmetric and asymmetric) were examined. For each case, a wide range of physiologically realistic flows were considered which corresponded to respiratory intensities for sedentary, light, and heavy activities. The results varied in a systematic manner. For example, at the lowest inspiratory flow rate of 14 L/min the effects were highly localized for all carinal ridge shapes, whereas at the highest inspiratory flow rate of 120 L/min the effects were propagated (i.e., to varying degrees) to distal regions. By relating fluid dynamics patterns to particle behavior, the results have inhalation toxicology and aerosol therapy relevance because, for example, bronchogenic carcinomas are selectively distributed within the tracheobronchial tree and certain receptors are known to be preferentially located at airway dividing sites.

Surface texture in electrodeposited films of cuprous oxide

The polycrystalline films of Cu2O, prepared by electrodeposition, consist of grains with well-defined geometrical shapes. The size and the orientation of grains which define the surface texture can be controlled by controlling the deposition parameters. Grains with a size ranging from a few tenths of a micrometer to about 10μm and with a preferential orientation of the (1 0 0) or (1 1 1) planes parallel to substrates can be obtained under certain deposition conditions. The effect of pH, bath temperature and the rate of deposition on the orientation and the size of grains is discussed.