Modeling soil system:complexity under your feet

This is an introductory articl

Disorder-induced magnetic memory: Experiments and theories

Beautiful theories of magnetic hysteresis based on random microscopic disorder have been developed over the past ten years. Our goal was to directly compare these theories with precise experiments. We first developed and then applied coherent x-ray speckle metrology to a series of thin multilayer perpendicular magnetic materials. To directly observe the effects of disorder, we deliberately introduced increasing degrees of disorder into our films. We used coherent x-rays to generate highly speckled magnetic scattering patterns. The apparently random arrangement of the speckles is due to the exact configuration of the magnetic domains in the sample. In effect, each speckle pattern acts as a unique fingerprint for the magnetic domain configuration. Small changes in the domain structure change the speckles, and comparison of the different speckle patterns provides a quantitative determination of how much the domain structure has changed. How is the magnetic domain configuration at one point on the major hysteresis loop related to the configurations at the same point on the loop during subsequent cycles? The microscopic return-point memory(RPM) is partial and imperfect in the disordered samples, and completely absent when the disorder was not present. We found the complementary-point memory(CPM) is also partial and imperfect in the disordered samples and completely absent when the disorder was not present. We found that the RPM is always a little larger than the CPM. We also studied the correlations between the domains within a single ascending or descending loop. We developed new theoretical models that do fit our experiments.Comment: 26 pages, 25 figures, Accepted by Physical Review B 01/25/0

Conservation and Paper Roughness of Art Artifacts

Worcester Art Museum has expressed large interest in knowing if they have causing any damage to the paper after applying some type of treatment to it in an attempt to clean the paper. This paper shows measurements on different types of paper samples provided by the Worcester Art Museum. After measurements are completed, area-scale and length-scale fractal analysis are performed by the patchwork method to investigate fundamental scales on adhesion on rough substrates, in order to find differences between two types of paper. Surface metrology is used to discriminate surface textures that were created under different conditions or that behave differently, and to understand functional correlations involving surface textures or roughness

Application of Fractal Dimension for Quantifying Noise Texture in Computed Tomography Images

Purpose Evaluation of noise texture information in CT images is important for assessing image quality. Noise texture is often quantified by the noise power spectrum (NPS), which requires numerous image realizations to estimate. This study evaluated fractal dimension for quantifying noise texture as a scalar metric that can potentially be estimated using one image realization. Methods The American College of Radiology CT accreditation phantom (ACR) was scanned on a clinical scanner (Discovery CT750, GE Healthcare) at 120 kV and 25 and 90 mAs. Images were reconstructed using filtered back projection (FBP/ASIR 0%) with varying reconstruction kernels: Soft, Standard, Detail, Chest, Lung, Bone, and Edge. For each kernel, images were also reconstructed using ASIR 50% and ASIR 100% iterative reconstruction (IR) methods. Fractal dimension was estimated using the differential box‐counting algorithm applied to images of the uniform section of ACR phantom. The two‐dimensional Noise Power Spectrum (NPS) and one‐dimensional‐radially averaged NPS were estimated using established techniques. By changing the radiation dose, the effect of noise magnitude on fractal dimension was evaluated. The Spearman correlation between the fractal dimension and the frequency of the NPS peak was calculated. The number of images required to reliably estimate fractal dimension was determined and compared to the number of images required to estimate the NPS‐peak frequency. The effect of Region of Interest (ROI) size on fractal dimension estimation was evaluated. Feasibility of estimating fractal dimension in an anthropomorphic phantom and clinical image was also investigated, with the resulting fractal dimension compared to that estimated within the uniform section of the ACR phantom. Results Fractal dimension was strongly correlated with the frequency of the peak of the radially averaged NPS curve, having a Spearman rank‐order coefficient of 0.98 (P‐value \u3c 0.01) for ASIR 0%. The mean fractal dimension at ASIR 0% was 2.49 (Soft), 2.51 (Standard), 2.52 (Detail), 2.57 (Chest), 2.61 (Lung), 2.66 (Bone), and 2.7 (Edge). A reduction in fractal dimension was observed with increasing ASIR levels for all investigated reconstruction kernels. Fractal dimension was found to be independent of noise magnitude. Fractal dimension was successfully estimated from four ROIs of size 64 × 64 pixels or one ROI of 128 × 128 pixels. Fractal dimension was found to be sensitive to non‐noise structures in the image, such as ring artifacts and anatomical structure. Fractal dimension estimated within a uniform region of an anthropomorphic phantom and clinical head image matched that estimated within the ACR phantom for filtered back projection reconstruction. Conclusions Fractal dimension correlated with the NPS‐peak frequency and was independent of noise magnitude, suggesting that the scalar metric of fractal dimension can be used to quantify the change in noise texture across reconstruction approaches. Results demonstrated that fractal dimension can be estimated from four, 64 × 64‐pixel ROIs or one 128 × 128 ROI within a head CT image, which may make it amenable for quantifying noise texture within clinical images

On Characterization and Optimization of Surface Topography in Additive Manufacturing Processes

With its ability to construct components through the layer-by-layer deposition of material, Additive Manufacturing (AM), more commonly known as "3D printing", has revolutionized the manufacturing industries. Not only can AM produce complex lightweight designs, but it can also streamline the supply chain, allowing businesses to more quickly and easily meet customer demand. Additionally, with the rising demand for low-volume customized products, and sustainable production, manufacturers are increasingly compelled to adopt AM to remain competitive in the global economy. Despite its popularity, AM has several significant drawbacks, one of the most notable being its poor surface topography quality. Most product failures can be traced back to the initial surface conditions, making the surface texture a crucial factor in determining how well a product will perform. Hence, this thesis presents a study on the surface topography of various AM processes mainly to understand the surface behavior in relation to the factors affecting it.\ua0\ua0\ua0 Every manufacturing process including AM generates distinct surface features referred to as “footprints” or process signatures which substantially affect the surface quality and function. These process signatures vary based on changes in AM processes and their process settings, materials, and geometrical design. The accuracy of identifying and analyzing these features becomes crucial in defining their relationship with manufacturing process variables. Usually, the best practice for defining surface quality is through parametric characterization which provides a quantitative description of either the stochastic or deterministic nature of manufactured surfaces. However, the challenge with AM is that it generates surfaces that often contain both the aforementioned surface features which make it particularly difficult to identify the manufacturing “footprints” through the parametric description. Therefore, the surface topography of AM may often require novel characterization methods to fully interpret the manufacturing process and thereby predict and optimize its product performance. The overall goal of this thesis is to provide an optimal approach toward the characterization of AM surfaces so that it gives a better understanding of the manufacturing process and also assists in process optimization to control the surface quality of the printed products. To realize this goal, the surface texture of AM processes was studied particularly Material Extrusion (MEX), Vat photopolymerization (VPP), and Powder Bed Fusion (PBF). These processes present topographical features that cover most of the surface scenarios in AM. Hence to explain these varied surface features, a diverse range of surface characterization tools such as Power Spectral Density (PSD), Scale-sensitive fractal analysis, feature-based characterization, and quantitative characterization by both profile and areal surface texture parameters were included in the analysis. Additionally, a methodology was developed using a statistical approach (Linear multiple regression) and a combination of the above-mentioned characterization techniques to identify the most significant parameters for discriminating different surfaces. Finally, the knowledge gained through the above-mentioned measurements and analysis is put to use to optimize the AM process to achieve enhanced surface quality. The results suggest that the developed approaches can be used as a guideline for AM users who are looking to optimize the process for gaining better surface quality and component functionality, as it works effectively in finding the significant parameters representing the unique signatures of the manufacturing process

Real-time pollen monitoring using digital holography

We present the first validation of the SwisensPoleno, currently the only operational automatic pollen mon-itoring system based on digital holography. The device pro-vides in-flight images of all coarse aerosols, and here wedevelop a two-step classification algorithm that uses theseimages to identify a range of pollen taxa. Deterministiccriteria based on the shape of the particle are applied toinitially distinguish between intact pollen grains and othercoarse particulate matter. This first level of discriminationidentifies pollen with an accuracy of 96 %. Thereafter, in-dividual pollen taxa are recognized using supervised learn-ing techniques. The algorithm is trained using data obtainedby inserting known pollen types into the device, and out ofeight pollen taxa six can be identified with an accuracy ofabove 90 %. In addition to the ability to correctly identifyaerosols, an automatic pollen monitoring system needs to beable to correctly determine particle concentrations. To fur-ther verify the device, controlled chamber experiments us-ing polystyrene latex beads were performed. This providedreference aerosols with traceable particle size and numberconcentrations in order to ensure particle size and samplingvolume were correctly characterized