Dislocation subgrain structures and modeling the plastic hardening of metallic single crystals

A single crystal plasticity theory for insertion into finite element simulation is formulated using sequential laminates to model subgrain dislocation structures. It is known that local models do not adequately account for latent hardening, as latent hardening is not only a material property, but a nonlocal property (e.g. grain size and shape). The addition of the nonlocal energy from the formation of subgrain structure dislocation walls and the boundary layer misfits provide both latent and self-hardening of a crystal slip. Latent hardening occurs as the formation of new dislocation walls limits motion of new mobile dislocations, thus hardening future slip systems. Self-hardening is accomplished by an evolution of the subgrain structure length scale. The substructure length scale is computed by minimizing the nonlocal energy. The minimization of the nonlocal energy is a competition between the dislocation wall energy and the boundary layer energies. The nonlocal terms are also directly minimized within the subgrain model as they affect deformation response. The geometrical relationship between the dislocation walls and slip planes affecting the dislocation mean free path is taken into account, giving a first-order approximation to shape effects. A coplanar slip model is developed due to requirements while modeling the subgrain structure. This subgrain structure plasticity model is noteworthy as all material parameters are experimentally determined rather than fit. The model also has an inherit path dependence due to the formation of the subgrain structures. Validation is accomplished by comparison with single crystal tension test results

Dark Photons from the Center of the Earth: Smoking-Gun Signals of Dark Matter

Dark matter may be charged under dark electromagnetism with a dark photon that kinetically mixes with the Standard Model photon. In this framework, dark matter will collect at the center of the Earth and annihilate into dark photons, which may reach the surface of the Earth and decay into observable particles. We determine the resulting signal rates, including Sommerfeld enhancements, which play an important role in bringing the Earth's dark matter population to their maximal, equilibrium value. For dark matter masses $m_X \sim$ 100 GeV - 10 TeV, dark photon masses $m_{A'} \sim$ MeV - GeV, and kinetic mixing parameters $\varepsilon \sim 10^{-10} - 10^{-8}$, the resulting electrons, muons, photons, and hadrons that point back to the center of the Earth are a smoking-gun signal of dark matter that may be detected by a variety of experiments, including neutrino telescopes, such as IceCube, and space-based cosmic ray detectors, such as Fermi-LAT and AMS. We determine the signal rates and characteristics, and show that large and striking signals---such as parallel muon tracks---are possible in regions of the $(m_{A'}, \varepsilon)$ plane that are not probed by direct detection, accelerator experiments, or astrophysical observations.Comment: 26 pages, 10 figures. v2: minor revisions to match published version; v3: updated direct detection and CMB constraints and corrected decay length in code, moving the region of experimental sensitivity to values of epsilon that are lower by an order of magnitud

Management of orthodontic emergencies in primary care – self-reported confidence of general dental practitioners

Objective: To determine general dental practitioners’ (GDPs) confidence in managing orthodontic emergencies. Design: Cross-sectional study. Setting: Primary dental care. Subjects and methods: An online survey was distributed to dentists practicing in Wales. The survey collected basic demographic information and included descriptions of ten common orthodontic emergency scenarios. Main outcome measure Respondents’ self-reported confidence in managing the orthodontic emergency scenarios on a 5‑point Likert scale. Differences between the Likert responses and the demographic variables were investigated using chi-squared tests. Results: The median number of orthodontic emergencies encountered by respondents over the previous six months was 1. Overall, the self-reported confidence of respondents was high with 7 of the 10 scenarios presented scoring a median of 4 indicating that GDPs were ‘confident’ in their management. Statistical analysis revealed that GDPs who saw more orthodontic emergencies in the previous six months were more confident when managing the presented scenarios. Other variables such as age, gender, geographic location of practice and number of years practising dentistry were not associated with self reported confidence. Conclusions: Despite GDPs encountering very few orthodontic emergencies in primary care, they appear to be confident in dealing with commonly arising orthodontic emergency situations

Enterprise Data Mining & Machine Learning Framework on Cloud Computing for Investment Platforms

Machine Learning and Data Mining are two key components in decision making systems which can provide valuable in-sights quickly into huge data set. Turning raw data into meaningful information and converting it into actionable tasks makes organizations profitable and sustain immense competition. In the past decade we saw an increase in Data Mining algorithms and tools for financial market analysis, consumer products, manufacturing, insurance industry, social networks, scientific discoveries and warehousing. With vast amount of data available for analysis, the traditional tools and techniques are outdated for data analysis and decision support. Organizations are investing considerable amount of resources in the area of Data Mining Frameworks in order to emerge as market leaders. Machine Learning is a natural evolution of Data Mining. The existing Machine Learning techniques rely heavily on the underlying Data Mining techniques in which the Patterns Recognition is an essential component. Building an efficient Data Mining Framework is expensive and usually culminates in multi-year project for the organizations. The organization pay a heavy price for any delay or inefficient Data Mining foundation. In this research, we propose to build a cost effective and efficient Data Mining (DM) and Machine Learning (ML) Framework on cloud computing environment to solve the inherent limitations in the existing design methodologies. The elasticity of the cloud architecture solves the hardware constraint on businesses. Our research is focused on refining and enhancing the current Data Mining frameworks to build an enterprise data mining and machine learning framework. Our initial studies and techniques produced very promising results by reducing the existing build time considerably. Our technique of dividing the DM and ML Frameworks into several individual components (5 sub components) which can be reused at several phases of the final enterprise build is efficient and saves operational costs to the organization. Effective Aggregation using selective cuboids and parallel computations using Azure Cloud Services are few of many proposed techniques in our research. Our research produced a nimble, scalable portable architecture for enterprise wide implementation of DM and ML frameworks

Review of two-photon exchange in electron scattering

We review the role of two-photon exchange (TPE) in electron-hadron scattering, focusing in particular on hadronic frameworks suitable for describing the low and moderate Q^2 region relevant to most experimental studies. We discuss the effects of TPE on the extraction of nucleon form factors and their role in the resolution of the proton electric to magnetic form factor ratio puzzle. The implications of TPE on various other observables, including neutron form factors, electroproduction of resonances and pions, and nuclear form factors, are summarized. Measurements seeking to directly identify TPE effects, such as through the angular dependence of polarization measurements, nonlinear epsilon contributions to the cross sections, and via e+ p to e- p cross section ratios, are also outlined. In the weak sector, we describe the role of TPE and gamma-Z interference in parity-violating electron scattering, and assess their impact on the extraction of the strange form factors of the nucleon and the weak charge of the proton.Comment: 73 pages, 40 figures, review article for Prog. Part. Nucl. Phys. (dedicated to the memory of John A. Tjon

Mini-Workshop: Mathematical Methods and Models of Continuum Biomechanics

The workshop Mathematical Methods and Models of Continuum Biomechanics focused on skills and tools providing a rational approach for integrating data that reductionist and molecular approaches in modern biological and medical science has recently provided. The workshop has provided contributions that brought together experts from the (bio-)mechanics and applied mathematics communities in order to highlight the mathematical needs and challenges especially in the fields of soft tissues and DNA mechanics

Cloud Instance Management and Resource Prediction For Computation-as-a-Service Platforms

Computation-as-a-Service (CaaS) offerings have gained traction in the last few years due to their effectiveness in balancing between the scalability of Software-as-a-Service and the customisation possibilities of Infrastructure-as-a-Service platforms. To function effectively, a CaaS platform must have three key properties: (i) reactive assignment of individual processing tasks to available cloud instances (compute units) according to availability and predetermined time-to-completion (TTC) constraints; (ii) accurate resource prediction; (iii) efficient control of the number of cloud instances servicing workloads, in order to optimize between completing workloads in a timely fashion and reducing resource utilization costs. In this paper, we propose three approaches that satisfy these properties (respectively): (i) a service rate allocation mechanism based on proportional fairness and TTC constraints; (ii) Kalman-filter estimates for resource prediction; and (iii) the use of additive increase multiplicative decrease (AIMD) algorithms (famous for being the resource management in the transport control protocol) for the control of the number of compute units servicing workloads. The integration of our three proposals into a single CaaS platform is shown to provide for more than 27% reduction in Amazon EC2 spot instance cost against methods based on reactive resource prediction and 38% to 60% reduction of the billing cost against the current state-of-the-art in CaaS platforms (Amazon Lambda and Autoscale)

Predicting and monitoring cancer treatment response with diffusion-weighted MRI

An imaging biomarker that would provide for an early quantitative metric of clinical treatment response in cancer patients would provide for a paradigm shift in cancer care. Currently, nonimage based clinical outcome metrics include morphology, clinical, and laboratory parameters, however, these are obtained relatively late following treatment. Diffusion-weighted MRI (DW-MRI) holds promise for use as a cancer treatment response biomarker as it is sensitive to macromolecular and microstructural changes which can occur at the cellular level earlier than anatomical changes during therapy. Studies have shown that successful treatment of many tumor types can be detected using DW-MRI as an early increase in the apparent diffusion coefficient (ADC) values. Additionally, low pretreatment ADC values of various tumors are often predictive of better outcome. These capabilities, once validated, could provide for an important opportunity to individualize therapy thereby minimizing unnecessary systemic toxicity associated with ineffective therapies with the additional advantage of improving overall patient health care and associated costs. In this report, we provide a brief technical overview of DW-MRI acquisition protocols, quantitative image analysis approaches and review studies which have implemented DW-MRI for the purpose of early prediction of cancer treatment response. J. Magn. Reson. Imaging 2010. © 2010 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77429/1/22167_ftp.pd