Diffusion Tensor Imaging in Alzheimer's disease

Attentional control and Information processing speed are central concepts in cognitive psychology and neuropsychology. Functional neuroimaging and neuropsychological assessment have depicted theoretical models considering attention as a complex and non-unitary process. One of its component processes, Attentional set-shifting ability, is commonly assessed using the Trail Making Test (TMT). Performance in the TMT decreases with increasing age in adults, Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). Besides, speed of information processing (SIP) seems to modulate attentional performance. While neural correlates of attentional control have been widely studied, there are few evidences about the neural substrates of SIP in these groups of patients. Different authors have suggested that it could be a property of cerebral white matter, thus, deterioration of the white matter tracts that connect brain regions related to set-shifting may underlie the age-related, MCI and AD decrease in performance. The aim of this study was to study the anatomical dissociation of attentional and speed mechanisms. Diffusion tensor imaging (DTI) provides a unique insight into the cellular integrity of the brain, offering an in vivo view into the microarchitecture of cerebral white matter. At the same time, the study of ageing, characterized by white matter decline, provides the opportunity to study the anatomical substrates speeded or slowed information processing. We hypothesized that FA values would be inversely correlated with time to completion on Parts A and B of the TMT, but not the derived scores B/A and B-A.

Diffusion of Zn into GaAs and AlGaAs from isothermal Liquid-phase epitaxy solutions

In this work we present results of zinc diffusion in GaAs using the liquid phase epitaxy technique from liquid solutions of Ga‐As‐Zn and Ga‐As‐Al‐Zn. Using silicon‐doped n‐GaAs substrates, working at a diffusion temperature of 850 °C, and introducing a dopant concentration ranging 1018–1019 cm−3, the most important findings regarding the diffusion properties are as follows: (a) zinc concentration in the solid depends on the square root of zinc atomic fraction in the liquid; (b) the diffusion is dominated by the interstitial‐substitutional process; (c) the diffusivity D varies as about C3 in the form D=2.9×10−67C3.05; (d) aluminum plays the role of the catalyst of the diffusion process, if it is introduced in the liquid solution, since it is found that D varies as (γAsXlAs)−1; (e) the zinc interstitial is mainly doubly ionized (Zn++i); (f) the zinc diffusion coefficient in Al0.85 Ga0.15 As is about four times greater than in GaAs; (g) by means of all these results, it is possible to control zinc diffusion processes in order to obtain optimized depth junctions and doping levels in semiconductor device fabrication.

Neighborhood-corrected interface discontinuity factors for multi-group pin-by-pin diffusion calculations for LWR

Performing three-dimensional pin-by-pin full core calculations based on an improved solution of the multi-group diffusion equation is an affordable option nowadays to compute accurate local safety parameters for light water reactors. Since a transport approximation is solved, appropriate correction factors, such as interface discontinuity factors, are required to nearly reproduce the fully heterogeneous transport solution. Calculating exact pin-by-pin discontinuity factors requires the knowledge of the heterogeneous neutron flux distribution, which depends on the boundary conditions of the pin-cell as well as the local variables along the nuclear reactor operation. As a consequence, it is impractical to compute them for each possible configuration; however, inaccurate correction factors are one major source of error in core analysis when using multi-group diffusion theory. An alternative to generate accurate pin-by-pin interface discontinuity factors is to build a functional-fitting that allows incorporating the environment dependence in the computed values. This paper suggests a methodology to consider the neighborhood effect based on the Analytic Coarse-Mesh Finite Difference method for the multi-group diffusion equation. It has been applied to both definitions of interface discontinuity factors, the one based on the Generalized Equivalence Theory and the one based on Black-Box Homogenization, and for different few energy groups structures. Conclusions are drawn over the optimal functional-fitting and demonstrative results are obtained with the multi-group pin-by-pin diffusion code COBAYA3 for representative PWR configurations.

Optimal fits of diffusion constants from single-time data points of Brownian trajectories

Experimental methods based on single particle tracking (SPT) are being increasingly employed in the physical and biological sciences, where nanoscale objects are visualized with high temporal and spatial resolution. SPT can probe interactions between a particle and its environment but the price to be paid is the absence of ensemble averaging and a consequent lack of statistics. Here we address the benchmark question of how to accurately extract the diffusion constant of one single Brownian trajectory. We analyze a class of estimators based on weighted functionals of the square displacement. For a certain choice of the weight function these functionals provide the true ensemble averaged diffusion coefficient, with a precision that increases with the trajectory resolution.

Understanding phosphorus diffusion into silicon in a MOVPE environment for III-V on silicon solar cells

Dual-junction solar cells formed by a GaAsP or GaInP top cell and a silicon bottom cell seem to be attractive candidates to materialize the long sought-for integration of III?V materials on silicon for photovoltaic applications. When manufacturing a multi-junction solar cell on silicon, one of the first processes to be addressed is the development of the bottom subcell and, in particular, the formation of its emitter. In this study, we analyze, both experimentally and by simulations, the formation of the emitter as a result of phosphorus diffusion that takes place during the first stages of the epitaxial growth of the solar cell. Different conditions for the Metal-Organic Vapor Phase Epitaxy (MOVPE) process have been evaluated to understand the impact of each parameter, namely, temperature, phosphine partial pressure, time exposure and memory effects in the final diffusion profiles obtained. A model based on SSupremIV process simulator has been developed and validated against experimental profiles measured by ECV and SIMS to calculate P diffusion profiles in silicon formed in a MOVPE environment taking in consideration all these factors.

Diffusion Gradient Temporal Difference for Cooperative Reinforcement Learning with Linear Function Approximation

We introduce a diffusion-based algorithm in which multiple agents cooperate to predict a common and global statevalue function by sharing local estimates and local gradient information among neighbors. Our algorithm is a fully distributed implementation of the gradient temporal difference with linear function approximation, to make it applicable to multiagent settings. Simulations illustrate the benefit of cooperation in learning, as made possible by the proposed algorithm.

Image processing methods for human brain connectivity analysis from in-vivo diffusion MRI

In this PhD Thesis proposal, the principles of diffusion MRI (dMRI) in its application to the human brain mapping of connectivity are reviewed. The background section covers the fundamentals of dMRI, with special focus on those related to the distortions caused by susceptibility inhomogeneity across tissues. Also, a deep survey of available correction methodologies for this common artifact of dMRI is presented. Two methodological approaches to improved correction are introduced. Finally, the PhD proposal describes its objectives, the research plan, and the necessary resources.

Brain tissue segmentation on Diffusion Weighted Magnetic Resonance data

Introduction Diffusion weighted Imaging (DWI) techniques are able to measure, in vivo and non-invasively, the diffusivity of water molecules inside the human brain. DWI has been applied on cerebral ischemia, brain maturation, epilepsy, multiple sclerosis, etc. [1]. Nowadays, there is a very high availability of these images. DWI allows the identification of brain tissues, so its accurate segmentation is a common initial step for the referred applications. Materials and Methods We present a validation study on automated segmentation of DWI based on the Gaussian mixture and hidden Markov random field models. This methodology is widely solved with iterative conditional modes algorithm, but some studies suggest [2] that graph-cuts (GC) algorithms improve the results when initialization is not close to the final solution. We implemented a segmentation tool integrating ITK with a GC algorithm [3], and a validation software using fuzzy overlap measures [4]. Results Segmentation accuracy of each tool is tested against a gold-standard segmentation obtained from a T1 MPRAGE magnetic resonance image of the same subject, registered to the DWI space. The proposed software shows meaningful improvements by using the GC energy minimization approach on DTI and DSI (Diffusion Spectrum Imaging) data. Conclusions The brain tissues segmentation on DWI is a fundamental step on many applications. Accuracy and robustness improvements are achieved with the proposed software, with high impact on the application’s final result.

Simulation-based evaluation of susceptibility distortion correction methods in diffusion MRI for connectivity analysis

Connectivity analysis on diffusion MRI data of the whole-brain suffers from distortions caused by the standard echo-planar imaging acquisition strategies. These images show characteristic geometrical deformations and signal destruction that are an important drawback limiting the success of tractography algorithms. Several retrospective correction techniques are readily available. In this work, we use a digital phantom designed for the evaluation of connectivity pipelines. We subject the phantom to a “theoretically correct” and plausible deformation that resembles the artifact under investigation. We correct data back, with three standard methodologies (namely fieldmap-based, reversed encoding-based, and registration- based). Finally, we rank the methods based on their geometrical accuracy, the dropout compensation, and their impact on the resulting connectivity matrices.

Modelling diffusion of photovoltaic: concepts and applications

Innovation studies have been interest of not only the scholars from various fields such as economics, management and sociology but also industrial practitioners and policy makers. In this vast and fruitful field, the theory of diffusion of innovations, which has been driven by a sociological approach, has played a vital role in our understanding of the mechanisms behind industrial change. In this paper, our aim is to give a state of art review of diffusion of innovation models in a structural and conceptual way with special reference to photovoltaic. We argue firstly, as an underlying background, how diffusion of innovations theory differs from other innovation studies. Secondly we give a brief taxonomical review of modelling methodologies together with comparative discussions. And finally we put the wealth of modelling in the context of photovoltaic diffusion and suggest some future directions.

Diffusion of photovoltaic in Germany: policy vs. consumers

In some countries photovoltaic (PV) technology has already achieved a stage of development at which it can compete with conventional electricity sources. Germany provides a good illustration of this where PV market has reached a mature stage. As a manifest of this, the German government has recently reduced subsidies for households and industry by decreasing the feed in tariff for PV. This development raises fundamental questions: could the PV industry survive? Will consumers be motivated to continue to adopt PV when feed-in tariff diminish? The point of departure for the relevant literature on diffusion of PV has been on the effect of subsidies but little attention has paid to consumer motives when the policy support is scaled down. This paper introduces an in-depth analysis on understanding the consumer motives for adopting photovoltaic applications. Anchored in an extensive exploratory case study on PV consumers and PV system providers, this study aims to provide an encompassing explanation of diffusion of PV by revealing the link between consumer motives and the impact of policy.

MMonCa: An Object Kinetic Monte Carlo simulator for damage irradiation evolution and defect diffusion

In this work, we introduce the Object Kinetic Monte Carlo (OKMC) simulator MMonCa and simulate the defect evolution in three different materials. We start by explaining the theory of OKMC and showing some details of how such theory is implemented by creating generic structures and algorithms in the objects that we want to simulate. Then we successfully reproduce simulated results for defect evolution in iron, silicon and tungsten using our simulator and compare with available experimental data and similar simulations. The comparisons validate MMonCa showing that it is powerful and flexible enough to be customized and used to study the damage evolution of defects in a wide range of solid materials.

Diffusion of Eco-innovations: exploring the literature

In recent years, the importance of the management of eco-innovations has been growing, more in practice than in academia. However, although in the literature there are already some evidences focussed on management of eco-innovations, there is no comprehensive review on the knowledge base of diffusion of eco-innovations. This paper provides a current overview of the existing body of literature, identifying the most active scholars and relevant publications in this field, and deepening in the major disciplines and research streams. Results show that the theory of diffusion of innovations which provided the philosophical underpinnings of how innovations are diffused is not the main knowledge base to explain the diffusion of eco-innovations. Lead market hypothesis, sustainable transitions and the ecological modernization appear as the initial base of the cognitive platform that can contribute to the understanding of diffusion of eco-innovations.

On ergodic least-squares estimators of the generalized diffusion coefficient for fractional Brownian motion

We analyse a class of estimators of the generalized diffusion coefficient for fractional Brownian motion Bt of known Hurst index H, based on weighted functionals of the single time square displacement. We show that for a certain choice of the weight function these functionals possess an ergodic property and thus provide the true, ensemble-averaged, generalized diffusion coefficient to any necessary precision from a single trajectory data, but at expense of a progressively higher experimental resolution. Convergence is fastest around H ? 0.30, a value in the subdiffusive regime.

Lifetime improvement after phosphorous diffusion gettering on upgraded metallurgical grade silicon

Wide experimental evidence of the phosphorus diffusion gettering beneficial effect on solar grade silicon is found by measuring electron effective lifetime and interstitial iron concentration in as-grown and post processed samples from two ingots of upgraded metallurgical grade silicon produced by Ferrosolar. Results after two different P-diffusion processes are compared: P emitter diffusion at 850ºC followed by fast cool-down (called “standard process”) or followed by slow cool-down (called “extended process”). It is shown that final lifetimes of this low cost material are in the range of those obtained with conventional material. The extended process can be beneficial for wafers with specific initial distribution and concentration of iron, e.g. materials with high concentration of big Fe precipitates, while for other cases the standard process is enough efficient. An analysis based on the comparison of measured lifetime and dissolved iron concentration with theoretical calculations helps to infer the initial iron distribution and concentration, and according to that, choose the more effective type of gettering.