Study of the water-based cooling system employed in the ALBA accelerator

The ALBA Synchrotron requires an important cooling system, as the emitted radiation heats the multiple mirrors, lenses, and magnets. The cooling system is based on water as a transportation fluid for most components, however, some components require more specialized cooling (such as Nitrogen or Helium). This project analyzes a part of this water-based cooling system, involving multiple pumps and heat exchangers, that use deionized water from an internal circuit (DW) and also cold untreated water coming from a District Heating & Cooling plant. In order to have optimal control over the outlet temperature of the heat exchangers, some solutions are proposed, with a three-way mixing valve decided. Using both experimental results and theoretical analysis, the pump and the heat exchanger are parameterized, in order to acquire their performance under different water flows. Both results have a slight expected deviation, due to hypotheses made, and suggest that the inclusion of a three-way will provide good thermal control, given the performance of the plate heat exchangers. Because of the multiple processes involved (quotation, manufacturing, transportation, system implementation...), the valve will not be installed within the scope of this project. Nonetheless, hypotheses and simulations are accomplished in order to evaluate its performanc

A New Ensemble Learning Framework for 3D Biomedical Image Segmentation

3D image segmentation plays an important role in biomedical image analysis. Many 2D and 3D deep learning models have achieved state-of-the-art segmentation performance on 3D biomedical image datasets. Yet, 2D and 3D models have their own strengths and weaknesses, and by unifying them together, one may be able to achieve more accurate results. In this paper, we propose a new ensemble learning framework for 3D biomedical image segmentation that combines the merits of 2D and 3D models. First, we develop a fully convolutional network based meta-learner to learn how to improve the results from 2D and 3D models (base-learners). Then, to minimize over-fitting for our sophisticated meta-learner, we devise a new training method that uses the results of the base-learners as multiple versions of "ground truths". Furthermore, since our new meta-learner training scheme does not depend on manual annotation, it can utilize abundant unlabeled 3D image data to further improve the model. Extensive experiments on two public datasets (the HVSMR 2016 Challenge dataset and the mouse piriform cortex dataset) show that our approach is effective under fully-supervised, semi-supervised, and transductive settings, and attains superior performance over state-of-the-art image segmentation methods.Comment: To appear in AAAI-2019. The first three authors contributed equally to the pape

On the interpretation of the equilibrium magnetization in the mixed state of high-Tc superconductors

We apply a recently developed scaling procedure to the analysis of equilibrium magnetization M(H) data that were obtained for T-2212 and Bi-2212single crystals and were reported in the literature. The temperature dependencies of the upper critical field and the magnetic field penetration depth resulting from our analysis are distinctly different from those obtained in the original publications. We argue that theoretical models, which are usually employed for analyses of the equilibrium magnetization in the mixed state of type-II superconductors are not adequate for a quantitative description of high-Tc superconductors. In addition, we demonstrate that the scaled equilibrium magnetization M(H) curve for a Tl-2212 sample reveals a pronounced kink, suggesting a phase transition in the mixed state.Comment: 9 pages, 5figure

Mechanisms and novel therapies in cervical spinal cord injury.

PhDRecent epidemiological data indicate that more than half of SCI patients have injuries of the cervical spine. There is no satisfactory treatment for these injuries either in the acute or the chronic phase. Docosahexaenoic acid (DHA) is an omega-3 polyunsaturated fatty acid that is essential in brain development and has structural and signalling roles. Acute DHA administration has been shown to improve neurological functional recovery following injury in rodent thoracic spinal cord injury (SCI) animal models. In this thesis, we characterized a cervical SCI model comprising a hemisection lesion applied at the C4-5 level of the rat spinal cord, and tested the effects of an acute treatment with 250 nmol/kg DHA delivered intravenously 30 minutes after injury. The acute intravenous bolus of DHA not only increased the number of neuronal cells spared at three weeks following injury but also resulted in robust sprouting of uninjured corticospinal and serotonergic fibres. Next, we used a mouse pyramidotomy model to confirm that this robust sprouting was not species or injury model specific. We demonstrated that the number of V2a interneurons contacted by collateral corticospinal sprouting fibres is positively correlated with skilled motor recovery. To address the mechanism behind the neuroplasticity-promoting effect of DHA, we investigated the expression of miR-21 and phosphatase and tensin homolog (PTEN) in cortical neurons and raphe nuclei after DHA treatment. We found that DHA significantly up-regulates miR-21 and down-regulates PTEN in corticospinal neurons one day after SCI. Downregulation of PTEN by DHA was also seen in dorsal root ganglion (DRG) neuron 3 cultures and was accompanied by increased neurite outgrowth. Lastly, we investigated whether DHA treatment combined with specific-task rehabilitation maximized the recovery of skilled forelimb function following cervical SCI. The rats receiving combined therapy achieved greater skilled forelimb functional recovery compared to DHA treatment or rehabilitation only. In summary, this study shows that DHA has therapeutic potential in cervical SCI and provides evidence that DHA could exert its beneficial effects in SCI via enhancement of neuroplasticityMemorial Hospital, Taiwan for funding this research (grant number CMRPG3A105

Statistical Vehicle Specific Power Profiling for Urban Freeways

AbstractVehicle Specific Power (VSP) is conventionally defined to represent the instantaneous vehicle engine power. It has been widely utilized to reveal the impact of vehicle operating conditions on emission and energy consumption estimates that are dependent upon speed, roadway grade and acceleration or deceleration on the basis of the second-by-second vehicle operation. VSP has hence been incorporated into a key contributing factor in the vehicle emission models including MOVES. To facilitate the preparation of MOVES vehicle operating mode distribution inputs, an enhanced understanding and modeling of VSP distribution versus roadway grade become indispensable. This paper presents a study in which previous studies are extended by deeply investigating the characteristics of VSP distributions and their impacts due to varying freeway grades, as well as time-of-day factors. Afterwards, statistical distribution models with a scope of bins is identified through a goodness of fit testing approach by using the sample data collected from the interstate freeway segments in Cincinnati area. The Global Positioning System (GPS) data were collected at a selected length of 30km urban freeway for AM, PM and Mid-day periods. The datasets representing the vehicle operating conditions for VSP calculation are then extracted from the GPS trajectory data. The distribution fit modeling results demonstrated that the Wakeby distribution with five parameters dominates the most fitting parameters with the samples. In addition, the speed variation lies behind the time of day differences is also identified to be a contributing factor of urban freeway VSP distribution

Effects of the interplay between fermionic interactions and disorders in the nodal-line superconductors

We study the interplay between fermion-fermion interactions and disorder scatterings beneath the superconducting dome of noncentrosymmetric nodal-line superconductors. With the application of renormalization group, several interesting low-energy behaviors are extracted from the coupled equations of all interaction parameters. At the clean limit, fermion-fermion interactions decrease with lowering the energy scales but conversely fermion velocities climb up and approach certain saturated values. This yields a slight decrease or increase of the anisotropy of fermion velocities depending upon their initial ratio. After bringing out four kinds of disorders designated by the random charge ($\Delta_{1}$), random mass ($\Delta_{2}$), random axial chemical potential ($\Delta_{3}$), and spin-orbit scatterers ($\Delta_{4}$) based on their own unique features, we begin with presenting the distinct low-energy fates of these disorders. For the presence of sole disorder, its strength becomes either relevant ($\Delta_{1,4}$) or irrelevant($\Delta_{2,3}$) in the low-energy regime. However, the competition for multiple sorts of disorders is capable of qualitatively reshaping the low-energy properties of disorders $\Delta_{2,3,4}$. Besides, it can generate an initially absent disorder as long as two of $\Delta_{1,2,3}$ are present. In addition, the fermion-fermion couplings are insensitive to the presence of $\Delta_4$ but rather substantially modified by $\Delta_1$, $\Delta_2$, or $\Delta_3$, and evolve towards zero or certain finite non-zero values under the coexistence of distinct disorders. Furthermore, the fermion velocities flow towards certain finite saturated value for the only presence of $\Delta_{2,3}$ and vanish for all other situations. As to their ratio, it acquires a little increase once the disorder is subordinate to fermionic interactions, otherwise keeps some fixed constant.Comment: 22 pages, 25 figures (accepted by European Physical Journal Plus

Model-Free Approach to Fair Solar PV Curtailment Using Reinforcement Learning

The rapid adoption of residential solar photovoltaics (PV) has resulted in regular overvoltage events, due to correlated reverse power flows. Currently, PV inverters prevent damage to electronics by curtailing energy production in response to overvoltage. However, this disproportionately affects households at the far end of the feeder, leading to an unfair allocation of the potential value of energy produced. Globally optimizing for fair curtailment requires accurate feeder parameters, which are often unknown. This paper investigates reinforcement learning, which gradually optimizes a fair PV curtailment strategy by interacting with the system. We evaluate six fairness metrics on how well they can be learned compared to an optimal solution oracle. We show that all definitions permit efficient learning, suggesting that reinforcement learning is a promising approach to achieving both safe and fair PV coordination

Anti-Inflammatory and Immunomodulatory Mechanism of Tanshinone IIA for Atherosclerosis

Tanshinone IIA (Tan II A) is widely used in the treatment of cardiovascular diseases as an active component of Salvia miltiorrhiza Bunge. It has been demonstrated to have pleiotropic effects for atherosclerosis. From the anti-inflammatory and immunomodulatory mechanism perspective, this paper reviewed major progresses of Tan IIA in antiatherosclerosis research, including immune cells, antigens, cytokines, and cell signaling pathways