Chalmers Research
Not a member yet
88095 research outputs found
Sort by
Extracellular interplay of amyloid fibrils and neural cells
Get PDFSome neurological disorders such e.g. as Alzheimer disease are accompanied by the appearance of amyloid fibrils inside and outside cells. Herein, I present a generic coarse-grained kinetic mean-field model describing at the extracellular level the interplay of fibrils and cells. It includes the formation and degradation of fibrils, activation of healthy cells with respect to the fabrication of fibrils, and death of activated cells. The corresponding analysis indicates that the disease development can occur in two qualitatively different regimes. The first one is controlled primarily by the intrinsic factors resulting in slow increase of fibril production inside cells. The second one implies faster self-promoted growth of the fibril population by analogy with explosion. This prediction reported as a hypothesis is of interest for conceptual understanding of the neurological disorders
Probability of rail break caused by out-of-round wheel loads
Get PDFA simulation procedure to predict the probability of rail break due to a measured wheel load spectrum is presented. The load distribution includes a representative proportion of high-magnitude dynamic loads generated by out-of-round wheels. Linear elastic fracture mechanics is applied to determine the stress intensities of preexisting rail head cracks in a continuously welded rail subjected to combined bending and temperature loading. Rail bending moments are evaluated using a validated time-domain model of dynamic vehicle-track interaction. The considered multi-dimensional stochastic parameter space includes field test data of dynamic loads from a wheel impact load detector and crack depths from eddy current data. Meta-models based on polyharmonic splines are applied to reduce the computational cost of the analysis. Supported by the extensive field test data, the simulation procedure is demonstrated by investigating the influences of freight traffic type, track support stiffness and rail temperature on the probability of a rail break initiated at a pre-existing rail head crack
On the Fundamental Diagram for Freeway Traffic: Exploring the Lower Bound of the Fitting Error and Correcting the Generalized Linear Regression Models
Get PDFIn traffic flow, the relationship between speed and density exhibits decreasing monotonicity and continuity, which is characterized by various models such as the Greenshields and Greenberg models. However, some existing models, i.e., the Underwood and Northwestern models, introduce bias by incorrectly utilizing linear regression for parameter calibration. Furthermore, the lower bound of the fitting errors for all these models remains unknown. To address above issues, this study first proves the bias associated with using linear regression in handling the Underwood and Northwestern models and corrects it, resulting in a significantly lower mean squared error (MSE). Second, a quadratic programming model is developed to obtain the lower bound of the MSE for these existing models. The relative gaps between the MSEs of existing models and the lower bound indicate that the existing models still have a lot of potential for improvement
Multiscale multimodal characterization and simulation of structural alterations in failed bioprosthetic heart valves
Get PDFCalcific degeneration is the most frequent type of heart valve failure, with rising incidence due to the ageing population. The gold standard treatment to date is valve replacement. Unfortunately, calcification oftentimes re-occurs in bioprosthetic substitutes, with the governing processes remaining poorly understood. Here, we present a multiscale, multimodal analysis of disturbances and extensive mineralisation of the collagen network in failed bioprosthetic bovine pericardium valve explants with full histoanatomical context. In addition to highly abundant mineralized collagen fibres and fibrils, calcified micron-sized particles previously discovered in native valves were also prevalent on the aortic as well as the ventricular surface of bioprosthetic valves. The two mineral types (fibres and particles) were detectable even in early-stage mineralisation, prior to any macroscopic calcification. Based on multiscale multimodal characterisation and high-fidelity simulations, we demonstrate that mineral occurrence coincides with regions exposed to high haemodynamic and biomechanical indicators. These insights obtained by multiscale analysis of failed bioprosthetic valves serve as groundwork for the evidence-based development of more durable alternatives. Statement of significance: Bioprosthetic valve calcification is a well-known clinically significant phenomenon, leading to valve failure. The nanoanalytical characterisation of bioprosthetic valves gives insights into the highly abundant, extensive calcification and disorganization of the collagen network and the presence of calcium phosphate particles previously reported in native cardiovascular tissues. While the collagen matrix mineralisation can be primarily attributed to a combination of chemical and mechanical alterations, the calcified particles are likely of host cellular origin. This work presents a straightforward route to mineral identification and characterization at high resolution and sensitivity, and with full histoanatomical context and correlation to hemodynamic and biomechanical indicators, hence providing design cues for improved bioprosthetic valve alternatives
Low-Vacuum Catalyst-Free Physical Vapor Deposition and Magnetotransport Properties of Ultrathin Bi2Se3 Nanoribbons
Get PDFIn this work, a simple catalyst-free physical vapor deposition method is optimized by adjusting source material pressure and evaporation time for the reliable obtaining of freestanding nanoribbons with thicknesses below 15 nm. The optimum synthesis temperature, time and pressure were determined for an increased yield of ultrathin Bi2Se3 nanoribbons with thicknesses of 8–15 nm. Physical and electrical characterization of the synthesized Bi2Se3 nanoribbons with thicknesses below 15 nm revealed no degradation of properties of the nanoribbons, as well as the absence of the contribution of trivial bulk charge carriers to the total conductance of the nanoribbons
Non-geometric pumping effects on the performance of interacting quantum-dot heat engines
Get PDFPeriodically driven quantum dots can act as counterparts of cyclic thermal machines at the nanoscale. In the slow-driving regime of geometric pumping, such machines have been shown to operate in analogy to a Carnot cycle. For larger driving frequencies, which are required to increase the cooling power, the efficiency of the operation decreases. Up to which frequency a close-to-optimal performance is still possible depends on the magnitude and sign of on-site electron–electron interaction. Extending our previous detailed study on cyclic quantum-dot refrigerators [Phys. Rev. B 106, 035405 (2022)], we here find that the optimal cooling power remains constant up to weak interaction strength compared to the cold-bath temperature. By contrast, the work cost depends on the interaction via the dot’s charge relaxation rate, as the latter sets the typical driving frequency for the onset of non-geometric pumping contributions
Comprehensive investigation of fission yields by using spallation- and (p,2p)induced fission reactions in inverse kinematics
Get PDFIn the last decades, measurements of spallation, fragmentation and Coulex induced fission reactions in inverse kinematics have provided valuable data to accurately investigate the fission dynamics and nuclear structure at large deformations of a large variety of stable and non -stable heavy nuclei. To go a step further, we propose now to induce fission by the use of quasi -free (p,2p) scattering reactions in inverse kinematics, which allows us to reconstruct the excitation energy of the compound fissioning system by using the four-momenta of the two outgoing protons. Therefore, this new approach might permit to correlate the excitation energy with the charge and mass distributions of the fission fragments and with the fission probabilities, given for the first time direct access to the simultaneous measurement of the fission yield dependence on temperature and fission barrier heights of exotic heavy nuclei, respectively. The first experiment based on this methodology was realized recently at the GM/FAIR facility and a detailed description of the experimental setup is given here
ProgDTD: Progressive Learned Image Compression with Double-Tail-Drop Training
No full textProgressive compression allows images to start loading as low-resolution versions, becoming clearer as more data is received. This increases user experience when, for example, network connections are slow. Today, most approaches for image compression, both classical and learned ones, are designed to be non-progressive. This paper introduces ProgDTD, a training method that transforms learned, non-progressive image compression approaches into progressive ones. The design of ProgDTD is based on the observation that the information stored within the bottleneck of a compression model commonly varies in importance. To create a progressive compression model, ProgDTD modifies the training steps to enforce the model to store the data in the bottleneck sorted by priority. We achieve progressive compression by transmitting the data in order of its sorted index. ProgDTD is designed for CNN-based learned image compression models, does not need additional parameters, and has a customizable range of progressiveness. For evaluation, we apply ProgDTD to the hyperprior model, one of the most common structures in learned image compression. Our experimental results show that ProgDTD performs comparably to its non-progressive counterparts and other state-of-the-art progressive models in terms of MS-SSIM and accuracy
A Service-Aware Autoscaling Strategy for Container Orchestration Platforms with Soft Resource Isolation
No full textContainer orchestration platforms like Kubernetes (K8s) allow easy deployment and management of cloud native services. When deploying their services, service providers need to specify a proper amount of resources to K8s, so that the desired Quality of Service (QoS) to their users can be maintained. To cope with the varying traffic demand coming from users, they can rely on the K8s Horizontal Pod Autoscaling (HPA) mechanism. To ensure that enough resources are available when needed, the standard HPA mechanism relies on resource overprovisioning. In this way, the required QoS is achieved most of (or all) the time but at the expense of additional resources that are allocated (and charged for), while they may stay idle for significant periods of time. A way to reduce overprovisioning is provided by the soft resource isolation of K8s, which allows services to compensate for a temporary lack of resources with shared resources, i.e., idle resources of the machines where services are running. However, during traffic spikes, these idle resources may not be enough to serve the whole demand, degrading the QoS. The HPA, which is not aware of how much demand could not be served, is not always able to correctly estimate the required additional resources, further degrading the QoS. To overcome this, service providers need to leverage overprovisioning, limiting the use of shared resources. In this paper, we propose a novel mechanism for autoscaling resources in K8s that relies on service-related data to avoid the additional degradation introduced by the HPA. The proposed strategy also offers a way to tune overprovisioning and shared resources. Simulation results show that our approach can reduce idle resources by up to 60% compared with the HPA mechanism