Study on serum fluorescence spectra based on wavelet transform

Blood plays an important role in clinical diagnosis and treatment and as such, the analysis of blood spectrum will be of very important practical significance. Serum fluorescence emission intensity is closely related with the excitation wavelength; when the excitation wavelength is 230 nm, the blood lipid concentration and fluorescence intensity was significantly correlated. On the contrary, blood sugar was almost with no effect on the strength. Wavelet analysis was used in signal de-noising to get a wide range of applications. In this paper, fluorescence spectrum was divided into four layers by db4 wavelet, according to the principle of stein unbiased likelihood estimate.To choose the threshold, noise was removed and reconstruction signal received. This paper studied the correlation between blood lipid concentration and original fluorescence intensity, reconstruction fluorescence intensity and the fourth layer fluorescence strength. Some significant results were achieved, providing an experimental basis for further study on the fluorescence spectrum of blood

The Universal Edge Physics in Fractional Quantum Hall Liquids

The chiral Luttinger liquid theory for fractional quantum Hall edge transport predicts universal power-law behavior in the current-voltage ($I$-$V$) characteristics for electrons tunneling into the edge. However, it has not been unambiguously observed in experiments in two-dimensional electron gases based on GaAs/GaAlAs heterostructures or quantum wells. One plausible cause is the fractional quantum Hall edge reconstruction, which introduces non-chiral edge modes. The coupling between counterpropagating edge modes can modify the exponent of the $I$-$V$ characteristics. By comparing the $\nu=1/3$ fractional quantum Hall states in modulation-doped semiconductor devices and in graphene devices, we show that the graphene-based systems have an experimental accessible parameter region to avoid the edge reconstruction, which is suitable for the exploration of the universal edge tunneling exponent predicted by the chiral Luttinger liquid theory.Comment: 7 pages, 6 figure

Atomic scale elastic textures coupled to electrons in superconductors

We present an atomic scale theory of lattice distortions using strain related variables and their constraint equations. Our approach connects constrained atomic length scale variations to continuum elasticity and describes elasticity at all length scales. We apply the general approach to a two-dimensional square lattice with a monatomic basis, and find the atomic scale elastic textures around a structural domain wall and a single defect, as exemplary textures. We clarify the microscopic origin of gradient terms, some of which are included phenomenologically in Landau-Ginzburg theory. The obtained elastic textures are used to investigate the effects of elasticity-driven lattice deformation on the nanoscale electronic structure in superconductor by solving the Bogliubov-de Gennes equations with the electronic degrees of freedom coupled to the lattice ones. It is shown that the order parameter is depressed in the regions where the lattice deformation takes place. The calculated local density of states suggests the electronic structure is strongly modulated as a response to the lattice deformation-- the elasticity propagates the electronic response over long distances. In particular, it is possible for the trapping of low-lying quasiparticle states around the defects. These predictions could be directly tested by STM experiments in superconducting materials.Comment: Proceeding paper for "Conference on Dynamic Inhomogeneities in Complex Oxides" (to appear in J. Superconductivity

Multi-Objective Robust Workflow Offloading in Edge-to-Cloud Continuum

Workflow offloading in the edge-to-cloud continuum copes with an extended calculation network among edge devices and cloud platforms. With the growing significance of edge and cloud technologies, workflow offloading among these environments has been investigated in recent years. However, the dynamics of offloading optimization objectives, i.e., latency, resource utilization rate, and energy consumption among the edge and cloud sides, have hardly been researched. Consequently, the Quality of Service(QoS) and offloading performance also experience uncertain deviation. In this work, we propose a multi-objective robust offloading algorithm to address this issue, dealing with dynamics and multi-objective optimization. The workflow request model in this work is modeled as Directed Acyclic Graph(DAG). An LSTM-based sequence-to-sequence neural network learns the offloading policy. We then conduct comprehensive implementations to validate the robustness of our algorithm. As a result, our algorithm achieves better offloading performance regarding each objective and faster adaptation to newly changed environments than fine-tuned typical singleobjective RL-based offloading methods

The superheated Melting of Grain Boundary

Based on a model of the melting of Grain Boundary (GB), we discuss the possibility of the existence of superheated GB state. A Molecular Dynamics simulation presented here shows that the superheated GB state can realized in the high symmetric tilt GB. Whether the sizes of liquid nuclei exceed a critical size determined the superheating grain boundary melting or not. Our results also indicate that the increase of melting point due to pressure is smaller than the superheating due to nucleation mechanism.Comment: Accepted by PRB, 7 pages and 5 figure

Provenance-enhanced Root Cause Analysis for Jupyter Notebooks

With Jupyter notebooks becoming more commonly used within scientific research, more Jupyter notebook-based use cases have evolved to be distributed. This trend makes it more challenging to analyze anomalies and debug notebooks. Provenance data is an ideal option that can create more context around anomalies and make it easier to find the root cause of the anomaly. However, provenance rarely gets investigated in the context of distributed Jupyter notebooks. In this paper, we propose a framework that integrates two data types, provenance and detected performance anomalies based on performance data. We use the combined information to visually show the enduser the provenance at the time of the anomaly and the root cause of the anomaly. We build and evaluate the framework with a notebook extended with anomaly-generating functions. The generated anomalies were automatically detected, and the combined information of provenance and anomaly creates a valuable subset of the provenance data around the time an anomaly occurred. Our experiments create a clear and confined context for the anomaly and enable the framework to find the root cause of performance anomalies in Jupyter notebooks.</p

Large deformation of spherical vesicle studied by perturbation theory and Surface evolver

With tangent angle perturbation approach the axial symmetry deformation of a spherical vesicle in large under the pressure changes is studied by the elasticity theory of Helfrich spontaneous curvature model.Three main results in axial symmetry shape: biconcave shape, peanut shape, and one type of myelin are obtained. These axial symmetry morphology deformations are in agreement with those observed in lipsome experiments by dark-field light microscopy [Hotani, J. Mol. Biol. 178, (1984) 113] and in the red blood cell with two thin filaments (myelin) observed in living state (see, Bessis, Living Blood Cells and Their Ultrastructure, Springer-Verlag, 1973). Furthermore, the biconcave shape and peanut shape can be simulated with the help of a powerful software, Surface Evolver [Brakke, Exp. Math. 1, 141 (1992) 141], in which the spontaneous curvature can be easy taken into account.Comment: 16 pages, 6 EPS figures and 2 PS figure