Effect of charged impurities on graphene thermoelectric power near the Dirac point

In graphene devices with a varying degree of disorders as characterized by their carrier mobility and minimum conductivity, we have studied the thermoelectric power along with the electrical conductivity over a wide range of temperatures. We have found that the Mott relation fails in the vicinity of the Dirac point in high-mobility graphene. By properly taking account of the high temperature effects, we have obtained good agreement between the Boltzmann transport theory and our experimental data. In low-mobility graphene where the charged impurities induce relatively high residual carrier density, the Mott relation holds at all gate voltages

Numerical simulation of pile geothermal heat exchanger with spiral tube considering its thermo-mechanical behavior

Pile geothermal heat exchanger (PGHE), which utilizes the building foundation piles as part of the geothermal heat exchangers (GHEs) for a ground-coupled heat pump (GCHP) system, has been attracting the interests of researchers and engineers. However, the continuous heat rejection/extraction of the PGHE to/from the piles will cause significant temperature variations (up to 25?) of piles and the surrounding soil, which can influence the mechanical behavior of the pile foundation severely. A modified direct shear apparatus has been developed to investigate the interface behavior between soil and pile. Then, based on the experiment results, the thermo-mechanical behavior of PGHE with spiral coils was investigated by a 3-D simulation model. The thermal loads induce additional compressive stress when the temperature rise, and the local compressive stress can reach to 9.35MPa near the heat exchanger pipe. Additionally, heat extraction led to a decrease of friction angle and normal contact pressure at the interface between soil and pile, and as a consequence, the shear force decreases with the temperature drop. Compared with no thermal disturbance, the ultimate friction resistance of pile is weakened by 15.37%

A Semianalytical Model Using MODIS Data to Estimate Cell Density of Red Tide Algae (Aureococcus anophagefferens)

A multiband and a single-band semianalytical model were developed to predict algae cell density distribution. The models were based on cell density (N) dependent parameterizations of the spectral backscattering coefficients, b ( ), obtained from in situ measurements. There was a strong relationship between b ( ) and N, with a minimum regression coefficient of 0.97 at 488 nm and a maximum value of 0.98 at other bands. The cell density calculated by the multiband inversion model was similar to the field measurements of the coastal waters (the average relative error was only 8.9%), but it could not accurately discern the red tide from mixed pixels, and this led to overestimation of the area affected by the red tide. While the single-band inversion model is less precise than the former model in the high chlorophyll water, it could eliminate the impact of the suspended sediments and make more accurate estimates of the red tide area. We concluded that the two models both have advantages and disadvantages; these methods lay the foundation for developing a remote sensing forecasting system for red tides

A Semianalytical Model Using MODIS Data to Estimate Cell Density of Red Tide Algae ( Aureococcus anophagefferens

A multiband and a single-band semianalytical model were developed to predict algae cell density distribution. The models were based on cell density (N) dependent parameterizations of the spectral backscattering coefficients, bb(λ), obtained from in situ measurements. There was a strong relationship between bb(λ) and N, with a minimum regression coefficient of 0.97 at 488 nm and a maximum value of 0.98 at other bands. The cell density calculated by the multiband inversion model was similar to the field measurements of the coastal waters (the average relative error was only 8.9%), but it could not accurately discern the red tide from mixed pixels, and this led to overestimation of the area affected by the red tide. While the single-band inversion model is less precise than the former model in the high chlorophyll water, it could eliminate the impact of the suspended sediments and make more accurate estimates of the red tide area. We concluded that the two models both have advantages and disadvantages; these methods lay the foundation for developing a remote sensing forecasting system for red tides

A Comparative Study of Two Prediction Models for Brain Tumor Progression

MR diffusion tensor imaging (DTI) technique together with traditional T1 or T2 weighted MRI scans supplies rich information sources for brain cancer diagnoses. These images form large-scale, high-dimensional data sets. Due to the fact that significant correlations exist among these images, we assume low-dimensional geometry data structures (manifolds) are embedded in the high-dimensional space. Those manifolds might be hidden from radiologists because it is challenging for human experts to interpret high-dimensional data. Identification of the manifold is a critical step for successfully analyzing multimodal MR images. We have developed various manifold learning algorithms (Tran et al. 2011; Tran et al. 2013) for medical image analysis. This paper presents a comparative study of an incremental manifold learning scheme (Tran. et al. 2013) versus the deep learning model (Hinton et al. 2006) in the application of brain tumor progression prediction. The incremental manifold learning is a variant of manifold learning algorithm to handle large-scale datasets in which a representative subset of original data is sampled first to construct a manifold skeleton and remaining data points are then inserted into the skeleton by following their local geometry. The incremental manifold learning algorithm aims at mitigating the computational burden associated with traditional manifold learning methods for large-scale datasets. Deep learning is a recently developed multilayer perceptron model that has achieved start-of-the-art performances in many applications. A recent technique named Dropout can further boost the deep model by preventing weight coadaptation to avoid over-fitting (Hinton et al. 2012). We applied the two models on multiple MRI scans from four brain tumor patients to predict tumor progression and compared the performances of the two models in terms of average prediction accuracy, sensitivity, specificity and precision. The quantitative performance metrics were calculated as average over the four patients. Experimental results show that both the manifold learning and deep neural network models produced better results compared to using raw data and principle component analysis (PCA), and the deep learning model is a better method than manifold learning on this data set. The averaged sensitivity and specificity by deep learning are comparable with these by the manifold learning approach while its precision is considerably higher. This means that the predicted abnormal points by deep learning are more likely to correspond to the actual progression region

Spin Relaxation in Single Layer Graphene with Tunable Mobility

Graphene is an attractive material for spintronics due to theoretical predictions of long spin lifetimes arising from low spin-orbit and hyperfine couplings. In experiments, however, spin lifetimes in single layer graphene (SLG) measured via Hanle effects are much shorter than expected theoretically. Thus, the origin of spin relaxation in SLG is a major issue for graphene spintronics. Despite extensive theoretical and experimental work addressing this question, there is still little clarity on the microscopic origin of spin relaxation. By using organic ligand-bound nanoparticles as charge reservoirs to tune mobility between 2700 and 12000 cm2/Vs, we successfully isolate the effect of charged impurity scattering on spin relaxation in SLG. Our results demonstrate that while charged impurities can greatly affect mobility, the spin lifetimes are not affected by charged impurity scattering.Comment: 13 pages, 5 figure

B serum proteome profiles revealed dysregulated proteins and mechanisms associated with insomnia patients: A preliminary study

BackgroundInsomnia is a clinical problem of significant public health importance; however, the underlying pathogenesis of this disorder is not comprehensively understood.MethodsTo identify potential treatment targets and unfold one of the gaps that were involved in insomnia pathological mechanisms, we employed a tandem mass tag-based (TMT) quantitative proteomics technology to detect differentially expressed proteins (DEPs) in serum from patients with insomnia and controls. DEPs were further analyzed by bioinformatics platforms. In addition, parallel reaction monitoring (PRM) was used to verify the TMT results.ResultsPatients with insomnia had poorer sleep quality compared with healthy controls. A total of 106 DEPs were identified among patients with insomnia and controls. They were mainly enriched in immune and inflammation-related biological functions and signaling pathways. Using the protein–protein interaction network, we screened the 10 most connected proteins as key DEPs. We predicted that four key DEPs were subject to targeted regulation by natural compounds of herbs. Eight key DEPs were validated using PRM in an additional 15 patients with insomnia and 15 controls, and the results also supported the experimental findings.ConclusionWe identified aberrantly expressed proteins in insomnia that may be involved in the immune-inflammatory response. The 10 key DEPs screened may be potential targets for insomnia, especially FN1, EGF, HP, and IGF1. The results of this study will broaden our understanding of the pathological mechanisms of insomnia and provide more possibilities for pharmacotherapy

Association of GSTM1 Null Allele with Prostate Cancer Risk: Evidence from 36 Case-Control Studies

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