Hydrological Drought Forecasting and Assessment Based on the Standardized Stream Index in the Southwest China

AbstractSouthwest China is abundant of rainfall and water resources, however, severe and extremely droughts hits it more frequently in recent years, caused huge loss of human lives and financial damages. To investigate the feasibility of the standardized stream index in Southwest China, the Nanpanjiang River basin above the Xiaolongtan hydrological station was selected as the case study site. Based on long-term daily hydrological and meteorological data series, the generated runoff was simulated by the daily Xinanjiang model, then the standardized stream index was calculated and its feasibility was explored by comparing it with other two hydrological drought index. The result revealed that the standardized stream index performed well in detecting the onset, severity and duration in 2009/2010 extremely drought. The output of the paper could provide valuable references for the regional and national drought monitoring and forecasting systems

An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis

Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions

Thermal emittance measurements of a cesium potassium antimonide photocathode

Thermal emittance measurements of a CsK2Sb photocathode at several laser wavelengths are presented. The emittance is obtained with a solenoid scan technique using a high voltage dc photoemission gun. The thermal emittance is 0.56+/-0.03 mm-mrad/mm(rms) at 532 nm wavelength. The results are compared with a simple photoemission model and found to be in a good agreement.Comment: APL 201

Integrative disease classification based on cross-platform microarray data

<p>Abstract</p> <p>Background</p> <p>Disease classification has been an important application of microarray technology. However, most microarray-based classifiers can only handle data generated within the same study, since microarray data generated by different laboratories or with different platforms can not be compared directly due to systematic variations. This issue has severely limited the practical use of microarray-based disease classification.</p> <p>Results</p> <p>In this study, we tested the feasibility of disease classification by integrating the large amount of heterogeneous microarray datasets from the public microarray repositories. Cross-platform data compatibility is created by deriving expression log-rank ratios within datasets. One may then compare vectors of log-rank ratios across datasets. In addition, we systematically map textual annotations of datasets to concepts in Unified Medical Language System (UMLS), permitting quantitative analysis of the phenotype "distance" between datasets and automated construction of disease classes. We design a new classification approach named ManiSVM, which integrates Manifold data transformation with SVM learning to exploit the data properties. Using the leave one dataset out cross validation, ManiSVM achieved the overall accuracy of 70.7% (68.6% precision and 76.9% recall) with many disease classes achieving the accuracy higher than 80%.</p> <p>Conclusion</p> <p>Our results not only demonstrated the feasibility of the integrated disease classification approach, but also showed that the classification accuracy increases with the number of homogenous training datasets. Thus, the power of the integrative approach will increase with the continuous accumulation of microarray data in public repositories. Our study shows that automated disease diagnosis can be an important and promising application of the enormous amount of costly to generate, yet freely available, public microarray data.</p

An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis

Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions

A Causal Intervention Scheme for Semantic Segmentation of Quasi-periodic Cardiovascular Signals

Precise segmentation is a vital first step to analyze semantic information of cardiac cycle and capture anomaly with cardiovascular signals. However, in the field of deep semantic segmentation, inference is often unilaterally confounded by the individual attribute of data. Towards cardiovascular signals, quasi-periodicity is the essential characteristic to be learned, regarded as the synthesize of the attributes of morphology (Am) and rhythm (Ar). Our key insight is to suppress the over-dependence on Am or Ar while the generation process of deep representations. To address this issue, we establish a structural causal model as the foundation to customize the intervention approaches on Am and Ar, respectively. In this paper, we propose contrastive causal intervention (CCI) to form a novel training paradigm under a frame-level contrastive framework. The intervention can eliminate the implicit statistical bias brought by the single attribute and lead to more objective representations. We conduct comprehensive experiments with the controlled condition for QRS location and heart sound segmentation. The final results indicate that our approach can evidently improve the performance by up to 0.41% for QRS location and 2.73% for heart sound segmentation. The efficiency of the proposed method is generalized to multiple databases and noisy signals.Comment: submitted to IEEE Journal of Biomedical and Health Informatics (J-BHI

Molecular cloning and characterization of an actindepolymerizing factor gene in Hevea brasiliensis

Actin-depolymerizing factor (ADF) plays important roles in regulating actin dynamics by maintaining the optimum equilibrium between unpolymerized actin molecules and assembled actin filaments in different cellular processes. In this study, the first ADF gene in Hevea brasiliensis designated as HbADF, was isolated. The HbADF contained an open reading frame (ORF) encoding 139 amino acids. The deduced HbADF showed high identities to plants ADF proteins. Besides a conserved ADF domain, HbADF also contained putative actin and specific F-actin binding sites, phosphorylation site and possible CAM (calmodulin) combining region. The phylogenetic analysis indicated that HbADF was clustered in the subclass I. Being consistent with  phylogenetic result, the expression of HbADF was constitutive. The HbADF transcripts were upregulated by ethephon and wounding treatments; whereas, HbADF was firstly induced, and then gradually downregulated by jasmonic acid. The expression profiles and characterizations of HbADF suggested that HbADF might be  associated with latex regeneration and flow in H. brasiliensis.Key words: Actin cytoskeleton, actin-depolymerizing factor, expression analysis, Hevea brasiliensis, semiquantitative reverse-transcription polymerase chain reaction

Effects of free heave motion on wave resonance inside a narrow gap between two boxes under wave actions

Fluid resonance inside a narrow gap between two side-by-side boxes is investigated based on an open-source CFD package, OpenFOAM. An upstream box heaves freely under wave actions and a downstream box remains fixed. The focus of this work is to study the influence of the motion of the upstream box on the hydrodynamic behavior of the resonant fluid inside the gap. The hydrodynamic behavior considered in this study includes the wave height inside the gap, heave displacement and their harmonic components, and reflection, transmission and energy loss coefficients. For comparison, the configuration in which the two boxes are fixed is considered. It was found that the heave motion of the upstream box increases the fluid resonant frequency and significantly reduces the resonant wave height in the gap. The frequencies at which the maximum and minimum heave displacements of the upstream box are observed to obviously deviate from the fluid resonant frequency. For the wave height in the gap and heave displacement, the effects of the incident wave height on their harmonic components are different. The heave motion of the upstream box results in a larger reflection coefficient and smaller energy loss coefficient