Photon and vibration synergism on planar defects induced 2D-graphitic carbon nitride for ultrafast remediation of dyes and antibiotic ampicillin

Journal of Materials Science - Hybrid oxidation methodologies (HOMs) and active site enrichment of 2D nanocatalyst through defects induction are ubiquitously used for generating adequate reactive...     

Design and characterization of asymetrical super-lattice Si/4H-SiC pin photo diode array: a potential opto-sensor for future applications in bio-medical domain

Microsystem Technologies - Photo-sensors are integral part of different bio-medical diagnostic equipment. Each type of bio-molecules possess unique spectral fingerprint in visible wavelength region...     

Generating vegetation leaf area index Earth system data record from multiple sensors. Part 2: Implementation,analysis and validation

The evaluation of a new global monthly leaf area index (LAI) data set for the period July 1981 to December 2006 derived from AVHRR Normalized Difference Vegetation Index (NDVI) data is described. The physically based algorithm is detailed in the first of the two part series. Here, the implementation, production and evaluation of the data set are described. The data set is evaluated both by direct comparisons to ground data and indirectly through inter-comparisons with similar data sets. This indirect validation showed satisfactory agreement with existing LAI products, importantly MODIS, at a range of spatial scales, and significant correlations with key climate variables in areas where temperature and precipitation limit plant growth. The data set successfully reproduced well-documented spatio-temporal trends and inter-annual variations in vegetation activity in the northern latitudes and semi-arid tropics. Comparison with plot scale field measurements over homogeneous vegetation patches indicated a 7% underestimation when all major vegetation types are taken into account. The error in mean values obtained from distributions of AVHRR LAI and high-resolution field LAI maps for different biomes is within 0.5 LAI for six out of the ten selected sites. These validation exercises though limited by the amount of field data, and thus less than comprehensive, indicated satisfactory agreement between the LAI product and field measurements. Overall, the inter-comparison with short-term LAI data sets, evaluation of long term trends with known variations in climate variables, and validation with field measurements together build confidence in the utility of this new 26 year LAI record for long term vegetation monitoring and modeling studies.     

Prognostics of machine condition using soft computing

This paper presents a system for monitoring and prognostics of machine conditions using soft computing (SC) techniques. The machine condition is assessed through a suitable ‘monitoring index’ extracted from the vibration signals. The progression of the monitoring index is predicted using an SC technique, namely adaptive neuro-fuzzy inference system (ANFIS). Comparison with a machine learning method, namely support vector regression (SVR), is also presented. The proposed prediction procedures have been evaluated through benchmark data sets. The prognostic effectiveness of the techniques has been illustrated through previously published data on several types of faults in machines. The performance of SVR was found to be better than ANFIS for the data sets used. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating features, the level of damage/degradation and their progression.     

Reduced complexity two stage vector quantization

We address the issue of complexity for vector quantization (VQ) of wide-band speech LSF (line spectrum frequency) parameters. The recently proposed switched split VQ (SSVQ) method provides better rate–distortion (R/D) performance than the traditional split VQ (SVQ) method, even at the requirement of lower computational complexity, but at the expense of much higher memory. We develop the two stage SVQ (TsSVQ) method, by which we gain both the memory and computational advantages and still retain good R/D performance. The proposed TsSVQ method uses a full dimensional quantizer in its first stage for exploiting all the higher dimensional coding advantages and then, uses an SVQ method for quantizing the residual vector in the second stage so as to reduce the complexity. We also develop a transform domain residual coding method in this two stage architecture such that it further reduces the computational complexity. To design an effective residual codebook in the second stage, variance normalization of Voronoi regions is carried out which leads to the design of two new methods, referred to as normalized two stage SVQ (NTsSVQ) and normalized two stage transform domain SVQ (NTsTrSVQ). These two new methods have complimentary strengths and hence, they are combined in a switched VQ mode which leads to the further improvement in R/D performance, but retaining the low complexity requirement. We evaluate the performances of new methods for wide-band speech LSF parameter quantization and show their advantages over established SVQ and SSVQ methods.     

Multilevel segmentation of Hippocampus images using global steered quantum inspired firefly algorithm

Applied Intelligence - Microscopic Image segmentation has a crucial role in detecting and diagnosing numerous critical diseases like Alzheimer’s disease, Kidney disease, Cancer, many...     

天然有机质对纳米碳管环境行为的影响研究进展

随着纳米技术的快速发展,大量的纳米碳管(CNTs)不可避免地释放到环境中。由于其较大的憎水性表面,CNTs与有机污染物和天然有机质(NOM)强烈地相互作用。综述了NOM存在下CNTs的环境行为,包括NOM对CNTs分散特性和吸附有机污染物特性的影响。着重论述了NOM的理化性质对CNTs分散的影响,"拉拉链"或"胶束包裹"是主要的分散机制。强调应该对不同分散机制下分散的CNTs与有机污染物的相互作用给予更多的关注,提出了目前在液相环境中直接测定CNTs表面积的新思路,并对今后的研究方向进行了展望。     

Construction of a radial basis function network using an evolutionary algorithm for grade estimation in a placer gold deposit

This paper presents a study highlighting the predictive performance of a radial basis function (RBF) network in estimating the grade of an offshore placer gold deposit. In applying the radial basis function network to grade estimation of the deposit, several pertinent issues regarding RBF model construction are addressed in this study. One of the issues is the selection of the RBF network along with its center and width parameters. Selection was done by an evolutionary algorithm that utilizes the concept of cooperative coevolutions of the RBFs and the associated network. Furthermore, the problem of data division, which arose during the creation of the training, calibration and validation of data sets for the RBF model development, was resolved with the help of an integrated approach of data segmentation and genetic algorithms (GA). A simulation study conducted showed that nearly 27% of the time, a bad data division would result if random data divisions were adopted in this study. In addition, the efficacy of the RBF network was tested against a feed-forward network and geostatistical techniques. The outcome of this comparative study indicated that the RBF model performed decisively better than the feed-forward network and the ordinary kriging (OK).