A review on the coordinative structure of human walking and the application of principal component analysis

Walking is a complex task which includes hundreds of muscles, bones and joints working together to deliver smooth movements. With the complexity, walking has been widely investigated in order to identify the pattern of multi-segment movement and reveal the control mechanism. The degree of freedom and dimensional properties provide a view of the coordinative structure during walking, which has been extensively studied by using dimension reduction technique. In this paper, the studies related to the coordinative structure, dimensions detection and pattern reorganization during walking have been reviewed. Principal component analysis, as a popular technique, is widely used in the processing of human movement data. Both the principle and the outcomes of principal component analysis were introduced in this paper. This technique has been reported to successfully reduce the redundancy within the original data, identify the physical meaning represented by the extracted principal components and discriminate the different patterns. The coordinative structure during walking assessed by this technique could provide further information of the body control mechanism and correlate walking pattern with injury.

Elemental Analysis of Soil Phosphorus Neighborhoods Using SEM, Spectral Mapping and GIS

The utility of GIS （geographic information system） methods and spatial statistical analysis on spectral maps of sediment samples were examined. Detailed elemental maps are often constructed using energy dispersive X-ray techniques and SEM （scanning electron microscopy）. The elemental neighborhood associations of a single element, P （phosphorus）, were quantified at a magnification of 3,000 ×. For each of the 170,000 pixels on the images which displayed a strong P concentration, neighborhoods from 0.1μm^2 to 12 μm^2 were examined for associated elemental concentrations. PCA （principal component analysis） revealed two significant neighborhood types associated with P in samples of pH 4, and three neighborhood types at pH 8. These neighborhoods corresponded to Mg-P associations commonly found to be chemically prevalent in river sediments impacted by agricultural operations. Discriminant analysis showed that the greatest accuracy in predicting sample pH could be achieved by using a neighborhood size of 12 ~m2. Potassium at relatively large neighborhood sizes was the element most significant in predicting pH. While many of the chemical associations in close proximity to P could be predicted and explained through mineral solubility, spatial analysis provided some interesting insights into the structure of the samples. Results also indicted differences in the spatial scale associated with different processes.

Single frame super-resolution reconstruction based on sparse representation

In order to effectively improve the quality of recovered images, a single frame super-resolution reconstruction method based on sparse representation is proposed. The combination method of local orientation estimation-based image patch clustering and principal component analysis is used to obtain a series of geometric dictionaries of different orientations in the dictionary learning process. Subsequently, the dictionary of the nearest orientation is adaptively assigned to each of the input patches that need to be represented in the sparse coding process. Moreover, the consistency of gradients is further incorporated into the basic framework to make more substantial progress in preserving more fine edges and producing sharper results. Two groups of experiments on different types of natural images indicate that the proposed method outperforms some state-of- the-art counterparts in terms of both numerical indicators and visual quality.

Evaluating journals' yearly impact with altmetric indicators

Purpose： Applying SSCI journals of library and information science （LIS） as the research sample, we explore the feasibility of measuring academic journals＇ yearly social impact by using altmetric indicators. Design/methodology/approach： Using a sample of 66 SSCI joumals in LIS published in 2013, statistics regarding journal mentions in social media and other online tools were retrieved from Altmetric.com and meanwhile citation data was also collected from JCR and Scopus. Based on the method of principal component analysis, data was analyzed for associations between the altmetric and traditional metrics to demonstrate the effect ofaltmetric indicators on measuring academic j oumals＇ yearly impact. Findings： The Spearman＇s rank correlation test results show that altmetric indicators and traditional citation counts were significantly correlated, indicating that altmetrics can be used to measure a journal＇s yearly social impact. Research limitations： The time frame of data collected from Altmetric.com may not be consistent with that of JCR and Scopus citation data. Practical implications： A new method is provided based on altmetrics for evaluating the social impact of academic journals, which can be applied to design new indicators of short-term journal impact. Originality value： In this paper, we have established a method for evaluating the social impact of academic journals based on altmetric indictors. Altmetrics can be complementary to traditional citation metrics in assessing a journal＇s impact within a year or even in a shorter period of time.

Relationship between Abnormal Characteristics of Sublingual Collateral and Portal Vein Hemodynamic Changes in Patients with Primary Hepatic Carcinoma

Objective： To investigate the relationship between the abnormal characteristics of sublingual collateral （SC） and portal vein hemodynamic changes in patients with primary hepatic carcinoma （PHC）. Methods： A total of 123 patients of PHC with abnormal SC were enrolled. The SC characteristics were classified and evaluated. The principal components （PC） of SC extracted from them by principal component analysis and the relationship between PC and the dynamic changes of portal vein flow were analyzed by correlation analysis. Results： Three groups of PC were extracted, namely PC-1 （length, width, presentation type of visualization）, PC-2 （circuitous, vesicular change）, and PC-3 （color, collateral hemostasis, petechiae, ecchymosis）. Their total accumulative contribution degree reached 56.803%. Correlation analysis shows that PC-1 was significantly positively correlated with the hemodynamic parameters of the portal vein （P〈0.01）, while PC-2 and PC-3 were not （P〉0.05）. Conclusion： Length, width and presentation type of SC could be used for predicting the changes of portal venous pressure in PHC patients.

Intelligent Multivariable Modeling of Blast Furnace Molten Iron Quality Based on Dynamic AGA-ANN and PCA

Blast furnace （BF） ironmaking process has complex and nonlinear dynamic characteristics. The molten iron temperature （MIT） as well as Si, P and S contents of molten iron is difficult to be directly measured online, and large-time delay exists in offline analysis through laboratory sampling. A nonlinear multivariate intelligent modeling method was proposed for molten iron quality （MIQ） based on principal component analysis （PCA） and dynamic ge- netic neural network. The modeling method used the practical data processed by PCA dimension reduction as inputs of the dynamic artificial neural network （ANN）. A dynamic feedback link was introduced to produce a dynamic neu- ral network on the basis of traditional back propagation ANN. The proposed model improved the dynamic adaptabili- ty of networks and solved the strong fluctuation and resistance problem in a nonlinear dynamic system. Moreover, a new hybrid training method was presented where adaptive genetic algorithms （AGA） and ANN were integrated, which could improve network convergence speed and avoid network into local minima. The proposed method made it easier for operators to understand the inside status of blast furnace and offered real-time and reliable feedback infor- mation for realizing close-loop control for MIQ. Industrial experiments were made through the proposed model based on data collected from a practical steel company. The accuracy could meet the requirements of actual operation.

Indoor carbonyl compounds in an academic building in Beijing, China： concentrations and influencing factors

Carbonyl compounds in indoor air are of great concern for their adverse health effects. Between February and May, 2009, concentrations of 13 carbonyl compounds were measured in an academic building in Beijing, China. Total concentration of the detected carbonyls ranged from 20.7 to 189.1 I.tg.m3, and among them acetone and formaldehyde were the most abundant, with mean concentrations of 26.4 and 22.6gg.m-3, respectively. Average indoor concentrations of other carbonyls were below I 0 gg. m^3. Principal component analysis identified a combined effect of common indoor carbonyl sources and ventilation on indoor carbonyl levels. Diurnal variations of the carbonyl compounds were investigated in one office room, and carbonyl concentrations tended to be lower in the daytime than at night, due to enhanced ventilation. Average concentrations of carbonyl compounds in the office room were generally higher in early May than in late February, indicating the influence of temperature. Carbo- nyl source emission rates from both the room and human occupants were estimated during two lectures, based on one-compartment mass balance model. The influence of human occupants on indoor carbonyl concentrations varies with environmental conditions, and may become signifi- cant in the case of a large human occupancy.

Simultaneous optimization of multiple performance characteristics in WEDM for machining ZC63/SiC_p MMC

Abstract The compliance of an integrated approach, principal component analysis （PCA）, coupled with Tagu chi＇s robust theory for simultaneous optimization of cor related multiple responses of wire electrical discharge machining （WEDM） process for machining SiCp rein forced ZC63 metal matrix composites （MMCs） is investi gated in this work. The WEDM is proven better for its efficiency to machine MMCs among others, while the particulate size and volume percentage of SiCp with the composite are the utmost important factors. These improve the mechanical properties enormously, however reduce the machining performance. Hence the WEDM experiments are conducted by varying the particulate size, volume fraction, pulseon time, pulseoff time and wire tension. In the view of quality cut, the most important performance indicators of WEDM as surface roughness （Ra）, metal removal rate （MRR）, wire wear ratio （WWR）, kerf （Kw） and white layer thickness （WLT） are measured as respon ses. PCA is used as multiresponse optimization technique to derive the composite principal component （CPC） which acts as the overall quality index in the process. Consequently, Taguchi＇s S/N ratio analysis is applied to optimize the CPC. The derived optimal process responses are confirmed by the experimental validation tests results. The analysis of vari ance is conducted to find the effects of choosing process variables on the overall quality of the machined component.The practical possibility of the derived optimal process conditions is also presented using SEM.