Do Chinese traditional values matter in regulating China’s company directors? Preliminary findings from surveys in Hong Kong

This article argues that Chinese traditional values do matter in Chinese corporate governance. The object is to report on the preliminary findings of a project supported by the General Research Fund in Hong Kong (HK). Thus far the survey results from HK respondents support the authors’ hypothesis. As such, traditional Chinese values should be on the agenda of the next round of company law reforms in China

Evaluation of spectral and texture features for object-based vegetation species classification using Support Vector Machines

The use of appropriate features to characterize an output class or object is critical for all classification problems. This paper evaluates the capability of several spectral and texture features for object-based vegetation classification at the species level using airborne high resolution multispectral imagery. Image-objects as the basic classification unit were generated through image segmentation. Statistical moments extracted from original spectral bands and vegetation index image are used as feature descriptors for image objects (i.e. tree crowns). Several state-of-art texture descriptors such as Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Patterns (LBP) and its extensions are also extracted for comparison purpose. Support Vector Machine (SVM) is employed for classification in the object-feature space. The experimental results showed that incorporating spectral vegetation indices can improve the classification accuracy and obtained better results than in original spectral bands, and using moments of Ratio Vegetation Index obtained the highest average classification accuracy in our experiment. The experiments also indicate that the spectral moment features also outperform or can at least compare with the state-of-art texture descriptors in terms of classification accuracy.

Empirical comparison of machine learning algorithms for image texture classification with application to vegetation management in power line corridors

This paper reports on the empirical comparison of seven machine learning algorithms in texture classification with application to vegetation management in power line corridors. Aiming at classifying tree species in power line corridors, object-based method is employed. Individual tree crowns are segmented as the basic classification units and three classic texture features are extracted as the input to the classification algorithms. Several widely used performance metrics are used to evaluate the classification algorithms. The experimental results demonstrate that the classification performance depends on the performance matrix, the characteristics of datasets and the feature used.

Advances in vegetation management for power line corridor monitoring using aerial remote sensing techniques

This paper presents a comprehensive discussion of vegetation management approaches in power line corridors based on aerial remote sensing techniques. We address three issues 1) strategies for risk management in power line corridors, 2) selection of suitable platforms and sensor suite for data collection and 3) the progress in automated data processing techniques for vegetation management. We present initial results from a series of experiments and, challenges and lessons learnt from our project.

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.

Depression among Chinese stroke survivors six months after discharge from a rehabilitation hospital

Aim and objective: The primary aim was to examine the prevalence of poststroke depression in Chinese stroke survivors six months after discharge from a rehabilitation hospital. A second aim was to determine whether six-month poststroke depression was associated with psychological, social and physical outcomes and demographic variables.---------- Background: There has been increasing recognition of the influence of depression on poststroke recovery. While some previous studies report associations between depression and social, psychological, physical and clinical outcomes, few studies had sufficient sample sizes for regression analysis thereby limiting the clinical applicability of their findings. ---------- Design: A cross-sectional design was used.---------- Method: Data were collected from 124 male and 86 female stroke survivors (mean age 71Æ7, SD 10Æ2 years). The Geriatric Depression Scale was used to measure depression, the State Self-esteem Scale to measure state self-esteem, the London Handicap Scale to measure participation restriction, the Social Support Questionnaire to measure satisfaction with social support and the Modified Barthel Index to measure functional ability. Results. Forty-two survivors (20Æ5%) reported mild and 33 (16Æ1%) reported severe depression. The presence of depression was associated with low levels of state self-esteem, social support satisfaction and functional ability. Logistic regression analysis revealed that these variables were statistically significant in predicting the probability of having depression (p < 0Æ05). ---------- Conclusions: Analyses in the present study revealed distinct patterns of correlates of depression, and the results were in agreement with prior studies that depression has a consistent positive ssociation with physical disability, living arrangements and social support and no significant association with the different types of brain lesion. Relevance to clinical practice. There is a need, routinely, to assess stroke survivors for depression and, where necessary, to intervene with the aim of enhancing psychological and social well-being.

Retracing the Roots and Ideals of Confucian Principles of Governance: the Art of Regulating Governance without Legal Rules in Chinese Societies

Confucius was and still is one of the most eminent Chinese philosophers. Such is the importance of Confucius’s teachings; it had influenced all aspects of social life in Chinese societies. In the post-Enron, post-Worldcom, and post-Global Financial Crisis era there are raising doubts in the mantra of the so-called conventional wisdom about law and economic order. Whilst many recent publications offered solutions to those problems like advocating for more laws, rules or reforms in regulatory institutions to enhance the regulation of corporate governance. What Confucius advocated was a non-legal, social mode of regulation based on moral ideals that should be embedded into the minds of every person. Whilst this is an ancient concept from primitive societies, its relevance and merits could be seen in modern Chinese societies like Hong Kong. In essence, Confucian principles of governance build on relational and paternalistic order based on moral ideals.

Xiamen Hai Cang tourism project : landscape planning and design of five islands

International practice-led design research in landscape architecture has identified the need for addressing the loss of biodiversity in urban environments. China has lost much of its biodiversity in rural and urban environments over thousands of years. However some Chinese cities have attempted to conserve what remains and enhance existing vegetation communities in isolated pockets. Island biogeography has been used as the basis for planning and designing landscapes in Australia and North America but not as yet in China, as far as we know. A gap in landscape design knowledge exists regarding how to apply landscape ecology concepts to urban islands of remaining biodiversity being developed for heavy Chinese domestic tourism impacts in the future. This project responded to the demands for harbour-side tourism opportunities in Xiamen City, Fujian Province, by proposing a range of eco-design innovations using concepts of patch, edge and interior to interconnect people and nature in a Chinese setting.

Motivating blue-collar employees : a case study of the Chinese workforce

The Katz and Kahn (1978) motivational framework is an open system management theory that underscores the importance of self-regulation while stressing the significance of using continuous feedback to adapt in a rapidly changing environment. This study aims to examine Katz and Kahn’s prepositions that the implementation of a system of rule compliance, external rewards, and internalized motivation can decrease employee turnover, increase quantitative and qualitative standards of performance, and enhance cooperation and creativeness. The results among 233 Chinese employees (96.6% response rate) indicated partial support for Katz and Kahn’s motivational framework. The implication for improving the Chinese workforce, in particular blue-collar occupations, is discussed.

A biologically inspired object spectral-texture descriptor and its application to vegetation classification in power-line corridors

The use of appropriate features to represent an output class or object is critical for all classification problems. In this paper, we propose a biologically inspired object descriptor to represent the spectral-texture patterns of image-objects. The proposed feature descriptor is generated from the pulse spectral frequencies (PSF) of a pulse coupled neural network (PCNN), which is invariant to rotation, translation and small scale changes. The proposed method is first evaluated in a rotation and scale invariant texture classification using USC-SIPI texture database. It is further evaluated in an application of vegetation species classification in power line corridor monitoring using airborne multi-spectral aerial imagery. The results from the two experiments demonstrate that the PSF feature is effective to represent spectral-texture patterns of objects and it shows better results than classic color histogram and texture features.

A hybrid Chinese information retrieval model

A distinctive feature of Chinese test is that a Chinese document is a sequence of Chinese with no space or boundary between Chinese words. This feature makes Chinese information retrieval more difficult since a retrieved document which contains the query term as a sequence of Chinese characters may not be really relevant to the query since the query term (as a sequence Chinese characters) may not be a valid Chinese word in that documents. On the other hand, a document that is actually relevant may not be retrieved because it does not contain the query sequence but contains other relevant words. In this research, we propose a hybrid Chinese information retrieval model by incorporating word-based techniques with the traditional character-based techniques. The aim of this approach is to investigate the influence of Chinese segmentation on the performance of Chinese information retrieval. Two ranking methods are proposed to rank retrieved documents based on the relevancy to the query calculated by combining character-based ranking and word-based ranking. Our experimental results show that Chinese segmentation can improve the performance of Chinese information retrieval, but the improvement is not significant if it incorporates only Chinese segmentation with the traditional character-based approach.

Waist circumference cut-off values for the prediction of cardiovascular risk factors clustering in Chinese school-aged children : a cross-sectional study

Background: Waist circumference has been identified as a valuable predictor of cardiovascular risk in children. The development of waist circumference percentiles and cut-offs for various ethnic groups are necessary because of differences in body composition. The purpose of this study was to develop waist circumference percentiles for Chinese children and to explore optimal waist circumference cut-off values for predicting cardiovascular risk factors clustering in this population.----- ----- Methods: Height, weight, and waist circumference were measured in 5529 children (2830 boys and 2699 girls) aged 6-12 years randomly selected from southern and northern China. Blood pressure, fasting triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and glucose were obtained in a subsample (n = 1845). Smoothed percentile curves were produced using the LMS method. Receiver-operating characteristic analysis was used to derive the optimal age- and gender-specific waist circumference thresholds for predicting the clustering of cardiovascular risk factors.----- ----- Results: Gender-specific waist circumference percentiles were constructed. The waist circumference thresholds were at the 90th and 84th percentiles for Chinese boys and girls respectively, with sensitivity and specificity ranging from 67% to 83%. The odds ratio of a clustering of cardiovascular risk factors among boys and girls with a higher value than cut-off points was 10.349 (95% confidence interval 4.466 to 23.979) and 8.084 (95% confidence interval 3.147 to 20.767) compared with their counterparts.----- ----- Conclusions: Percentile curves for waist circumference of Chinese children are provided. The cut-off point for waist circumference to predict cardiovascular risk factors clustering is at the 90th and 84th percentiles for Chinese boys and girls, respectively.

The role of vegetation in enhancing radon concentration and ion production in the atmosphere

The role of ions in the production of atmospheric particles has gained wide interest due to their profound impact on climate. Away from anthropogenic sources, molecules are ionized by alpha radiation from radon exhaled from the ground and cosmic gamma radiation from space. These molecular ions quickly form into ‘cluster ions’, typically smaller than about 1.5 nm. Using our measurements and the published literature, we present evidence to show that cluster ion concentrations in forest areas are consistently higher than outside. Since alpha radiation cannot penetrate more than a few centimetres of soil, radon present deep in the ground cannot directly contribute to the measured cluster ion concentrations. We propose an additional mechanism whereby radon, which is water soluble, is brought up by trees and plants through the uptake of groundwater and released into the atmosphere by transpiration. We estimate that, in a forest comprising eucalyptus trees spaced 4m apart, approximately 28% of the radon in the air may be released by transpiration. Considering that 24% of the earth’s land area is still covered in forests; these findings have potentially important implications for atmospheric aerosol formation and climate.

Spectral–texture feature extraction using statistical moments with application to object-based vegetation species classification

The use of appropriate features to characterise an output class or object is critical for all classification problems. In order to find optimal feature descriptors for vegetation species classification in a power line corridor monitoring application, this article evaluates the capability of several spectral and texture features. A new idea of spectral–texture feature descriptor is proposed by incorporating spectral vegetation indices in statistical moment features. The proposed method is evaluated against several classic texture feature descriptors. Object-based classification method is used and a support vector machine is employed as the benchmark classifier. Individual tree crowns are first detected and segmented from aerial images and different feature vectors are extracted to represent each tree crown. The experimental results showed that the proposed spectral moment features outperform or can at least compare with the state-of-the-art texture descriptors in terms of classification accuracy. A comprehensive quantitative evaluation using receiver operating characteristic space analysis further demonstrates the strength of the proposed feature descriptors.