Description:
Current design methods propose that reinforcement loads distribution within reinforced soil slope is affected by slope height instead of slope inclination, which is not supported by the results of field and laboratorial tests. This paper addresses the influence of slope height and inclination on the distribution of reinforcement loads within reinforced soil slopes. Based on centrifuge model test results, finite element numerical models of reinforced soil slopes with different slope heights and inclinations were established. Maximum reinforcement load in each layer was calculated when the factor of safety of each model was 1.3. The influence of slope height and inclination on the distribution of reinforcement loads was analyzed by normalizing reinforcement loads and slope heights. The results show that the computed location and shape of failure surface and factor of safety at slope failure are in agreement with the experimental results. The distribution of reinforcement loads is little influenced by slope height, whereas greatly influenced by slope inclination. With the increase of slope inclination, the location of maximum reinforcement load transfers from the mid height to the bottom of slopes.
Description:
Evidence shows the understanding of green marketing and corporate green decision-making in China is still underdeveloped. The purpose of this paper is to investigate the perception, motivation and marketing practices of a large joint-operated cashmere firm in the textile industry. Both Chinese domestic factors and international economic trends have contributed to the rapid restructuring of the Chinese cashmere industry into a highly dynamic, flexible and international openness towards green development. Drawing on the literature on firms’ motivations to go green and characteristics of firms which induce green initiatives, this paper selected a large hybrid mixture of ownership and corporate governance state-owned cashmere producer as basis for a case study. This study illustrates the central role of the top management team as the firm operator within the jointly owned state-owned firm and the influence of firm’s past history and value in their green initiatives. While findings from this case have confirmed some of the literature on green motivation, they also exposed the reform of state-owned firms’ ownership and governance for the firm contributes to more effective management and more efficient operations pursuing cost saving and profit-making, more responsiveness to market demand and in turn to increase the resource utilization efficiency and environmental performance. This research has hence provided new insights and policy implication for a successful transition towards a market economy in China with firms that are both economically strong and also socially and ecologically sustainable.
Description:
Scoring systems such as the Glasgow-Coma scale used to assess consciousness AusDrisk to assess the risk of diabetes, are prevalent in clinical practice. Scoring systems typically include relevant variables with ordinal values where each value is assigned a weight. Weights for selected values are summed and compared to thresholds for health care professionals to rapidly generate a score. Scoring systems are prevalent in clinical practice because they are easy and quick to use. However, most scoring systems comprise many variables and require some time to calculate an final score. Further, expensive population-wide studies are required to validate a scoring system. In this article, we present a new approach for the generation of a scoring system. The approach uses a search procedure invoking iterative decision tree induction to identify a suite of scoring rules, each of which requires values on only two variables. Twelve scoring rules were discovered using the approach, from an Australian screening program for the assessment of Type 2 Diabetes risk. However, classifications from the 12 rules can conflict. In this paper we argue that a simple rule preference relation is sufficient for the resolution of rule conflicts.
Description:
This paper presents a proposed adaptive technique for nonholonomic wheeled mobile robot (NWMR) using the sliding-mode control (SMC) method. The proposed control system based on the backstepping kinematic controller and PI sliding mode dynamic control. With an adaptive fuzzy logic to adjust adaptation gain of SMC for trajectory tracking control of nonholonomic mobile robot. Parametric and nonparametric uncertainties of mobile robot can be solved by using the proposed control which take advantages of stability and robustness in sliding mode control. The adaptation gain of SMC is adjusted by using Mamdani type inference system with adaptive tuning algorithm, which improves the adaptability for uncertainness and eliminate input chattering of the SMC. The stability and convergence of the control system are proved using Lypanouve criteria, and the comparison of the proposed controller with the other controllers ensures the validity and superiority of my own controller
Description:
As a broader effort to build a holistic biopsychosocial health metric, reaction time data obtained from participants undertaking neurocognitive tests have been examined using Exploratory Data Analysis (EDA) for assessing its distribution. Many of the known existing methods assume, that the reaction time data follows a Gaussian distribution and thus commonly use statistical measures such as Analysis of Variance (ANOVA) for analysis. However, it is not mandatory for the reaction time data, to necessarily follow Gaussian distribution and in many instances, it can be better modeled by other representations such as Gamma distribution. Unlike Gaussian distribution which is defined using mean and variance, the Gamma distribution is defined using shape and scale parameters which also considers higher order moments of data such as skewness and kurtosis. Generalized Linear Models (GLM), based on the family exponential distributions such as Gamma distribution, which have been used to model reaction time in other domains, have not been fully explored for modeling reaction time data in psychology domain. While limited use of Gamma distribution have been reported [5, 17, 21], for analyzing response times, their application has been somewhat ad-hoc rather than systematic. For this proposed research, we use a real life biopsychosocial dataset, generated from the 'digital health' intervention programs conducted by the Faculty of Health, Federation University, Australia. The two digital intervention programs were the 'Mindfulness' program and 'Physical Activity' program. The neurocognitive tests were carried out as part of the 'Mindfulness' program. In this paper, we investigate the participants' reaction time distributions in neurocognitive tests such as the Psychology Experiment Building Language (PEBL) Go/No-Go test [19], which is a subset of the larger biopsychosocial data set. PEBL is an open source software system for designing and running psychological experiments. Analysis of participants' reaction time in the PEBL Go/No-Go test, shows that the reaction time data are more compatible with a Gamma distribution and clearly demonstrate that these can be better modeled by Gamma distribution.
Description:
Genome-wide association studies (GWAS) and next-generation sequencing (NGS) has led to an increase in information about the human genome and cardiovascular disease. Understanding the role of genes in cardiac function and pathology requires modeling gene interactions and identification of regulatory genes as part of a gene regulatory network (GRN). Feature selection and data reduction not sufficient and require domain knowledge to deal with large data. We propose three novel innovations in constructing a GRN based on heuristics. A 2D Visualised Co-regulation function. Post-processing to identify gene-gene interactions. Finally a threshold algorithm is applied to identify the hub genes that provide the backbone of the GRN. The 2D Visualized Co-regulation function performed significantly better compared to the Pearson's correlation for measuring pairwise associations (t=3.46, df=5, p=0.018). The F-measure, improved from 0.11 to 0.12. The hub network provided a 60% improvement to that reported in the literature. The performance of the hub network was then also compared against ARACNe and performed significantly better (p=0.024). We conclude that a heuristics approach in developing GRNs has potential to improve our understanding of gene regulation and interaction in diverse biological function and disease.
Description:
The biopsychosocial (BPS) model proposes that health is best understood as a combination of bio-physiological, psychological and social determinants, and thus advocates for a far more comprehensive investigation of the relationships between ‘mind-body’ health. For this holistic analysis, we need a suitable measure to indicate participants’ ‘biological’ stress. With the advent of wearable sensor devices, health monitoring is becoming easier. In this study, we focus on bio-physiological indicators of stress, from wearable devices using the heart-rate data. The analysis of such heart-rate data presents a set of practical challenges. We review various measures currently in use for stress measurement and their relevance and significance with the wearables’ heart-rate data. In this paper, we propose to use the novel ‘peak heart-rate count’ metric to quantify level of ‘biological’ stress. Real life biometric data obtained from digital health intervention program was considered for the study. Our study indicates the significance of using frequency of ‘peak heart-rate count’ as a ‘biological’ stress measure.
Description:
Texture feature is important in describing tire pattern image which provides useful clue in solving crime cases and traffic accidents. In this paper, we propose a novel texture feature extraction method based on HOG (Histogram of Oriented Gradient) and dominant gradient (DG) in tire pattern images, named HOG-DG. The proposed HOG-DG is not only robust to illumination and scale changes but also is rotation-invariant. In the proposed HOG-DG, HOG features are first computed from circular local cells, and HOG features from an image are concatenated and normalized using the DG to construct the HOG-DG feature. HOG-DG is used to train a support-vector-machine (SVM) classifier for tire pattern classification. Experimental results demonstrate its outstanding performance for tire pattern description.
Description:
Kalman filter estimates the desired signal from the amount of measurement related to the extracted signal, which is widely used in engineering due to its simple calculation and easy programming on a computer. However, the basic theory originally proposed by Rudolf E. Kalman is for linear systems only, whereas a realistic physical system is often nonlinear. Extended Kalman Filter (EKF) solves nonlinear filtering problems. In this paper, we focus on issues related with targeted object being occluded We combine EKF and Meanshift to track the moving object. Once the object position is predicted by EKF in the center of the object, then the Meanshift algorithm iterates over the initial value of EKF estimation to track the object. Experiments show that the method reduces the object search time and improves the accuracy of the object tracking.
Description:
Convolutional Neural Networks are highly effective for image classification. However, it is still vulnerable to image distortion. Even a small amount of noise or blur can severely hamper the performance of these CNNs. Most work in the literature strives to mitigate this problem simply by fine-tuning a pre-trained CNN on mutually exclusive or a union set of distorted training data. This iterative fine-tuning process with all known types of distortion is exhaustive and the network struggles to handle unseen distortions. In this work, we propose distortion robust DCT-Net, a Discrete Cosine Transform based module integrated into a deep network which is built on top of VGG16 [1]. Unlike other works in the literature, DCT-Net is "blind" to the distortion type and level in an image both during training and testing. The DCT-Net is trained only once and applied in a more generic situation without further retraining. We also extend the idea of dropout and present a training adaptive version of the same. We evaluate our proposed DCT-Net on a number of benchmark datasets. Our experimental results show that once trained, DCT-Net not only generalizes well to a variety of unseen distortions but also outperforms other comparable networks in the literature.
Description:
Malware authors are known to reuse existing code, this development process results in software evolution and a sequence of versions of a malware family containing functions that show a divergence from the initial version. This paper proposes the term evolved similarity to account for this gradual divergence of similarity across the version history of a malware family. While existing techniques are able to match functions in different versions of malware, these techniques work best when the version changes are relatively small. This paper introduces the concept of evolved similarity and presents automated Evolved Similarity Techniques (EST). EST differs from existing malware function similarity techniques by focusing on the identification of significantly modified functions in adjacent malware versions and may also be used to identify function similarity in malware samples that differ by several versions. The challenge in identifying evolved malware function pairs lies in identifying features that are relatively invariant across evolved code. The research in this paper makes use of the function call graph to establish these features and then demonstrates the use of these techniques using Zeus malware.
Description:
The Family Life Cycle (FLC) model is frequently used for understanding travel patterns and consumer behaviour. The model outlines the stages many people are considered to transition through from singles, to couples, to having children, retiring and then being a solitary survivor. The aim of this research was to consider the relationship between life cycle stages and travel engagement in Australia. In particular, this research aimed to determine whether there were any differences in travel behaviour between Visiting Friends and Relatives (VFR) travellers and nonVFRs across the FLC stages. Based on a sample size of 102,029 domestic visitors, an analysis revealed that VFRs and non-VFRs were significantly different in their FLC composition ( =6851.60, df=20, p<.05). Z tests of column proportions revealed differences across a number of the groups, with the strongest level of association identified for the older non-working single FLC stage (
Description:
This paper offers the design and prototype technology of a tactile sensor, based on the principle of surface texture recognition, capable of monitoring slip in robotic grasping. The sensor has been mounted onto a parallel gripper jaw of a robot. The integrated system of tactile sensor, gripper, robot and the system control, in real life experiments, has proven itself capable of slip detection and adjusting adequate grasping force preventing objects from falling down. Several experiments have been carried out with the newly developed system for grasping a number of various object-samples. Success rate of the system for testing in slip detection and adjusting adequate grasping force in experiments was about 85% in average.
Description:
As a 'new' research discipline, the creative arts challenges ethics understandings within the context of its emergent research methodologies and the interactive and polyvalent nature of knowledge produced this mode of research. In this paper we focus on a current learning and teaching project that attends to ethical know-how in creative practice research in order to address the gaps between institutional research knowhow and the practices of creative practitioners in the world. Graduate creative practice researchers working in the university are required to observe the University's Code of Conduct for Research and adhere to the guidelines provided by the National Statement, however, practicing artists working in the community are not similarly constrained. Once creative practice PhD graduates leave the university, they are no longer required to gain ethics clearance for their work but use their own developed sense of ethics to make 'judgment calls.' Ethical know-how is situated, contextual, and a mainstay of all professional practices in action. In order to address the disjuncture between institutional ethics and compliance, what we call 'know-what,' and the ethical know-how required in the real world by artists, this paper sets out the principles and an approach to developing ethical know-how. Through a case study that adapts real world art practice to the research context of the Academy, this essay demonstrates how institutional know-what can be brought into play with ethical know-how. We propose that 'the hypothetical' enables us to shift perceptions and practice around ethics. This approach raises issues specific to the creative arts disciplines and prepares our graduate researchers to become ethical and innovative practitioners in the real world.