An input-output-based linkage measurement framework

This study proposed an input-output-based linkage measurement framework, which is a multi-level hierarchy, omni-direction decision-making model and takes the impact of capital into account. This framework had resolved the critical deficiencies and inherent limitations of the existing methods and was used to explore the real estate and construction linkages.

Linear functional observers with guaranteed ε-convergence for discrete time-delay systems with input/output disturbances

The problem of designing linear functional observers for discrete time-delay systems with unknown-but-bounded disturbances in both the plant and the output is considered for the first time in this paper. A novel approach to design a minimum-order observer is proposed to guarantee that the observer error is ϵ-convergent, which means that the estimate converges robustly within an ϵ-bound of the true state. Conditions for the existence of this observer are first derived. Then, by utilising an extended Lyapunov-Krasovskii functional and the free-weighting matrix technique, a sufficient condition for ϵ-convergence of the observer error system is given. This condition is presented in terms of linear matrix inequalities with two parameters needed to be tuned, so that it can be efficiently solved by incorporating a two-dimensional search method into convex optimisation algorithms to obtain the smallest possible value for ϵ. Three numerical examples, including the well-known single-link flexible joint robotic system, are given to illustrate the feasibility and effectiveness of our results.

Measuring output structures of multinational construction industries using the World Input‒Output Database

In the context of international movement towards trade liberalization, increasing technological progress, open competition and social development have impacted deeply on the construction industry in all economies. Using the World Input–Output Database (WIOD), a multinational comparison of the construction industry is estimated from 1995 to 2011 to provide accurate and valid information on the changing patterns of its output structure. The output coefficients for 37 countries and regions are formulated to allow for inter-industry comparisons and to identify the major components of construction output. Changes of output structure are then elaborated over time across countries and regions. The research findings presented in this paper would provide a framework for identifying the output structure of a nation's construction industry and its change trends at an international level, which may help policymakers and enterprises with the formulation of their future development strategies.

Output and labor productivity in organized manufacturing: a panel cointegration analysis for India

One of the policy puzzles faced in India during the last two and half decades has been the weak association between output and labor markets, particularly in the manufacturing sector. In this research, we investigate the long-run relationship between output, labor productivity and real wages in the case of organized manufacturing. We adjust the measure of labor productivity incorporating bottlenecks, such as lack of infrastructure, access to external finance, and labor regulations, which all may influence labor market outcomes. Using panel data from seventeen manufacturing industries, we establish long-run dynamics for the output-labor productivity-real wages series over a period of nearly three decades. We employ recently developed panel unit root and cointegration tests for cross-sectional dependence to incorporate heterogeneity across industries. Long-run elasticities are generally found to be low for labor productivity compared to real wages due to the changes in manufacturing output. There are variations across industries within the manufacturing sector for the effects of the labor market on manufacturing output. In some industries, lower wages are associated with higher output, and the reason for the positive relationship in other industries could be due to workers' bargaining power.

Smartphone applications, malware and data theft

The growing popularity of smartphone devices has led to development of increasing numbers of applications which have subsequently become targets for malicious authors. Analysing applications in order to identify malicious ones is a current major concern in information security; an additional problem connected with smart-phone applications is that their many advertising libraries can lead to loss of personal information. In this paper, we relate the current methods of detecting malware on smartphone devices and discuss the problems caused by malware as well as advertising.

What have sampling and data collection got to do with good qualitative research?

Objective: To highlight the importance of sampling and data collection  processes in qualitative interview studies, and to discuss the contribution of  these processes to determining the strength of the evidence generated and  thereby to decisions for public health practice and policy.

Approach: This discussion is informed by a hierarchy-of-evidence-for-practice  model. The paper provides succinct guidelines for key sampling and data  collection considerations in qualitative research involving interview studies. The  importance of allowing time for immersion in a given community to become  familiar with the context and population is discussed, as well as the practical  constraints that sometimes operate against this stage. The role of theory in  guiding sample selection is discussed both in terms of identifying likely sources  of rich data and in understanding the issues emerging from the data. It is noted  that sampling further assists in confirming the developing evidence and also  illuminates data that does not seem to fit. The importance of reporting sampling  and data collection processes is highlighted clearly to enable others to assess  both the strength of the evidence and the broader applications of the findings.

Conclusion: Sampling and data collection processes are critical to determining  the quality of a study and the generalisability of the findings. We argue that  these processes should operate within the parameters of the research goal, be  guided by emerging theoretical considerations, cover a range of relevant   participant perspectives, and be clearly outlined in research reports with an  explanation of any research limitations.

Agents and data mining: mutual enhancement by integration

This paper tells a story of synergism of two cutting edge technologies — agents and data mining. By integrating these two technologies, the power for each of them is enhanced. Integrating agents into data mining systems, or constructing data mining systems from agent perspectives, the flexibility of data mining systems can be greatly improved. New data mining techniques can add to the systems dynamically in the form of agents, while the out-of-date ones can also be deleted from systems at run-time. Equipping agents with data mining capabilities, the agents are much smarter and more adaptable. In this way, the performance of these agent systems can be improved. A new way to integrate these two techniques –ontology-based integration is also discussed. Case studies will be given to demonstrate such mutual enhancement.

Fuzzy neural networks for control of dynamic systems

This thesis provides a unified and comprehensive treatment of the fuzzy neural networks as the intelligent controllers. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty. Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, direct fuzzy neural controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective. A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, the e-completeness requirement and the use of the fuzzy similarity measure were also investigated. Main emphasis of the thesis has been on the applications to the real-world problems such as the industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the temperature and the number-average molecular weight control in the continuous stirred tank polymerization reactor, and the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed fuzzy neural controller shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior. In addition, the applicability of the proposed method beyond the strictly control area has also been investigated, in particular to the data mining and the knowledge elicitation. When compared to the decision tree method and the pruned neural network method for the data mining, the proposed fuzzy neural network is able to achieve a comparable accuracy with a more compact set of rules. In addition, the performance of the proposed fuzzy neural network is much better for the classes with the low occurrences in the data set compared to the decision tree method. Thus, the proposed fuzzy neural network may be very useful in situations where the important information is contained in a small fraction of the available data.

Intravenous AICAR administration reduces hepatic glucose output and inhibits whole body lipolysis in type 2 diabetic patients

Aims/hypothesis: The 5′-AMP-activated protein kinase (AMPK) pathway is intact in type 2 diabetic patients and is seen as a target for diabetes treatment. In this study, we aimed to assess the impact of the AMPK activator 5-aminoimidazole-4-carboxamide riboside (AICAR) on both glucose and fatty acid metabolism in vivo in type 2 diabetic patients.

Methods: Stable isotope methodology and blood and muscle biopsy sampling were applied to assess blood glucose and fatty acid kinetics following continuous i.v. infusion of AICAR (0.75 mg kg−1 min−1) and/or NaCl (0.9%) in ten male type 2 diabetic patients (age 64 ± 2 years; BMI 28 ± 1 kg/m2).
Results Plasma glucose rate of appearance (R a) was reduced following AICAR administration, while plasma glucose rate of disappearance (R d) was similar in the AICAR and control test. Consequently, blood glucose disposal (R d expressed as a percentage of R a) was increased following AICAR infusion (p < 0.001). Accordingly, a greater decline in plasma glucose concentration was observed following AICAR infusion (p < 0.001). Plasma NEFA R a and R d were both significantly reduced in response to AICAR infusion, and were accompanied by a significant decline in plasma NEFA concentration. Although AMPK phosphorylation in skeletal muscle was not increased, we observed a significant increase in acetyl-CoA carboxylase phosphorylation (p < 0.001).

Conclusions/interpretation: The i.v. administration of AICAR reduces hepatic glucose output, thereby lowering blood glucose concentrations in vivo in type 2 diabetic patients. Furthermore, AICAR administration stimulates hepatic fatty acid oxidation and/or inhibits whole body lipolysis, thereby reducing plasma NEFA concentration.

Management framework for corporate telecommunication and data centre information security

Network security, particularly Internet security, is at the forefront of business and government networks. This research has discovered weaknesses in current professional practice, particularly in mitigation strategies to reduce the impacts of security violations in corporate telecommunications and data centres. The importance of integrating security policies, processes and operational practice is demonstrated. Leadership models and innovation mechanisms best suited to improved security design are also identified.

Subjective wellbeing and families : issues of measurement and data interpretation

Since April 2001 we have been monitoring the Subjective Wellbeing (SWB) of the Australian population using the Personal Wellbeing Index. Our aims are to establish normative values and to identify people with abnormally low SWB. Each of 18 surveys has involved a new sample of 2,000 people, randomly chosen but representing the geographical distribution of the population. The data are remarkable for their stability, with the variation in population mean scores being just 3.2 percentage points. The cause of such high reliability is Subjective Wellbeing Homeostasis. Here, in a manner analogous to the management of body temperature, the SWB for each person is normally held positive and within a narrow set-point range. However, all homeostatic systems have a limited capacity to absorb challenge and when aversive experiences are both strong and sustained, homeostasis fails. If this occurs, people lose their normal positive view of themselves and become depressed. Therefore, the second aim of these studies is to reveal the demographic character of families in distress, who are in need of additional resources. Our data reveal the extent to which family structure and responsibilities impact on wellbeing. They also yield important diagnostic information about individuals, and point to SWB as a crucial measure of intervention outcome. In sum, the Personal Wellbeing Index is a simple, reliable and valid measure of SWB. The measures it yields are theoretically embedded, they can be compared against solid normative data, and their interpretation is enhanced through an understanding of SWB homeostasis.

Animal migration : linking models and data beyond taxonomic limits

An international workshop on animal migration was held at the Lorentz Center in Leiden, The Netherlands, 2–6 March 2009, bringing together leading theoreticians and empiricists from the major migratory taxa, aiming at the identification of cutting-edge questions in migration research that cross taxonomic borders.