Fault-tolerant dynamic job scheduling policy

In this paper, we propose a scalable and fault-tolerant job scheduling framework for grid computing. The proposed framework loosely couples a dynamic job scheduling approach with the hybrid replications approach to schedule jobs efficiently while at the same time providing fault-tolerance. The novelty of the proposed framework is that it uses passive replication approach under high system load and active replication approach under low system loads. The switch between these two replication methods is also done dynamically and transparently.

Dynamic simulation of collisions of heavy high-speed trucks with concrete barriers

Real vehicle collision experiments on full-scale road safety barriers are important to determine the outcome of a vehicle versus barrier impact accident. However, such experiments require large investment of time and money. Numerical simulation has therefore been imperative as an alternative method for testing concrete barriers. In this research, spring subgrade models were first developed to simulate the ground boundary of concrete barriers. Both heavy trucks and concrete barriers were modeled using finite element methods (FEM) to simulate dynamic collision performances. Comparison of the results generated from computer simulations and on-site full-scale experiments demonstrated that the developed models could be applied to simulate the collision of heavy trucks with concrete barriers to provide the data to design new road safety barriers and analyze existing ones.

An authorization policy management framework for dynamic medical data sharing

In this paper, we propose a novel feature reduction approach to group words hierarchically into clusters which can then be used as new features for document classification. Initially, each word constitutes a cluster. We calculate the mutual confidence between any two different words. The pair of clusters containing the two words with the highest mutual confidence are combined into a new cluster. This process of merging is iterated until all the mutual confidences between the un-processed pair of words are smaller than a predefined threshold or only one cluster exists. In this way, a hierarchy of word clusters is obtained. The user can decide the clusters, from a certain level, to be used as new features for document classification. Experimental results have shown that our method can perform better than other methods.

Dynamic pacing strategies during the cycle phase of an ironman triathlon

Introduction: A nonlinear dynamic systems model has previously been proposed to explain pacing strategies employed during exercise.

Purpose: This study was conducted to examine the pacing strategies used under varying conditions during the cycle phase of an Ironman triathlon.

Methods: The bicycles of six well-trained male triathletes were equipped with SRM power meters set to record power output, cadence, speed, and heart rate. The flat, three-lap, out-and-back cycle course, coupled with relatively consistent wind conditions (17-30 km·h-1), enabled comparisons to be made between three consecutive 60-km laps and relative wind direction (headwind vs tailwind).

Results: Participants finished the cycle phase (180 km) with consistently fast performance times (5 h, 11 ± 2 min; top 10% of all finishers). Average power output (239 ± 25 to 203 ± 20 W), cadence (89 ± 6 to 82 ± 8 rpm), and speed (36.5 ± 0.8 to 33.1 ± 0.8 km·h-1) all significantly decreased with increasing number of laps (P < 0.05). These variables, however, were not significantly different between headwind and tailwind sections. The deviation (SD) in power output and cadence did not change with increasing number of laps; however, the deviations in torque (6.8 ± 1.6 and 5.8 ± 1.3 N·m) and speed (2.1 ± 0.5 and 1.6 ± 0.3 km·h-1) were significantly greater under headwind compared with tailwind conditions, respectively. The median power frequency tended to be lower in headwind (0.0480 ± 0.0083) compared with tailwind (0.0531 ± 0.0101) sections.

Conclusion: These data show evidence that a nonlinear dynamic pacing strategy is used by well-trained triathletes throughout various segments and conditions of the Ironman cycle phase. Moreover, an increased variation in torque and speed was found in the headwind versus the tailwind condition.

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.

Dynamic due diligence : an entrepreneurial business planning method for evaluating mergers and acquisitions: theory, case application and general implications

This paper presents a method for conducting dynamic due diligence to evaluate Mergers and Acquisitions; demonstrates its effectiveness in a particular case; and extrapolates its theoretical and practical implications to the general case. It may be called the ‘ECIPP’ method - an acronym for: Establishing mandates; Creating projections; Identifying issues; Prioritizing procedures and Performing them.

Two established alternative due diligence methods are examined. The prevailing finance-theory-based procedure has the virtues of simplicity and elegance; the vice is abstraction. The prevailing practitioner-based regime has the virtues of thoroughness and concreteness but the vices of rigidity and inefficiency. Resolving the tradeoffs inherent in both static prescriptions provides an opportunity for a dynamic, innovative approach derived from grounded theory and an application of Hindle’s (1993) theory of venture renaissance through application of an enhanced paradigm of Entrepreneurial Business Planning. The ECIPP method retains simplicity, concreteness and thoroughness but eliminates abstraction, rigidity and inefficiency.

This is demonstrated in a case. ChildCo’s CEO had only one month to complete his M&A evaluation; no expertise or previous experience; severely limited budget for the exercise and had been flatly informed by prevailing M&A experts that what he wanted could not be done. Using the ECIPP method, the CEO and the author did it: on time, within budget and to the satisfaction of a previously skeptical board of one of the world’s largest multi-national companies including arguably the world’s most professional corporate M&A division.

The replicability logic of the case research permits two generalisations. (1) ECIPP extends the range and utility of Entrepreneurial Business Planning as a management technology, well beyond the constraints to which it is usually confined. (2) The ECIPP method of dynamic due diligence is an innovation worthy of mature consideration and further investigation by theorists and practitioners in the M&A field, in the disciplines of both Finance and Entrepreneurship and, well beyond, in the realms of general management theory, methodology and practice.

Data-based dynamic haptic interaction model with deformable 3D objects

The data-based modeling of the haptic interaction simulation is a growing trend in research. These techniques offer a quick alternative to parametric modeling of the simulation. So far, most of the use of the data-based techniques was applied to static simulations. This paper introduces how to use data-based model in dynamic simulations. This ensures realistic behavior and produce results that are very close to parametric modeling. The results show that a quick and accurate response can be achieved using the proposed methods.

Dynamic privacy assessment in a smart house environment using multimodal sensing

Surveillance applications in private environments such as smart houses require a privacy management policy if such systems are to be accepted by the occupants of the environment. This is due to the invasive nature of surveillance, and the private nature of the home. In this article, we propose a framework for dynamically altering the privacy policy applied to the monitoring of a smart house based on the situation within the environment. Initially the situation, or context, within the environment is determined; we identify several factors for determining environmental context, and propose methods to quantify the context using audio and binary sensor data. The context is then mapped to an appropriate privacy policy, which is implemented by applying data hiding techniques to control access to data gathered from various information sources. The significance of this work lies in the examination of privacy issues related to assisted-living smart house environments. A single privacy policy in such applications would be either too restrictive for an observer, for example, a carer, or too invasive for the occupants. We address this by proposing a dynamic method, with the aim of decreasing the invasiveness of the technology, while retaining the purpose of the system.

Dynamic threshold determination by local and global edge evaluation

In this paper we consider two methods for automatically determining values for thresholding edge maps. In contrast to most other related work they are based on the figural rather than statistical properties of the edges. The first approach applies a local edge evaluation measure based on edge continuity and edge thinness to determine the threshold on edge magnitude. The second approach is more global and considers complete connected edge curves. The curves are mapped onto an edge curve length/average magnitude feature space, and a robust technique is developed to partition this feature space into true and false edge regions. A quantitative assessment of the results on synthetic data shows that the global method performs better than the local method. Furthermore, a qualitative assessment of its application to a variety of real images shows that it reliably produces good results.

On the incremental learning and recognition of the pattern of movement of multiple labelled objects in dynamic scenes

In this paper we discuss combining incremental learning and incremental recognition to classify patterns consisting of multiple objects, each represented by multiple spatio-temporal features. Importantly the technique allows for ambiguity in terms of the positions of the start and finish of the pattern. This involves a progressive classification which considers the data at each time instance in the query and thus provides a probable answer before all the query information becomes available. We present two methods that combine incremental learning and incremental recognition: a time instance method and an overall best match method.

Dynamic threshold determination by local and global edge evaluation

In this paper we consider two methods for automatically determining values for thresholding edge maps. Rather than use statistical methods they are based on the figural properties of the edges. Two approaches are taken. We investigate applying an edge evaluation measure based on edge continuity and edge thinness to determine the threshold on edge strength. However, the technique is not valid when applied to edge detector outputs that are one-pixel wide. In this case, we use a measure based on work by Lowe for assessing edges. This measure is based on length and average strength of complete linked edge lists.

Static and dynamic tests for damage identification of a slab-girder structure

Slab-girder bridges are widely used in Australia. The shear connection between reinforced concrete slab and steel girder plays an important role in composite action. In order to test the suitability and efficiency of various vibration-based damage identification methods to assess the integrity of the structure, a scaled composite bridge model was constructed in the laboratory. Some removable shear connectors were specially designed and fabricated to link the beam and slab that were cast separately. In this test, two static loads were acted in the 1/3 points of the structure. In the first stage, dynamic test was conducted under different damage scenarios, where a number of shear connectors were removed step by step. In the second stage, the static load is increased gradually until concrete slab cracked. Static tests were conducted continuously to monitor the deflection and loading on the beam. Dynamic test was carried out before and after concrete cracking. Both static and dynamic results can be used to identify damage in the structure.

Prospective randomized controlled trial comparing dynamic hip screw and screw fixation for undisplaced subcapital hip fractures

Background
Neck of femur fractures (NOFFs) are a common cause of morbidity and mortality in our community. Minimally displaced intracapsular fractures are treated with internal fixation by a two-hole dynamic hip screw (DHS) or three partially threaded cancellous screws. Data to support the superiority of one are limited. This prospective randomized controlled trial compares outcomes with these two fixation methods.

Methods
We prospectively recruited patients over 50 years, with an acute fracture subcapital NOFF, who walked and lived independently, and were cognitively intact. They were randomized into DHS or cancellous screw groups and followed up for 2 years (overall 75.9%). Outcomes of mortality, revision, loss of fixation, avascular necrosis, surgical complications, WOMAC, Harris hip score and SF-12 were measured.

Results
We recruited 62 patients (31 DHS, 29 cancellous screws, 2 failed consent). Six deaths (19.3%) were seen in each group. A total of 3.2% of DHS (1 out of 31) and 10.3% (3 out of 29) of cancellous screw patients required re-operation (P = 0.272). There was no statistical significant difference in patient satisfaction, quality of life (QoL), radiological union or osteonecrosis. There are trends towards better functional scores and QoL in cancellous screws, particularly at 1 year (P = 0.0061), but with a higher re-operation rate. There was a combined mortality and transition to institutional care of 40.0% (24 out of 60) at 2 years.

Conclusions
This study found no difference in outcomes between DHS and cancellous screws in the treatment of subcapital NOFFs in a fit, independent population, but we found a high level of physical decline in previously fit, independently ambulating patients. A large, multicentre trial will be required to differentiate between these two fixation methods.

Limited population structure, genetic drift and bottlenecks characterise an endangered bird species in a dynamic, fire-prone ecosystem

Fire is a major disturbance process in many ecosystems world-wide, resulting in spatially and temporally dynamic landscapes. For populations occupying such environments, fire-induced landscape change is likely to influence population processes, and genetic patterns and structure among populations. The Mallee Emu-wren Stipiturus mallee is an endangered passerine whose global distribution is confined to fire-prone, semi-arid mallee shrublands in south-eastern Australia. This species, with poor capacity for dispersal, has undergone a precipitous reduction in distribution and numbers in recent decades. We used genetic analyses of 11 length-variable, nuclear loci to examine population structure and processes within this species, across its global range. Populations of the Mallee Emu-wren exhibited a low to moderate level of genetic diversity, and evidence of bottlenecks and genetic drift. Bayesian clustering methods revealed weak genetic population structure across the species' range. The direct effects of large fires, together with associated changes in the spatial and temporal patterns of suitable habitat, have the potential to cause population bottlenecks, serial local extinctions and subsequent recolonisation, all of which may interact to erode and homogenise genetic diversity in this species. Movement among temporally and spatially shifting habitat, appears to maintain long-term genetic connectivity. A plausible explanation for the observed genetic patterns is that, following extensive fires, recolonisation exceeds in-situ survival as the primary driver of population recovery in this species. These findings suggest that dynamic, fire-dominated landscapes can drive genetic homogenisation of populations of species with low-mobility and specialised habitat that otherwise would be expected to show strongly structured populations. Such effects must be considered when formulating management actions to conserve species in fire-prone systems.

Methods and technologies to promote information-centered knowledge construction

This alternative event for the 2013 iConference is a combination of lightning talks, a demonstration of an assessment technology for knowledge construction in complex domains, and a hands-on exercise in using the tools discussed. The unifying logic for this presentation is that meaningful learning often involves solving challenging and complex problems that allow for multiple solution approaches and a variety of acceptable solutions. While it is important to prepare students to solve such problems, it is difficult to determine the extent to which various interventions and programs are contributing to the development of appropriate problem-solving strategies and attitudes. Simply testing domain knowledge or the ability to solve simple, single-solution problems may not provide support for improving individual student ability or relevant programs and activities. A reliable and robust methodology for assessing the relevant knowledge constructions of students engaged in solving challenging problems is needed, and that is our focus.