Swarm intelligence-based solver for parameter estimation of laboratory through-diffusion transport of contaminants

Theoretical approaches are of fundamental importance to predict the potential impact of waste disposal facilities on ground water contamination. Appropriate design parameters are generally estimated be fitting theoretical models to data gathered from field monitoring or laboratory experiments. Transient through-diffusion tests are generally conducted in the laboratory to estimate the mass transport parameters of the proposed barrier material. Thes parameters are usually estimated either by approximate eye-fitting calibration or by combining the solution of the direct problem with any available gradient-based techniques. In this work, an automated, gradient-free solver is developed to estimate the mass transport parameters of a transient through-diffusion model. The proposed inverse model uses a particle swarm optimization (PSO) algorithm that is based on the social behavior of animals searching for food sources. The finite difference numerical solution of the forward model is integrated with the PSO algorithm to solve the inverse problem of parameter estimation. The working principle of the new solver is demonstrated and mass transport parameters are estimated from laboratory through-diffusion experimental data. An inverse model based on the standard gradient-based technique is formulated to compare with the proposed solver. A detailed comparative study is carried out between conventional methods and the proposed solver. The present automated technique is found to be very efficient and robust. The mass transport parameters are obtained with great precision.

Symposium on participatory approaches to reservoir fisheries management: issues, challenges and policies. Dambulla, Sri Lanka, 04-06 Oct. 2004. Session I: Community-based fisheries management: experience in other countries

This paper advocates strategies, processes and practices that enable: livelihoods approaches rather than resource-based approaches, ‘direct’ institutional and policy development, rather than ‘project demonstrations’, and support for regional, national and local communications. (Pdf contains 12 pages).

The role of self-monitoring of blood glucose in glucagon-like Peptide-1-based treatment approaches: a European expert recommendation

The role of glucagon-like peptide (GLP)-1-based treatment approaches for type 2 diabetes mellitus (T2DM) is increasing. Although self-monitoring of blood glucose (SMBG) has been performed in numerous studies on GLP-1 analogs and dipeptidyl peptidase-4 inhibitors, the potential role of SMBG in GLP-1-based treatment strategies has not been elaborated. The expert recommendation suggests individualized SMBG strategies in GLP-1-based treatment approaches and suggests simple and clinically applicable SMBG schemes. Potential benefits of SMBG in GLP-1-based treatment approaches are early assessment of treatment success or failure, timely modification of treatment, detection of hypoglycemic episodes, assessment of glucose excursions, and support of diabetes management and diabetes education. Its length and frequency should depend on the clinical setting and the quality of metabolic control. It is considered to play an important role for the optimization of diabetes management in T2DM patients treated with GLP-1-based approaches.

Quantification of network perfusion in ASL cerebral blood flow data with seed based and ICA approaches

Independent component analysis (ICA) or seed based approaches (SBA) in functional magnetic resonance imaging blood oxygenation level dependent (BOLD) data became widely applied tools to identify functionally connected, large scale brain networks. Differences between task conditions as well as specific alterations of the networks in patients as compared to healthy controls were reported. However, BOLD lacks the possibility of quantifying absolute network metabolic activity, which is of particular interest in the case of pathological alterations. In contrast, arterial spin labeling (ASL) techniques allow quantifying absolute cerebral blood flow (CBF) in rest and in task-related conditions. In this study, we explored the ability of identifying networks in ASL data using ICA and to quantify network activity in terms of absolute CBF values. Moreover, we compared the results to SBA and performed a test-retest analysis. Twelve healthy young subjects performed a fingertapping block-design experiment. During the task pseudo-continuous ASL was measured. After CBF quantification the individual datasets were concatenated and subjected to the ICA algorithm. ICA proved capable to identify the somato-motor and the default mode network. Moreover, absolute network CBF within the separate networks during either condition could be quantified. We could demonstrate that using ICA and SBA functional connectivity analysis is feasible and robust in ASL-CBF data. CBF functional connectivity is a novel approach that opens a new strategy to evaluate differences of network activity in terms of absolute network CBF and thus allows quantifying inter-individual differences in the resting state and task-related activations and deactivations.

Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

Assessment of lifetime cumulative sun exposure using a self-administered questionnaire : reliability of two approaches

Few studies have evaluated the reliability of lifetime sun exposure estimated from inquiring about the number of hours people spent outdoors in a given period on a typical weekday or weekend day (the time-based approach). Some investigations have suggested that women have a particularly difficult task in estimating time outdoors in adulthood due to their family and occupational roles. We hypothesized that people might gain additional memory cues and estimate lifetime hours spent outdoors more reliably if asked about time spent outdoors according to specific activities (an activity-based approach). Using self-administered, mailed questionnaires, test-retest responses to time-based and to activity-based approaches were evaluated in 124 volunteer radiologic technologist participants from the United States: 64 females and 60 males 48 to 80 years of age. Intraclass correlation coefficients (ICC) were used to evaluate the test-retest reliability of average number of hours spent outdoors in the summer estimated for each approach. We tested the differences between the two ICCs, corresponding to each approach, using a t test with the variance of the difference estimated by the jackknife method. During childhood and adolescence, the two approaches gave similar ICCs for average numbers of hours spent outdoors in the summer. By contrast, compared with the time-based approach, the activity-based approach showed significantly higher ICCs during adult ages (0.69 versus 0.43, P = 0.003) and over the lifetime (0.69 versus 0.52, P = 0.05); the higher ICCs for the activity-based questionnaire were primarily derived from the results for females. Research is needed to further improve the activity-based questionnaire approach for long-term sun exposure assessment. (Cancer Epidemiol Biomarkers Prev 2009;18(2):464–71)

A Two-stage information filtering based on rough decision rule and pattern mining

Information Overload and Mismatch are two fundamental problems affecting the effectiveness of information filtering systems. Even though both term-based and patternbased approaches have been proposed to address the problems of overload and mismatch, neither of these approaches alone can provide a satisfactory solution to address these problems. This paper presents a novel two-stage information filtering model which combines the merits of term-based and pattern-based approaches to effectively filter sheer volume of information. In particular, the first filtering stage is supported by a novel rough analysis model which efficiently removes a large number of irrelevant documents, thereby addressing the overload problem. The second filtering stage is empowered by a semantically rich pattern taxonomy mining model which effectively fetches incoming documents according to the specific information needs of a user, thereby addressing the mismatch problem. The experimental results based on the RCV1 corpus show that the proposed twostage filtering model significantly outperforms the both termbased and pattern-based information filtering models.

Rough sets based reasoning and pattern mining for a two-stage information filtering system

This paper presents a novel two-stage information filtering model which combines the merits of term-based and pattern- based approaches to effectively filter sheer volume of information. In particular, the first filtering stage is supported by a novel rough analysis model which efficiently removes a large number of irrelevant documents, thereby addressing the overload problem. The second filtering stage is empowered by a semantically rich pattern taxonomy mining model which effectively fetches incoming documents according to the specific information needs of a user, thereby addressing the mismatch problem. The experiments have been conducted to compare the proposed two-stage filtering (T-SM) model with other possible "term-based + pattern-based" or "term-based + term-based" IF models. The results based on the RCV1 corpus show that the T-SM model significantly outperforms other types of "two-stage" IF models.

Optimal budget allocation in budget-based access control

In dynamic and uncertain environments, where the needs of security and information availability are difficult to balance, an access control approach based on a static policy will be suboptimal regardless of how comprehensive it is. Risk-based approaches to access control attempt to address this problem by allocating a limited budget to users, through which they pay for the exceptions deemed necessary. So far the primary focus has been on how to incorporate the notion of budget into access control rather than what or if there is an optimal amount of budget to allocate to users. In this paper we discuss the problems that arise from a sub-optimal allocation of budget and introduce a generalised characterisation of an optimal budget allocation function that maximises organisations expected benefit in the presence of self-interested employees and costly audit.

New perspectives on context-based chemistry education : using a dialectical sociocultural approach to view teaching and learning

Context-based chemistry education aims to improve student interest and motivation in chemistry by connecting canonical chemistry concepts with real-world contexts. Implementation of context-based chemistry programmes began 20 years ago in an attempt to make the learning of chemistry meaningful for students. This paper reviews such programmes through empirical studies on six international courses, ChemCom (USA), Salters (UK), Industrial Science (Israel), Chemie im Kontext (Germany), Chemistry in Practice (The Netherlands) and PLON (The Netherlands). These studies are categorised through emergent characteristics of: relevance, interest/attitudes motivation and deeper understanding. These characteristics can be found to an extent in a number of other curricular initiatives, such as science-technology-society approaches and problem-based learning or project based science, the latter of which often incorporates an inquiry-based approach to science education. These initiatives in science education are also considered with a focus on the characteristics of these approaches that are emphasised in context-based education. While such curricular studies provide a starting point for discussing context-based approaches in chemistry, to advance our understanding of how students connect canonical science concepts with the real-world context, a new theoretical framework is required. A dialectical sociocultural framework originating in the work of Vygotsky is used as a referent for analysing the complex human interactions that occur in context-based classrooms, providing teachers with recent information about the pedagogical structures and resources that afford students the agency to learn.

Empirical evaluation of alternative approaches in identifying crash hot spots

This study proposes a framework of a model-based hot spot identification method by applying full Bayes (FB) technique. In comparison with the state-of-the-art approach [i.e., empirical Bayes method (EB)], the advantage of the FB method is the capability to seamlessly integrate prior information and all available data into posterior distributions on which various ranking criteria could be based. With intersection crash data collected in Singapore, an empirical analysis was conducted to evaluate the following six approaches for hot spot identification: (a) naive ranking using raw crash data, (b) standard EB ranking, (c) FB ranking using a Poisson-gamma model, (d) FB ranking using a Poisson-lognormal model, (e) FB ranking using a hierarchical Poisson model, and (f) FB ranking using a hierarchical Poisson (AR-1) model. The results show that (a) when using the expected crash rate-related decision parameters, all model-based approaches perform significantly better in safety ranking than does the naive ranking method, and (b) the FB approach using hierarchical models significantly outperforms the standard EB approach in correctly identifying hazardous sites.

Neural network-based detection of virtual environment anomalies

The increasingly widespread use of large-scale 3D virtual environments has translated into an increasing effort required from designers, developers and testers. While considerable research has been conducted into assisting the design of virtual world content and mechanics, to date, only limited contributions have been made regarding the automatic testing of the underpinning graphics software and hardware. In the work presented in this paper, two novel neural network-based approaches are presented to predict the correct visualization of 3D content. Multilayer perceptrons and self-organizing maps are trained to learn the normal geometric and color appearance of objects from validated frames and then used to detect novel or anomalous renderings in new images. Our approach is general, for the appearance of the object is learned rather than explicitly represented. Experiments were conducted on a game engine to determine the applicability and effectiveness of our algorithms. The results show that the neural network technology can be effectively used to address the problem of automatic and reliable visual testing of 3D virtual environments.

Risk-based approach to the management of on-site wastewater treatment facilities

Background On-site wastewater treatment system (OWTS) siting, design and management has traditionally been based on site specific conditions with little regard to the surrounding environment or the cumulative effect of other systems in the environment. The general approach has been to apply the same framework of standards and regulations to all sites equally, regardless of the sensitivity, or lack thereof, to the receiving environment. Consequently, this has led to the continuing poor performance and failure of on-site systems, resulting in environmental and public health consequences. As a result, there is increasing realisation that more scientifically robust evaluations in regard to site assessment and the underlying ground conditions are needed. Risk-based approaches to on-site system siting, design and management are considered the most appropriate means of improvement to the current standards and codes for on-site wastewater treatment systems. The Project Research in relation to this project was undertaken within the Gold Coast City Council region, the major focus being the semi-urban, rural residential and hinterland areas of the city that are not serviced by centralised treatment systems. The Gold Coast has over 15,000 on-site systems in use, with approximately 66% being common septic tank-subsurface dispersal systems. A recent study evaluating the performance of these systems within the Gold Coast area showed approximately 90% were not meeting the specified guidelines for effluent treatment and dispersal. The main focus of this research was to incorporate strong scientific knowledge into an integrated risk assessment process to allow suitable management practices to be set in place to mitigate the inherent risks. To achieve this, research was undertaken focusing on three main aspects involved with the performance and management of OWTS. Firstly, an investigation into the suitability of soil for providing appropriate effluent renovation was conducted. This involved detailed soil investigations, laboratory analysis and the use of multivariate statistical methods for analysing soil information. The outcomes of these investigations were developed into a framework for assessing soil suitability for effluent renovation. This formed the basis for the assessment of OWTS siting and design risks employed in the developed risk framework. Secondly, an assessment of the environmental and public health risks was performed specifically related the release of contaminants from OWTS. This involved detailed groundwater and surface water sampling and analysis to assess the current and potential risks of contamination throughout the Gold Coast region. Additionally, the assessment of public health risk incorporated the use of bacterial source tracking methods to identify the different sources of fecal contamination within monitored regions. Antibiotic resistance pattern analysis was utilised to determine the extent of human faecal contamination, with the outcomes utilised for providing a more indicative public health assessment. Finally, the outcomes of both the soil suitability assessment and ground and surface water monitoring was utilised for the development of the integrated risk framework. The research outcomes achieved through this project enabled the primary research aims and objects to be accomplished. This in turn would enable Gold Coast City Council to provide more appropriate assessment and management guidelines based on robust scientific knowledge which will ultimately ensure that the potential environmental and public health impacts resulting from on-site wastewater treatment is minimised. As part of the implementation of suitable management strategies, a critical point monitoring program (CPM) was formulated. This entailed the identification of the key critical parameters that contribute to the characterised risks at monitored locations within the study area. The CPM will allow more direct procedures to be implemented, targeting the specific hazards at sensitive areas throughout Gold Coast region.

A hardware virtualization-based component sandboxing architecture

Modern applications comprise multiple components, such as browser plug-ins, often of unknown provenance and quality. Statistics show that failure of such components accounts for a high percentage of software faults. Enabling isolation of such fine-grained components is therefore necessary to increase the robustness and resilience of security-critical and safety-critical computer systems. In this paper, we evaluate whether such fine-grained components can be sandboxed through the use of the hardware virtualization support available in modern Intel and AMD processors. We compare the performance and functionality of such an approach to two previous software based approaches. The results demonstrate that hardware isolation minimizes the difficulties encountered with software based approaches, while also reducing the size of the trusted computing base, thus increasing confidence in the solution's correctness. We also show that our relatively simple implementation has equivalent run-time performance, with overheads of less than 34%, does not require custom tool chains and provides enhanced functionality over software-only approaches, confirming that hardware virtualization technology is a viable mechanism for fine-grained component isolation.

Graph-based concept weighting for medical information retrieval

This paper presents a graph-based method to weight medical concepts in documents for the purposes of information retrieval. Medical concepts are extracted from free-text documents using a state-of-the-art technique that maps n-grams to concepts from the SNOMED CT medical ontology. In our graph-based concept representation, concepts are vertices in a graph built from a document, edges represent associations between concepts. This representation naturally captures dependencies between concepts, an important requirement for interpreting medical text, and a feature lacking in bag-of-words representations. We apply existing graph-based term weighting methods to weight medical concepts. Using concepts rather than terms addresses vocabulary mismatch as well as encapsulates terms belonging to a single medical entity into a single concept. In addition, we further extend previous graph-based approaches by injecting domain knowledge that estimates the importance of a concept within the global medical domain. Retrieval experiments on the TREC Medical Records collection show our method outperforms both term and concept baselines. More generally, this work provides a means of integrating background knowledge contained in medical ontologies into data-driven information retrieval approaches.