Benefit cost analysis applied to behavioral and engineering safety countermeasures in San Francisco, California

The state of the practice in safety has advanced rapidly in recent years with the emergence of new tools and processes for improving selection of the most cost-effective safety countermeasures. However, many challenges prevent fair and objective comparisons of countermeasures applied across safety disciplines (e.g. engineering, emergency services, and behavioral measures). These countermeasures operate at different spatial scales, are funded often by different financial sources and agencies, and have associated costs and benefits that are difficult to estimate. This research proposes a methodology by which both behavioral and engineering safety investments are considered and compared in a specific local context. The methodology involves a multi-stage process that enables the analyst to select countermeasures that yield high benefits to costs, are targeted for a particular project, and that may involve costs and benefits that accrue over varying spatial and temporal scales. The methodology is illustrated using a case study from the Geary Boulevard Corridor in San Francisco, California. The case study illustrates that: 1) The methodology enables the identification and assessment of a wide range of safety investment types at the project level; 2) The nature of crash histories lend themselves to the selection of both behavioral and engineering investments, requiring cooperation across agencies; and 3) The results of the cost-benefit analysis are highly sensitive to cost and benefit assumptions, and thus listing and justification of all assumptions is required. It is recommended that a sensitivity analyses be conducted when there is large uncertainty surrounding cost and benefit assumptions.

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.

Developing a framework for work integrated research higher degree studies in an Australian engineering context

The umbrella of Australian research higher degree (RHD) offerings has broadened from the traditional MPhil/PhD programmes to include a range of professional masters and doctoral degrees. This article reports on the experiences of three PhD students, engaged in an informally managed industry partnered research programme, described in this article as the work integrated research higher degree (WIRHD). Their learning process shares the attributes from both the traditional PhD programme and professional doctorates. However, because of the blended nature of the learning contexts, candidates engaged in the WIRHD programme must address a wider range of issues than those following the traditional RHD pathway. An exploratory case study approach was adopted with the view to develop an integrative framework to explain the various contexts that influence the learning experience of WIRHD candidates, as well as a structured approach to guide this contemporary form of industry partnered WIRHD process.

Adopting relevance feature to learn personalized ontologies

Relevance feature and ontology are two core components to learn personalized ontologies for concept-based retrievals. However, how to associate user native information with common knowledge is an urgent issue. This paper proposes a sound solution by matching relevance feature mined from local instances with concepts existing in a global knowledge base. The matched concepts and their relations are used to learn personalized ontologies. The proposed method is evaluated elaborately by comparing it against three benchmark models. The evaluation demonstrates the matching is successful by achieving remarkable improvements in information filtering measurements.

Providing query support to leverage BIM for construction

Building information modeling (BIM) is an emerging technology and process that provides rich and intelligent design information models of a facility, enabling enhanced communication, coordination, analysis, and quality control throughout all phases of a building project. Although there are many documented benefits of BIM for construction, identifying essential construction-specific information out of a BIM in an efficient and meaningful way is still a challenging task. This paper presents a framework that combines feature-based modeling and query processing to leverage BIM for construction. The feature-based modeling representation implemented enriches a BIM by representing construction-specific design features relevant to different construction management (CM) functions. The query processing implemented allows for increased flexibility to specify queries and rapidly generate the desired view from a given BIM according to the varied requirements of a specific practitioner or domain. Central to the framework is the formalization of construction domain knowledge in the form of a feature ontology and query specifications. The implementation of our framework enables the automatic extraction and querying of a wide-range of design conditions that are relevant to construction practitioners. The validation studies conducted demonstrate that our approach is significantly more effective than existing solutions. The research described in this paper has the potential to improve the efficiency and effectiveness of decision-making processes in different CM functions.

Semantic labelling for document feature patterns using ontological subjects

Finding and labelling semantic features patterns of documents in a large, spatial corpus is a challenging problem. Text documents have characteristics that make semantic labelling difficult; the rapidly increasing volume of online documents makes a bottleneck in finding meaningful textual patterns. Aiming to deal with these issues, we propose an unsupervised documnent labelling approach based on semantic content and feature patterns. A world ontology with extensive topic coverage is exploited to supply controlled, structured subjects for labelling. An algorithm is also introduced to reduce dimensionality based on the study of ontological structure. The proposed approach was promisingly evaluated by compared with typical machine learning methods including SVMs, Rocchio, and kNN.

Matching relevance features with ontological concepts

In order to comprehend user information needs by concepts, this paper introduces a novel method to match relevance features with ontological concepts. The method first discovers relevance features from user local instances. Then, a concept matching approach is developed for matching these features to accurate concepts in a global knowledge base. This approach is significant for the transition of informative descriptor and conceptional descriptor. The proposed method is elaborately evaluated by comparing against three information gathering baseline models. The experimental results shows the matching approach is successful and achieves a series of remarkable improvements on search effectiveness.

Reasoning about component similarity in building product models from the construction perspective

Identifying the design features that impact construction is essential to developing cost effective and constructible designs. The similarity of building components is a critical design feature that affects method selection, productivity, and ultimately construction cost and schedule performance. However, there is limited understanding of what constitutes similarity in the design of building components and limited computer-based support to identify this feature in a building product model. This paper contributes a feature-based framework for representing and reasoning about component similarity that builds on ontological modelling, model-based reasoning and cluster analysis techniques. It describes the ontology we developed to characterize component similarity in terms of the component attributes, the direction, and the degree of variation. It also describes the generic reasoning process we formalized to identify component similarity in a standard product model based on practitioners' varied preferences. The generic reasoning process evaluates the geometric, topological, and symbolic similarities between components, creates groupings of similar components, and quantifies the degree of similarity. We implemented this reasoning process in a prototype cost estimating application, which creates and maintains cost estimates based on a building product model. Validation studies of the prototype system provide evidence that the framework is general and enables a more accurate and efficient cost estimating process.

Deriving construction features from an IFC model

In recent years, there has been a growing interest from the design and construction community to adopt Building Information Models (BIM). BIM provides semantically-rich information models that explicitly represent both 3D geometric information (e.g., component dimensions), along with non-geometric properties (e.g., material properties). While the richness of design information offered by BIM is evident, there are still tremendous challenges in getting construction-specific information out of BIM, limiting the usability of these models for construction. In this paper, we describe our approach for extracting construction-specific design conditions from a BIM model based on user-defined queries. This approach leverages an ontology of features we are developing to formalize the design conditions that affect construction. Our current implementation analyzes the component geometry and topological relationships between components in a BIM model represented using the Industry Foundation Classes (IFC) to identify construction features. We describe the reasoning process implemented to extract these construction features, and provide a critique of the IFC’s to support the querying process. We use examples from two case studies to illustrate the construction features, the querying process, and the challenges involved in deriving construction features from an IFC model.

Teams rather than individuals : collaborative intrusion detection

We propose CIMD (Collaborative Intrusion and Malware Detection), a scheme for the realization of collaborative intrusion detection approaches. We argue that teams, respectively detection groups with a common purpose for intrusion detection and response, improve the measures against malware. CIMD provides a collaboration model, a decentralized group formation and an anonymous communication scheme. Participating agents can convey intrusion detection related objectives and associated interests for collaboration partners. These interests are based on intrusion objectives and associated interests for collaboration partners. These interests are based on intrusion detection related ontology, incorporating network and hardware configurations and detection capabilities. Anonymous Communication provided by CIMD allows communication beyond suspicion, i.e. the adversary can not perform better than guessing an IDS to be the source of a message at random. The evaluation takes place with the help of NeSSi² (www.nessi2.de), the Network Security Simulator, a dedicated environment for analysis of attacks and countermeasures in mid-scale and large-scale networks. A CIMD prototype is being built based on the JIAC agent framework(www.jiac.de).

An ensemble method for predicting subnuclear localizations from primary protein structures

Background Predicting protein subnuclear localization is a challenging problem. Some previous works based on non-sequence information including Gene Ontology annotations and kernel fusion have respective limitations. The aim of this work is twofold: one is to propose a novel individual feature extraction method; another is to develop an ensemble method to improve prediction performance using comprehensive information represented in the form of high dimensional feature vector obtained by 11 feature extraction methods. Methodology/Principal Findings A novel two-stage multiclass support vector machine is proposed to predict protein subnuclear localizations. It only considers those feature extraction methods based on amino acid classifications and physicochemical properties. In order to speed up our system, an automatic search method for the kernel parameter is used. The prediction performance of our method is evaluated on four datasets: Lei dataset, multi-localization dataset, SNL9 dataset and a new independent dataset. The overall accuracy of prediction for 6 localizations on Lei dataset is 75.2% and that for 9 localizations on SNL9 dataset is 72.1% in the leave-one-out cross validation, 71.7% for the multi-localization dataset and 69.8% for the new independent dataset, respectively. Comparisons with those existing methods show that our method performs better for both single-localization and multi-localization proteins and achieves more balanced sensitivities and specificities on large-size and small-size subcellular localizations. The overall accuracy improvements are 4.0% and 4.7% for single-localization proteins and 6.5% for multi-localization proteins. The reliability and stability of our classification model are further confirmed by permutation analysis. Conclusions It can be concluded that our method is effective and valuable for predicting protein subnuclear localizations. A web server has been designed to implement the proposed method. It is freely available at http://bioinformatics.awowshop.com/snlpr​ed_page.php.

Higher degree research at Australian Universities : Responding to diversity in engineering and information technology

BACKGROUND There is increasing enrolment of international students in the Engineering and Information Technology disciplines and anecdotal evidence of a need for additional understanding and support for these students and their supervisors due to differences both in academic and social cultures. While there is a growing literature on supervisory styles and guidelines on effective supervision, there is little on discipline-specific, cross-cultural supervision responding to the growing diversity. In this paper, we report findings from a study of Engineering and Information technology Higher Degree Research (HDR)students and supervision in three Australian universities. PURPOSE The aim was to assess perceptions of students and supervisors of factors influencing success that are particular to international or culturally and linguistically diverse (CaLD) HDR students in Engineering and Information technology. DESIGN/METHOD Online survey and qualitative data was collected from international and CaLD HDR students and supervisors at the three universities. Bayesian network analysis, inferential statistics, and qualitative analysis provided the main findings. RESULTS Survey results indicate that both students and supervisors are positive about their experiences, and do not see language or culture as particularly problematic. The survey results also reveal strong consistency between the perceptions of students and supervisors on most factors influencing success. Qualitative analysis of critical supervision incidents has provided rich data that could help improve support services. CONCLUSIONS In contrast with anecdotal evidence, HDR completion data from the three universities reveal that international students, on average, complete in shorter time periods than domestic students. The analysis suggests that success is linked to a complex set of factors involving the student, supervision, the institution and broader community.

Preparing nanocomposite fibrous scaffolds of P3HB/nHA for bone tissue engineering

Nanocomposites are recently known to be among the most successful materials in biomedical applications. In this work we sought to fabricate fibrous scaffolds which can mimic the extra cellular matrix of cartilaginous connective tissue not only to a structural extent but with a mechanical and biological analogy. Poly(3-hydroxybutyrate) (P3HB) matrices were reinforced with 5, 10 and 15 %wt hydroxyapatite (HA) nanoparticles and electrospun into nanocomposite fibrous scaffolds. Mechanical properties of each case were compared with that of a P3HB scaffold produced in the same processing condition. Spectroscopic and morphological observations were used for detecting the interaction quality between the constituents. Nanoparticles rested deep within the fibers of 1 μm in diameter. Chemical interactions of hydrogen bonds linked the constituents through the interface. Maximum elastic modulus and mechanical strength was obtained with the presence of 5%wt hydroxyapatite nanoparticles. Above 10%wt, nanoparticles tended to agglomerate and caused the entity to lose its mechanical performance; however, viscoelasticity interfered at this concentration and lead to a delayed failure. In other words, higher elongation at break and a massive work of rupture was observed at 10%wt.

Service management and engineering in information systems research

Service research in information systems (IS) has received attention over many years (e.g. Kettinger and Lee, 1994), but more recently has increased substantially in both diversity and volume (Rai and Sambamurthy, 2006). A service-oriented view of information technology (IT) is gradually taking hold in both academia and industry. This is concomitant with the growth of service-related phenomena and concepts (Lusch and Vargo, 2006), stimulating a global discourse about 'service science' as a new, cross-disciplinary field of research (Chesbrough and Spohrer, 2006).

Qualitative methods in information systems research

Qualitative research methods are widely accepted in Information Systems and multiple approaches have been successfully used in IS qualitative studies over the years. These approaches include narrative analysis, discourse analysis, grounded theory, case study, ethnography and phenomenological analysis. Guided by critical, interpretive and positivist epistemologies (Myers 1997), qualitative methods are continuously growing in importance in our research community. In this special issue, we adopt Van Maanen's (1979: 520) definition of qualitative research as an umbrella term to cover an “array of interpretive techniques that can describe, decode, translate, and otherwise come to terms with the meaning, not the frequency, of certain more or less naturally occurring phenomena in the social world”. In the call for papers, we stated that the aim of the special issue was to provide a forum within which we can present and debate the significant number of issues, results and questions arising from the pluralistic approach to qualitative research in Information Systems. We recognise both the potential and the challenges that qualitative approaches offers for accessing the different layers and dimensions of a complex and constructed social reality (Orlikowski, 1993). The special issue is also a response to the need to showcase the current state of the art in IS qualitative research and highlight advances and issues encountered in the process of continuous learning that includes questions about its ontology, epistemological tenets, theoretical contributions and practical applications.