Experimental studies of lipped channel beams subject to web crippling under ETF and ITF load cases

Lipped channel beams (LCBs) are commonly used as floor joists and bearers in buildings. However, they are subjected to specific failure modes such as web crippling. Despite considerable web crippling research, recent studies [1-6] have shown that the current web crippling design rules are unable to predict the test capacities under ETF and ITF load cases. In many instances, the predictions by the available design standards such as AISI S100, AS/NZS 4600 and Eurocode 3 Part 1-3 [7-9] are inconsistent. Hence thirty-six tests were conducted to assess the web crippling behaviour and strengths of LCBs under two flange load cases. Experimental web crippling capacities were then compared with the predictions from the current design rules. These comparisons showed that AS/NZS 4600 and AISI S100 design equations are very unconservative for LCB sections under ETF load case and are conservative for ITF load case. Hence improved equations were proposed to determine the web crippling capacities of LCBs. Suitable design rules were also developed using the direct strength method. This paper presents the details of this study and the results including improved design rules.

Improving deep convultional neural networks with unsupervised feature learning

The latest generation of Deep Convolutional Neural Networks (DCNN) have dramatically advanced challenging computer vision tasks, especially in object detection and object classification, achieving state-of-the-art performance in several computer vision tasks including text recognition, sign recognition, face recognition and scene understanding. The depth of these supervised networks has enabled learning deeper and hierarchical representation of features. In parallel, unsupervised deep learning such as Convolutional Deep Belief Network (CDBN) has also achieved state-of-the-art in many computer vision tasks. However, there is very limited research on jointly exploiting the strength of these two approaches. In this paper, we investigate the learning capability of both methods. We compare the output of individual layers and show that many learnt filters and outputs of the corresponding level layer are almost similar for both approaches. Stacking the DCNN on top of unsupervised layers or replacing layers in the DCNN with the corresponding learnt layers in the CDBN can improve the recognition/classification accuracy and training computational expense. We demonstrate the validity of the proposal on ImageNet dataset.

Delay tolerant routing protocols for energy-neutral animal tracking

This paper investigates communication protocols for relaying sensor data from animal tracking applications back to base stations. While Delay Tolerant Networks (DTNs) are well suited to such challenging environments, most existing protocols do not consider the available energy that is particularly important when tracking devices can harvest energy. This limits both the network lifetime and delivery probability in energy-constrained applications to the point when routing performance becomes worse than using no routing at all. Our work shows that substantial improvement in data yields can be achieved through simple yet efficient energy-aware strategies. Conceptually, there is need for balancing the energy spent on sensing, data mulling, and delivery of direct packets to destination. We use empirical traces collected in a flying fox (fruit bat) tracking project and show that simple threshold-based energy-aware strategies yield up to 20% higher delivery rates. Furthermore, these results generalize well for a wide range of operating conditions.

A framework for freeway incident modeling and traffic re-routing

This paper proposes an analytical Incident Traffic Management framework for freeway incident modeling and traffic re-routing. The proposed framework incorporates an econometric incident duration model and a traffic re-routing optimization module. The incident duration model is used to estimate the expected duration of the incident and thus determine the planning horizon for the re-routing module. The re-routing module is a CTM-based Single Destination System Optimal Dynamic Traffic Assignment model that generates optimal real-time strategies of re-routing freeway traffic to its adjacent arterial network during incidents. The proposed framework has been applied to a case study network including a freeway and its adjacent arterial network in South East Queensland, Australia. The results from different scenarios of freeway demand and incident blockage extent have been analyzed and advantages of the proposed framework are demonstrated.

A systematic review of axillary web syndrome (AWS)

Introduction Axillary web syndrome (AWS) can result in early post-operative and long-term difficulties following lymphadenectomy for cancer and should be recognised by clinicians. This systematic review was conducted to synthesise information on AWS clinical presentation and diagnosis, frequency, natural progression, grading, pathoaetiology, risk factors, symptoms, interventions and outcomes. Methods Electronic searches were conducted using Cochrane, Pubmed, MEDLINE, CINAHL, EMBASE, AMED, PEDro and Google Scholar until June 2013. The methodological quality of included studies was determined using the Downs and Black checklist. Narrative synthesis of results was undertaken. Results Thirty-seven studies with methodological quality scores ranging from 11 to 26 on a 28-point scale were included. AWS diagnosis relies on inspection and palpation; grading has not been validated. AWS frequency was reported in up to 85.4 % of patients. Biopsies identified venous and lymphatic pathoaetiology with five studies suggesting lymphatic involvement. Twenty-one studies reported AWS occurrence within eight post-operative weeks, but late occurrence of greater than 3 months is possible. Pain was commonly reported with shoulder abduction more restricted than flexion. AWS symptoms usually resolve within 3 months but may persist. Risk factors may include extensiveness of surgery, younger age, lower body mass index, ethnicity and healing complications. Low-quality studies suggest that conservative approaches including analgesics, non-steroidal anti-inflammatory drugs and/or physiotherapy may be safe and effective for early symptom reduction. Conclusions AWS appears common. Current evidence for the treatment of AWS is insufficient to provide clear guidance for clinical practice. Implications for Cancer Survivors Cancer survivors should be informed about AWS. Further investigation is needed into pathoaetiology, long-term outcomes and to determine effective treatment using standardised outcomes.

Australian patients using a cardiac-diabetes web-based intervention program: Contributions to person-centered clinical practice

Rationale, aims and objectives: Patients with both cardiac disease and diabetes have poorer health outcomes than patients with only one chronic condition. While evidence indicates that internet based interventions may improve health outcomes for patients with a chronic disease, there is no literature on internet programs specific to cardiac patients with comorbid diabetes. Therefore this study aimed to develop a specific web-based program, then to explore patients’ perspectives on the usefulness of a new program. Methods: The interpretive approach using semi-structured interviews on a purposive sample of eligible patients with type 2 diabetes and a cardiac condition in a metropolitan hospital in Brisbane, Australia. Thematic analysis was undertaken to describe the perceived usefulness of a newly developed Heart2heart webpage. Results: Themes identified included confidence in hospital health professionals and reliance on doctors to manage conditions. Patients found the webpage useful for managing their conditions at home. Conclusions: The new Heart2heart webpage provided a positive and useful resource. Further research on to determine the potential influence of this resource on patients’ self-management behaviours is paramount. Implications for practice include using multimedia strategies for providing information to patients’ comorbidities of cardiac disease and type 2 diabetes, and further development on enhancement of such strategies

Pre-lithiation of onion-like carbon/MoS2 nano-urchin anodes for high-performance rechargeable lithium ion batteries

Hybrid urchin-like nanostructures composed of a spherical onion-like carbon (OLC) core and MoS2 nanoleaves were synthesized by a simple solvothermal method followed by thermal annealing treatment. Compared to commercial MoS2 powder, MoS2/OLC nanocomposites exhibit enhanced electrochemical performance as anode materials of lithium-ion batteries (LIBs) with a specific capacity of 853 mA h g−1 at a current density of 50 mA g−1 after 60 cycles, and a moderate initial coulombic efficiency of 71.1%. Furthermore, a simple pre-lithiation method based on direct contact of lithium foil with MoS2/OLC nano-urchins was used to achieve a very high coulombic efficiency of 97.6% in the first discharge/charge cycle, which is at least 26% higher compared to that of pristine MoS2/OLC nano-urchins. This pre-lithiation method can be generalized to develop other carbon-metal sulfide nanohybrids for LIB anode materials. These results may open up a new avenue for the development of the next-generation high-performance LIBs.

Doing Digital Composition on the Social Web: Knowledge Processes in Literacy Learning

The power to influence others in ever-expanding social networks in the new knowledge economy is tied to capabilities with digital media production. This chapter draws on research in elementary classrooms to examine the repertoires of cross-disciplinary knowledge that literacy learners need to produce innovative digital media via the “social web”. It focuses on the knowledge processes that occurred when elementary students engaged in multimodal text production with new digital media. It draws on Kalantzis and Cope’s (2008) heuristic for theorizing “Knowledge Processes” in the Learning by Design approach to pedagogy. Learners demonstrate eight “Knowledge Processes” across different subject domains, skills areas, and sensibilities. Drawing data from media-based lessons across several classroom and schools, this chapter examines what kinds of knowledge students utilize when they produce digital, multimodal texts in the classroom. The Learning by Design framework is used as an analytic tool to theorize how students learn when they engaged in a specific domain of learning – digital media production.

Automating marine mammal detection in aerial images captured during wildlife surveys: A deep learning approach

Aerial surveys conducted using manned or unmanned aircraft with customized camera payloads can generate a large number of images. Manual review of these images to extract data is prohibitive in terms of time and financial resources, thus providing strong incentive to automate this process using computer vision systems. There are potential applications for these automated systems in areas such as surveillance and monitoring, precision agriculture, law enforcement, asset inspection, and wildlife assessment. In this paper, we present an efficient machine learning system for automating the detection of marine species in aerial imagery. The effectiveness of our approach can be credited to the combination of a well-suited region proposal method and the use of Deep Convolutional Neural Networks (DCNNs). In comparison to previous algorithms designed for the same purpose, we have been able to dramatically improve recall to more than 80% and improve precision to 27% by using DCNNs as the core approach.

Finding within-organisation spatial information on the web

Information available on company websites can help people navigate to the offices of groups and individuals within the company. Automatically retrieving this within-organisation spatial information is a challenging AI problem This paper introduces a novel unsupervised pattern-based method to extract within-organisation spatial information by taking advantage of HTML structure patterns, together with a novel Conditional Random Fields (CRF) based method to identify different categories of within-organisation spatial information. The results show that the proposed method can achieve a high performance in terms of F-Score, indicating that this purely syntactic method based on web search and an analysis of HTML structure is well-suited for retrieving within-organisation spatial information.

Design and a deep customer insight approach to innovation

This paper proposes and explores the Deep Customer Insight Innovation Framework in order to develop an understanding as to how design can be integrated within existing innovation processes. The Deep Customer Insight Innovation Framework synthesises the work of Beckman and Barry (2007) as a theoretical foundation, with the framework explored within a case study of Australian Airport Corporation seeking to drive airport innovations in operations and retail performance. The integration of a deep customer insight approach develops customer-centric and highly integrated solutions as a function of concentrated problem exploration and design-led idea generation. Businesses’ facing complex innovation challenges or seeking to making sense of future opportunities will be able to integrate design into existing innovation processes, anchoring the new approach between existing market research and business development activities. This paper contributes a framework and novel understanding as to how design methods are integrated into existing innovation processes for operationalization within industry.

An information accountability perspective to eHealth security and privacy

Rigid security boundaries hinder the proliferation of eHealth. Through active audit logs, accountable-eHealth systems alleviate privacy concerns and enhance information availability.

Chronicling the patient journey: Co-creating value with digital health ecosystems

With the introduction of the PCEHR (Personally Controlled Electronic Health Record), the Australian public is being asked to accept greater responsibility for the management of their health information. However, the implementation of the PCEHR has occasioned poor adoption rates underscored by criticism from stakeholders with concerns about transparency, accountability, privacy, confidentiality, governance, and limited capabilities. This study adopts an ethnographic lens to observe how information is created and used during the patient journey and the social factors impacting on the adoption of the PCEHR at the micro-level in order to develop a conceptual model that will encourage the sharing of patient information within the cycle of care. Objective: This study aims to firstly, establish a basic understanding of healthcare professional attitudes toward a national platform for sharing patient summary information in the form of a PCEHR. Secondly, the studies aims to map the flow of patient related information as it traverses a patient’s personal cycle of care. Thus, an ethnographic approach was used to bring a “real world” lens to information flow in a series of case studies in the Australian healthcare system to discover themes and issues that are important from the patient’s perspective. Design: Qualitative study utilising ethnographic case studies. Setting: Case studies were conducted at primary and allied healthcare professionals located in Brisbane Queensland between October 2013 and July 2014. Results: In the first dimension, it was identified that healthcare professionals’ concerns about trust and medico-legal issues related to patient control and information quality, and the lack of clinical value available with the PCEHR emerged as significant barriers to use. The second dimension of the study which attempted to map patient information flow identified information quality issues, clinical workflow inefficiencies and interoperability misconceptions resulting in duplication of effort, unnecessary manual processes, data quality and integrity issues and an over reliance on the understanding and communication skills of the patient. Conclusion: Opportunities for process efficiencies, improved data quality and increased patient safety emerge with the adoption of an appropriate information sharing platform. More importantly, large scale eHealth initiatives must be aligned with the value proposition of individual stakeholders in order to achieve widespread adoption. Leveraging an Australian national eHealth infrastructure and the PCEHR we offer a practical example of a service driven digital ecosystem suitable for co-creating value in healthcare.

Deep transverse metatarsal ligaments mechanical response during landing

The deep transverse metatarsal ligaments play an important role in stabilizing the metatarsal bones and manipulating foot transverse arch deformation. However, the biomechanical research about transverse metatarsal ligaments in the foot maneuver is quite few. Due to the difficulties and lack of better measurement technology for these ligaments experimental monitor, the load transfer mechanism and internal stress state also hadn't been well addressed. The purpose of this study was to develop a detailing foot finite element model including transverse metatarsal ligaments tissues, to investigate the mechanical response of transverse metatarsal ligaments during the landing condition. The transverse metatarsal ligaments were considered as hyperelastic material model was used to represent the nonlinear and nearly incompressible nature of the ligament tissue. From the simulation results, it is clearly to find that the peak maiximal principal stress of transverse metatarsal ligaments was between the third and fourth metatarsals. Meanwhile, it seems the transverse metatarsal ligaments in the middle position experienced higher tension than the sides transverse metatarsal ligaments.

Finite element analysis of deep transverse metatarsal ligaments mechanical response during landing

The deep transverse metatarsal ligaments (DTML) play an important role in stabilizing the metatarsal bones and manipulating foot transverse arch deformation. However, the biomechanical research about DTML in the foot maneuver is quite few. Due to the difficulties and lack of better measurement technology for these ligaments experimental monitor, the load transfer mechanism and internal stress state also hadn't been well addressed. The purpose of this study was to develop a detailing foot finite element model including DTML tissues, to investigate the mechanical response of DTML during the landing condition. The DTML was considered as hyperelastic material model was used to represent the nonlinear and nearly incompressible nature of the ligament tissue. From the simulation results, it is clearly to find that the peak maiximal principal stress of DTML was between the third and fourth metatarsals. Meanwhile, it seems the DTML in the middle position experienced higher tension than the sides DTML.