Large area noise evaluation

A software tool (DRONE) has been developed to evaluate road traffic noise in a large area with the consideration of network dynamic traffic flow and the buildings. For more precise estimation of noise in urban network where vehicles are mainly in stop and go running conditions, vehicle sound power level (for acceleration/deceleration cruising and ideal vehicle) is incorporated in DRONE. The calculation performance of DRONE is increased by evaluating the noise in two steps of first estimating the unit noise database and then integrating it with traffic simulation. Details of the process from traffic simulation to contour maps are discussed in the paper and the implementation of DRONE on Tsukuba city is presented

Conifer defence against insects: microarray gene expression profiling of Sitka spruce (Picea sitchensis) induced by mechanical wounding or feeding by spruce budworms (Choristoneura occidentalis) or white pine weevils (Pissodes strobi) reveals large

Conifers are resistant to attack from a large number of potential herbivores or pathogens. Previous molecular and biochemical characterization of selected conifer defence systems support a model of multigenic, constitutive and induced defences that act on invading insects via physical, chemical, biochemical or ecological (multitrophic) mechanisms. However, the genomic foundation of the complex defence and resistance mechanisms of conifers is largely unknown. As part of a genomics strategy to characterize inducible defences and possible resistance mechanisms of conifers against insect herbivory, we developed a cDNA microarray building upon a new spruce (Picea spp.) expressed sequence tag resource. This first-generation spruce cDNA microarray contains 9720 cDNA elements representing c. 5500 unique genes. We used this array to monitor gene expression in Sitka spruce (Picea sitchensis) bark in response to herbivory by white pine weevils (Pissodes strobi, Curculionidae) or wounding, and in young shoot tips in response to western spruce budworm (Choristoneura occidentalis, Lepidopterae) feeding. Weevils are stem-boring insects that feed on phloem, while budworms are foliage feeding larvae that consume needles and young shoot tips. Both insect species and wounding treatment caused substantial changes of the host plant transcriptome detected in each case by differential gene expression of several thousand array elements at 1 or 2 d after the onset of treatment. Overall, there was considerable overlap among differentially expressed gene sets from these three stress treatments. Functional classification of the induced transcripts revealed genes with roles in general plant defence, octadecanoid and ethylene signalling, transport, secondary metabolism, and transcriptional regulation. Several genes involved in primary metabolic processes such as photosynthesis were down-regulated upon insect feeding or wounding, fitting with the concept of dynamic resource allocation in plant defence. Refined expression analysis using gene-specific primers and real-time PCR for selected transcripts was in agreement with microarray results for most genes tested. This study provides the first large-scale survey of insect-induced defence transcripts in a gymnosperm and provides a platform for functional investigation of plant-insect interactions in spruce. Induction of spruce genes of octadecanoid and ethylene signalling, terpenoid biosynthesis, and phenolic secondary metabolism are discussed in more detail.

An alginate-based hybrid system for growth factor delivery in the functional repair of large bone defects

The treatment of challenging fractures and large osseous defects presents a formidable problem for orthopaedic surgeons. Tissue engineering/regenerative medicine approaches seek to solve this problem by delivering osteogenic signals within scaffolding biomaterials. In this study, we introduce a hybrid growth factor delivery system that consists of an electrospun nanofiber mesh tube for guiding bone regeneration combined with peptide-modified alginate hydrogel injected inside the tube for sustained growth factor release. We tested the ability of this system to deliver recombinant bone morphogenetic protein-2 (rhBMP-2) for the repair of critically-sized segmental bone defects in a rat model. Longitudinal [mu]-CT analysis and torsional testing provided quantitative assessment of bone regeneration. Our results indicate that the hybrid delivery system resulted in consistent bony bridging of the challenging bone defects. However, in the absence of rhBMP-2, the use of nanofiber mesh tube and alginate did not result in substantial bone formation. Perforations in the nanofiber mesh accelerated the rhBMP-2 mediated bone repair, and resulted in functional restoration of the regenerated bone. [mu]-CT based angiography indicated that perforations did not significantly affect the revascularization of defects, suggesting that some other interaction with the tissue surrounding the defect such as improved infiltration of osteoprogenitor cells contributed to the observed differences in repair. Overall, our results indicate that the hybrid alginate/nanofiber mesh system is a promising growth factor delivery strategy for the repair of challenging bone injuries.

Identifying large truck hot spots using crash counts and PDOEs

Large trucks are involved in a disproportionately small fraction of the total crashes but a disproportionately large fraction of fatal crashes. Large truck crashes often result in significant congestion due to their large physical dimensions and from difficulties in clearing crash scenes. Consequently, preventing large truck crashes is critical to improving highway safety and operations. This study identifies high risk sites (hot spots) for large truck crashes in Arizona and examines potential risk factors related to the design and operation of the high risk sites. High risk sites were identified using both state of the practice methods (accident reduction potential using negative binomial regression with long crash histories) and a newly proposed method using Property Damage Only Equivalents (PDOE). The hot spots identified via the count model generally exhibited low fatalities and major injuries but large minor injuries and PDOs, while the opposite trend was observed using the PDOE methodology. The hot spots based on the count model exhibited large AADTs, whereas those based on the PDOE showed relatively small AADTs but large fractions of trucks and high posted speed limits. Documented site investigations of hot spots revealed numerous potential risk factors, including weaving activities near freeway junctions and ramps, absence of acceleration lanes near on-ramps, small shoulders to accommodate large trucks, narrow lane widths, inadequate signage, and poor lighting conditions within a tunnel.

Quantitative evaluation of route allocation solutions at large-scale passenger stations

Routing trains within passenger stations in major cities is a common scheduling problem for railway operation. Various studies have been undertaken to derive and formulate solutions to this route allocation problem (RAP) which is particularly evident in mainland China nowadays because of the growing traffic demand and limited station capacity. A reasonable solution must be selected from a set of available RAP solutions attained in the planning stage to facilitate station operation. The selection is however based on the experience of the operators only and objective evaluation of the solutions is rarely addressed. In order to maximise the utilisation of station capacity while maintaining service quality and allowing for service disturbance, quantitative evaluation of RAP solutions is highly desirable. In this study, quantitative evaluation of RAP solutions is proposed and it is enabled by a set of indices covering infrastructure utilisation, buffer times and delay propagation. The proposed evaluation is carried out on a number of RAP solutions at a real-life busy railway station in mainland China and the results highlight the effectiveness of the indices in pinpointing the strengths and weaknesses of the solutions. This study provides the necessary platform to improve the RAP solution in planning and to allow train re-routing upon service disturbances.

Efficient querying of large process model repositories

Recent years have seen an increased uptake of business process management technology in industries. This has resulted in organizations trying to manage large collections of business process models. One of the challenges facing these organizations concerns the retrieval of models from large business process model repositories. For example, in some cases new process models may be derived from existing models, thus finding these models and adapting them may be more effective and less error-prone than developing them from scratch. Since process model repositories may be large, query evaluation may be time consuming. Hence, we investigate the use of indexes to speed up this evaluation process. To make our approach more applicable, we consider the semantic similarity between labels. Experiments are conducted to demonstrate that our approach is efficient.

Strategies to facilitate grading consistency in large classes

First year undergraduate university classes can be very large, and big student numbers often creates a challenge for instructors to ensure assignments are graded consistently across the cohort. This session describes and demonstrates the use of interactive audience response technology (ART) with assessors (rather than students) to moderate assignment grading. Results from preliminary research indicate this method of moderating the grading of assignments is effective, and achieves more consistent outcomes for students.

Meshless TL and UL approaches for large deformation analysis

To accurately and effectively simulate large deformation is one of the major challenges in numerical modeling of metal forming. In this paper, an adaptive local meshless formulation based on the meshless shape functions and the local weak-form is developed for the large deformation analysis. Total Lagrangian (TL) and the Updated Lagrangian (UL) approaches are used and thoroughly compared each other in computational efficiency and accuracy. It has been found that the developed meshless technique provides a superior performance to the conventional FEM in dealing with large deformation problems for metal forming. In addition, the TL has better computational efficiency than the UL. However, the adaptive analysis is much more efficient using the UL approach than using in the TL approach.

An evidence-based approach to professional development planning in large state educational systems

This paper presents a systems-level approach for adjudicating the prioritization, selection, and planning of inservcie professional development (PD) for teachers. We present a step-by-step model for documenting and assessing system-wide 'bids' for professional development programs

Lean project management in large scale industrial project via standardization

Lean project management is the comprehensive adaption of other lean concept like lean construction, lean manufacturing and lean thinking into project management context. Execution of many similar industrial projects creates the idea of lean project management in companies and rapidly growing in industries. This paper offers the standardization method in order to achieve Lean project management in large scale industrial project. Standardization refers to all activity which makes two projects most identical and unify to each other like standardization of design, reducing output variability, value analysis and strategic management. Although standard project may have minor effi ciency decrease, compare to custom built project; but great advantage of standard project like cost saving, time reduction and quality improvement justify standardization methodology. This paper based on empirical experience in industrial project and theoretical analysis of benefi ts of project standardization.

Psychiatric and substance abuse comorbidity among a large sample of DUI Texas offenders

Research is indicating that individuals who present for DUI treatment may have competing substance abuse and mental health needs. This study aimed to examine the extent of such comorbidity issues among a sample of Texas DUI offenders. Method: Records of 36,372 DUI clients and 308,695 non-DUI clients admitted to Texas treatment programs between 2005 and 2008 were obtained from the State's administrative dataset. The data were analysed to identify the relationship between substance use, psychiatric problems, program completion and recidivism rates. Results: Analysis indicated that while non-DUI clients were more likely to present with more severe illicit substance use problems, DUI clients were more likely to have a primary problem with alcohol. Additionally, a cannabis use problem was also found to be significantly associated with DUI recidivism in the last year. In regards to mental health needs, a major finding was that depression was the most common psychiatric condition reported by DUI clients, including those with more than one DUI offence in the past year. This group were also more at risk of being diagnosed with Bipolar Disorder compared to the general population, and such a diagnosis was also associated with an increased likelihood of not completing treatment. Interestingly, female DUI and non-DUI clients were also more likely to be diagnosed with mental health problems compared to males, as well as more likely to be placed on medications at admission and have problems with methamphetamine, cocaine, and opiates. Conclusion: The findings highlight the complex competing needs of some DUI offenders who enter treatment. The results also suggest that there is a need to utilise mental health and substance abuse screening methods to ensure DUI offenders are directed towards appropriate treatment pathways as well as ensure that such interventions adequately cater for complex substance abuse and psychiatric needs.

Multi-objective four-dimensional vehicle motion planning in large dynamic environments

This paper presents Multi-Step A* (MSA*), a search algorithm based on A* for multi-objective 4D vehicle motion planning (three spatial and one time dimension). The research is principally motivated by the need for offline and online motion planning for autonomous Unmanned Aerial Vehicles (UAVs). For UAVs operating in large, dynamic and uncertain 4D environments, the motion plan consists of a sequence of connected linear tracks (or trajectory segments). The track angle and velocity are important parameters that are often restricted by assumptions and grid geometry in conventional motion planners. Many existing planners also fail to incorporate multiple decision criteria and constraints such as wind, fuel, dynamic obstacles and the rules of the air. It is shown that MSA* finds a cost optimal solution using variable length, angle and velocity trajectory segments. These segments are approximated with a grid based cell sequence that provides an inherent tolerance to uncertainty. Computational efficiency is achieved by using variable successor operators to create a multi-resolution, memory efficient lattice sampling structure. Simulation studies on the UAV flight planning problem show that MSA* meets the time constraints of online replanning and finds paths of equivalent cost but in a quarter of the time (on average) of vector neighbourhood based A*.