Hybrid genetic algorithm for elimination of severe stress concentration in railhead ends

This paper presents a computational method for eliminating severe stress concentration at the unsupported railhead ends in rail joints through innovative shape optimization of the contact zone, which is complex due to near field nonlinear contact. With a view to minimizing the computational efforts, hybrid genetic algorithm method coupled with parametric finite element has been developed and compared with the traditional genetic algorithm (GA). The shape of railhead top surface where the wheel contacts nonlinearly was optimized using the hybridized GA method. Comparative study of the optimal result and the search efficiency between the traditional and hybrid GA methods has shown that the hybridized GA provides the optimal shape in fewer computational cycles without losing accuracy. The method will be beneficial to solving complex engineering problems involving contact nonlinearity.

Composite SaaS scaling in cloud computing using a hybrid genetic algorithm

A Software-as-a-Service or SaaS can be delivered in a composite form, consisting of a set of application and data components that work together to deliver higher-level functional software. Components in a composite SaaS may need to be scaled – replicated or deleted, to accommodate the user’s load. It may not be necessary to replicate all components of the SaaS, as some components can be shared by other instances. On the other hand, when the load is low, some of the instances may need to be deleted to avoid resource underutilisation. Thus, it is important to determine which components are to be scaled such that the performance of the SaaS is still maintained. Extensive research on the SaaS resource management in Cloud has not yet addressed the challenges of scaling process for composite SaaS. Therefore, a hybrid genetic algorithm is proposed in which it utilises the problem’s knowledge and explores the best combination of scaling plan for the components. Experimental results demonstrate that the proposed algorithm outperforms existing heuristic-based solutions.

A bayesian approach for place recognition

This paper presents a robust place recognition algorithm for mobile robots that can be used for planning and navigation tasks. The proposed framework combines nonlinear dimensionality reduction, nonlinear regression under noise, and Bayesian learning to create consistent probabilistic representations of places from images. These generative models are incrementally learnt from very small training sets and used for multi-class place recognition. Recognition can be performed in near real-time and accounts for complexity such as changes in illumination, occlusions, blurring and moving objects. The algorithm was tested with a mobile robot in indoor and outdoor environments with sequences of 1579 and 3820 images, respectively. This framework has several potential applications such as map building, autonomous navigation, search-rescue tasks and context recognition.

Tensile properties of a boron/nitrogen-doped carbon nanotube–graphene hybrid structure

Doping is an effective approach that allows for the intrinsic modification of the electrical and chemical properties of nanomaterials. Recently, a graphene and carbon nanotube hybrid structure (GNHS) has been reported, which extends the excellent properties of carbon-based materials to three dimensions. In this paper, we carried out a first-time investigation on the tensile properties of the hybrid structures with different dopants. It is found that with the presence of dopants, the hybrid structures usually exhibit lower yield strength, Young’s modulus, and earlier yielding compared to that of a pristine hybrid structure. For dopant concentrations below 2.5% no significant reduction of Young’s modulus or yield strength could be observed. For all considered samples, the failure is found to initiate at the region where the nanotubes and graphene sheets are connected. After failure, monatomic chains are normally observed around the failure region. Dangling graphene layers without the separation of a residual CNT wall are found to adhere to each other after failure with a distance of about 3.4 Å. This study provides a fundamental understanding of the tensile properties of the doped graphene–nanotube hybrid structures, which will benefit the design and also the applications of graphene-based hybrid materials.

Hybrid frequency–time domain models for dynamic response analysis of marine structures

Time-domain models of marine structures based on frequency domain data are usually built upon the Cummins equation. This type of model is a vector integro-differential equation which involves convolution terms. These convolution terms are not convenient for analysis and design of motion control systems. In addition, these models are not efficient with respect to simulation time, and ease of implementation in standard simulation packages. For these reasons, different methods have been proposed in the literature as approximate alternative representations of the convolutions. Because the convolution is a linear operation, different approaches can be followed to obtain an approximately equivalent linear system in the form of either transfer function or state-space models. This process involves the use of system identification, and several options are available depending on how the identification problem is posed. This raises the question whether one method is better than the others. This paper therefore has three objectives. The first objective is to revisit some of the methods for replacing the convolutions, which have been reported in different areas of analysis of marine systems: hydrodynamics, wave energy conversion, and motion control systems. The second objective is to compare the different methods in terms of complexity and performance. For this purpose, a model for the response in the vertical plane of a modern containership is considered. The third objective is to describe the implementation of the resulting model in the standard simulation environment Matlab/Simulink.

An R2R3 MYB transcription factor determines red petal colour in an Actinidia (kiwifruit) hybrid population

Background Red colour in kiwifruit results from the presence of anthocyanin pigments. Their expression, however, is complex, and varies among genotypes, species, tissues and environments. An understanding of the biosynthesis, physiology and genetics of the anthocyanins involved, and the control of their expression in different tissues, is required. A complex, the MBW complex, consisting of R2R3-MYB and bHLH transcription factors together with a WD-repeat protein, activates anthocyanin 3-O-galactosyltransferase (F3GT1) to produce anthocyanins. We examined the expression and genetic control of anthocyanins in flowers of Actinidia hybrid families segregating for red and white petal colour. Results Four inter-related backcross families between Actinidia chinensis Planch. var. chinensis and Actinidia eriantha Benth. were identified that segregated 1:1 for red or white petal colour. Flower pigments consisted of five known anthocyanins (two delphinidin-based and three cyanidin-based) and three unknowns. Intensity and hue differed in red petals from pale pink to deep magenta, and while intensity of colour increased with total concentration of anthocyanin, no association was found between any particular anthocyanin data and hue. Real time qPCR demonstrated that an R2R3 MYB, MYB110a, was expressed at significant levels in red-petalled progeny, but not in individuals with white petals. A microsatellite marker was developed that identified alleles that segregated with red petal colour, but not with ovary, stamen filament, or fruit flesh colour in these families. The marker mapped to chromosome 10 in Actinidia. The white petal phenotype was complemented by syringing Agrobacterium tumefaciens carrying Actinidia 35S::MYB110a into the petal tissue. Red pigments developed in white petals both with, and without, co-transformation with Actinidia bHLH partners. MYB110a was shown to directly activate Actinidia F3GT1 in transient assays. Conclusions The transcription factor, MYB110a, regulates anthocyanin production in petals in this hybrid population, but not in other flower tissues or mature fruit. The identification of delphinidin-based anthocyanins in these flowers provides candidates for colour enhancement in novel fruits.

On display : Ravensthorpe Wildflower Show and the assembly of place

Each September since 1983 in the rural Shire of Ravensthorpe, Western Australia, volunteers collect samples of up to 700 wildfl ower species which are then displayed in the Ravensthorpe Senior Citizens Centre from 9.00 am to 4.00 pm daily over a two-week period. This chapter offers an ethnographic interpretation of this enduring annual event focusing on the 25th show held in 2007. The study contributes to understanding the complex and nuanced role of local wildflower shows in shaping and supporting rural senses of place and of community. Importantly, this particular type of festival, and more specifically this local instance, foregrounds a less-remarked aspect of festivals, namely the (re)production and celebration of place-specific knowledge through validations of, and interconnections between, scientific flower classification and emotive experience. This feature, encapsulated in Laurel Lamperd’s poem above, invites consideration of the ways in which local place knowledge and the simultaneous (re)production of ‘place’ are constituted by a complex layering of rational, objective ways of knowing and those which emphasize emotions, aesthetics and memories. This rural wildflower show not only mobilises both the rational and the emotional in ‘making sense of the world’ for local residents and for tourists, but also offers insights into the production of place as constituted in and through relations between humans and non-human life forms (Cloke & Jones, 2001; Conradson, 2005; see also Chapter 6).

Hyperbranched polymer-gold nanoparticle assemblies : role of polymer architecture in hybrid assembly formation and SERS activity

Plasmonic gold nano-assemblies that self-assemble with the aid of linking molecules or polymers have the potential to yield controlled hierarchies of morphologies and consequently result in materials with tailored optical (e.g. localized surface plasmon resonances (LSPR)) and spectroscopic properties (e.g. surface enhanced Raman scattering (SERS)). Molecular linkers that are structurally well-defined are promising for forming hybrid nano-assemblies which are stable in aqueous solution and are increasingly finding application in nanomedicine. Despite much ongoing research in this field, the precise role of molecular linkers in governing the morphology and properties of the hybrid nano-assemblies remains unclear. Previously we have demonstrated that branched linkers, such as hyperbranched polymers, with specific anchoring end groups can be successfully employed to form assemblies of gold NPs demonstrating near-infrared SPRs and intense SERS scattering. We herein introduce a tailored polymer as a versatile molecular linker, capable of manipulating nano-assembly morphologies and hot-spot density. In addition, this report explores the role of the polymeric linker architecture, specifically the degree of branching of the tailored polymer in determining the formation, morphology and properties of the hybrid nano-assemblies. The degree of branching of the linker polymer, in addition to the concentration and number of anchoring groups, is observed to strongly influence the self-assembly process. The assembly morphology shifts primarily from 1D-like chains to 2D plates and finally to 3D-like globular structures, with increase in degree of branching. Insights have been gained into how the morphology influences the SERS performance of these nano-assemblies with respect to hot-spot density. These findings supplement the understanding of the morphology determining nano-assembly formation and pave the way for the possible application of these nano-assemblies as SERS bio-sensors for medical diagnostics.

All-environment visual place recognition with SMART

This paper presents Sequence Matching Across Route Traversals (SMART); a generally applicable sequence-based place recognition algorithm. SMART provides invariance to changes in illumination and vehicle speed while also providing moderate pose invariance and robustness to environmental aliasing. We evaluate SMART on vehicles travelling at highly variable speeds in two challenging environments; firstly, on an all-terrain vehicle in an off-road, forest track and secondly, using a passenger car traversing an urban environment across day and night. We provide comparative results to the current state-of-the-art SeqSLAM algorithm and investigate the effects of altering SMART’s image matching parameters. Additionally, we conduct an extensive study of the relationship between image sequence length and SMART’s matching performance. Our results show viable place recognition performance in both environments with short 10-metre sequences, and up to 96% recall at 100% precision across extreme day-night cycles when longer image sequences are used.

Gender dimensions of resource extraction : the place of women

This chapter examines two core dimensions of women’s gendered experiences of mining in Australia and more specifically in Western Australia (WA). First, the chapter explores what has been and continues to be women’s principal relationship to mining encapsulated in the social and cultural identity of the ‘mining wife’ and, more recently, ‘fly-in/fly-out (FIFO) wife’. Second, the chapter addresses the fraught emergence of women as mineworkers. As the research presented in this chapter makes clear, the human cost of developmentalism was and continues to be deeply gendered.

A sparse hybrid map for vision-guided mobile robots

This paper introduces a minimalistic approach to produce a visual hybrid map of a mobile robot’s working environment. The proposed system uses omnidirectional images along with odometry information to build an initial dense posegraph map. Then a two level hybrid map is extracted from the dense graph. The hybrid map consists of global and local levels. The global level contains a sparse topological map extracted from the initial graph using a dual clustering approach. The local level contains a spherical view stored at each node of the global level. The spherical views provide both an appearance signature for the nodes, which the robot uses to localize itself in the environment, and heading information when the robot uses the map for visual navigation. In order to show the usefulness of the map, an experiment was conducted where the map was used for multiple visual navigation tasks inside an office workplace.

A place to make, hack, and learn: makerspaces in Australian public libraries

Content-creation spaces, or ‘makerspaces’, are an emerging phenomenon in public libraries worldwide. This study investigated the current state of makerspaces in Australian public libraries. Qualitative interviews with three information professionals formed the data collection. Thematic analysis of interviews addressed two research questions: What are the issues and challenges of creating makerspaces within Australian public libraries? How can they be addressed? Findings revealed the substantive benefits of these spaces, including enhanced community engagement, development of a new form of library as ‘third place’, and transforming the library's image from that of a place where works are consumed to that of a place where works are created. Additionally the study highlighted significant challenges to creating these spaces, including budgetary constraints, resistance to change within organisations and proving the relevance of such spaces within a library context. The study provides suggestions for overcoming these obstacles and provides areas for further research in the area, including larger studies across a broader geographic area and further investigation and follow-up into upcoming programs within existing makerspaces.

Multi-scale bio-inspired place recognition

This paper presents a novel place recognition algorithm inspired by the recent discovery of overlapping and multi-scale spatial maps in the rodent brain. We mimic this hierarchical framework by training arrays of Support Vector Machines to recognize places at multiple spatial scales. Place match hypotheses are then cross-validated across all spatial scales, a process which combines the spatial specificity of the finest spatial map with the consensus provided by broader mapping scales. Experiments on three real-world datasets including a large robotics benchmark demonstrate that mapping over multiple scales uniformly improves place recognition performance over a single scale approach without sacrificing localization accuracy. We present analysis that illustrates how matching over multiple scales leads to better place recognition performance and discuss several promising areas for future investigation.

An hybrid approach for efficient multicast stream authentication over unsecured channels

We study the multicast stream authentication problem when an opponent can drop, reorder and inject data packets into the communication channel. In this context, bandwidth limitation and fast authentication are the core concerns. Therefore any authentication scheme is to reduce as much as possible the packet overhead and the time spent at the receiver to check the authenticity of collected elements. Recently, Tartary and Wang developed a provably secure protocol with small packet overhead and a reduced number of signature verifications to be performed at the receiver. In this paper, we propose an hybrid scheme based on Tartary and Wang’s approach and Merkle hash trees. Our construction will exhibit a smaller overhead and a much faster processing at the receiver making it even more suitable for multicast than the earlier approach. As Tartary and Wang’s protocol, our construction is provably secure and allows the total recovery of the data stream despite erasures and injections occurred during transmission.