Aspects of Keno modelling

In two earlier papers, an intricate Jackpot structure and analysis of pseudo-random numbers for Keno in the Australian state of Queensland circa 2000 were described. Aspects of the work were also reported at an international conference . Since that time, many aspects of the game in Australia have changed. The present paper presents more up-to-date details of Keno throughout the states of Queensland, New South Wales and Victoria. A much simpler jackpot structure is now in place and this is described. Two add-ons or side-bets to the game are detailed: the trivial Heads or Tails and the more interesting Keno Bonus, which leads to consideration of the subset sum problem. The most intricate structure is where Heads or Tails and Keno Bonus are combined, and here, the issue of independence arises. Closed expressions for expected return to player (ERTP) are presented in all cases.

Aspects of elementary number theory illustrated in the spreadsheet environment

Number theory has in recent decades assumed a great practical importance, due primarily to its application to cryptography. This chapter discusses how elementary concepts of number theory may be illuminated and made accessible to upper secondary school students via appropriate spreadsheet models. In such environments, students can observe patterns, gain structural insight, form and test conjectures, and solve problems. The chapter begins by reviewing literature on the use of spreadsheets in general and the use of spreadsheets in number theory in particular. Two sample applications are then discussed. The first, factoring factorials, is presented and instructions are given to construct a model in Excel 2007. The second application, the RSA cryptosystem, is included because of its importance to Science, Technology, Engineering, and Mathematics (STEM) students. Number theoretic concepts relevant to RSA are discussed, and an outline of RSA. is given, with example. The chapter ends with instructions on how to construct a simple spreadsheet illustrating RSA.

Executive function and postural instability in people with Parkinson’s disease

The specific aspects of cognition contributing to balance and gait have not been clarified in people with Parkinson’s disease (PD). Twenty PD participants and twenty age- and gender-matched healthy controls were assessed on cognition and clinical mobility tests. General cognition was assessed with the Mini Mental State Exam and the Addenbrooke’s Cognitive Exam. Executive function was evaluated using the Trail Making Tests (TMT-A and TMT-B) and a computerized cognitive battery which included a series of choice reaction time (CRT) tests. Clinical gait and balance measures included the Tinetti, Timed Up & Go, Berg Balance and Functional Reach tests. PD participants performed significantly worse than the controls on the tests of cognitive and executive function, balance and gait. PD participants took longer on Trail Making Tests, CRT-Location and CRT-Colour (inhibition response). Furthermore, executive function, particularly longer times on CRT-Distracter and greater errors on the TMT-B were associated with worse balance and gait performance in the PD group. Measures of general cognition were not associated with balance and gait measures in either group. For PD participants, attention and executive function were impaired. Components of executive function, particularly those involving inhibition response and distracters, were associated with poorer balance and gait performance in PD.

Plasma break-down and re-build : same functional vertical graphenes from diverse natural precursors

Plasmas, the 4th state of matter, uniformly transform natural precursors with different chemical composition in solid, liquid, and gas states into the same functional vertical graphenes in a single-step process within a few minutes. Functional vertical graphenes show reliable biosensing properties, strong binding with proteins, and improved adhesion to substrates.

Carbon nanostructures for hard tissue engineering

The primary goal in hard tissue engineering is to combine high-performance scaffold materials with living cells to develop biologically active substitutes that can restore tissue functions. This requires relevant knowledge in multidisciplinary fields encompassing chemical engineering, material science, chemistry, biology and nanotechnology. Here we present an overview on the recent progress of how two representative carbon nanostructures, namely, carbon nanotubes and graphene, aid and advance the research in hard tissue engineering. The article focuses on the advantages and challenges of integrating these carbon nanostructures into functional scaffolds for repairing and regenerative purposes. It includes, but is not limited to, the critical physico-chemical properties of carbon nanomaterials for enhanced cell interactions such as adhesion, morphogenesis, proliferation and differentiation; the novel designs of two- and three-dimensional nanostructured scaffolds; multifunctional hybrid materials; and the biocompatible aspects of carbon nanotubes and graphene. Perspectives on the future research directions are also given, in an attempt to shed light on the innovative and rational design of more effective biomedical devices in hard tissue engineering.

Highly NO2 sensitive caesium doped graphene oxide conductometric sensors

Here we report on the synthesis of caesium doped graphene oxide (GO-Cs) and its application to the development of a novel NO2 gas sensor. The GO, synthesized by oxidation of graphite through chemical treatment, was doped with Cs by thermal solid-state reaction. The samples, dispersed in DI water by sonication, have been drop-casted on standard interdigitated Pt electrodes. The response of both pristine and Cs doped GO to NO2 at room temperature is studied by varying the gas concentration. The developed GO-Cs sensor shows a higher response to NO2 than the pristine GO based sensor due to the oxygen functional groups. The detection limit measured with GO-Cs sensor is ≈90 ppb.

Functional impairments characterising mild, moderate and severe hallux valgus

Objective Hallux valgus has been linked to functional disability and increased falls risk, but mechanisms underpinning functional disability are unclear. This study investigated functional performance, muscle strength and plantar pressures in adults with mild, moderate, and severe HV compared to controls, while considering the influence of foot pain. Methods Sixty adults with hallux valgus (classified as mild, moderate and severe on dorsalplantar radiographs) and 30 controls participated. Measures included: hallux plantarflexion and abduction strength, walking performance, postural sway and forefoot plantar pressures. Multiple analysis of covariance and pairwise comparisons (p<0.05, Bonferroni adjustment) were used to investigate differences between groups, adjusting for age, sex, body mass index and foot pain. Results Hallux plantarflexion and abduction strength was significantly reduced in those with moderate (mean differences: plantarflexion -45.8N, abduction -12.3N, p<0.001) and severe hallux valgus (plantarflexion -50.1N, p<0.001; abduction -11.2N, p=0.01) compared to controls. A significant reduction in hallux peak pressure and pressure-time integral was evident in moderate (peak pressure -90.8kPa, p<0.001) and severe hallux valgus (peak pressure -106.2kPa, p<0.001) compared to controls. Those with severe hallux valgus also demonstrated increased mediolateral postural sway in single leg stance compared to controls (3.5cm, p=0.01). Conclusion Moderate to severe hallux valgus is associated with reduced hallux plantar pressures and strength measures, while relatively normal function compared to controls was found in those with mild deformity. Greater understanding of specific functional deficits associated with different stages of hallux valgus will help inform clinical management and future research.

Synthesis of functional nanoassemblies in reactive plasmas

Synthesis of various functional nanoassemblies, by using a combination of low-pressure reactive plasma-enhanced chemical deposition and plasma-assisted rf magnetron sputtering deposition is reported. This paper details how selective generation and manipulation of the required building blocks and management of unwanted nanoparticle contaminants, can be used for plasma-aided nanofabrication of carbon nanotip microemitter structures, ultra-high aspect ratio semiconductor nanowires, ordered quantum dot arrays, and microporous hydroxyapatite bioceramics. Emerging challenges of the plasma-aided synthesis of functional nanofilms and nanoassemblies are also discussed.

Integrated plasma-aided nanofabrication facility: Operation, parameters, and assembly of quantum structures and functional nanomaterials

The development, operation, and applications of two configurations of an integrated plasma-aided nanofabrication facility (IPANF) comprising low-frequency inductively coupled plasma-assisted, low-pressure, multiple-target RF magnetron sputtering plasma source, are reported. The two configurations of the plasma source have different arrangements of the RF inductive coil: a conventional external flat spiral "pancake" coil and an in-house developed internal antenna comprising two orthogonal RF current sheets. The internal antenna configuration generates a "unidirectional" RF current that deeply penetrates into the plasma bulk and results in an excellent uniformity of the plasma over large areas and volumes. The IPANF has been employed for various applications, including low-temperature plasma-enhanced chemical vapor deposition of vertically aligned single-crystalline carbon nanotips, growth of ultra-high aspect ratio semiconductor nanowires, assembly of optoelectronically important Si, SiC, and Al1-xInxN quantum dots, and plasma-based synthesis of bioactive hydroxyapatite for orthopedic implants.

Ion-assisted functional monolayer coating of nanorod arrays in hydrogen plasmas

Uniformity of postprocessing of large-area, dense nanostructure arrays is currently one of the greatest challenges in nanoscience and nanofabrication. One of the major issues is to achieve a high level of control in specie fluxes to specific surface areas of the nanostructures. As suggested by the numerical experiments in this work, this goal can be achieved by manipulating microscopic ion fluxes by varying the plasma sheath and nanorod array parameters. The dynamics of ion-assisted deposition of functional monolayer coatings onto two-dimensional carbon nanorod arrays in a hydrogen plasma is simulated by using a multiscale hybrid numerical simulation. The numerical results show evidence of a strong correlation between the aspect ratios and nanopattern positioning of the nanorods, plasma sheath width, and densities and distributions of microscopic ion fluxes. When the spacing between the nanorods and/or their aspect ratios are larger, and/or the plasma sheath is wider, the density of microscopic ion current flowing to each of the individual nanorods increases, thus reducing the time required to apply a functional monolayer coating down to 11 s for a 7-μm-wide sheath, and to 5 s for a 50-μm-wide sheath. The computed monolayer coating development time is consistent with previous experimental reports on plasma-assisted functionalization of related carbon nanostructures [B. N. Khare et al., Appl. Phys. Lett. 81, 5237 (2002)]. The results are generic in that they can be applied to a broader range of plasma-based processes and nanostructures, and contribute to the development of deterministic strategies of postprocessing and functionalization of various nanoarrays for nanoelectronic, biomedical, and other emerging applications.

Brain decoding based on functional magnetic resonance imaging using machine learning : a comparative study

Brain decoding of functional Magnetic Resonance Imaging data is a pattern analysis task that links brain activity patterns to the experimental conditions. Classifiers predict the neural states from the spatial and temporal pattern of brain activity extracted from multiple voxels in the functional images in a certain period of time. The prediction results offer insight into the nature of neural representations and cognitive mechanisms and the classification accuracy determines our confidence in understanding the relationship between brain activity and stimuli. In this paper, we compared the efficacy of three machine learning algorithms: neural network, support vector machines, and conditional random field to decode the visual stimuli or neural cognitive states from functional Magnetic Resonance data. Leave-one-out cross validation was performed to quantify the generalization accuracy of each algorithm on unseen data. The results indicated support vector machine and conditional random field have comparable performance and the potential of the latter is worthy of further investigation.

Anterior temporal cortex and semantic memory : reconciling findings from neuropsychology and functional imaging

Studies of semantic impairment arising from brain disease suggest that the anterior temporal lobes are critical for semantic abilities in humans; yet activation of these regions is rarely reported in functional imaging studies of healthy controls performing semantic tasks. Here, we combined neuropsychological and PET functional imaging data to show that when healthy subjects identify concepts at a specific level, the regions activated correspond to the site of maximal atrophy in patients with relatively pure semantic impairment. The stimuli were color photographs of common animals or vehicles, and the task was category verification at specific (e.g., robin), intermediate (e.g., bird), or general (e.g., animal) levels. Specific, relative to general, categorization activated the antero-lateral temporal cortices bilaterally, despite matching of these experimental conditions for difficulty. Critically, in patients with atrophy in precisely these areas, the most pronounced deficit was in the retrieval of specific semantic information.

Comparison of block and event-related experimental designs in diffusion-weighted functional MRI

PURPOSE To compare diffusion-weighted functional magnetic resonance imaging (DfMRI), a novel alternative to the blood oxygenation level-dependent (BOLD) contrast, in a functional MRI experiment. MATERIALS AND METHODS Nine participants viewed contrast reversing (7.5 Hz) black-and-white checkerboard stimuli using block and event-related paradigms. DfMRI (b = 1800 mm/s2 ) and BOLD sequences were acquired. Four parameters describing the observed signal were assessed: percent signal change, spatial extent of the activation, the Euclidean distance between peak voxel locations, and the time-to-peak of the best fitting impulse response for different paradigms and sequences. RESULTS The BOLD conditions showed a higher percent signal change relative to DfMRI; however, event-related DfMRI showed the strongest group activation (t = 21.23, P < 0.0005). Activation was more diffuse and spatially closer to the BOLD response for DfMRI when the block design was used. DfMRIevent showed the shortest TTP (4.4 +/- 0.88 sec). CONCLUSION The hemodynamic contribution to DfMRI may increase with the use of block designs.

Part 2. MOO methods for multidisciplinary design using parallel evolutionary algorithms, game theory and hierarchical topology. (Part 2.) Numerical aspects and implementation of model test cases

These lecture notes describe the use and implementation of a framework in which mathematical as well as engineering optimisation problems can be analysed. The foundations of the framework and algorithms described -Hierarchical Asynchronous Parallel Evolutionary Algorithms (HAPEAs) - lie upon traditional evolution strategies and incorporate the concepts of a multi-objective optimisation, hierarchical topology, asynchronous evaluation of candidate solutions , parallel computing and game strategies. In a step by step approach, the numerical implementation of EAs and HAPEAs for solving multi criteria optimisation problems is conducted providing the reader with the knowledge to reproduce these hand on training in his – her- academic or industrial environment.