QoE modelling for VP9 and H.265 videos on mobile devices

Current mobile devices and streaming video services support high definition (HD) video, increasing expectation for more contents. HD video streaming generally requires large bandwidth, exerting pressures on existing networks. New generation of video compression codecs, such as VP9 and H.265/HEVC, are expected to be more effective for reducing bandwidth. Existing studies to measure the impact of its compression on users’ perceived quality have not been focused on mobile devices. Here we propose new Quality of Experience (QoE) models that consider both subjective and objective assessments of mobile video quality. We introduce novel predictors, such as the correlations between video resolution and size of coding unit, and achieve a high goodness-of-fit to the collected subjective assessment data (adjusted R-square >83%). The performance analysis shows that H.265 can potentially achieve 44% to 59% bit rate saving compared to H.264/AVC, slightly better than VP9 at 33% to 53%, depending on video content and resolution.

Modelling interregional links in electricity price spikes

Abnormally high price spikes in spot electricity markets represent a significant risk to market participants. As such, a literature has developed that focuses on forecasting the probability of such spike events, moving beyond simply forecasting the level of price. Many univariate time series models have been proposed to dealwith spikes within an individual market region. This paper is the first to develop a multivariate self-exciting point process model for dealing with price spikes across connected regions in the Australian National Electricity Market. The importance of the physical infrastructure connecting the regions on the transmission of spikes is examined. It is found that spikes are transmitted between the regions, and the size of spikes is influenced by the available transmission capacity. It is also found that improved risk estimates are obtained when inter-regional linkages are taken into account.

Recurrence risk modelling of the genetic susceptibility to ankylosing spondylitis

Objectives - It has long been suspected that susceptibility to ankylosing spondylitis (AS) is influenced by genes lying distant to the major histocompatibility complex. This study compares genetic models of AS to assess the most likely mode of inheritance, using recurrence risk ratios in relatives of affected subjects. Methods - Recurrence risk ratios in different degrees of relatives were determined using published data from studies specifically designed to address the question. The methods of Risch were used to determine the expected recurrence risk ratios in different degrees of relatives, assuming equal first degree relative recurrence risk between models. Goodness of fit was determined by χ2 comparison of the expected number of affected subjects with the observed number, given equal numbers of each type of relative studied. Results - The recurrence risks in different degrees of relatives were: monozygotic (MZ) twins 63% (17/27), first degree relatives 8.2% (441/5390), second degree relatives 1.0% (8/834), and third degree relatives 0.7% (7/997). Parent-child recurrence risk (7.9%, 37/466) was not significantly different from the sibling recurrence risk (8.2%, 404/4924), excluding a significant dominance genetic component to susceptibility. Poor fitting models included single gene, genetic heterogeneity, additive, two locus multiplicative, and one locus and residual polygenes (χ2 > 32 (two degrees of freedom), p < 10-6 for all models). The best fitting model studied was a five locus model with multiplicative interaction between loci (χ2 = 1.4 (two degrees of freedom), p = 0.5). Oligogenic multiplicative models were the best fitting over a range of population prevalences and first degree recurrence risk rates. Conclusions - This study suggests that of the genetic models tested, the most likely model operating in AS is an oligogenic model with predominantly multiplicative interaction between loci.

Systems modelling of the socio-technical aspects of residential electricity use and network peak demand

Provision of network infrastructure to meet rising network peak demand is increasing the cost of electricity. Addressing this demand is a major imperative for Australian electricity agencies. The network peak demand model reported in this paper provides a quantified decision support tool and a means of understanding the key influences and impacts on network peak demand. An investigation of the system factors impacting residential consumers’ peak demand for electricity was undertaken in Queensland, Australia. Technical factors, such as the customers’ location, housing construction and appliances, were combined with social factors, such as household demographics, culture, trust and knowledge, and Change Management Options (CMOs) such as tariffs, price,managed supply, etc., in a conceptual ‘map’ of the system. A Bayesian network was used to quantify the model and provide insights into the major influential factors and their interactions. The model was also used to examine the reduction in network peak demand with different market-based and government interventions in various customer locations of interest and investigate the relative importance of instituting programs that build trust and knowledge through well designed customer-industry engagement activities. The Bayesian network was implemented via a spreadsheet with a tick box interface. The model combined available data from industry-specific and public sources with relevant expert opinion. The results revealed that the most effective intervention strategies involve combining particular CMOs with associated education and engagement activities. The model demonstrated the importance of designing interventions that take into account the interactions of the various elements of the socio-technical system. The options that provided the greatest impact on peak demand were Off-Peak Tariffs and Managed Supply and increases in the price of electricity. The impact in peak demand reduction differed for each of the locations and highlighted that household numbers, demographics as well as the different climates were significant factors. It presented possible network peak demand reductions which would delay any upgrade of networks, resulting in savings for Queensland utilities and ultimately for households. The use of this systems approach using Bayesian networks to assist the management of peak demand in different modelled locations in Queensland provided insights about the most important elements in the system and the intervention strategies that could be tailored to the targeted customer segments.

Nanostructured hydroxyapatite surfaces-mediated adsorption alters recognition of BMP receptor IA and bioactivity of bone morphogenetic protein-2

Highly efficient loading of bone morphogenetic protein-2 (BMP-2) onto carriers with desirable performance is still a major challenge in the field of bone regeneration. Till now, the nanoscaled surface-induced changes of the structure and bioactivity of BMP-2 remains poorly understood. Here, the effect of nanoscaled surface on the adsorption and bioactivity of BMP-2 was investigated with a series of hydroxyapatite surfaces (HAPs): HAP crystal-coated surface (HAP), HAP crystal-coated polished surface (HAP-Pol), and sintered HAP crystal-coated surface (HAP-Sin). The adsorption dynamics of recombinant human BMP-2 (rhBMP-2) and the accessibility of the binding epitopes of adsorbed rhBMP-2 for BMP receptors (BMPRs) were examined by a quartz crystal microbalance with dissipation. Moreover, the bioactivity of adsorbed rhBMP-2 and the BMP-induced Smad signaling were investigated with C2C12 model cells. A noticeably high mass-uptake of rhBMP-2 and enhanced recognition of BMPR-IA to adsorbed rhBMP-2 were found on the HAP-Pol surface. For the rhBMP-2-adsorbed HAPs, both ALP activity and Smad signaling increased in the order of HAP-Sin < HAP < HAP-Pol. Furthermore, hybrid molecular dynamics and steered molecular dynamics simulations validated that BMP-2 tightly anchored on the HAP-Pol surface with a relative loosened conformation, but the HAP-Sin surface induced a compact conformation of BMP-2. In conclusion, the nanostructured HAPs can modulate the way of adsorption of rhBMP-2, and thus the recognition of BMPR-IA and the bioactivity of rhBMP-2. These findings can provide insightful suggestions for the future design and fabrication of rhBMP-2-based scaffolds/implants.

Induction of a Th1 immune response and suppression of IgE via immunotherapy with a recombinant hybrid molecule encapsulated in liposome-protamine-DNA nanoparticles in a model of experimental allergy

Liposome-protamine-DNA nanoparticles (LPD) are safe, effective, and non-toxic adjuvants that induce Th1-like immune responses. We hypothesized that encapsulation of allergens into liposomes could be an appropriate option for immunotherapy. The present study evaluated the immunotherapeutic potential of a recombinant hybrid molecule (rHM) encapsulated in LPD nanoparticles in a murine model of Chenopodium album allergy. BALB/c mice were sensitized with the allergen in alum, and the immunotherapy procedure was performed by subcutaneous injections of LPD-rHM, rHM, or empty LPD at weekly intervals. Sensitized mice developed a Th2-biased immune response characterized by strong specific IgG1 and IgE production, IL-4, and the transcription factor GATA3 in spleen cell cultures. Treatment with the LPD-rHM resulted in a reduction in IgE and a marked increase in IgG2a. The LPD-rHM induced allergen-specific responses with relatively high interferon-gamma production, as well as expression of the transcription factor T-bet in stimulated splenocytes. In addition, lymphoproliferative responses were higher in the LPD-rHM-treated mice than in the other groups. Removal of the nanoparticles from the rHM resulted in a decrease in the allergen's immunogenicity. These results indicate that the rHM complexed with LPD nanoparticles has a marked suppressive effect on the allergic response and caused a shift toward a Th1 pathway.

Exploring accuracy factors in cost estimating practice towards implementing Building Information Modelling (BIM)

Cost estimating has been acknowledged as a crucial component of construction projects. Depending on available information and project requirements, cost estimates evolve in tandem with project lifecycle stages; conceptualisation, design development, execution and facility management. The premium placed on the accuracy of cost estimates is crucial to producing project tenders and eventually in budget management. Notwithstanding the initial slow pace of its adoption, Building Information Modelling (BIM) has successfully addressed a number of challenges previously characteristic of traditional approaches in the AEC, including poor communication, the prevalence of islands of information and frequent reworks. Therefore, it is conceivable that BIM can be leveraged to address specific shortcomings of cost estimation. The impetus for leveraging BIM models for accurate cost estimation is to align budgeted and actual cost. This paper hypothesises that the accuracy of BIM-based estimation, as more efficient, process-mirrors of traditional cost estimation methods, can be enhanced by simulating traditional cost estimation factors variables. Through literature reviews and preliminary expert interviews, this paper explores the factors that could potentially lead to more accurate cost estimates for construction projects. The findings show numerous factors that affect the cost estimates ranging from project information and its characteristic, project team, clients, contractual matters, and other external influences. This paper will make a particular contribution to the early phase of BIM-based project estimation.

Cost estimating practice incorporating Building Information Modelling (BIM): Malaysian Quantity Surveyors perspectives

Cost estimating is a key task within Quantity Surveyors’ (QS) offices. Provision of an accurate estimate is vital to ensure that the objectives of the client are met by staying within the client’s budget. Building Information Modelling (BIM) is an evolving technology that has gained attention in the construction industries all over the world. Benefits from the use of BIM include cost and time savings if the processes used by the procurement team are adapted to maximise the benefits of BIM. BIM can be used by QSs to automate aspects of quantity take-off and the preparation of estimates, decreasing turnaround time and assist in controlling errors and inaccuracies. The Malaysian government has decided to require the use of BIM for its projects beginning from 2016. However, slow uptake is reported in the use of BIM both within companies and to support collaboration within the Malaysian industry. It has been recommended that QSs to start evaluating the impact of BIM on their practices. This paper reviews the perspectives of QSs in Malaysia towards the use of BIM to achieve more dependable results in their cost estimating practice. The objectives of this paper include identifying strategies in improving practice and potential adoption drivers that lead QSs to BIM usage in their construction projects. From the expert interviews, it was found out that, despite still using traditional methods and not practising BIM, the interviewees still acquire limited knowledge related to BIM. There are some drivers that potentially motivate them to employ BIM in their practices. These include client demands, innovation in traditional methods, speed in estimating costs, reduced time and costs, improvement in practices and self-awareness, efficiency in projects, and competition from other companies. The findings of this paper identify the potential drivers in encouraging Malaysian Quantity Surveyors to exploit BIM in their construction projects.

Trans-scale granular modelling of cytoskeleton: A mini-review

Living cells are the functional unit of organs that controls reactions to their exterior. However, the mechanics of living cells can be difficult to characterize due to the crypticity of their microscale structures and associated dynamic cellular processes. Fortunately, multiscale modelling provides a powerful simulation tool that can be used to study the mechanical properties of these soft hierarchical, biological systems. This paper reviews recent developments in hierarchical multiscale modeling technique that aimed at understanding cytoskeleton mechanics. Discussions are expanded with respects to cytoskeletal components including: intermediate filaments, microtubules and microfilament networks. The mechanical performance of difference cytoskeleton components are discussed with respect to their structural and material properties. Explicit granular simulation methods are adopted with different coarse-grained strategies for these cytoskeleton components and the simulation details are introduced in this review.

Review and future options for computer modelling in the sugar industry

Computer modelling has been used extensively in some processes in the sugar industry to achieve significant gains. This paper reviews the investigations carried out over approximately the last twenty five years, including the successes but also areas where problems and delays have been encountered. In that time the capability of both hardware and software have increased dramatically. For some processes such as cane cleaning, cane billet preparation, and sugar drying, the application of computer modelling towards improved equipment design and operation has been quite limited. A particular problem has been the large number of particles and particle interactions in these…

Generalizing the use of geographical weights in biodiversity modelling

Aim Determining how ecological processes vary across space is a major focus in ecology. Current methods that investigate such effects remain constrained by important limiting assumptions. Here we provide an extension to geographically weighted regression in which local regression and spatial weighting are used in combination. This method can be used to investigate non-stationarity and spatial-scale effects using any regression technique that can accommodate uneven weighting of observations, including machine learning. Innovation We extend the use of spatial weights to generalized linear models and boosted regression trees by using simulated data for which the results are known, and compare these local approaches with existing alternatives such as geographically weighted regression (GWR). The spatial weighting procedure (1) explained up to 80% deviance in simulated species richness, (2) optimized the normal distribution of model residuals when applied to generalized linear models versus GWR, and (3) detected nonlinear relationships and interactions between response variables and their predictors when applied to boosted regression trees. Predictor ranking changed with spatial scale, highlighting the scales at which different species–environment relationships need to be considered. Main conclusions GWR is useful for investigating spatially varying species–environment relationships. However, the use of local weights implemented in alternative modelling techniques can help detect nonlinear relationships and high-order interactions that were previously unassessed. Therefore, this method not only informs us how location and scale influence our perception of patterns and processes, it also offers a way to deal with different ecological interpretations that can emerge as different areas of spatial influence are considered during model fitting.

Two-scale computational modelling of water flow in unsaturated soils containing irregular-shaped inclusions

The focus of this paper is two-dimensional computational modelling of water flow in unsaturated soils consisting of weakly conductive disconnected inclusions embedded in a highly conductive connected matrix. When the inclusions are small, a two-scale Richards’ equation-based model has been proposed in the literature taking the form of an equation with effective parameters governing the macroscopic flow coupled with a microscopic equation, defined at each point in the macroscopic domain, governing the flow in the inclusions. This paper is devoted to a number of advances in the numerical implementation of this model. Namely, by treating the micro-scale as a two-dimensional problem, our solution approach based on a control volume finite element method can be applied to irregular inclusion geometries, and, if necessary, modified to account for additional phenomena (e.g. imposing the macroscopic gradient on the micro-scale via a linear approximation of the macroscopic variable along the microscopic boundary). This is achieved with the help of an exponential integrator for advancing the solution in time. This time integration method completely avoids generation of the Jacobian matrix of the system and hence eases the computation when solving the two-scale model in a completely coupled manner. Numerical simulations are presented for a two-dimensional infiltration problem.

A novel fluorescent probe involving a graphene quantum dot-enzyme hybrid system for the analysis of hydroquinone in the presence of toxic resorcinol and catechol

An efficient method for the analysis of hydroquinone at trace levels in water samples has been developed in the form of a fluorescent probe based on graphene quantum dots (GQDs). The analytical variable, fluorescence quenching, was generated from the formation of benzoquinone intermediates, which formed during the catalytic oxidation of hydroquinone by horseradish peroxidase (HRP). In general, the reaction mechanism involved hydroquinone, as an electron acceptor, which affected the surface state of GQDs via an electron transfer effect. The water-soluble GQDs were directly prepared by the pyrolysis of citric acid and with the use of the mentioned hybrid enzyme system, the detection limit for hydroquinone was as low as 8.4 × 10−8 M. Furthermore, this analysis was almost unaffected by other phenol and quinine compounds, such as phenol, resorcinol and other quinines, and therefore, the developed GQD method produced satisfactory results for the analysis of hydroquinone in several different lake water samples.

Hybrid metaheuristic techniques for optimising sugarcane rail operations

Mixed integer programming and parallel-machine job shop scheduling are used to solve the sugarcane rail transport scheduling problem. Constructive heuristics and metaheuristics were developed to produce a more efficient scheduling system and so reduce operating costs. The solutions were tested on small and large size problems. High-quality solutions and improved CPU time are the result of developing new hybrid techniques which consist of different ways of integrating simulated annealing and Tabu search techniques.

Multipurpose nanoporous alumina-carbon nanowall bi-dimensional nano-hybrid platform via catalyzed and catalyst-free plasma CVD

Simple, rapid, plasma-assisted synthesis of large-area arrays of vertically-aligned carbon nanowalls on highly-porous, transparent bare and gold-coated alumina membranes with the two pore sizes is reported. It is demonstrated that the complex patterns of vertically aligned nanowalls can nucleate and form different morphologies in the low-temperature plasmas. The process is stable, and the twofold change in the gas flow (10 and 20 sccm) does not noticeably influence the morphology of the nanowall pattern. Application of a thin (5 nm) gold layer to nanoporous membrane prior to the nanowall growth allows controlling the network morphology.