Thrust allocation with linear constrained quadratic cost function

In this paper, a method of thrust allocation based on a linearly constrained quadratic cost function capable of handling rotating azimuths is presented. The problem formulation accounts for magnitude and rate constraints on both thruster forces and azimuth angles. The advantage of this formulation is that the solution can be found with a finite number of iterations for each time step. Experiments with a model ship are used to validate the thrust allocation system.

Stochastic output feedback model predictive control

This paper addresses the issue of output feedback model predictive control for linear systems with input constraints and stochastic disturbances. We show that the optimal policy uses the Kalman filter for state estimation, but the resultant state estimates are not utilized in a certainty equivalence control law

kmth price sealed-bid auctions with general independent values and equilibrium linear mark-ups

A generalised bidding model is developed to calculate a bidder’s expected profit and auctioners expected revenue/payment for both a General Independent Value and Independent Private Value (IPV) kmth price sealed-bid auction (where the mth bidder wins at the kth bid payment) using a linear (affine) mark-up function. The Common Value (CV) assumption, and highbid and lowbid symmetric and asymmetric First Price Auctions and Second Price Auctions are included as special cases. The optimal n bidder symmetric analytical results are then provided for the uniform IPV and CV models in equilibrium. Final comments concern implications, the assumptions involved and prospects for further research.

A new approach to spatial data interpolation using higher-order statistics

Interpolation techniques for spatial data have been applied frequently in various fields of geosciences. Although most conventional interpolation methods assume that it is sufficient to use first- and second-order statistics to characterize random fields, researchers have now realized that these methods cannot always provide reliable interpolation results, since geological and environmental phenomena tend to be very complex, presenting non-Gaussian distribution and/or non-linear inter-variable relationship. This paper proposes a new approach to the interpolation of spatial data, which can be applied with great flexibility. Suitable cross-variable higher-order spatial statistics are developed to measure the spatial relationship between the random variable at an unsampled location and those in its neighbourhood. Given the computed cross-variable higher-order spatial statistics, the conditional probability density function (CPDF) is approximated via polynomial expansions, which is then utilized to determine the interpolated value at the unsampled location as an expectation. In addition, the uncertainty associated with the interpolation is quantified by constructing prediction intervals of interpolated values. The proposed method is applied to a mineral deposit dataset, and the results demonstrate that it outperforms kriging methods in uncertainty quantification. The introduction of the cross-variable higher-order spatial statistics noticeably improves the quality of the interpolation since it enriches the information that can be extracted from the observed data, and this benefit is substantial when working with data that are sparse or have non-trivial dependence structures.

Understanding Uncertainties in Non-Linear Population Trajectories: A Bayesian Semi-Parametric Hierarchical Approach to Large-Scale Surveys of Coral Cover

Recently, attempts to improve decision making in species management have focussed on uncertainties associated with modelling temporal fluctuations in populations. Reducing model uncertainty is challenging; while larger samples improve estimation of species trajectories and reduce statistical errors, they typically amplify variability in observed trajectories. In particular, traditional modelling approaches aimed at estimating population trajectories usually do not account well for nonlinearities and uncertainties associated with multi-scale observations characteristic of large spatio-temporal surveys. We present a Bayesian semi-parametric hierarchical model for simultaneously quantifying uncertainties associated with model structure and parameters, and scale-specific variability over time. We estimate uncertainty across a four-tiered spatial hierarchy of coral cover from the Great Barrier Reef. Coral variability is well described; however, our results show that, in the absence of additional model specifications, conclusions regarding coral trajectories become highly uncertain when considering multiple reefs, suggesting that management should focus more at the scale of individual reefs. The approach presented facilitates the description and estimation of population trajectories and associated uncertainties when variability cannot be attributed to specific causes and origins. We argue that our model can unlock value contained in large-scale datasets, provide guidance for understanding sources of uncertainty, and support better informed decision making

A continent-wide study reveals clear relationships between regional abiotic conditions and post-dispersal seed predation

Aim Large-scale patterns linking energy availability, biological productivity and diversity form a central focus of ecology. Despite evidence that the activity and abundance of animals may be limited by climatic variables associated with regional biological productivity (e.g. mean annual precipitation and annual actual evapotranspiration), it is unclear whether plant–granivore interactions are themselves influenced by these climatic factors across broad spatial extents. We evaluated whether climatic conditions that are known to alter the abundance and activity of granivorous animals also affect rates of seed removal. Location Eleven sites across temperate North America. Methods We used a common protocol to assess the removal of the same seed species (Avena sativa) over a 2-day period. Model selection via the Akaike information criterion was used to determine a set of candidate binomial generalized linear mixed models that evaluated the relationship between local climatic data and post-dispersal seed predation. Results Annual actual evapotranspiration was the single best predictor of the proportion of seeds removed. Annual actual evapotranspiration and mean annual precipitation were both positively related to mean seed removal and were included in four and three of the top five models, respectively. Annual temperature range was also positively related to seed removal and was an explanatory variable in three of the top four models. Main conclusions Our work provides the first evidence that energy and precipitation, which are known to affect consumer abundance and activity, also translate to strong, predictable patterns of seed predation across a continent. More generally, these findings suggest that future changes in temperature and precipitation could have widespread consequences for plant species composition in grasslands, through impacts on plant recruitment.

Identifying weak linear features with the "coalescing shortest path image transform"

The detection of line-like features in images finds many applications in microanalysis. Actin fibers, microtubules, neurites, pilis, DNA, and other biological structures all come up as tenuous curved lines in microscopy images. A reliable tracing method that preserves the integrity and details of these structures is particularly important for quantitative analyses. We have developed a new image transform called the "Coalescing Shortest Path Image Transform" with very encouraging properties. Our scheme efficiently combines information from an extensive collection of shortest paths in the image to delineate even very weak linear features. © Copyright Microscopy Society of America 2011.

Models of protein linear molecular motors for dynamic nanodevices

Protein molecular motors are natural nano-machines that convert the chemical energy from the hydrolysis of adenosine triphosphate into mechanical work. These efficient machines are central to many biological processes, including cellular motion, muscle contraction and cell division. The remarkable energetic efficiency of the protein molecular motors coupled with their nano-scale has prompted an increasing number of studies focusing on their integration in hybrid micro- and nanodevices, in particular using linear molecular motors. The translation of these tentative devices into technologically and economically feasible ones requires an engineering, design-orientated approach based on a structured formalism, preferably mathematical. This contribution reviews the present state of the art in the modelling of protein linear molecular motors, as relevant to the future design-orientated development of hybrid dynamic nanodevices. © 2009 The Royal Society of Chemistry.

The BAD project: data mining, database and prediction of protein adsorption on surfaces

Protein adsorption at solid-liquid interfaces is critical to many applications, including biomaterials, protein microarrays and lab-on-a-chip devices. Despite this general interest, and a large amount of research in the last half a century, protein adsorption cannot be predicted with an engineering level, design-orientated accuracy. Here we describe a Biomolecular Adsorption Database (BAD), freely available online, which archives the published protein adsorption data. Piecewise linear regression with breakpoint applied to the data in the BAD suggests that the input variables to protein adsorption, i.e., protein concentration in solution; protein descriptors derived from primary structure (number of residues, global protein hydrophobicity and range of amino acid hydrophobicity, isoelectric point); surface descriptors (contact angle); and fluid environment descriptors (pH, ionic strength), correlate well with the output variable-the protein concentration on the surface. Furthermore, neural network analysis revealed that the size of the BAD makes it sufficiently representative, with a neural network-based predictive error of 5% or less. Interestingly, a consistently better fit is obtained if the BAD is divided in two separate sub-sets representing protein adsorption on hydrophilic and hydrophobic surfaces, respectively. Based on these findings, selected entries from the BAD have been used to construct neural network-based estimation routines, which predict the amount of adsorbed protein, the thickness of the adsorbed layer and the surface tension of the protein-covered surface. While the BAD is of general interest, the prediction of the thickness and the surface tension of the protein-covered layers are of particular relevance to the design of microfluidics devices.

Evidence for a simple linear relationship between MRSA rates and hand-washing compliance [Comment]

Is there a threshold above which hand-rub solution consumption is efficient for decreasing MRSA incidence? [J Hosp Infect. 2009] Association between an index of consumption of hand-rub solution and the incidence of acquired meticillin-resistant Staphylococcus aureus in an intensive care unit.

Psychological well-being status among medical and dental students in Makkah, Saudi Arabia: A cross-sectional study

Objectives Medical and dental students experience poor psychological well-being relative to their peers. This study aimed to assess the psychological well-being among medical and dental students in Saudi Arabia, identify the high-risk groups and assess the association between the psychological well-being and the academic performance. Methods In this cross-sectional study, 422 preclinical medical and dental students at Umm Al-Qura University, Saudi Arabia, were recruited to assess their depression, anxiety, stress, self-efficacy and satisfaction with life levels using 21-items Depression Anxiety Stress Scale (DASS-21), General Self-Efficacy (GSE) scale and Satisfaction With Life Scale (SWLS). Students’ academic weighted grades were obtained later. Descriptive statistics and univariate general linear model were used to analyse data. Results High levels of depression (69.9%), anxiety (66.4%) and stress (70.9%) were indicated, whereas self-efficacy (mean = 27.22, sd = 4.85) and life satisfaction (mean = 23.60, sd = 6.37) were within the normal range. Female medical students had higher psychological distress in contrast to dental students. In general, third-year students were more depressed and stressed in comparison with second-year students, except for stress among dental students. Moreover, all females had higher self-efficacy than males. Life satisfaction was higher within the second-year and high family income students. Depression was the only psychological variable correlated with the academic performance. Conclusion High levels of psychological distress were found. Female medical students had higher psychological distress than males, whereas male dental students had higher distress than female. Medical students at third year were more depressed and stressed. Dental students were more depressed in the third year, but more stressed in the second year. Attention should be directed towards reducing the alarming levels of depression, anxiety and stress among medical and dental students.

Optimizing preventative maintenance strategies for linear assets

Linear assets are engineering infrastructure, such as pipelines, railway lines, and electricity cables, which span long distances and can be divided into different segments. Optimal management of such assets is critical for asset owners as they normally involve significant capital investment. Currently, Time Based Preventive Maintenance (TBPM) strategies are commonly used in industry to improve the reliability of such assets, as they are easy to implement compared with reliability or risk-based preventive maintenance strategies. Linear assets are normally of large scale and thus their preventive maintenance is costly. Their owners and maintainers are always seeking to optimize their TBPM outcomes in terms of minimizing total expected costs over a long term involving multiple maintenance cycles. These costs include repair costs, preventive maintenance costs, and production losses. A TBPM strategy defines when Preventive Maintenance (PM) starts, how frequently the PM is conducted and which segments of a linear asset are operated on in each PM action. A number of factors such as required minimal mission time, customer satisfaction, human resources, and acceptable risk levels need to be considered when planning such a strategy. However, in current practice, TBPM decisions are often made based on decision makers’ expertise or industrial historical practice, and lack a systematic analysis of the effects of these factors. To address this issue, here we investigate the characteristics of TBPM of linear assets, and develop an effective multiple criteria decision making approach for determining an optimal TBPM strategy. We develop a recursive optimization equation which makes it possible to evaluate the effect of different maintenance options for linear assets, such as the best partitioning of the asset into segments and the maintenance cost per segment.

High order unconditionally stable difference schemes for the Riesz space-fractional telegraph equation

In this paper, a class of unconditionally stable difference schemes based on the Pad´e approximation is presented for the Riesz space-fractional telegraph equation. Firstly, we introduce a new variable to transform the original dfferential equation to an equivalent differential equation system. Then, we apply a second order fractional central difference scheme to discretise the Riesz space-fractional operator. Finally, we use (1, 1), (2, 2) and (3, 3) Pad´e approximations to give a fully discrete difference scheme for the resulting linear system of ordinary differential equations. Matrix analysis is used to show the unconditional stability of the proposed algorithms. Two examples with known exact solutions are chosen to assess the proposed difference schemes. Numerical results demonstrate that these schemes provide accurate and efficient methods for solving a space-fractional hyperbolic equation.