Simple Series Research Articles

Dynamic modelling of a large-scale horizontal-axis wind turbine coupled to a hydraulic pump

The recent increase in demand for wind turbines has given rise to concerns about the availability of materials used in their construction. The combination of a wind turbine coupled to a hydraulic pump, instead of an electrical generator, offers an alternative application for this renewable energy technology. This article analyses the dynamic response of a wind/hydraulic system consisting of a wind turbine driving a swash-plate pump. This is done by means of mathematical models utilising MATLAB® and Simulink®. The operational aspects of the proposed wind/hydraulic pumping system are validated against the standard baseline 5-MW wind turbine model of the National Renewable Energy Laboratory. Control issues encountered in wind turbines and hydraulic systems are analysed, with the wind turbine control systems used to run electrical generators being adopted to control the pressure-controlled swash-plate pump. The pump lends itself to such applications as it can be controlled by varying the swash-plate angle, thus controlling the pump’s output power and torque. Methods for the generation of artificial wind data are also included, and a simple wind speed time series is developed. This time series is used to test the wind turbine hydraulic system under varying wind conditions using the concept of ‘control regions’. The models developed show the standard steady-state response of the wind turbine together with the dynamic behaviour of the wind turbine. Once the control parameters are established, the system also presents a wind turbine controller and analyses the behaviour of the system under typical and extreme wind conditions. This study concludes that such a system has significant potential to be technically achievable.

A new box iterative method for a class of nonlinear interface problems with application in solving Poisson–Boltzmann equation

In this paper, a new box iterative method for solving a class of nonlinear interface problems is proposed by intermixing linear and nonlinear boundary value problems based on a special seven-overlapped-boxes partition. It is then applied to the construction of a new finite element and finite difference hybrid scheme for solving the Poisson–Boltzmann equation (PBE) — a second order nonlinear elliptic interface problem for computing electrostatics of an ionic solvated protein. Furthermore, a modified Newton minimization algorithm accelerated by a multigrid preconditioned conjugate gradient method is presented to efficiently solve each involved nonlinear boundary value problem. In addition, the analytical solution of a Poisson dielectric test model with a spherical solute region containing multiple charges is expressed in a simple series of Legendre polynomials, resulting in a new PBE test model that works for a large number of point charges. The new PBE hybrid solver is programmed as a software package, and numerically validated on the new PBE test model with 892 point charges. It is also compared to a commonly used finite difference scheme in the accuracy of computing solution and electrostatic free energy for three proteins with up to 2124 atomic charges. Numerical results on six proteins demonstrate its high performance in comparison to the PBE finite element program package reported in Xie (2014).

Adaptive Time Series Momentum: An Objective Benchmark for Systematic Trend-Following Strategies

We propose a market-based benchmark for Managed Futures funds that relies on simple time series momentum strategies. Our benchmark mimics managers’ allocation process by increasing exposure when trends are developing and decreasing exposure when trends are fading by allocating to short and long-term trend-following strategies simultaneously. In addition, we take into account transaction costs, allowing a meaningful assessment of fund alpha. Our approach offers investors an alternative to widely used manager-based CTA benchmarks. Our model outperforms the explanatory power of existing market-based factor models applied to Managed Futures funds.

A chaotic analyzing method based on the dependence of neighbor sub-sequences in the data series

Ever since the special characteristics hidden in the chaos was discovered, the chaotic behavior has been extensively studied as a ubiquitous and complex nonlinear dynamic phenomenon, which is gradually extending to various disciplines of natural and social science, and the significant values in the theoretical and the practical application have attracted much attention from scholars of different fields in the recent decades. Conventional methods of analyzing chaotic dynamic systems, including the Lyapunov exponent, correlation dimension, Poincar map, unavoidably encounter some common problems, such as reconstruction of the phase space, determination of the linear area, etc. Besides, the current approaches each also possess a poor capability of balancing the direct observation and the quantitative calculation. Based on the fact that the neighbor data relate to each other to some degree, taking those shortages into consideration, aiming at depicting the chaotic features efficiently, a new method of analyzing the complicated chaotic motion is proposed. During the processing of that novel approach, the Euclidean distance is continuously computed to represent the dependence of the adjacent unit, after that, the original complicated array is converted into a simpler series composed of the distance of neighbor sub-sequences with more distinct characteristics. The mean value and the standard deviation of the newborn series are exacted to assist in describing the chaotic changing law. The method is adopted for studying the typical chaotic models, like Logistic model, Chebychev model, Duffing oscillator, Lorenz system, etc., which proves the good performances in explaining the chaotic variation rules in different systems. Based on the model verification, it could be seen that the method could detect the chaotic motion both qualitatively and quantitatively, and the ability for that method to resist the noise is improved up to some degree, what is more, the information about the real model is not required, thereby simplifying the analysis of the complicated chaotic behavior whose authentic model is unavailable. In addition, the method is applied to decomposing the vibration signal to monitor the working condition of the rotating rotor, and the results show that the conditional variation could be detected obviously. The analyses above show that the proposed method, on the basis of the dependence between nearby data, could perform well in observing the chaotic feature in an efficient way which simplifies the operation and clarifies the chaotic variation, moreover, the application potential of this method is worthy of great attention.

The coefficient of restitution of pressurized balls: a mechanistic model

Pressurized, inflated balls used in professional sports are regulated so that their behaviour upon impact can be anticipated and allow the game to have its distinctive character. However, the dynamics governing the impacts of such balls, even on stationary hard surfaces, can be extremely complex. The energy transformations, which arise from the compression of the gas within the ball and from the shear forces associated with the deformation of the wall, are examined in this paper. We develop a simple mechanistic model of the dependence of the coefficient of restitution, e, upon both the gauge pressure, PG, of the gas and the shear modulus, G, of the wall. The model is validated using the results from a simple series of experiments using three different sports balls. The fits to the data are extremely good for PG > 25 kPa and consistent values are obtained for the value of G for the wall material. As far as the authors can tell, this simple, mechanistic model of the pressure dependence of the coefficient of restitution is the first in the literature.

Role of a multimodal educational strategy on health care workers' handwashing

Good hand hygiene is the single most important strategy used to prevent health care-associated infections (HAIs); however, health care workers' (HCWs') hand hygiene compliance rates range between 25% and 51%. This study aims to determine if a multimodal strategy using the World Health Organization's (WHO's) My 5 Moments for Hand Hygiene methodology increases HCWs' compliance with handwashing and awareness of the importance of good hand hygiene in the prevention of HAIs. A quasi-experimental, 1-group pre-post survey design was used to test awareness and knowledge. A simple interrupted time series methodology at baseline and 3 months was used to monitor hand hygiene compliance. Overall, HCWs' hand hygiene compliance increased from 51.3% to 98.6%, with an odds ratio of 71.10. The pre-post survey demonstrated HCWs were aware and knowledgeable of the importance of good hand hygiene. Eight postsurvey questions focusing on the strategies used to promote hand hygiene demonstrated statistical significance using a 1-sample t test, with P values ranging from .000-.024. A multimodal approach using the WHO's My 5 Moments for Hand Hygiene does increase HCWs' hand hygiene compliance and awareness and knowledge of the importance of hand hygiene in the prevention of HAIs. Using this approach can produce a positive social change by reducing preventable disease and decreasing HAIs not only within a facility but also in the community.

Addendum to: An introduction to functional data analysis and a principal component approach for testing the equality of mean curves

We give an introduction to functional data analysis, with examples, and provide a brief review of the literature. We explain how principal component analysis (PCA) can be used to transform curves into finite dimensional data. An application of PCA is developed to test for the equality of the means of several populations (functional analysis of variance). Asymptotics are derived under the null hypothesis that the populations have the same mean curves. The selection of the basis for the projections and the power of the test is discussed for simple random samples and stationary time series samples of curves. We review the part of the literature which is needed to establish the validity of the PCA method. Two data sets, magnetogram records and stock returns, are used to illustrate the applicability of our limit results.

Thin Flexible Thermal Ground Planes: Fabrication and Scaling Characterization

Thermal ground planes (TGPs) are passive thermal management devices that utilize the latent heat associated with phase change to achieve high effective thermal conductance, similar to heat pipes. In this paper, we develop flexible TGPs with an ultra-thin thickness of 0.5 mm using copper-cladded polyimide as the encasing material, woven copper mesh as a wick, and electroplated copper pillars to support a vapor core. The lowest thermal resistance of one TGP is characterized to be only 1/3 that of an equivalently sized copper heat spreader. The effects of size scaling of evaporator and condenser, and overall TGP sizes on the thermal resistances of TGPs are experimentally characterized. A simple series thermal resistance model, which accounts for vapor core thermal resistance, is developed to predict the measured results. This experimentally validated model can be used for the design of TGPs with varying sizes of evaporator and condenser, and overall size. [2015-0032]

Affine Term Structure Model with Macroeconomic Factors: Do No‐Arbitrage Restriction and Macroeconomic Factors Imply Better Out‐of‐Sample Forecasts?

AbstractThis study extends the affine dynamic Nelson–Siegel model for the inclusion of macroeconomic variables. Five macroeconomic variables are included in affine term structure model, derived under the arbitrage‐free restriction, to evaluate their role in the in‐sample fitting and out‐of‐sample forecasting of the term structure. We show that the relationship between the macroeconomic factors and yield data has an intuitive interpretation, and that there is interdependence between the yield and macroeconomic factors. Moreover, the macroeconomic factors significantly improve the forecast performance of the model. The affine Nelson–Siegel type models outperform the benchmark simple time series forecast models. The out‐of‐sample predictability of the affine Nelson–Siegel model with macroeconomic factors for the short horizon is superior to the simple affine yield model for all maturities, and for longer horizons the former is still compatible to the latter, particularly for medium and long maturities. Copyright © 2015 John Wiley & Sons, Ltd.

Achievable rates and outage probability of cognitive radio with dynamic frequency hopping under imperfect spectrum sensing

In this study, the authors propose simple methods to evaluate the achievable rates and outage probability of a cognitive radio (CR) link that takes into account the imperfectness of spectrum sensing. In the considered system, the CR transmitter and receiver correlatively sense and dynamically exploit the spectrum pool via dynamic frequency hopping. Under imperfect spectrum sensing, false-alarm and miss-detection occur which cause impulsive interference emerged from collisions due to the simultaneous spectrum access of primary and cognitive users. That makes it very challenging to evaluate the achievable rates. By first examining the static link where the channel is assumed to be constant over time, they show that the achievable rate using a Gaussian input can be calculated accurately through a simple series representation. In the second part of this study, they extend the calculation of the achievable rate to wireless fading environments. To take into account the effect of fading, they introduce a piece-wise linear curve fitting-based method to approximate the instantaneous achievable rate curve as a combination of linear segments. It is then demonstrated that the ergodic achievable rate in fast fading and the outage probability in slow fading can be calculated to achieve any given accuracy level.

A Simple but Accurate Method for Analytical Estimation of Splice Loss in Single-Mode Triangular Index Fibers for Different V Numbers Including the Low Ones

AbstractWe report formulation of simple but accurate analytical expressions at the splice for both angular and transverse mismatches in case of single mode triangular index fibers. Here, we employ the simple series expression for fundamental modal field of such fibers. The analysis takes care of large

Multiple Avirulence Loci and Allele-Specific Effector Recognition Control the Pm3 Race-Specific Resistance of Wheat to Powdery Mildew.

In cereals, several mildew resistance genes occur as large allelic series; for example, in wheat (Triticum aestivum and Triticum turgidum), 17 functional Pm3 alleles confer agronomically important race-specific resistance to powdery mildew (Blumeria graminis). The molecular basis of race specificity has been characterized in wheat, but little is known about the corresponding avirulence genes in powdery mildew. Here, we dissected the genetics of avirulence for six Pm3 alleles and found that three major Avr loci affect avirulence, with a common locus_1 involved in all AvrPm3-Pm3 interactions. We cloned the effector gene AvrPm3(a2/f2) from locus_2, which is recognized by the Pm3a and Pm3f alleles. Induction of a Pm3 allele-dependent hypersensitive response in transient assays in Nicotiana benthamiana and in wheat demonstrated specificity. Gene expression analysis of Bcg1 (encoded by locus_1) and AvrPm3 (a2/f2) revealed significant differences between isolates, indicating that in addition to protein polymorphisms, expression levels play a role in avirulence. We propose a model for race specificity involving three components: an allele-specific avirulence effector, a resistance gene allele, and a pathogen-encoded suppressor of avirulence. Thus, whereas a genetically simple allelic series controls specificity in the plant host, recognition on the pathogen side is more complex, allowing flexible evolutionary responses and adaptation to resistance genes.

Exploring Carbohydrates with Bananas

Bananas can be used in many classroom lab activities to make carbohydrates easier for students to understand. I detail a simple series of banana activities that can be used to investigate carbohydrates across a wide range of levels of organization and that serve to connect carbohydrate concepts that might otherwise seem disparate to students. For example, the taste of a banana is linked to carbohydrate hydrolysis, as well as to organelle content within banana cells. Bananas can be used safely in any classroom, and inquiry-based learning can be used to progress through related course content. In addition, students will gain expertise in understanding cells viewed through the microscope as they try to examine the starch in the bananas.

On modular transformations of toric conformal blocks

We derive and solve the difference equations on the toric modular kernel following from the consistency relations in the fusion algebra. The result is explicit and simple series expansion for the toric modular kernel of non-degenerate Virasoro conformal blocks. We show that this expansion is equivalent to the celebrated integral representation due to B. Ponsot and J. Teschner. We also interpret obtained series representation as a non-perturbative expansion and note that this raises further questions.

Chapter 5 – Using simple data: surplus production models

<p>QUESTIONS ASKED</p><ul><li>How to assess the state of a fishery with a simple series of catch and effort?</li><li>How is a fishery model different from a trend analysis?</li><li>Do the assumptions of the model affect the management values of interest?</li><li>How to word a management advice?</li></ul><p> </p><p><a href="/index.php/SapEdu/article/downloadSuppFile/3600/166">Surplus production equilibrium and dynamic models</a></p>

Churn Prediction in Online Games Using Players’ Login Records: A Frequency Analysis Approach

The rise of free-to-play and other service-based business models in the online gaming market brought to game publishers problems usually associated to markets like mobile telecommunications and credit cards, especially customer churn. Predictive models have long been used to address this issue in these markets, where companies have a considerable amount of demographic, economic, and behavioral data about their customers, while online game publishers often only have behavioral data. Simple time series’ feature representation schemes like RFM can provide reasonable predictive models solely based on online game players’ login records, but maybe without fully exploring the predictive potential of these data. We propose a frequency analysis approach for feature representation from login records for churn prediction modeling. These entries (from real data) were converted into fixed-length data arrays using four different methods, and then these were used as input for training probabilistic classifiers with the $k$ -nearest neighbors machine learning algorithm. The classifiers were then evaluated and compared using predictive performance metrics. One of the methods, the time-frequency plane domain analysis, showed satisfactory results, being able to theoretically increase the retention campaigns profits in more than 20% over the RFM approach.

CORR Insights(®): Should High-grade Extraosseous Osteosarcoma Be Treated With Multimodality Therapy Like Other Soft Tissue Sarcomas?

O rthopaedic oncology is a specialty defined by uncertainty. Although we are able to diagnose sarcoma subtypes reliably and recommend treatment plans, the reality is that even tumors with the same name can act dramatically differently. Therefore, the clinician must use many details about the individual scenario—grade, histologic subtype, size, depth, location, stage, and patient demographics—to make a logical guess as to the anticipated behavior of the diagnosed condition. As described by Fan and colleagues [3], extraosseous osteosarcoma is a representative example of a common dilemma in orthopaedic oncology: How do we select the most appropriate management strategy when we are unable to accurately predict the natural history or response to treatment in a rare cancer? The first attempt at understanding a rare tumor is by a simple descriptive case report or case series to reveal general observations about the patients in which the tumor occurs, the common features of presentation, the treatments attempted, and the observations on oncologic outcomes (survival and recurrence). The current study is the most recent in a line of such case series. In attempt to view this problem from a novel perspective, Fan and colleagues question if extraosseous osteosarcoma behaves more similarly to a soft tissue sarcoma than a skeletal osteosarcoma. This is a reasonable approach, and has worked well for most subtypes of highgrade soft tissue sarcoma as their biologic aggressiveness, response to treatment, and patient outcomes are similar. They found that some features, such as a therapeutic response to radiation, are more characteristic of soft tissue sarcoma than skeletal osteosarcoma. As the authors note, the heterogeneity of treatment increases the difficulty in interpretation, as there was This CORR Insights is a commentary on the article ‘‘Should High-grade Extraosseous Osteosarcoma Be Treated With Multimodality TherapyLikeOther Soft Tissue Sarcomas?’’ by Fan and colleagues available at: DOI: 10.1007/s11999-015-4463-y. The author certifies that he, or a member of his immediate family, has no funding or commercial associations (eg, consultancies, stock ownership, equity interest, patent/ licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article. All ICMJE Conflict of Interest Forms for authors andClinical Orthopaedics and Related Research editors and board members are on file with the publication and can be viewed on request. The opinions expressed are those of thewriters, and do not reflect the opinion or policy of CORR or The Association of Bone and Joint Surgeons. This CORR Insights comment refers to the article available at DOI: 10.1007/s11999-0154463-y. B. J. Miller MD, MS (&) Department of Orthopaedics and Rehabilitation, University of Iowa, 200 Hawkins Dr, 01015 JPP, Iowa City, IA 52242, USA e-mail: benjamin-j-miller@uiowa.edu CORR Insights Published online: 27 August 2015 The Association of Bone and Joint Surgeons1 2015

Machine Learning in Fine Wine Price Prediction

AbstractAdvanced machine learning techniques like Gaussian process regression and multi-task learning are novel in the area of wine price prediction; previous research in this area being restricted to parametric linear regression models when predicting wine prices. Using historical price data of the 100 wines in the Liv-Ex 100 index, the main contributions of this paper to the field are, firstly, a clustering of the wines into two distinct clusters based on autocorrelation. Secondly, an implementation of Gaussian process regression on these wines with predictive accuracy surpassing both the trivial and simple ARMA and GARCH time series prediction benchmarks. Lastly, an implementation of an algorithm which performs multi-task feature learning with kernels on the wine returns as an extension to our optimal Gaussian process regression model. Using the optimal covariance kernel from Gaussian process regression, we achieve predictive results which are comparable to that of Gaussian process regression. Altogether, our research suggests that there is potential in using advanced machine learning techniques in wine price prediction. (JEL Classifications: C6, G12)

Study on unblocked reliability of Beijing, China, urban road network

The urban road network is the basis of urban transportation and its reliability plays an extremely important role in relieving congestion. The present study took unblocked reliability as the index for a road network reliability study. As the saturation cannot fully reflect the traffic situation, first, the formula for unblock reliability was improved and second, the minimal road set algorithm in graph theory was used to simplify the road network as a simple series–parallel network, and by combination with real-time data of the road network in Beijing, China, a calculation method of urban road network reliability was established. Finally, as an extension of the unblocked reliability evaluation, a sensitivity analysis was conducted for each road network and the critical sections in the road network were determined. The simulation results show that the improved unblocked reliability can sensitively reflect the traffic congestion situation and sensitivity analysis based on the unblocked reliability evaluation can effectively locate the sections in the road network that are critical for solving the congestion problem. The results of the study provide reasonable decision-making and theoretic support for traffic management departments.

Predicting high-cost pediatric patients: derivation and validation of a population-based model.

Health care administrators often lack feasible methods to prospectively identify new pediatric patients with high health care needs, precluding the ability to proactively target appropriate population health management programs to these children. To develop and validate a predictive model identifying high-cost pediatric patients using parent-reported health (PRH) measures that can be easily collected in clinical and administrative settings. Retrospective cohort study using 2-year panel data from the 2001 to 2011 rounds of the Medical Expenditure Panel Survey. A total of 24,163 children aged 5-17 with family incomes below 400% of the federal poverty line were included in this study. Predictive performance, including the c-statistic, sensitivity, specificity, and predictive values, of multivariate logistic regression models predicting top-decile health care expenditures over a 1-year period. Seven independent domains of PRH measures were tested for predictive capacity relative to basic sociodemographic information: the Children with Special Health Care Needs (CSHCN) Screener; subjectively rated health status; prior year health care utilization; behavioral problems; asthma diagnosis; access to health care; and parental health status and access to care. The CSHCN screener and prior year utilization domains exhibited the highest incremental predictive gains over the baseline model. A model including sociodemographic characteristics, the CSHCN screener, and prior year utilization had a c-statistic of 0.73 (95% confidence interval, 0.70-0.74), surpassing the commonly used threshold to establish sufficient predictive capacity (c-statistic>0.70). The proposed prediction tool, comprising a simple series of PRH measures, accurately stratifies pediatric populations by their risk of incurring high health care costs.