Optimal Size Selection Research Articles

Detection of air gaps around the cylinder by postinsertion computed tomography in vaginal cuff brachytherapy: A prospective series, systematic review, and meta-analysis

PurposePostinsertion computed tomography (CT) can identify air gaps (AGs) around the cylinder in vaginal cuff brachytherapy (VCB). This study investigates the incidence and location of AGs. Methods and MaterialsPlanning CTs of 22 prospectively recruited patients (NCT02091050) treated with 2.6 cm (n = 8) and 3.0 cm (n = 14) cylinders were evaluated. In addition, a systematic literature review and meta-analysis was performed (PubMed and EMBASE). The pooled incidence of AGs was calculated by using the random-effects model weighted by inverse variance. ResultsIn 18 cases (82%), a total of 45 AGs were found: 26 within the 2 cm cranial length and 19 between 2 and 4 cm of the cylinder. The mean AG diameter was 3.7 mm (range: 1.3–11.8). Cylinder diameter, primary tumor site, and use of external beam radiotherapy were not associated with AG incidence. Systematic literature review revealed nine additional relevant studies, totaling 657 patients. The pooled incidence of patients with ≥1 AG was 67% (95% confidence interval: 50–83). AGs were located at the apex in 43.4%–94.4% of cases. In patients with ≥1 AG (n = 244), the pooled mean number of AGs was 2.18 per patient. The mean dose reduction varied from 9.6% to 29.3%. ConclusionMore than two-thirds of VCB cases present with AGs, which are most commonly at the apex and can potentially reduce mucosal dose. By identifying AGs, postinsertion CT can facilitate selection of optimal cylinder size in VCB.

Optimal Conductor Size Selection in Distribution Networks with High Penetration of Distributed Generation Using Adaptive Genetic Algorithm

The widespread deployment of distributed generation (DG) has significantly impacted the planning and operation of current distribution networks. The environmental benefits and the reduced installation cost have been the primary drivers for the investment in large-scale wind farms and photovoltaics (PVs). However, the distribution network operators (DNOs) face the challenge of conductor upgrade and selection problems due to the increasing capacity of DG. In this paper, a hybrid optimization approach is introduced to solve the optimal conductor size selection (CSS) problem in the distribution network with high penetration of DGs. An adaptive genetic algorithm (AGA) is employed as the primary optimization strategy to find the optimal conductor sizes for distribution networks. The aim of the proposed approach is to minimize the sum of life-cycle cost (LCC) of the selected conductor and the total energy procurement cost during the expected operation periods. Alternating current optimal power flow (AC-OPF) analysis is applied as the secondary optimization strategy to capture the economic dispatch (ED) and return the results to the primary optimization process when a certain conductor arrangement is assigned by AGA. The effectiveness of the proposed algorithm for optimal CSS is validated through simulations on modified IEEE 33-bus and IEEE 69-bus distribution systems.

Expectation Maximization Indoor Localization Utilizing Supporting Set for Internet of Things

With the growth of WLAN infrastructure, received signal strength (RSS) fingerprint-based WiFi indoor positioning systems have received considerable attention recently. Some existing RSS fingerprint-based localization methods estimate locations by directly matching an online testing sample with an offline database, and thus show low accuracy because RSS is known to be vulnerable to variations caused by changing environment and heterogeneous hardware. To overcome the above drawbacks, we propose an expectation maximization indoor localization approach by leveraging supporting set (EMSS). In the offline phase, we first divide a positioning area into ${G}$ grid points and index each grid by a label. All the indices of grid points form a label set $ {\Psi =\{1,2,\ldots, G\}}$ . Then, we collect the RSS fingerprints to construct an offline database for all labeled grid points. In the online phase, given an online RSS testing sample, we first construct a supporting set (SS), which is a subset of $ {\Psi }$ , selected by the similarity between the online RSS sample and offline database. So, SS is a latent space that likely includes the true label (location) of the user. Based on the SS, we then derive an expectation maximization (EM) algorithm by incorporating the fingerprint quality into the estimation of the true label. EM can intelligently estimate the location of the user by evaluating the fingerprint quality of SS. Furthermore, we propose an optimal size selection algorithm based on Bayesian information criterion to adaptively determine the size of SS. Our method can effectively mitigate the impacts of changing environment and heterogeneous hardware without fingerprint and hardware calibrations, and can thus be practically applied. Experimental results verify that EMSS performs significantly better than some existing fingerprint-based methods.

The Curve of Optimal Sample Size (COSS): A Graphical Representation of the Optimal Sample Size from a Value of Information Analysis.

Value of information (VOI) analysis quantifies the opportunity cost associated with decision uncertainty, and thus informs the value of collecting further information to avoid this cost. VOI can inform study design, optimal sample size selection, and research prioritization. Recent methodological advances have reduced the computational burden of conducting VOI analysis and have made it easier to evaluate the expected value of sample information, the expected net benefit of sampling, and the optimal sample size of a study design ([Formula: see text]). The volume of VOI analyses being published is increasing, and there is now a need for VOI studies to conduct sensitivity analyses on VOI-specific parameters. In this practical application, we introduce the curve of optimal sample size (COSS), which is a graphical representation of [Formula: see text] over a range of willingness-to-pay thresholds and VOI parameters (example data and R code are provided). In a single figure, the COSS presents summary data for decision makers to determine the sample size that optimizes research funding given their operating characteristics. The COSS also presents variation in the optimal sample size given variability or uncertainty in VOI parameters. The COSS represents an efficient and additional approach for summarizing results from a VOI analysis.

Self-Adaptive Digital Volume Correlation for Unknown Deformation Fields

Digital volume correlation (DVC) has evolved into a powerful tool for quantifying full-field internal deformation. In existing subvolume-based local DVC, subvolume size and shape function are two key user-defined parameters closely related to the DVC measurement errors. In routine implementation, the user must define fixed subvolume size and shape function according to prior experience and intuition, which cannot ensure accurate measurements, particularly for unknown complex heterogeneous deformation fields. Self-adaptive selection of optimal subvolume size and the best shape function is therefore highly desirable to realize full-automatic and quality DVC measurements. In this work, we first establish theoretical error models that relate total displacement errors to subvolume sizes and shape functions. By minimizing the V-shaped models of theoretically predicted total errors, optimal subvolume size and the best shape function can be identified as inputs for self-adaptive DVC analysis at each calculation point. The accuracy advantage of the presented self-adaptive DVC approach over classic one using fixed subvolume size and shape function is demonstrated through numerically simulated three-point bending tests.

Larger Sizes Matter More! Applying the Matteo Mathematics Method for Endovascular Aortic Bifurcation Reconstruction to Large Venous Vascular Repair.

Endovascular aortobifemoral bypass repair with aortic bifurcation reconstruction is a well-established option with mortality benefits compared to conventional surgical management. The same theory, formulas, and techniques can be applied to the central venous system as long as there are commercially available devices. Using mathematically derived criteria for optimal stent size selection, endovascular aortic bifurcation reconstruction with kissing stents was extrapolated to the inferior vena cava (IVC). This report describes a traumatic case of IVC injury that was successfully repaired using the standard aortic grafts while adhering to the guidelines for proper stent size selection.

Solvable integration problems and optimal sample size selection

We compute the integral of a function or the expectation of a random variable with minimal cost and use, for our new algorithm and for upper bounds of the complexity, i.i.d. samples. Under certain assumptions it is possible to select a sample size based on a variance estimation, or – more generally – based on an estimation of a (central absolute) p-moment. That way one can guarantee a small absolute error with high probability, the problem is thus called solvable. The expected cost of the method depends on the p-moment of the random variable, which can be arbitrarily large. In order to prove the optimality of our algorithm we also provide lower bounds. These bounds apply not only to methods based on i.i.d. samples but also to general randomized algorithms. They show that – up to constants – the cost of the algorithm is optimal in terms of accuracy, confidence level, and norm of the particular input random variable. Since the considered classes of random variables or integrands are very large, the worst case cost would be infinite. Nevertheless one can define adaptive stopping rules such that for each input the expected cost is finite. We contrast these positive results with examples of integration problems that are not solvable.

Optimality of training/test size and resampling effectiveness in cross-validation

An important question in cross-validation (CV) is whether rules can be established to allow optimal sample size selection of the training/test set, for fixed values of the total sample size n. We study the cases of repeated train–test CV and k-fold CV for certain decision rules that are used frequently. We begin by defining the resampling effectiveness of repeated train–test CV estimators of the generalization error and study its relation to optimal training sample size selection. We then define optimality via simple statistical rules that allow us to select the optimal training sample size and the number of folds. We show that: (1) there exist decision rules for which closed form solutions of the optimal training/test sample size can be obtained; (2) in a broad class of loss functions the optimal training sample size equals half of the total sample size, independently of the data distribution and the data analytic task. We study optimal selection of the number of folds in k-fold CV and address the case of classification via logistic regression and support vector machines, substantiating our claims theoretically and empirically in both, small and large sample sizes. We contrast our results with standard practice in the use of CV.

Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures

This paper focuses on the use of ultra-high resolution Unmanned Aircraft Systems (UAS) imagery to classify tree species. Multispectral surveys were performed on a plant nursery to produce Digital Surface Models and orthophotos with ground sample distance equal to 0.01 m. Different combinations of multispectral images, multi-temporal data, and texture measures were employed to improve classification. The Grey Level Co-occurrence Matrix was used to generate texture images with different window sizes and procedures for optimal texture features and window size selection were investigated. The study evaluates how methods used in Remote Sensing could be applied on ultra-high resolution UAS images. Combinations of original and derived bands were classified with the Maximum Likelihood algorithm, and Principal Component Analysis was conducted in order to understand the correlation between bands. The study proves that the use of texture features produces a significant increase of the Overall Accuracy, whose values change from 58% to 78% or 87%, depending on components reduction. The improvement given by the introduction of texture measures is highlighted even in terms of User’s and Producer’s Accuracy. For classification purposes, the inclusion of texture can compensate for difficulties of performing multi-temporal surveys.

Optimal Design of Bypass Line for an Industrial-Scale 8-Leg Polyolefin Loop Reactor to Manage Slurry Dispersion Using Hydraulic and CFD Simulations

The pump power oscillation caused by slugs in slurry phase polymerization reactors limits production capacity. A bypass line connecting two locations of a loop reactor is suggested in patent literature to dissipate slugs. The optimal design of such bypass lines is studied through a hierarchy of modeling approaches applied to an industrial-scale reactor. In the design process, 1D hydraulic calculations guide the selection of optimal bypass pipe sizes to maximize slurry velocity. 2D CFD simulations provide data on slug dissipation processes. 3D simulations guide selection of optimal installation angle to maximize solid intake into the bypass line. By combining these approaches, a bypass line connecting the second and sixth legs by 6 in. pipes at a 45° angle is found to be optimal for reactor considered here. Such designs achieves high slurry velocity in the bypass line while maximizing slug dissipation, and the approach is applicable for other loop reactor configurations.

Correlation between lumbar intervertebral disc height and lumbar spine sagittal alignment among asymptomatic Asian young adults

BackgroundTo investigate the distribution and characteristics of the lumbar intervertebral disc height (IDH) in asymptomatic Asian population and to determine whether the lumbar IDH is related to the lumbar spine sagittal alignment.MethodsA cohort of 169 cases of asymptomatic volunteers was enrolled from January 2014 to July 2016. All participants underwent magnetic resonance imaging of the lumbar spine and panoramic radiography of the spine. Panoramic radiographs of the spine were taken to evaluate pelvic incidence (PI), sacral slope (SS), and pelvic tilt (PT) using Surgimap® software. Roussouly classification was utilized to categorize all subjects according to the four subtypes of sagittal alignment. The IDH was measured on the MRI mid-saggital section of the vertebral body. The relationships between lumbar IDH and spine-pelvic parameters were also assessed using the Spearman correlation analysis.ResultsThe reference value ranges of IDH in asymptomatic Asian volunteers between L1/2, L2/3, L3/4, L4/5, and L5/S1 were (6.25, 10.99), (6.97, 12.08), (7.42, 13.3), (7.76, 14.57),and (7.11, 13.12) mm, respectively. Based on the above reference value, the high lumbar intervertebral space is defined as more than 14 mm. According to the Roussouly Classification, there are 33 cases in type I, 48 in type II, 66 in type III, and 22 in type IV. According to the definition of the high IDH, there are two cases in type I, three in type II, nine in type III, and eight in type IV. The results indicated that people in the Roussouly III and IV subtypes had greater values for IDH compared to those of Roussouly I and II subtypes, and the spinopelvic parameters were partly correlated with IDH in different subtypes. In addition, levels L4–L5 showed the highest IDH for all four groups followed by the L3–L4 and L5–S1 levels, and the value of L3–L4 is equivalent to that of L5–S1. All type groups showed moderate and positive correlations between the PI and IDH except the level of L1–L2 in type IV.ConclusionsThe IDH may influence the lumbar spine sagittal alignment in asymptomatic Asian adults. Moreover, pre-operative evaluation of IDH is useful for selection of optimal cage size and reconstruction of spinal alignment.

Inductive analysis of the deflection of a beam truss allowing kinematic variation

A scheme of a statically determinate planar truss is proposed and an analytical calculation of its deflection and displacement of the mobile support are obtained. The forces in the rods from the external load, uniformly distributed over the nodes of the lower or upper belt, are determined by the method of cutting out nodes using the computer mathematic system Maple. In the generalization of a number of solutions of trusses with a different number of panels to the general case, the general terms of the sequence of coefficients in the formulas are found from solutions of linear homogeneous recurrence equations. To compose and solve these equations, Maple operators were used. In the process of calculation it was revealed that for even numbers of panels in half the span, the determinant of the system of equations degenerates. This corresponds to the kinematic degeneracy of the structure. The corresponding scheme of possible speeds of the truss is given. The displacement was determined by the Maxwell-Mohr’s formula. The graphs of the obtained dependences have appreciable jumps, which in principle can be used in the selection of optimal design sizes.

Rapid and scalable synthesis of crystalline tin oxide nanoparticles with superior photovoltaic properties by flame oxidation

Abstract

Statistical power in two-level hierarchical linear models with arbitrary number of factor levels

As the US health care system undergoes unprecedented changes, the need for adequately powered studies to understand the multiple levels of main and interaction factors that influence patient and other care outcomes in hierarchical settings has taken center stage. We consider two-level models where n lower-level units are nested within each of J higher-level clusters (e.g. patients within practices and practices within networks) and where two factors may have arbitrary a and b factor levels, respectively. Both factors may represent a×b treatment combinations, or one of them may be a pretreatment covariate. Consideration of both factors at the same higher or lower hierarchical level, or one factor per hierarchical level yields a cluster (C), multisite (M) or split-plot randomized design (S). We express statistical power to detect main, interaction, or any treatment effects as a function of sample sizes (n,J), a and b factor levels, intraclass correlation ρ and effect sizes δ given each design d∈{C,M,S}. The power function given a,b,ρ, δ and d determines adequate sample sizes to achieve a minimum power requirement. Next, we compare the impact of the designs on power to facilitate selection of optimal design and sample sizes in a way that minimizes the total cost given budget and logistic constraints. Our approach enables accurate and conservative power computation with a priori knowledge of only three effect size differences regardless of how large a×b is, simplifying previously available computation methods for health services and other researches.

CuRRBS: simple and robust evaluation of enzyme combinations for reduced representation approaches.

DNA methylation is an important epigenetic modification in many species that is critical for development, and implicated in ageing and many complex diseases, such as cancer. Many cost-effective genome-wide analyses of DNA modifications rely on restriction enzymes capable of digesting genomic DNA at defined sequence motifs. There are hundreds of restriction enzyme families but few are used to date, because no tool is available for the systematic evaluation of restriction enzyme combinations that can enrich for certain sites of interest in a genome. Herein, we present customised Reduced Representation Bisulfite Sequencing (cuRRBS), a novel and easy-to-use computational method that solves this problem. By computing the optimal enzymatic digestions and size selection steps required, cuRRBS generalises the traditional MspI-based Reduced Representation Bisulfite Sequencing (RRBS) protocol to all restriction enzyme combinations. In addition, cuRRBS estimates the fold-reduction in sequencing costs and provides a robustness value for the personalised RRBS protocol, allowing users to tailor the protocol to their experimental needs. Moreover, we show in silico that cuRRBS-defined restriction enzymes consistently out-perform MspI digestion in many biological systems, considering both CpG and CHG contexts. Finally, we have validated the accuracy of cuRRBS predictions for single and double enzyme digestions using two independent experimental datasets.

Application of Convolutional Neural Networks to Four-Class Motor Imagery Classification Problem

In this paper the use of a novel feature extraction method oriented to convolutional neural networks (CNN) is discussed in order to solve four-class motor imagery classification problem. Analysis of viable CNN architectures and their influence on the obtained accuracy for the given task is argued. Furthermore, selection of optimal feature map image dimension, filter sizes and other CNN parameters used for network training is investigated. Methods for generating 2D feature maps from 1D feature vectors are presented for commonly used feature types. Initial results show that CNN can achieve high classification accuracy of 68% for the four-class motor imagery problem with less complex feature extraction techniques. It is shown that optimal accuracy highly depends on feature map dimensions, filter sizes, epoch count and other tunable factors, therefore various fine-tuning techniques must be employed. Experiments show that simple FFT energy map generation techniques are enough to reach the state of the art classification accuracy for common CNN feature map sizes. This work also confirms that CNNs are able to learn a descriptive set of information needed for optimal electroencephalogram (EEG) signal classification.DOI: http://dx.doi.org/10.5755/j01.itc.46.2.17528

ServAR: An augmented reality tool to guide the serving of food

BackgroundAccurate estimation of food portion size is a difficult task. Visual cues are important mediators of portion size and therefore technology-based aids may assist consumers when serving and estimating food portions. The current study evaluated the usability and impact on estimation error of standard food servings of a novel augmented reality food serving aid, ServAR.MethodsParticipants were randomised into one of three groups: 1) no information/aid (control); 2) verbal information on standard serving sizes; or 3) ServAR, an aid which overlayed virtual food servings over a plate using a tablet computer. Participants were asked to estimate the standard serving sizes of nine foods (broccoli, carrots, cauliflower, green beans, kidney beans, potato, pasta, rice, and sweetcorn) using validated food replicas. Wilcoxon signed-rank tests compared median served weights of each food to reference standard serving size weights. Percentage error was used to compare the estimation of serving size accuracy between the three groups. All participants also performed a usability test using the ServAR tool to guide the serving of one randomly selected food.ResultsNinety adults (78.9% female; a mean (95%CI) age 25.8 (24.9–26.7) years; BMI 24.2 (23.2–25.2) kg/m2) completed the study. The median servings were significantly different to the reference portions for five foods in the ServAR group, compared to eight foods in the information only group and seven foods for the control group. The cumulative proportion of total estimations per group within ±10%, ±25% and ±50% of the reference portion was greater for those using ServAR (30.7, 65.2 and 90.7%; respectively), compared to the information only group (19.6, 47.4 and 77.4%) and control group (10.0, 33.7 and 68.9%). Participants generally found the ServAR tool easy to use and agreed that it showed potential to support optimal portion size selection. However, some refinements to the ServAR tool are required to improve the user experience.ConclusionsUse of the augmented reality tool improved accuracy and consistency of estimating standard serve sizes compared to the information only and control conditions. ServAR demonstrates potential as a practical tool to guide the serving of food. Further evaluation across a broad range of foods, portion sizes and settings is warranted.

Special issue in honor of Kathryn Chaloner

We are honored to be guest editors for this special issue of Applied Stochastic Models in Business and Industry, dedicated to Kathryn Chaloner's life and achievements. Contributions from her colleagues and friends in this volume are devoted to Kathryn's early research interest in Bayesian optimal design. She was a pioneer in this field and had a broad impact on the subsequent literature on experimental design. Kathryn was internationally known for her research in Bayesian Statistics, which included design of experiments, outlier detection, prior elicitation, and clinical trials, and for her advancements in the study of HIV/AIDS. She was an accomplished researcher, teacher, and mentor. She actively encouraged women and under-represented minority students to join the field of biostatistics and provided mentorship, support, and encouragement. The special issue contains seven papers. The first three papers present some of the recent work in Bayesian optimal designs for nonlinear models, binary responses, and experiments with adversarial components. This is followed by two articles on Bayesian designs in clinical trials. The last two articles of the special issue involve engineering design problems. The paper by Giovagnoli considers the binary response models of Chaloner and Larntz (1989, Journal of Statistical Planning and Inference, 21:191–208) and develops an adaptive version of A-optimal Bayesian designs that are revised as additional data become available during the experiment. Adaptive Bayesian compound designs is also discussed as another extension. Konstantinou and Dette develop approximate Bayesian D-optimal designs for nonlinear regression models that involve covariates that cannot be observed directly. Applications to specific nonlinear models including the exponential regression model are considered and characterizations of the D-optimal saturated designs are presented. Optimal sample size selection for experiments is considered by De Santis and Gubbiotti in an adversarial setting where the decision makers have different prior opinions while having common utility functions and data. Using a Bayesian decision approach with a quadratic loss function, the authors present results for the one-parameter exponential family and investigate effect of the priors on the optimal sample size. Müller, Xu, and Thall discuss advantages and limitations of Bayesian decision theoretic approach in design of clinical trials. In doing so, the authors present a specific case study and point out the limitations such as the computational difficulties involved in solving sequential problems as well as selection of utility functions capturing different stakeholder interests. The article by Ventz, Parmigiani, and Trippa also consider limitations of the Bayesian framework in design of clinical trials and propose a strategy to alleviate these. The proposed strategy involves combining Bayesian designs with frequentist metrics such as error rates, confidence intervals, etc. that are commonly used by medical community and regulatory agencies. The authors show that this can be achieved by an inclusion of frequentist constraints into the Bayesian formulation. The constrained decision theoretic framework is illustrated via several applications and computational algorithms are presented for implementing the proposed approach. Nakamura, Seepaul, Kadane, and Reeja-Jayan discuss design of experiments in materials science. The authors consider use of a batch-sequential experiment and a regression model to determine optimal levels of input variables for synthesis of ceramic materials. It is illustrated that the optimal settings produce more reliable results than other experiments. The final article by Polson and Soyer considers Bayesian design of accelerated life tests for reliability assessment. An augmented probability simulation (APS) approach using Lindley's (1976, Annals of Statistics, 4:1–10) conjugate utility functions is proposed to compute optimal designs in an efficient manner. The proposed approach is illustrated using single and multiple point designs. Use of particle based methods for APS is also discussed as an alternative to Markov chain Monte Carlo methods.

Generalized multigranulation rough sets and optimal granularity selection

Multigranulation rough set theory is a desirable direction in the field of rough set, in which upper and lower approximations are approximated by multiple granular structures. However, classic multigranulation rough set is studied from two kinds of qualitative combination rules which were generated by pessimistic and optimistic viewpoints, respectively. The two combination rules seem to lack of practicability since one is too restrictive and the other too relaxed. To overcome this disadvantage, we propose a generalized multigranulation rough set model in this paper. First, we discuss upper and lower approximation sets of a generalized multigranulation rough set by introducing a support characteristic function and an information level. Then, as one of the most important problems in granular computing, we carefully study how to select optimal granularity in generalized multigranulation rough sets. Furthermore, algorithms of optimal granularity selection are constructed, by which we can provide an efficient approach to compute the optimal granularity based on generalized multigranulation rough sets. Finally, an illustrative example is given to show the effectiveness of the proposed approach. The main contribution of this paper is to construct the model of the optimal particle size selection on account of the generalized multi granularity, and overcome the limitation of the classical multi granularity.

Simple, Accurate, Analytical Time Modeling and Optimal Tile Size Selection for GPGPU Stencils

Stencil computations are an important class of compute and data intensive programs that occur widely in scientific and engineeringapplications. A number of tools use sophisticated tiling, parallelization, and memory mapping strategies, and generate code that relies on vendor-supplied compilers. This code has a number of parameters, such as tile sizes, that are then tuned via empirical exploration. We develop a model that guides such a choice. Our model is a simple set of analytical functions that predict the execution time of the generated code. It is deliberately optimistic, since tile sizes and, moreover, the optimistic assumptions are intended to enable we are targeting modeling and parameter selections yielding highly tuned codes. We experimentally validate the model on a number of 2D and 3D stencil codes, and show that the root mean square error in the execution time is less than 10% for the subset of the codes that achieve performance within 20% of the best. Furthermore, based on using our model, we are able to predict tile sizes that achieve a further improvement of 9% on average.