Optimal Design Criteria Research Articles

Enhancing Natural Product Clinical Trials (P13-037-19)

ObjectivesTo discuss good practices and criteria for optimal design and interpretation of pre-clinical and clinical natural product (NP) research in order to increase benefit from our investment in NP clinical trials (CT). BackgroundLarge, randomized, controlled CT often fail to reject the null hypothesis or show rigorous evidence of benefit. This includes recent large, NIH-supported CT of nutrients such as vitamin D and selenium, and of botanical dietary supplements. Negative and positive outcomes may be equally important for public health, but because large CT cost at least $20 M each, plus opportunity costs, it is important that CT designs maximize the yield of actionable information regardless of outcome. MethodsExperts and stakeholders from academia, government and the private sector collaboratively developed a broadly attended workshop in which good practices to enhance rigor, reproducibility and translational relevance were discussed. ResultsN/A. ConclusionsCritical issues in CT design include product identity, reproducibility and pharmacology (where feasible), power to test a primary outcome significant to consumers, and placebo controls. When basing a CT on traditional use or prior in vitro or in vivo studies, similarity of product (e.g., source identity, methods of preparation, form and intake), health outcome and population (e.g., age, sex, genetics, diet and environment), require careful consideration. Appropriate controls for known types of in vitro assay interference (e.g., aggregation, membrane disruption, protein denaturation) are imperative. Compounds with limited bioavailability, or activity only at concentrations above those achievable by ingestion, are likely poor candidates for dietary CT. Translational validity of model systems should be carefully assessed. Appropriate analyses (e.g., p-curve and meta-regression methods) should be used to obtain bias-corrected effect size estimates, and to identify research areas where the evidence base may be weaker than published findings suggest. Finally, CT prioritization should consider expected impact on public health, and whether known NP causal mechanisms of action are such that useful information, e.g., on product bioavailability or biological activity, are generated even if the completed CT fails to reject the null hypothesis. Funding SourcesNIH, FDA, USDA.

A novel computational framework for structural optimization with patched laminates

Fiber patch placement (FPP) is a manufacturing technique for discrete variable stiffness composites. In the FPP approach, a structural component is assembled from a multitude of discrete fiber patches. However, due to the discontinuous fibers at patch edges, complex stress distributions occur. To date, a holistic FPP design framework that combines a tailored patch placement method with a dedicated mechanical model for the analysis of patched laminates does not exist. This article introduces a novel approach for the design of fiber patched laminates. It is based on the sequential placement of patches on a finite element shell mesh, using a critical element and angle selection routine in order to optimally locate and orientate fiber patches. They are added to the 3D mesh by employing a highly efficient kinematic draping algorithm. Strength-critical regions of the resulting fiber patched laminates are identified by state-of-the-art finite element analysis and extracted to a shear-lag–based mechanical submodel dedicated to the detailed analysis of patched laminates. The patch placement routine terminates once all design optimization criteria are met. The efficiency of applying optimized patch reinforcements on a continuous fiber-reinforced base laminate is demonstrated using the example of an individualized biomedical component. The work at hand presents the first patched laminate design framework combining a patch placement strategy coupled with a dedicated mechanical model. As a consequence, a substantial progress in the design of patch laminated structures is achieved.

Thermal comfort improvement in urban spaces with water spray systems: Field measurements and survey

An experimental study was conducted to improve the comfort and liveability of urban open spaces during the hottest months of the summer by implementing an overhead water mist cooling system. The campaign was conducted in two different Italian urbanscapes (Ancona and Rome, representative of Cfa and Csa climates) one after the other.Monitoring data and comfort questionnaires were combined to extract useful information by means of statistical tests, regressions and data mining algorithms and, ultimately, to delineate design and operating guidelines to maximize people's satisfaction with the misted environment.The cloud of droplets reduced the temperature and the UTCI by 8.2 °C and 7.9 °C respectively, against a 7% mean humidity premium. The vertical cooling and humidification profiles obeyed a Lorentzian distribution, peaking within approximately 0.5 m of the injection. The severe overheating experienced outside of the cooled areas evanished under the spray, with 67% (Ancona) to 90.6% (Rome) of respondents reporting only slight bending from thermal neutrality. Perception and preferences towards solar radiation, humidity level and wind all improved within the droplets mist.In terms of comfort-oriented, optimized design criteria, the system proved to work best with a dominant and steady light breeze (1–2 m/s), in highly irradiated sites and suspended at 1.2–1.5 m above the average height of users.

High Rate CCK Modulation Design for Bandwidth Efficient Link Adaptation

This letter presents a novel design of high rate complementary code keying (CCK) modulation scheme (named QAM-CCK) that is suitable for bandwidth efficient wireless link adaptation. The proposed QAM-CCK can increase the bandwidth efficiency of CCK signaling by simultaneously implementing quadrant and phase modulations. We introduce an optimal design criterion based on weighted minimum Euclidean distance to optimize the QAM-CCK mapping with minimum average bit error probability. In order to utilize the inherent coding gain of QAM-CCK, we propose an iterative soft maximum likelihood receiver to enhance the decoding performance. Compared with traditional QPSK of the same bandwidth efficiency, the proposed QAM-CCK achieves a 4-dB SNR gain under additive white Gaussian noise channels when used in conjunction with proposed iterative receiver.

Nonparametric Bayesian optimal designs for exponential regression model

Constructing the Bayesian optimal design depends on the choice of a prior distribution for the unknown parameter. Lacking informative or historical knowledge of the parameter, a parametric Bayesian approach cannot be expected in complex statistical problems. In this regard, a nonparametric Bayesian approach can be used, in which random prior distribution is considered. The Dirichlet process is employed as a prior on the space of distribution functions. In this paper, a non-parametric Bayesian approach is incorporated into an optimal design criterion. This method is illustrated by an example.

Efficient parameter estimation in multiresponse models measuring radioactivity retention.

After incorporation of radioactive substances, workers are routinely checked by bioassays (isotopic activity excreted via urine, measurements of radionuclides retained in the whole body or in the lungs, etc.). From the results, the isotopic activity incorporated by the worker is inferred, as well as the values of other parameters related to the metabolism of the incorporated substance, using the 'response function'. This function depends on several factors and it is usually obtained by solving a system of linear differential equations, resulting from the compartmental model which describes the human body (or a part of it). The possibility of using different types of bioassays from the same worker improves estimation of some of the parameters that characterize the solution of the system of equations, specially the unknown incorporated activity to the system. The transfer coefficients are usually considered to be known, using the values that are published in the corresponding International Commission of Radiological Protection (ICRP) publication. In the present study some practical cases will be presented, and optimal design criteria are developed that allow taking the bio-samples at the most informative times. The methodology presented here requires solving the models of element distribution in the human organism as a function of time, for which the recently updated models recommended by the ICRP have been used. Initially thought for workers in facilities dealing with radioactive substances, the study results, procedures and conclusions can be applied to other clinical or laboratory settings, and to the design of action protocols in case of environmental public exposure.

Bayesian C-optimal life testing plans under progressive type-I interval censoring scheme

• Optimal Bayesian life tests plans are presented under Progressive Type-I Interval Censoring Scheme. • Bayesian C -optimal design criterion is used. • An algorithm is presented for log-location-scale family of distributions. • A comparative study is provided for Weibull and log-normal lifetime distributions. • Sampling variations are visualized through MCMC methods. This work considers optimal planning of progressive type-I interval censoring schemes for log-location-scale family of distributions. Optimum schemes are obtained by using a Bayesian C -optimality design criterion. The C -optimality criterion is formed to attain precision in estimating a particular lifetime quantile . An algorithm is proposed to obtain the optimal censoring schemes. Optimal schemes are obtained under two different scenarios for the Weibull and log-normal models, which are two popular special cases of log-location-scale family of distributions. A sensitivity analysis is conducted to study the effect of various prior inputs on the optimal censoring schemes. Furthermore, a simulation study is undertaken to illustrate the sampling variations resulting from the optimal censoring schemes.

Equivalence of weighted and partial optimality of experimental designs

The recently introduced weighted optimality criteria for experimental designs allow one to place various emphasis on different parameters or functions of parameters of interest. However, various emphasis on parameter functions can also be expressed by considering the well-developed optimality criteria for estimating a parameter system of interest (the partial optimality criteria). We prove that the approaches of weighted optimality and of partial optimality are in fact equivalent for any eigenvalue-based optimality criterion. This opens up the possibility to use the large body of existing theoretical and computational results for the partial optimality to derive theorems and numerical algorithms for the weighted optimality of experimental designs. We demonstrate the applicability of the proven equivalence on a few examples. We also propose a slight generalization of the weighted optimality so that it can represent the experimental objective consisting of any system of linear estimable functions.

Bregman divergences based on optimal design criteria and simplicial measures of dispersion

In previous work the authors defined the k-th order simplicial distance between probability distributions which arises naturally from a measure of dispersion based on the squared volume of random simplices of dimension k. This theory is embedded in the wider theory of divergences and distances between distributions which includes Kullback–Leibler, Jensen–Shannon, Jeffreys–Bregman divergence and Bhattacharyya distance. A general construction is given based on defining a directional derivative of a function $$\phi $$ from one distribution to the other whose concavity or strict concavity influences the properties of the resulting divergence. For the normal distribution these divergences can be expressed as matrix formula for the (multivariate) means and covariances. Optimal experimental design criteria contribute a range of functionals applied to non-negative, or positive definite, information matrices. Not all can distinguish normal distributions but sufficient conditions are given. The k-th order simplicial distance is revisited from this aspect and the results are used to test empirically the identity of means and covariances.

Improved Design of a Photon Enhanced Thermionic Energy Converter

In this letter, the model of photon enhanced thermionic energy converter is improved to design a new vacuum thermionic energy converter (VTEC) consisting of a cathode and an anode, in which the two electrode materials are made of p-type silicon semiconductor coated negative electron affinity material and boron doped diamond. Formulas for the power output density and efficiency of the VTEC are derived. The electron affinity is optimized to obtain the maximum power output density and efficiency of the VTEC. Making a trade-off between the power output density and efficiency, the parametric optimum design criteria are determined. The results obtained in this letter are helpful for developing novel VTECs.

Stochastic coordinate-exchange optimal designs with complex constraints

The paper provides a method to construct the exact optimal experimental designs for both physical and computer experiments with any regular or irregularly constrained design space. We modify the original greedy coordinate-exchange algorithm into the stochastic version by adding the random selection of the “bad” coordinates that can be exchanged for “better” values to improve the optimal design criterion. More importantly, we adapt it to accommodate the non-convex or disconnected constrained design space. We implement the stochastic coordinate-exchange (SCE) algorithm for D- and linear- (A- and I-) optimal designs for physical experiments, and -optimal space-filling designs for computer experiments.

Optimal Path Planning for Two Unmanned Aerial Vehicles in DRSS Localization

Optimal path planning for three or more unmanned aerial vehicles (UAVs) in radio source localization has been studied extensively; but path planning for two UAVs in signal strength based localization is still a challenge. This paper investigates the autonomous path planning for a pair of UAVs equipped with received signal strength (RSS) sensors applying optimum experimental design criteria based on the fisher information matrix (FIM). The control strategy steers the UAVs along the paths that minimize the emitter location uncertainty. Since there is one differential received signal strength (DRSS) measurement (two RSS sensors) in each time step, the emitter location uncertainty is infinite for a single measurement and the FIM is singular. The FIM is approximated at successive waypoints using the estimated location of the emitter produced by the extended kalman filter (EKF). The objective of this paper is to propose path planning approaches for minimum number of UAVs in DRSS-based localization based on different optimum criteria including D-, E-, A-optimality and sensitivity. Each criterion is related to a different characteristic of the estimation uncertainty ellipsoid and consequently generates different trajectories. The proposed steering algorithms are evaluated through extensive simulations.

Some sequential third order response surface designs

Sequential third order designs are very important due to their nice sequential property. These designs become even more important in case of second order model lack of fit. There is possibility that an experimenter uses a second order design to explore the second order response surfaces, however, lack of fit problem may arise. If one is using the sequential third order design, then there is an opportunity to use the next portion of that design to estimate the third order terms and remove the second order model lack of fit. Das and Narasimham (1962) and Arshad, Akhtar, and Gilmour (2012) have proposed sequential third order designs that can be used to estimate a complete third order model, in case we face second order model lack of fit. However, these designs have very large sizes. Sometime, it becomes difficult for an experimenter, in real world situation, to use a design with such a large size due to different issues like high cost of experimental material. Arshad, Akhtar, and Gilmour (2012) augmented the very popular designs of Box and Behnken (1958 and 1960) and made them third order. Those designs, called augmented Box–Behnken designs (ABBDs), are sequential in nature and can be used for estimation of a complete third order model in case of second order model lack of fit. These designs have been developed by augmenting the Box–Behnken designs using different combinations of factorial points, axial points and complementary design points. In this research paper, we have also selected the Box–Behnken designs due to their popularity and augmented them using the concept of doubly balanced incomplete block design provided by Calvin (1954). We have developed 9 designs one for each from 4 to 12 number of factors (design for 3 factors was not achievable). These third order designs are also sequential in nature, and can be used for estimation of complete third order model in case the second order model lack of fit is exhibited. The newly proposed designs have smaller sizes as compared to Das–Narasimham designs (DNDs) in all cases, and smaller than ABBDs in many cases. In addition to the designs sizes, some other design optimality criteria have also been explored for these designs, and compared with the DNDs and ABBDs. Mostly the new designs have been found better than DNDs and ABBDs.

Bit-Interleaved Coded SCMA With Iterative Multiuser Detection: Multidimensional Constellations Design

This paper investigates the constellation/codebook design of a promising uplink multiple access technique, sparse code multiple access (SCMA), proposed for the fifth generation mobile networks. The application of bit-interleaved coded modulation with iterative multiuser detection is considered for uplink SCMA over fading channels. Extrinsic information transfer chart is used to aid the analysis and the design of multidimensional constellations, and the design criteria for multidimensional constellations and labelings optimization are thus established. Furthermore, a new and simple approach of multi-stage optimization for the multidimensional constellation design is proposed for SCMA, to improve the bit-error rate performance and alleviate the complexity of turbo multiuser detection. Numerical and simulation results are also provided to demonstrate the performance and verify the efficiency of the proposed scheme, compared with the state of the art.

Joint Optimization of Protograph LDPC Code Pair for Joint Source and Channel Coding

Double protograph low-density parity-check code has been shown to be a good solution for joint source-channel coding. Moreover, the source code and channel code (as one code pair) can be jointly optimized to achieve a good bit error rate (BER) performance. In this paper, first, an optimal design criterion of this code pair based on an extended curve-fitting algorithm is proposed. Second, a joint optimization procedure which searches for both the optimal decoding threshold and the optimal code pair is presented. In addition, for short-to-moderate-length codewords, the finite-length effect cannot be ignored and the procedure has to be modified with a new notion named length-N decoding threshold . The protograph extrinsic information transfer analysis and the BER simulation have shown that our optimized code pairs acquire lower decoding thresholds than conventional code pairs. Moreover, the optimized code pairs with short-to-moderate-length codewords can mitigate the finite-length effect and obtain low length-N decoding thresholds .

Optimal Design of Needle Array for Effective Drug Delivery.

Recently, the multi-needle drug injection has been adopted to overcome the shortcomes of conventional single-needle injection, enhancing the efficiency of drug delivery. However, the effect of needle array on the efficacy of drug delivery has not been fully elucidated. In this study, the interactions of drug analogous solution injected from a pair of needles were analyzed to examine the design criteria of effective multi-needle devices for drug delivery. Temporal and spatial variations of relative contents of the solution in the tissues were compared according to the distance between two adjacent needles (DN). As the DN increases from 5 to 20 D, where D is the needle diameter, the solution from each needle encounters 3.5 times faster, and 4.22 times more solution was accumulated. At the same time, the effective spreading area was continuously increased from 54.2 to 177.8mm2 and RCS gradient decreases from 0.087 to 0.037, due to the overlapping effect of the spreading solution from neighboring needles. Finally, based on the experimental results, an optimal design criterion of needle array for effective drug delivery was proposed. The present results would be helpful in the design of multi-needle injection devices and eventually offer advantage to patients with effective drug delivery.

Spatio-Temporal Multi-Criteria Optimization of Reservoir Water Quality Monitoring Network Using Value of Information and Transinformation Entropy

Identifying optimal Water Quality Monitoring Stations (WQMS) with high values of information on the entire reservoir status, instead of all potential WQMS would significantly reduce the monitoring network expenditure while providing adequate spatial coverage. This study presented a new methodology for spatio-temporal multi-criteria optimization of reservoir WQMS based on Value of Information (VOI), Transinformation Entropy (TE), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), and IRanian Water Quality Index (IRWQI). Although, all mentioned methods and concepts are well-known and have been used in water resources management, but their integration into a specific application for spatio-temporal multi-criteria optimization of reservoir WQMS is definitely an innovation and a contribution to improvement of WQMS design. More specifically, maximizing VOI as a decision-makers’ design criteria for optimization of WQMS, and considering spatial and temporal variations of water quality at different reservoir depths are new innovations in this research. The multi-objective optimization model was based on three objectives: 1) minimizing costs; 2) maximizing VOI; and 3) minimizing TE (redundant information). Considering these objectives, the NSGA-II multi-objective optimization method was used to find Pareto-optimal solutions. The most preferable solution was then determined using PROMETHEE multi-criteria decision making method. The proposed methodology was applied to Karkheh Reservoir with more than 5 billion cubic meter capacity and 60 km length that is one of the largest reservoirs in Southwestern Iran, however, the proposed approach has the ability to be generalized for any generic reservoir. Considering equal weights for criteria, PROMETHEE method resulted in 6 optimized WQMS out of 60 potential ones and a period of 25 days for optimal sampling interval. The optimized monitoring stations were mainly located at deep parts where most water quality variations are expected to occur. To show sensitivity of the model to different weights, 4 scenarios with various relative weights were evaluated in the PROMETHEE method. Results indicated that by increasing the weight of the second criterion (maximizing VOI), the number of optimized WQMS increased and the sampling interval decreased.

Experimental Data Based Current Transformer Mathematical Simulation in Micro-Cap Program

The present article provides detailed information on current transformer mathematical simulation using the well-known software product Micro-Cap that meets industrial standards. Basic issues of creating a mathematical model of current transformer used for measuring currents and supplying information for power metering and relay protection equipment are discussed. The described method of mathematical simulation in Micro-Cap program reflects peculiarities of a magnetic-core current transformer loaded onto various filters and relays both in quality and quantity terms and allows estimation of its operating parameters depending on the magnetic core material. The mathematical model is built using the finite element method that makes it possible to calculate the required parameters of a current transformer with the optimum output characteristics. Simulation results are used to provide recommendations for selection of output parameters of a current transformer used with relay protection devices depending on the external load value. It also contains the criteria for magnetic-core current transformer design optimization. The paper also discusses main advantages of using Micro-Cap program for simulation of primary instrument current transducers. The methods of calculating current transformer core model parameters for simulation in Micro-Cap program are described. Experimental results are compared with simulation results obtained with the mathematical model.

Multi-objective CFD optimizations of water spray injection in gas-turbine combustors

In this research, the water spray injection process in a natural-gas-fueled gas turbine combustor is studied numerically using an Eulerian-Lagrangian formulation, and the combined effect of different design variables, including the swirl number, water injection mass flow rate, injection partitioning between a pre- and a post-flame injector, and injection direction, are investigated on several combustor performance objectives by systematic multi-objective optimizations. Sensitivity analyses reveal that the most effectual parameter changing the combustion efficiency and overall emission is the swirl number while the outlet maximum temperature and liquid water mass loading are most sensitive to the post-flame and pre-flame injection mass flow rates, respectively. In addition, the outlet temperature uniformity depends on all the four design variables nearly the same. As the optimal design criteria, multi-objective optimizations propose a high swirl number, a small injection angle, and introducing 81% of the total water at the post-flame injector. The optimal total water-to-fuel ratio is 3.4. However, if the flame instability due to the pressure pulsation becomes critical, this value should be lowered to 2. The optimal designs can reduce both maximum temperature and temperature non-uniformity by 19% and suppress NOx and overall emission by 87% and 110%, respectively.

Construction of D-Optimal Experimental Designs for Nonparametric Regression Models

Under study is the problem of a D-optimal experimental design for the problem of nonparametric kernel smoothing. Modification is proposed for the process of calculating the Fisher information matrix. D-optimal designs are constructed for one and several target points for the problems of nonparametric kernel smoothing using a uniform kernel, the Gauss and Epanechnikov kernels. Comparison is performed between Fedorov’s algorithm and direct optimization methods (such as the Nelder–Mead method and the method of differential evolution). The features of the application of the optimality criterion for the experimental design of the problems with several target points were specified for the cases of various kernels and bandwidths.