Optimal Sampling Method Research Articles

PATIENT RECRUITMENT USING ELECTRONIC HEALTH RECORDS UNDER SELECTION BIAS: A TWO-PHASE SAMPLING FRAMEWORK.

Electronic health records (EHRs) are increasingly recognized as a cost-effective resource for patient recruitment in clinical research. However, how to optimally select a cohort from millions of individuals to answer a scientific question of interest remains unclear. Consider a study to estimate the mean or mean difference of an expensive outcome. Inexpensive auxiliary covariates predictive of the outcome may often be available in patients' health records, presenting an opportunity to recruit patients selectively, which may improve efficiency in downstream analyses. In this paper we propose a two-phase sampling design that leverages available information on auxiliary covariates in EHR data. A key challenge in using EHR data for multiphase sampling is the potential selection bias, because EHR data are not necessarily representative of the target population. Extending existing literature on two-phase sampling design, we derive an optimal two-phase sampling method that improves efficiency over random sampling while accounting for the potential selection bias in EHR data. We demonstrate the efficiency gain from our sampling design via simulation studies and an application evaluating the prevalence of hypertension among U.S. adults leveraging data from the Michigan Genomics Initiative, a longitudinal biorepository in Michigan Medicine.

Hybrid uncertainty propagation for mechanical dynamics problems via polynomial chaos expansion and Legendre interval inclusion function

This paper investigates a non-intrusive hybrid uncertainty propagation framework in mechanical dynamic systems, utilizing polynomial chaos expansion (PCE) and Legendre inclusion function. Uncertainties with substantial knowledge and information are conceptualized as stochastic parameters and described using PCE, while Legendre polynomials are employed to represent uncertain, bounded parameters, specifically interval uncertainties. During the computation, the PCE model for each time step is developed through Galerkin projection and sparse grid numerical integration. Consequently, the statistical moments of the dynamic responses relative to the stochastic parameters are derived from the orthogonality of the PCE and transformed into functions reflecting the interval parameters. The interval bounds for these statistical moments are further determined using the Legendre inclusion function, which is obtained through optimal Latin hypercube sampling and collocation methods. Three dynamics examples, respectively, modeled by differential–algebraic equations, finite element method, and commercial software proves its applicability and superiority. Detailed assessment demonstrates that this method offers high computational efficiency and accuracy. As a non-intrusive approach, it poses no particular limitations on numerical methods for solving differential equations, making it universally applicable.

Saccades and Microsaccades Coupling During Free-Throw Shots in Basketball Players.

We investigated the role of saccades and microsaccades when different levels of basketball players were engaged in an ecological free-throw condition. All participants made more correct than incorrect shoots, with a movement time initiation shorter in amateurs than in near-expert groups. Near-experts had more stable gaze fixation than amateurs, with higher microsaccade rate and duration and lower peak velocity. Amateurs showed higher saccade rate, peak velocity, and amplitude than near-experts. The temporal sequence of near-experts' microsaccade rate increased after the saccade peak; on the contrary, in amateurs, the saccade peak is shown after the decrement in microsaccade rates. The spatiotemporal characteristics of microsaccades and saccades may reflect an optimal sampling method by which the brain discretely acquires visual information and can differentiate between participants who use a fixation before the critical movement time and participants who move their eyes to catch more visual cues to make decisions.

Comparative evaluation of two clinical sampling techniques for HPV detection in male genital sites: a randomized controlled study.

The optimal sampling methods for detecting human papillomavirus (HPV) in male genital sites remain unclear. This study aimed to assess the performance, acceptability, and comfort of two sampling techniques for male genital HPV detection. A total of 490 men aged 18-45 were randomly assigned in a 1:1 ratio to undergo either the rub-brush (nail file followed by swab) or brush-only method (swab only) for sampling at external genitalia sites (PGS) and perineum/perianal (PA) sites. HPV distribution, specimen validity (β-globin as a quality reference), and participant acceptability and comfort were evaluated between the two sampling methods. The brush-only method demonstrated non-inferiority in detecting 14 high-risk HPV types (16/18/31/33/35/39/45/51/52/56/58/59/66/68) compared to the rub-brush method in both PGS (18.9%vs. 16.9%) and PA (10.5%vs. 11.9%). Although no significant differences were observed in positive rates for other HPV types, the brush-only method had a significantly higher invalid rate in PA (8.5%vs. 1.5%). Approximately 85.0% of participants reported good acceptability and comfort with both sampling methods, regardless of anatomical sites. This study suggests comparable performance, acceptability and comfort between the two sampling techniques for HPV detection. However, the rub-brush method may offer an advantage in higher sample validity.

Microplastic pollution in the North-east Atlantic Ocean surface water: How the sampling approach influences the extent of the issue

A lack of standardization in monitoring protocols has hindered the accurate evaluation of microplastic (MP) pollution in the open sea and its potential impacts. As sampling techniques significantly influence the amounts of MPs contained in the sample, the aim of this study was to compare two sampling methods: Manta trawl (size selective approach) and grab sampling (volume selective approach). Both approaches were applied in the open sea surface waters of the North-east Atlantic Ocean. Onshore sample processing was carried out using the innovative tape lifting technique, which affords a series of advantages, including prevention of airborne contamination during analysis, without compromising integrity of the results. The results obtained indicated an MP concentration over four orders of magnitude higher using grab sampling compared to the Manta net approach (mean values equal to 0.24 and 4050 items/m3, respectively). Consequently, the sole quantification of MPs using results obtained with the Manta trawl resulted in a marked underestimation of abundance. Nevertheless, the grab sampling technique is intricately linked to a risk of collecting non-representative water volumes, consequently leading to an overestimation of MPs abundance and a significant inter-sample variability. Moreover, the latter method is unsuitable for use in sampling larger MPs or in areas with low concentrations of MP pollution. The optimal sampling method therefore is dependent on the specific objectives of the study, often resulting in a combination of size and volume selective methods. The results of this study have the potential to contribute to the standardization of monitoring protocols for microplastics, both during the sampling phase and sample processing.

Hydrodynamic shape optimization of an autonomous and remotely-operated vehicle via a multi-surrogate model

The design requirements for the hydrodynamic performance of underwater vehicles vary significantly depending on the application. Optimization without an initial model results in challenges such as large design domains, nonlinear complexity, and high data requirements. To optimize the hydrodynamic performance during the design process, in this paper, a multi-surrogate model was employed to progressively design the shape of an autonomous and remotely-operated vehicle. Based on the data characteristics of different stages, this approach strikes a balance among data quantity, prediction accuracy, and multi-objective requirements. An artificial neural network prediction surrogate model was constructed based on the principle of the minimum prediction factor using the optimal Latin hypercube sampling method. During the optimization of the design domain, the optimum objective was to minimize the dimensionless force (G(x)). During the multi-objective optimization stage, a regression kriging surrogate model was constructed based on a support vector product. The optimization objectives were to maximize volume and minimize dimensionless forces. This enabled the overall design process to attain optimal Pareto solutions within the design domain while simultaneously ensuring high prediction accuracy and minimum data requirements. The results obtained are consistent with the simulation comparison, thus verifying the reliability of the entire optimization process.

Optimization of Centrifugal Pump Impeller Based on Neural Network and NSGA-II Algorithm

Abstract This article focuses on the multistage centrifugal pump secondary impeller as the research object, and the two performance parameters of head and efficiency are taken as the optimization objectives. Firstly, the range analysis of significant influence degree of impeller geometric parameters was carried out by partial factor test method. The blade inlet angle at the front and rear cover boards of the impeller, blade outlet angle and blade wrap angle as the optimization variables, two optimization schemes with different blade numbers were proposed. On this basis, the optimal Latin hypercube sampling method was used to extract samples from the two schemes, CFD technology was used to calculate the corresponding target value of each sample, BP neural networks with different structures were used to train the samples, and the agent model with the highest prediction accuracy was optimized as the population evaluation function of NSGA-II algorithm to search for optimization. The results show that the pump performance parameters have been improved after optimization of Scheme II, and the flow of the internal flow field conforms to the law. Finally, through the experimental verification, the optimized centrifugal pump head and efficiency increased by 3.21m and 3.05%.

Quantitative Assessment of Preanalytic Variables on Clinical Evaluation of PI3/AKT/mTOR Signaling Activity in Diffuse Glioma

Biomarker-driven therapeutic clinical trials require the implementation of standardized, evidence-based practices for sample collection. In diffuse glioma, phosphatidylinositol 3 (PI3)-kinase/AKT/mTOR (PI3/AKT/mTOR) signaling is an attractive therapeutic target for which window-of-opportunity clinical trials could facilitate the identification of promising new agents. Yet, the relevant preanalytic variables and optimal tumor sampling methods necessary to measure pathway activity are unknown. To address this, we used a murine model for isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GBM) and human tumor tissue, including IDH-wildtype GBM and IDH-mutant diffuse glioma. First, we determined the impact of delayed time-to-formalin fixation, or cold ischemia time (CIT), on the quantitative assessment of cellular expression of 6 phosphoproteins that are readouts of PI3K/AK/mTOR activity (phosphorylated-proline-rich Akt substrate of 40 kDa (p-PRAS40, T246), -mechanistic target of rapamycin (p-mTOR; S2448); -AKT (p-AKT, S473); -ribosomal protein S6 (p-RPS6, S240/244 and S235/236), and -eukaryotic initiation factor 4E–binding protein 1 (p-4EBP1, T37/46). With CITs ≥ 2 hours, typical of routine clinical handling, all had reduced or altered expression with p-RPS6 (S240/244) exhibiting relatively greater stability. A similar pattern was observed using patient tumor samples from the operating room with p-4EBP1 more sensitive to delayed fixation than p-RPS6 (S240/244). Many clinical trials utilize unstained slides for biomarker evaluation. Thus, we evaluated the impact of slide storage conditions on the detection of p-RPS6 (S240/244), p-4EBP1, and p-AKT. After 5 months, storage at −80°C was required to preserve the expression of p-4EBP1 and p-AKT, whereas p-RPS6 (240/244) expression was not stable regardless of storage temperature. Biomarker heterogeneity impacts optimal tumor sampling. Quantification of p-RPS6 (240/244) expression in multiple regionally distinct human tumor samples from 8 patients revealed significant intratumoral heterogeneity. Thus, the accurate assessment of PI3K/AKT/mTOR signaling in diffuse glioma must overcome intratumoral heterogeneity and multiple preanalytic factors, including time-to-formalin fixation, slide storage conditions, and phosphoprotein of interest.

Multiobjective optimization of laser welding parameters for P92 steel based on MFO/MOEAD-Kriging

P92 steel is used in steam equipment of thermal power plants and nuclear power plants. It needs to withstand high temperature and high pressure during service, so high-quality welds are required. Laser welding can easily obtain good weld quality and reduce the probability of coarse grains and welding defects in the heat-affected zone. Post-weld deformation, residual stress, and weld hardness are important indexes for evaluating weld quality, which largely depends on the combination of laser welding process parameters. The multiobjective optimization methods of moth-flame optimization (MFO) and multiobjective evolutionary algorithm based on decomposition under the Kriging model were compared in this research. It was found that the MFO algorithm has a faster optimization speed and better overall effect. The Optimal Latin hypercube sampling method was used to design the simulation test. The welding seam-forming process was simulated by the SYSWELD software. The Kriging model was used to make the nonlinear relationship between the process parameters and the weld quality indexes significant. A new data set was designed to evaluate the accuracy of the model. On this basis, the efficiency of the combination of process parameters optimized by the two algorithms was compared. The simulation and experimental results were in good agreement with the multiobjective optimization results of the MFO. In the experimental verification, the optimized weld quality was analyzed from the aspects of microstructure and mechanical properties. The results showed that the multiobjective optimization method quickly and effectively reduces the probability of weld quality defects in actual welding, thereby improving weld quality. This multiobjective optimization method provides valuable research ideas for engineering research and development to effectively shorten its development cycle.

Structural optimization design of ice abrasive water jet nozzle based on multi-objective algorithm

To enhance the cleaning efficiency of the ice abrasive water jet (IAWJ), the nozzle in the jet structure was optimized. The cleaning effect of nozzle with different structural design is different. The design variables include the geometric parameters of the nozzle's structure and the flow process parameters of the jet. The maximum wall static pressure, outlet velocity and wash width are the optimization objectives. Based on the Optimal Latin Hypercube Sampling (OLHS) method, 50 sets of sample capacity were designed for simulation calculation. The parameter functions associated with the three objectives were fitted by MATLAB. The Competitive mechanism multi-objective particle swarm optimizer (CMOPSO) algorithm was used to optimize the nozzle to improve the cleaning effect, which was verified with the test results. The inlet pressure and outlet diameter are critical factors influencing the cleaning effect of the IAWJ. The maximum wall static pressure produced by the optimized nozzle is about 4.5% higher than that of the ordinary cosine nozzle. The cleaning effect of the optimized nozzle is 10% higher than that of the ordinary cosine nozzle, which is verified by the experiments. The optimization of the nozzle serves as a valuable reference for the nozzle structure design of the cleaning machine.

What Are the Limitations and Challenges of Swab-Based DNA Sampling?

Selecting the optimal sampling method is an essential component of the DNA analysis process. Errors or omissions in targeting and gathering relevant samples can significantly reduce the likelihood of obtaining a valuable DNA profile, affecting the profile’s quality and evidential value and ultimately hindering its ability to support the justice system. While DNA typing techniques have become significantly more sensitive in recent years, there is an ongoing need for further advancements in the recovery of DNA from crime scenes. It is essential to improve the accuracy and reliability of forensic investigations, particularly in cases where only tiny amounts of DNA are present, such as touch DNA samples or degraded forensic evidence. Parameters, including swab material, type of substrate, and swabbing protocol, that influence the efficiency of a swab are discussed in this review. This is followed by a literature review of studies that have compared swab types and/or other sampling conditions. While swabs are the most-used collection tools at a crime scene, alternatives are available. These alternatives are reviewed, including their advantages and disadvantages. A critical discussion and conclusions make clear that, unfortunately, neither swabs nor their alternatives are highly effective in recovering DNA from a substrate.

Derivative-Variance Hybrid Global Sensitivity Measure with Optimal Sampling Method Selection

This paper proposes a derivative-variance hybrid global sensitivity measure with optimal sampling method selection. The proposed sensitivity measure is as computationally efficient as the derivative-based global sensitivity measure, which also serves as the conservative estimation of the corresponding variance-based global sensitivity measure. Moreover, the optimal sampling method for the proposed sensitivity measure is studied. In search of the optimal sampling method, we investigated the performances of six widely used sampling methods, namely Monte Carlo sampling, Latin hypercube sampling, stratified sampling, Latinized stratified sampling, and quasi-Monte Carlo sampling using the Sobol and Halton sequences. In addition, the proposed sensitivity measure is validated through its application to a rural bridge.

Fish ectoparasite detection, collection and curation.

Fish parasitology is a dynamic and internationally important discipline with numerous biological, ecological and practical applications. We reviewed optimal fish and parasite sampling methods for key ectoparasite phyla (i.e. Ciliophora, Platyhelminthes, Annelida and Arthropoda) as well as recent advances in molecular detection of ectoparasites in aquatic environments. Ideally, fish capture and anaesthesia as well as parasite recovery methods should be validated to eliminate potential sampling bias and inaccuracy in determining ectoparasite population parameters. There are considerable advantages to working with fresh samples and live parasites, when combined with appropriate fixation methods, as sampling using dead or decaying materials can lead to rapid decomposition of soft-bodied parasites and subsequent challenges for identification. Sampling methods differ between target phyla, and sometimes genera, with optimum techniques largely associated with identification of parasite microhabitat and the method of attachment. International advances in fish parasitology can be achieved through the accession of whole specimens and/or molecular voucher specimens (i.e. hologenophores) in curated collections for further study. This approach is now critical for data quality because of the increased application of environmental DNA (eDNA) for the detection and surveillance of parasites in aquatic environments where the whole organism may be unavailable. Optimal fish parasite sampling methods are emphasised to aid repeatability and reliability of parasitological studies that require accurate biodiversity and impact assessments, as well as precise surveillance and diagnostics.

Aerodynamic shape optimization of passenger car fender based on the FFD method

The research of this work focuses on optimizing the body fender of the CAERI AERO car model to decrease aerodynamic drag and improve aerodynamic performance. Five design regions of the body fender were selected and studied. The Free-Form Deformation (FFD) method was used to perform the mesh deformation process without re-modeling and re-gridding. The optimal Latin Hypercube Sampling method and Computational Fluid Dynamics (CFD) simulations are used to generate the sample points and obtain their responses. The relationships between the design variables and their responses, as well as the contributions of the main factors, were analyzed. After then, an optimization of decreasing drag using the Adaptive Simulated Annealing (ASA) algorithm was conducted based on the Radial Basis Function (RBF) model, and a set of convergent solution space sets is obtained. Finally, CFD simulations were performed on the ASA algorithm search results, and a comparison of the aerodynamic performance between the original fender and an optimal fender that selected from the solution spaces is presented. Using the FFD method and the surrogate model dramatically improves the computational efficiency in this study.

A construction strategy of Kriging surrogate model based on Rosenblatt transformation of associated random variables and its application in groundwater remediation

When using simulation-optimization models for optimizing the design of groundwater pumping-treatment plans for pollution, building a surrogate model for the numerical simulation model has become an effective means of overcoming the computational load of such models. However, previous studies often treated pumping time as a single optimization variable, leading to unnecessary excessive pumping. This paper considers the location, pumping rate, start time, and end time of each candidate pumping well as optimization variables, and proposes a Rosenblatt-transform-based optimal Latin hypercube sampling method for the associated random variables to ensure that the start time is less than or equal to the end time. This method is coupled with an adaptive sampling method based on batch local optimal solutions to construct a dynamic adaptive Kriging surrogate model for the numerical model, ensuring that the true value of the optimal remediation scheme is not lost. The results show that, at the final stage of remediation, the pollutant concentration in the 4 scenarios achieves comprehensive compliance. However, when considering the minimization of remediation costs as the evaluation criterion, the remediation scheme in scenario 1 (the pumping start and end times are independent optimization variables for all candidate pumping wells) is optimal. In the optimization design of groundwater pumping-treatment plans, the pumping wells should be arranged in the midstream and downstream regions of the contaminant plume and parallel to the regional flow direction. This paper provides a method reference for the construction and adaptive updating of surrogate models involving associated random variables, as well as guidance for the dynamic optimization of groundwater pumping and treatment at contaminated sites.

Design and optimization of bionic Nautilus volute for a hydrodynamic retarder

To reduce the energy loss of volute and improve the emergency braking performance of hydrodynamic retarder under design condition, a bionic volute optimization design method is proposed based on the Nautilus shell. First, the accuracy of the numerical method is verified by grid convergence analysis and experiments. Thereafter, the bionic parametric expression is performed based on the cross-section shape of the Nautilus shell. Subsequently, sample points are created using the Optimal Latin Hypercube sampling method and numerically simulated. Radial basis function neural network and multi-objective genetic algorithm are used for optimization. Finally, the flow field inside the volute and the braking performance of the hydrodynamic retarder are compared by numerical calculation and experiment. Results show that the internal flow field of the bionic volute is more uniform, the energy loss is reduced by 78.95%, and the oil flow speed is increased by 40.61%. In addition, the stable flow field can be established faster during braking, the peak torque can be increased by 6.15%, and the onset time can be advanced by 6.58%. The analysis of energy characteristics shows that the improved performance of the bionic volute can be attributed to its improved internal oil flow conditions, thus reducing the energy loss.

Intelligent Prediction of Drilling Rate of Penetration Based on Method-Data Dual Validity Analysis

Summary The rate of penetration (ROP) is a critical parameter in drilling operations, essential for optimizing the drilling process and enhancing drilling speed and efficiency. Traditional and statistical models are inadequate for predicting ROP in complex formations, as they fail to conduct a comprehensive analysis of method validity and data validity. In this study, geological conditions parameters, mechanical parameters, and drilling fluid parameters were extracted as prediction parameters, and an intelligent ROP prediction method was constructed under method-data dual validity analysis. The effectiveness of the ROP prediction method is studied by comparing five machine learning algorithms. The data validity of ROP prediction is also studied by changing the input data type, input data dimension, and input data sampling method. The results show that the effectiveness of the long short-term memory (LSTM) neural network method was found to be superior to support vector regression (SVR), backpropagation (BP) neural network, deep belief neural network (DBN), and convolutional neural network (CNN) methods. For data validity, the best input data type for ROP prediction is geological conditions parameters after principal component analysis (PCA) combined with mechanical parameters and drilling fluid parameters. The lower limit of input data dimension validity is seven input parameters, and the accuracy of prediction results increases with the increase of data dimension. The optimal data sampling method is one point per meter, and the error of the prediction result increases and then decreases with the increase of sampling points. Through step-by-step analysis of method validity, input data type, input data dimension, and input data sampling method, the range, size, and mean of error values of ROP prediction results were significantly reduced, and the mean absolute percentage error (MAPE) of the prediction results of the test set is only 18.40%, while the MAPE of the prediction results of the case study is only 11.60%. The results of this study can help to accurately predict ROP, achieve drilling speedup in complex formations, and promote the efficient development of hydrocarbons in the study area.

Intelligent Optimization Design of a Phononic Crystal Air-Coupled Ultrasound Transducer.

To further improve the operational performance of a phononic crystal air-coupled ultrasonic transducer while reducing the number of simulations, an intelligent optimization design strategy is proposed by combining finite element simulation analysis and artificial intelligence (AI) methods. In the proposed strategy, the structural design parameters of 1-3 piezoelectric composites and acoustic impedance gradient matching layer are sampled using the optimal Latin hypercube sampling (OLHS) method. Moreover, the COMSOL software is utilized to calculate the performance parameters of the transducer. Based on the simulation data, a radial basis function neural network (RBFNN) model is trained to establish the relationship between the design parameters and the performance parameters. The accuracy of the approximation model is verified through linear regression plots and statistical methods. Finally, the NSGA-II algorithm is used to determine the design parameters of the transducer. After optimization, the band gap widths of the piezoelectric composites and acoustic impedance gradient matching layer are increased by 16 kHz and 13.5 kHz, respectively. Additionally, the -6 dB bandwidth of the transducer is expanded by 11.5%. The simulation results and experimental results are consistent with the design objectives, which confirms the effectiveness of the design strategy. This work provides a feasible strategy for the design of high-performance air-coupled ultrasonic transducers, which is of great significance for the development of non-destructive testing technology.

Multi-fidelity data-driven optimization design framework for self-piercing riveting process parameters

Due to its excellent performance, the self-piercing riveting (SPR) process has been widely used in auto body manufacturing in recent years. The need for efficient and reliable design of SPR process parameters is growing in industry. Traditional trial-and-error design methods rely on historical experience and can be limited by physical experimental materials and equipment. Data-driven methods rely on data accuracy and data volume, and data from a single source is often inadequate to meet design needs. To address these issues, a multi-fidelity data-driven optimization design framework is proposed in this paper. The most significant feature of this framework is the fusion of physical experiment data and simulation data to build surrogate models. In this framework, a modified optimal Latin hypercube sampling method and a multi-fidelity surrogate model based on transfer learning and neural networks are proposed for SPR process. This multi-fidelity surrogate model can use a very small amount of experiment data to modify the surrogate model built on simulation data based on transfer learning, so that the model predictions can more closely match the physical experiment results at a lower modeling cost. Benefit from this method, the proposed framework can balance the contradiction between design accuracy and development cost compared to a single-fidelity data-driven framework. The application cases show that the prediction errors of the multi-fidelity models are <0.1 mm for the key geometric parameters of the SPR joint. As verified by physical experiments, the rivets and dies selected by the framework are the optimal solutions within the optional range. To the authors' knowledge, this is the first time that a multi-fidelity modeling method has been introduced to the field of SPR process, to solve the process parameter optimization design problem. Further, the method is not limited to the use of SPR process but can be applied as a paradigm in other engineering optimization design problems, especially in joining process parameter design problems.

Effects of Spatial Data Acquisition on Determination of a Gravel-Bed River Geomorphology

Bed irregularities of water bodies play a significant role in many hydraulic and river engineering experiments and models. Accurate measurement of river geomorphology requires great fieldwork effort. Optimizing the dataset size of measured points will reduce the time and costs involved. In this study, the geomorphology of a gravel bed river reach was measured using different spatial acquisition methods. Digital elevation models were created for each measurement method and the volumes of under/overestimation were calculated. The results show that the sampling methods had more effect on the accuracy of the interpolated geomorphology than the density of the measured points. By choosing an optimized sampling method, the measurement efforts decreased to less than 50%, with negligible errors of around 15 m3 and 10 m3 over and underestimation, respectively, in a water body area of around 2200 m2. These findings help to provide more accurate geomorphological data with less effort as inputs for experimental and numerical models to derive better results.