Sequential Experimental Design Research Articles

Prevalence of Current Chronic Pain in Royal Canadian Mounted Police Cadets

ABSTRACT Background Nearly half of active duty Royal Canadian Mounted Police (RCMP) officers (i.e. 43%) report experiencing current chronic pain (i.e. pain lasting longer than 3 months). Most RCMP officers (i.e. 91%) who report chronic pain indicate that the pain started after working as RCMP officers. Baseline data on chronic pain prevalence among RCMP cadets has not been available. Aims The current study was designed to provide cross-sectional estimates of chronic pain prevalence among RCMP cadets starting the Cadet Training Program and to assess for sociodemographic differences among participants. Methods The RCMP Study uses a longitudinal prospective sequential experimental cohort design to create a clustered randomized trial that engages individual participants for 5.5 years. The current manuscript provides cross-sectional associations between chronic pain prevalence and sociodemographic characteristics. Participants were RCMP cadets (n=770) starting the Cadet Training Program. Location, intensity (i.e. on a 0-10 scale, and days per week experienced), and duration (i.e. number of months) of chronic pain was reported. Differences across sociodemographic characteristics were examined. Results Few RCMP cadets reported experiencing chronic pain (i.e. 10%); lower back pain was rated as the most severe in terms of intensity and duration, and second most frequently reported in number of days experienced per week. Prevalence of chronic pain was lower among RCMP cadets than RCMP officers. Conclusions Chronic pain prevalence among active duty RCMP officers may result from or be moderated by operational duties, as well as routine aging. Future researchers could examine ways to mitigate chronic pain development during RCMP officer careers.

Investigation of Deposition Parameters for Near-Beta Alloy Ti-55511 Fabricated by Directed Energy Deposition

The directed energy deposition (DED) parameters were determined for near-β alloy Ti-55511 by employing statistical design of experiments (DOEs) methods. Parameters resulting in fully dense freeform deposits were identified using two sequential DOEs. Single laser tracks were printed with several laser power, traverse rate, and powder feed rate settings in an initial DOE to identify promising build parameters. The capture efficiency and effective deposition rate were used to characterize and rank the single track deposits. The best parameters were then used to print a solid cube with various X–Y and Z overlaps (different hatch spacing, HS, and layer thickness, ZS) in a second DOE. Suitable deposition parameters were selected based on the cube density and microstructure and were used to fabricate larger tensile samples for mechanical testing. Multiple parameter sets were found to provide dense Ti-55511 deposits with acceptable mechanical properties and the parametric models showed statistical significance.

Bayesian reinforcement learning reliability analysis

A Bayesian reinforcement learning reliability method that combines Bayesian inference for the failure probability estimation and reinforcement learning-guided sequential experimental design is proposed. The reliability-oriented sequential experimental design is framed as a finite-horizon Markov decision process (MDP), with the associated utility function defined by a measure of epistemic uncertainty about Kriging-estimated failure probability, referred to as integrated probability of misclassification (IPM). On this basis, a one-step Bayes optimal learning function termed integrated probability of misclassification reduction (IPMR), along with a compatible convergence criterion, is defined. Three effective strategies are implemented to accelerate IPMR-informed sequential experimental design: (i) Analytical derivation of the inner expectation in IPMR, simplifying it to a single expectation. (ii) Substitution of IPMR with its upper bound IPMRU to avoid element-wise computation of its integrand. (iii) Rational pruning of both quadrature set and candidate pool in IPMRU to alleviate computer memory constraint. The efficacy of the proposed approach is demonstrated on two benchmark examples and two numerical examples. Results indicate that IPMRU facilitates a much more rapid reduction of IPM compared to other existing learning functions, while requiring much less computational time than IPMR itself. Therefore, the proposed reliability method offers a substantial advantage in both computational efficiency and accuracy, especially in complex dynamic reliability problems.

Optimisation of DNA electroporation protocols for different plant-associated bacteria

Electroporation is a vital process that facilitates the use of modern recombineering and other high-throughput techniques in a wide array of microorganisms, including non-model bacteria like plant growth-promoting bacteria (PGPB). These microorganisms play a significant role in plant health by colonizing plants and promoting growth through nutrient exchange and hormonal regulation. In this study, we introduce a sequential Design of Experiments (DOE) approach to obtain highly competent cells swiftly and reliably for electroporation. Our method focuses on optimizing the three stages of the electroporation procedure—preparing competent cells, applying the electric pulse field, and recovering transformed cells—separately. We utilized a split-plot fractional design with five factors and a covariate to optimize the first step, response surface methodology (RSM) for the second step, and Plackett-Burman design for two categorical factors and one continuous factor for the final step. Following the experimental sequence with three bacterial models, we achieved efficiencies 10 to 100 times higher, reaching orders of 105 to 106 CFU/μg of circular plasmid DNA. These results highlight the significant potential for enhancing electroporation protocols for non-model bacteria.

Structural reliability analysis based on probability density evolution method and stepwise truncated variance reduction

To address the substantial computational burden associated with probability density evolution method (PDEM) in structural reliability analysis, this study proposes a novel look-ahead learning function named stepwise truncated variance reduction (STVR), integrating polynomial chaos Kriging (PCK) and PDEM. Three key features of STVR are highlighted. First, it enables quantifying the maximum reduction in predictive errors of PCK within the regions of interest (ROI) when adding a new point. Second, closed-form expression for STVR is derived through Kriging update formulas, eliminating the need for computationally intensive Gauss–Hermite quadrature or extensive conditional simulations of PCK. Third, a dynamic adjustment procedure is proposed for the probability level-related parameter in STVR, with the aim of achieving a good balance between the exploitation and exploration of ROI during the sequential experimental design process. The performance of STVR is demonstrated through two benchmark analytical functions and three numerical examples of varying complexity. Results indicate that the dynamic adjustment procedure for the probability level-related parameter in STVR outperforms the empirical setting of a minor value. Then, STVR proves more advantageous than existing pointwise and look-ahead learning functions, particularly in addressing complex dynamic reliability problems.

Integrating a tailored recurrent neural network with Bayesian experimental design to optimize microbial community functions.

Microbiomes interact dynamically with their environment to perform exploitable functions such as production of valuable metabolites and degradation of toxic metabolites for a wide range of applications in human health, agriculture, and environmental cleanup. Developing computational models to predict the key bacterial species and environmental factors to build and optimize such functions are crucial to accelerate microbial community engineering. However, there is an unknown web of interactions that determine the highly complex and dynamic behavior of these systems, which precludes the development of models based on known mechanisms. By contrast, entirely data-driven machine learning models can produce physically unrealistic predictions and often require significant amounts of experimental data to learn system behavior. We develop a physically-constrained recurrent neural network that preserves model flexibility but is constrained to produce physically consistent predictions and show that it can outperform existing machine learning methods in the prediction of certain experimentally measured species abundance and metabolite concentrations. Further, we present a closed-loop, Bayesian experimental design algorithm to guide data collection by selecting experimental conditions that simultaneously maximize information gain and target microbial community functions. Using a bioreactor case study, we demonstrate how the proposed framework can be used to efficiently navigate a large design space to identify optimal operating conditions. The proposed methodology offers a flexible machine learning approach specifically tailored to optimize microbiome target functions through the sequential design of informative experiments that seek to explore and exploit community functions.

Sequential Bayesian Experimental Design for Calibration of Expensive Simulation Models

Simulation models of critical systems often have parameters that need to be calibrated using observed data. For expensive simulation models, calibration is done using an emulator of the simulation model built on simulation output at different parameter settings. Using intelligent and adaptive selection of parameters to build the emulator can drastically improve the efficiency of the calibration process. The article proposes a sequential framework with a novel criterion for parameter selection that targets learning the posterior density of the parameters. The emergent behavior from this criterion is that exploration happens by selecting parameters in uncertain posterior regions while simultaneously exploitation happens by selecting parameters in regions of high posterior density. The advantages of the proposed method are illustrated using several simulation experiments and a nuclear physics reaction model.

Phosphorus Recovery from Wastewater Aiming Fertilizer Production: Struvite Precipitation Optimization Using a Sequential Plackett–Burman and Doehlert Design

The precipitation of struvite from wastewater is a potential alternative for the recovery of nutrients, especially phosphorus, which is an essential macronutrient for agriculture but can be harmful to the environment when improperly disposed of in water bodies. In addition, struvite has elements of great added value for agricultural activity (P, N, and Mg) and is, therefore, considered a sustainable alternative fertilizer. In its formation process, several intervening physicochemical factors may be responsible for the production yield levels. Optimization processes can help to define and direct the factors that truly matter for precipitation. In this context, a sequential design of experiments (DOE) methodology was applied to select and optimize the main struvite precipitation factors in wastewater. Initially, a screening was performed with eight factors with the aid of Plackett–Burman design, and the factors with a real influence on the process were identified. Then, a Doehlert design was used for optimization by applying the response surface methodology and the desirability function. The results were used to identify the optimal points of the pH (10.2), N/P ratio (≥4), and initial phosphorus concentration (183.5 mg/L); these values had a greater effect on phosphorus recovery and the production of struvite, which was confirmed through thermochemical analysis of the decomposition of its structure by differential scanning calorimeter—glass transition temperature (DSC-TG) and phase identification by X-ray diffraction (XRD). The determination of the best synthesis conditions is an enormous contribution to the control of the process because these conditions lead to better yields and higher levels of phosphorus recovery.

Bayesian sequential optimal experimental design for nonlinear models using policy gradient reinforcement learning

We present a mathematical framework and computational methods for optimally designing a finite sequence of experiments. This sequential optimal experimental design (sOED) problem is formulated as a finite-horizon partially observable Markov decision process (POMDP) under a Bayesian setting and with information-theoretic utilities. The formulation is general and may accommodate continuous random variables, non-Gaussian posteriors, and nonlinear forward models. The sOED design policy incorporates elements of feedback and lookahead simultaneously, and we show it to generalize the commonly-used batch and greedy design strategies. We solve for the sOED policy using the policy gradient (PG) method from reinforcement learning, and provide a derivation for the PG expression in the sOED context. Adopting an actor–critic approach, the policy and value functions are parameterized using deep neural networks and improved via PG estimates produced from simulated episodes of designs and observations. The new PG-sOED algorithm is first validated on a linear-Gaussian benchmark, and then compared against other design baselines on a sensor movement problem for contaminant source inversion in a convection–diffusion field. We provide explanation for the policy behaviors using knowledge of the underlying physical process.

Sequential Experiment Design Method Based on Uniformity and Distortion of Responses

Sequential Experiment Design Method Based on Uniformity and Distortion of Responses

Uncertainty Quantification and Experimental Design for Large-Scale Linear Inverse Problems under Gaussian Process Priors

We consider the use of Gaussian process (GP) priors for solving inverse problems in a Bayesian framework. As is well known, the computational complexity of GPs scales cubically in the number of datapoints. Here we show that in the context of inverse problems involving integral operators, one faces additional difficulties that hinder inversion on large grids. Furthermore, in that context, covariance matrices can become too large to be stored. By leveraging recent results about sequential disintegrations of Gaussian measures, we are able to introduce an implicit representation of posterior covariance matrices that reduces the memory footprint by only storing low rank intermediate matrices, while allowing individual elements to be accessed on-the-fly without needing to build full posterior covariance matrices. Moreover, it allows for fast sequential inclusion of new observations. These features are crucial when considering sequential experimental design tasks. We demonstrate our approach by computing sequential data collection plans for excursion set recovery for a gravimetric inverse problem, where the goal is to provide fine resolution estimates of high density regions inside the Stromboli volcano, Italy. Sequential data collection plans are computed by extending the weighted integrated variance reduction (wIVR) criterion to inverse problems. Our results show that this criterion is able to significantly reduce the uncertainty on the excursion volume, reaching close to minimal levels of residual uncertainty. Overall, our techniques allow the advantages of probabilistic models to be brought to bear on large-scale inverse problems arising in the natural sciences. Particularly, applying the latest developments in Bayesian sequential experimental design on realistic large-scale problems opens new venues of research at a crossroads between mathematical modelling of natural phenomena, statistical data science, and active learning.

AN ADAPTIVE STRATEGY FOR SEQUENTIAL DESIGNS OF MULTILEVEL COMPUTER EXPERIMENTS

Investigating uncertainties in computer simulations can be prohibitive in terms of computational costs, since the simulator needs to be run over a large number of input values. Building an emulator, i.e., a statistical surrogate model of the simulator constructed using a design of experiments made of a comparatively small number of evaluations of the forward solver, greatly alleviates the computational burden to carry out such investigations. Nevertheless, this can still be above the computational budget for many studies. Two major approaches have been used to reduce the budget needed to build the emulator: efficient design of experiments, such as sequential designs, and combining training data of different degrees of sophistication in a so-called multifidelity method, or multilevel in case these fidelities are ordered typically for increasing resolutions. We present here a novel method that combines both approaches, the multilevel adaptive sequential design of computer experiments in the framework of Gaussian process (GP) emulators. We make use of reproducing kernel Hilbert spaces as a tool for our GP approximations of the differences between two consecutive levels. This dual strategy allows us to allocate efficiently limited computational resources over simulations of different levels of fidelity and build the GP emulator. The allocation of computational resources is shown to be the solution of a simple optimization problem in a special case where we theoretically prove the validity of our approach. Our proposed method is compared to other existing models of multifidelity Gaussian process emulation. Gains in orders of magnitudes in accuracy or computing budgets are demonstrated in some numerical examples for some settings.

Multi-level emulation of tsunami simulations over Cilacap, South Java, Indonesia

Carrying out a Probabilistic Tsunami Hazard Assessment (PTHA) requires a large number of simulations done at a high resolution. Statistical emulation builds a surrogate to replace the simulator and thus reduces computational costs when propagating uncertainties from the earthquake sources to the tsunami inundations. To reduce further these costs, we propose here to build emulators that exploit multiple levels of resolution and a sequential design of computer experiments. By running a few tsunami simulations at high resolution and many more simulations at lower resolutions we are able to provide realistic assessments whereas, for the same budget, using only the high resolution tsunami simulations do not provide a satisfactory outcome. As a result, PTHA can be considered with higher precision using the highest spatial resolutions, and for impacts over larger regions. We provide an illustration to the city of Cilacap in Indonesia that demonstrates the benefit of our approach.

Sequential design of multi-fidelity computer experiments with effect sparsity

Sequential design of multi-fidelity computer experiments with effect sparsity

Hierarchical Bayesian approach to experimental data fusion: Application to strength prediction of high entropy alloys from hardness measurements

The discovery of materials with improved properties can be accelerated by models with the ability to combine data from multiple experimental information sources. A recurring task in the toolbox of practitioners is to map input physical descriptors to output properties of interest. Typically, both the outputs and many of the inputs are experimentally measured and, thus, noisy. Probabilistic regression methods, e.g., Gaussian process regression, can easily deal with noisy outputs, even if the noise is input-dependent. However, most regression methods cannot process noisy inputs. Ignoring input uncertainty leads to inaccurate predictive uncertainty, a crucial ingredient for the sequential design of experiments. The objective of this paper is to develop a regression methodology that can deal with input uncertainty when one wishes to correlate an inexpensive experimental measurement (e.g., hardness) to an expensive one (e.g., yield strength). Our hierarchical Bayesian approach uses two Gaussian processes. The first one maps noiseless physical descriptors to the inexpensive experimental measurement. The second Gaussian process maps noiseless physical descriptors and the inexpensive experimental measurement to the expensive experimental measurement. The two Gaussian processes form a nested model that is not analytically tractable. To overcome this issue, we propose semi-analytical approximations to both the marginal likelihood and the posterior predictive distribution. The result is a model that is practical to train and use. We demonstrate the merits of the proposed method through a synthetic dataset in which we control all the uncertainties. The statistical tests clearly show that standard Gaussian process regression cannot cope with input uncertainty whereas our proposed method consistently yields better predictive distributions. Finally, we apply the method to the task of predicting the yield strength of high entropy alloys from hardness on an exhaustive dataset compiled from the available literature.

Adaptive Sequential Experiments with Unknown Information Arrival Processes

Problem definition: Sequential experiments that are deployed in a broad range of practices are characterized by an exploration-exploitation trade-off that is well understood when in each time period feedback is received only on the action that was selected in that period. However, in many practical settings, additional information may become available between decision epochs. We study the performance that one may achieve when leveraging such auxiliary information and the design of algorithms that effectively do so without prior knowledge of the information arrival process. Methodology/results: Our formulation considers a broad class of distributions that are informative about rewards from actions and allows auxiliary observations from these distributions to arrive according to an arbitrary and a priori unknown process. When it is known how to map auxiliary observations to reward estimates, we characterize the best achievable performance as a function of the information arrival process. In terms of achieving optimal performance, we establish that upper confidence bound and Thompson sampling algorithms possess natural robustness with respect to the information arrival process, which uncovers a novel property of these popular algorithms. When the mappings connecting auxiliary observations and rewards are a priori unknown, we characterize a necessary and sufficient condition under which auxiliary information allows performance improvement and devise an adaptive policy (termed 2UCBs) that guarantees near optimality. We use a data set from a large media site to analyze the value that may be captured by leveraging auxiliary observations in the design of content recommendations. Managerial implications: Our study highlights the importance of utilizing auxiliary information in the design of sequential experiments and characterizes how salient features of the auxiliary information stream impact performance. Our study also emphasizes the risk in processing auxiliary information using nonadaptive approaches that are predicated on correct interpretation of this information, as opposed to deploying flexible, adaptive methods.

Assessment of Community Health Volunteers contribution and factors affecting their health care service delivery in selected urban wards of Puducherry – A mixed-methods operational research study

IntroductionCommunity participation gained its importance from the Alma Ata declaration in 1978. Improving coverage of these services depends on factors like availability, accessibility, and quality of service delivery. Involving Community Health Volunteers to deliver health services would help in decentralizing health care delivery. MethodsA mixed-method approach (Sequential experimental design) was employed to evaluate the contributions of CHVs. The data collection methods included Key Informant Interviews (KII) and a pre-tested semi-structured self-introspection questionnaire to capture both volunteer's and health system side perspectives of the intervention. The study was conducted from October 2017 to May 2018. ResultsAt the end of the intervention, only 33 volunteers (51%) had contributed to health care delivery in their respective anganwadis. We compiled the contributions made by the CHV during the intervention period. We explored the facilitating, hindering factors that influenced the outcome and gathered suggestions for the same. The CHVs felt that they have contributed adequately to the delivery of health care. Discussion and conclusionThe study explores the contributions of CHVs and their evaluation using a mixed-methods design capturing the perspectives of various stakeholders. Our study results highlight the spectrum of contributions that can be rendered through CHV's if adequately trained, who can be utilized as an effective workforce in resource-poor settings.

Bioprocess development as a sustainable platform for eco-friendly alkaline phosphatase production: an approach towards crab shells waste management

BackgroundThere are substantial environmental and health risks associated with the seafood industry's waste of crab shells. In light of these facts, shellfish waste management is critical for environmental protection against hazardous waste produced from the processing industries. Undoubtedly, improved green production strategies, which are based on the notion of "Green Chemistry," are receiving a lot of attention. Therefore, this investigation shed light on green remediation of the potential hazardous crab shell waste for eco-friendly production of bacterial alkaline phosphatase (ALP) through bioprocessing development strategies.ResultsIt was discovered that by utilizing sequential statistical experimental designs, commencing with Plackett–Burman design and ending with spherical central composite design, and then followed by pH-uncontrolled cultivation conditions in a 7 L bench-top bioreactor, an innovative medium formulation could be developed that boosted ALP production from Bacillus licheniformis strain ALP3 to 212 U L−1. The highest yield of ALP was obtained after 22 h of incubation time with yield coefficient Yp/s of 795 U g−1, which was 4.35-fold higher than those obtained in the shake-flask system. ALP activity has a substantial impact on the volatilization of crab shell particles, as shown by the results of several analytical techniques such as atomic absorption spectrometry, TGA, DSC, EDS, FTIR, and XRD.ConclusionsWe highlighted in the current study that the biovalorization of crab shell waste and the production of cost-effective ALP were being combined and that this was accomplished via the use of a new and innovative medium formulation design for seafood waste management as well as scaling up production of ALP on the bench-top scale.

A novel machine learning-based multiobjective robust optimisation strategy for quality improvement of multivariate manufacturing processes

The primary objective of this study was to develop a novel data-driven machine learning-based multiobjective robust optimisation strategy to improve the overall quality of multivariate manufacturing processes. The new strategy was conceptualised considering a manufacturing environment with unreplicated non-normal data observations and limited opportunity for off-line sequential design of experiments. At a macro level, the new strategy adopts suitable artificial intelligence-based process models and a fine-tuned non-dominated sorting genetic algorithm-II (NSGA-II) to derive robust efficient process setting conditions. These robust solutions are iteratively derived considering process model predictive uncertainties, process setting sensitivities, and variance-covariance structure of uncontrollable multivariate non-normal inputs (or covariates). These solutions are also ranked based on multicriteria decision-making (MCDM) techniques to facilitate implementation. In this study, the quality of the best-ranked solutions was compared (w.r.t. closeness to specified multiple targets and predicted multivariate output variabilities) with those of the solutions obtained from parametric and commercial software-based approaches using three different real-life manufacturing cases.

Reinforcement Learning-Based Sequential Batch-Sampling for Bayesian Optimal Experimental Design

Abstract Engineering problems that are modeled using sophisticated mathematical methods or are characterized by expensive-to-conduct tests or experiments are encumbered with limited budget or finite computational resources. Moreover, practical scenarios in the industry, impose restrictions, based on logistics and preference, on the manner in which the experiments can be conducted. For example, material supply may enable only a handful of experiments in a single-shot or in the case of computational models one may face significant wait-time based on shared computational resources. In such scenarios, one usually resorts to performing experiments in a manner that allows for maximizing one’s state-of-knowledge while satisfying the above-mentioned practical constraints. Sequential design of experiments (SDOE) is a popular suite of methods that have yielded promising results in recent years across different engineering and practical problems. A common strategy that leverages Bayesian formalism is the Bayesian SDOE, which usually works best in the one-step-ahead or myopic scenario of selecting a single experiment at each step of a sequence of experiments. In this work, we aim to extend the SDOE strategy, to query the experiment or computer code at a batch of inputs. To this end, we leverage deep reinforcement learning (RL)-based policy gradient methods, to propose batches of queries that are selected taking into account the entire budget in hand. The algorithm retains the sequential nature, inherent in the SDOE while incorporating elements of reward based on task from the domain of deep RL. A unique capability of the proposed methodology is its ability to be applied to multiple tasks, for example, optimization of a function, once its trained. We demonstrate the performance of the proposed algorithm on a synthetic problem and a challenging high-dimensional engineering problem.