Search

Fluorescence-encoded infrared (FEIR) spectroscopy is an emerging technique for performing vibrational spectroscopy in solution with detection sensitivity down to single molecules. FEIR experiments use ultrashort pulses to excite a fluorescent molecule's vibrational and electronic transitions in a sequential, time-resolved manner, and are therefore sensitive to intervening vibrational dynamics on the ground state, vibronic coupling, and the relative orientation of vibrational and electronic transition dipole moments. This series of papers presents a theoretical treatment of FEIR spectroscopy that describes these phenomena and examines their manifestation in experimental data. This first paper develops a nonlinear response function description of Fourier-transform FEIR experiments for a two-level electronic system coupled to multiple vibrations, which is then applied to interpret experimental measurements in the second paper [L. Whaley-Mayda et al., J. Chem. Phys. 159, 194202 (2023)]. Vibrational coherence between pairs of modes produce oscillatory features that interfere with the vibrations' population response in a manner dependent on the relative signs of their respective Franck–Condon wavefunction overlaps, leading to time-dependent distortions in FEIR spectra. The orientational response of population and coherence contributions are analyzed and the ability of polarization-dependent experiments to extract relative transition dipole angles is discussed. Overall, this work presents a framework for understanding the full spectroscopic information content of FEIR measurements to aid data interpretation and inform optimal experimental design. [ABSTRACT FROM AUTHOR]

Over recent decades, optimal experimental design has surged in popularity due to affordable computing and efficient algorithms. However, some researchers highlight that this focus has often side‐lined the importance of including pure experimental replication in designs, as many optimality criteria do not inherently suggest designs with replicates. In this study, we introduce an approach that allows the designer to input a desired replication structure into the optimal design search. We list all potential replication structures, indicate the corresponding budget for pure‐error (PE) and lack‐of‐fit (LOF) degrees‐of‐freedom, and use particle swarm optimization to search for the optimal design in each scenario. We demonstrate our approach on various unexplored I$I$‐optimal design scenarios and several scenarios recently discussed in literature. Our findings reveal the existence of highly‐efficient I$I$‐optimal designs with unique replication structures, offering designers informed choices on allocating a limited budget for PE and LOF degrees‐of‐freedom. This method is versatile and compatible with other optimizers, such as the coordinate exchange. [ABSTRACT FROM AUTHOR]

Tissue engineering predominantly relies on trial and error in vitro and ex vivo experiments to develop protocols and bioreactors to generate functional tissues. As an alternative, in silico methods have the potential to significantly reduce the timelines and costs of experimental programs for tissue engineering. In this paper, we propose a methodology to formulate, select, calibrate, and test mathematical models to predict cell population growth as a function of the biochemical environment and to design optimal experimental protocols for model inference of in silico model parameters. We systematically combine methods from the experimental design, mathematical statistics, and optimization literature to develop unique and explainable mathematical models for cell population dynamics. The proposed methodology is applied to the development of this first published model for a population of the airway-relevant bronchio-alveolar epithelial (BEAS-2B) cell line as a function of the concentration of metabolic-related biochemical substrates. The resulting model is a system of ordinary differential equations that predict the temporal dynamics of BEAS-2B cell populations as a function of the initial seeded cell population and the glucose, oxygen, and lactate concentrations in the growth media, using seven parameters rigorously inferred from optimally designed in vitro experiments. [ABSTRACT FROM AUTHOR]

X-ray tomography is an effective non-destructive testing method for industrial quality control. Limited-angle tomography can be used to reduce the amount of data that need to be acquired and thereby speed up the process. In some industrial applications, however, objects are flat and layered, and laminography is preferred. It can deliver 2D images of the structure of a layered object at a particular depth from very limited data. An image at a particular depth is obtained by summing those parts of the data that contribute to that slice. This produces a sharp image of that slice while superimposing a blurred version of structures present at other depths. In this paper, we investigate an Optimal Experimental Design (OED) problem for laminography that aims to determine the optimal source positions. Not only can this be used to mitigate imaging artifacts, it can also speed up the acquisition process in cases where moving the source and detector is time-consuming (e.g., in robotic arm imaging systems). We investigate the imaging artifacts in detail through a modified Fourier Slice Theorem. We address the experimental design problem within the Bayesian risk framework using empirical Bayes risk minimization. Finally, we present numerical experiments on simulated data. [ABSTRACT FROM AUTHOR]

This paper addresses the following problem: given m potential observations to determine n parameters, m > n , what is the best choice of n observations. The problem can be formulated as finding the n × n submatrix of the complete m × n observation matrix that has maximum determinant. An algorithm by Gu and Eisenstat for a determining a strongly rank-revealing QR factorisation of a matrix can be adapted to address this latter formulation. The algorithm starts with an initial selection of n rows of the observation matrix and then performs a sequence of row interchanges, with the determinant of the current submatrix strictly increasing at each step until no further improvement can be made. The algorithm implements rank-one updating strategies, which leads to a compact and efficient algorithm. The algorithm does not necessarily determine the global optimum but provides a practical approach to designing an effective measurement strategy. In this paper, we describe how the Gu–Eisenstat algorithm can be adapted to address the problem of optimal experimental design and used with the QR algorithm with column pivoting to provide effective designs. We also describe implementations of sequential algorithms to add further measurements that optimise the information gain at each step. We illustrate performance on several metrology examples. [ABSTRACT FROM AUTHOR]

Nuclear data are fundamental inputs to radiation transport codes used for reactor design and criticality safety. The design of experiments to reduce nuclear data uncertainty has been a challenge for many years, but advances in the sensitivity calculations of radiation transport codes within the last two decades have made optimal experimental design possible. The design of integral nuclear experiments poses numerous challenges not emphasized in classical optimal design, in particular, constrained design spaces (in both a statistical and engineering sense), severely under‐determined systems, and optimality uncertainty. We present a design pipeline to optimize critical experiments that uses constrained Bayesian optimization within an iterative expert‐in‐the‐loop framework. We show a successfully completed experiment campaign designed with this framework that involved two critical configurations and multiple measurements that targeted compensating errors in 239Pu nuclear data. [ABSTRACT FROM AUTHOR]

In this paper, the ordinary reinforced UHPC T‐shaped beam, steel plate reinforced UHPC T‐shaped beam and prestressed UHPC T‐shaped beam were compared by conducting the flexure test. The vertical displacement, crack development, strain change, and crack resistance were discussed. The steel plate reinforced UHPC T‐shaped beam shown better cracking resistance and mechanical properties. Based on the research, the multi‐scale finite element model of the steel plate reinforced UHPC T‐shaped beam was established, and the influence parameters were analyzed. The beam height, steel plate thickness and web thickness shown great influence on the ultimate bearing capacity of the beam. The bearing capacity calculation formulas of the steel plate reinforced UHPC T‐shaped beam based on the plastic strengthening model of the steel were proposed. The calculation results shown good agreement with the test value. [ABSTRACT FROM AUTHOR]

Accelerated degradation testing has considerable significance in reliability inference due to its ability to provide accurate estimation of lifetime characteristics of highly reliable systems within a relatively short testing time period. The measured data from particular experiments at high stress conditions are extrapolated, through a technically reasonable statistical model, to obtain estimates of certain reliability properties under normal use levels. In this work, we consider repeated measures accelerated degradation tests with multiple stress variables, where the degradation path is assumed to follow a linear mixed effects model which is quite common in settings when repeated measures are made. As opposed to the vast majority of numerical derivations in the literature, we contribute in this work with analytical results in regards to optimal experimental designs for minimizing the asymptotic variance for estimating the median failure time under normal use conditions when the time points for measurements are fixed in advance. [ABSTRACT FROM AUTHOR]

This study focused on extracting antioxidants from hazelnut skin, an industrial food waste, using two different methods: conventional extraction (CE) and ultrasound-assisted extraction (UAE). The Response Surface Methodology (RSM) has been utilized using the total phenolic content (TPC) and antioxidant capacity (AC) results as responses in optimizing both method conditions. The independent variables and their levels for the optimal experimental design were adjusted as follows: temperature (50-90°C), time (2-62 min), and loading capacity (5-15%) for CE; and temperature (25-50°C), time (1-30 min), loading capacity (5-15%), and ultrasonic amplitude (20-50%) for UAE. The optimum conditions were determined to be 90°C for 35 min with a 5% loading capacity for CE, and 50°C for 27 min with a 5% loading capacity and 50% amplitude for UAE. The TPC of the extracts were found to be 142.62 mg GAE/g and 129.69 mg GAE/g, while the AC values were 127.02 µmol TE/g and 116.00 µmol TE/g for CE and UAE methods, respectively. In conclusion, it has been demonstrated that hazelnut skin extracts obtained by optimizing CE and UAE methods can serve as natural antioxidant alternatives in food products and may hold significant potential for further applications. [ABSTRACT FROM AUTHOR]

Due to their cost, experiments for inertial confinement fusion (ICF) heavily rely on numerical simulations to guide design. As simulation technology progresses, so too can the fidelity of models used to plan for new experiments. However, these high-fidelity models are by themselves insufficient for optimal experimental design, because their computational cost remains too high to efficiently and effectively explore the numerous parameters required to describe a typical experiment. Therefore, traditionally, ICF design has relied on low-fidelity modeling to initially identify potentially interesting design regions, which are then subsequently explored via selected high-fidelity modeling. In this paper, we demonstrate that this two-step approach can be insufficient: even for simple design problems, a two-step optimization strategy can lead high-fidelity searching toward incorrect regions and consequently waste computational resources on parameter regimes far away from the true optimal solution. We reveal that a primary cause of this behavior in ICF design problems is the presence of low-fidelity optima in different regions of the parameter space far away from high-fidelity optima. To address this issue, we propose an iterative multifidelity Bayesian optimization method based on Gaussian Process Regression that leverages both low- and high-fidelity models simultaneously. We demonstrate, using both two- and eight-dimensional ICF test problems, that our algorithm can effectively utilize both low-fidelity and high-fidelity models to refine the designs. This approach proves to be more efficient than relying solely on high-fidelity modeling for optimization. [ABSTRACT FROM AUTHOR]

Precise and accurate determination of equilibrium constants in chemical systems plays a crucial role in understanding their behavior, predicting reactions, and optimizing processes, leading to advancements in materials science, pharmaceuticals, and environmental studies. Various procedures, such as the mole ratio method, continuous variation, and titration, are commonly employed to investigate equilibrium systems and calculate equilibrium constants involving host–guest complexes. However, the impact of different experimental designs on the accuracy and precision of the fitted parameters has not been extensively explored. In this study, we focus on the role of experimental design in monitoring chemical equilibria and its influence on model parameter estimation. The indicator displacement assay and guest displacement assay are investigated as typical monitoring methods, and the optimal experimental design approach is introduced to identify suitable probe systems. The global analysis is also explored to improve parameter estimation by simultaneously fitting multiple datasets. Furthermore, non-regular procedures are examined, demonstrating the potential for robust model fitting with minimal measurements and providing insights into selecting appropriate probe systems. The results indicate that with appropriate design for monitoring equilibrium positions in competitive reactions, the probe equilibrium can differ by several orders of magnitude (more than 3 units) from the target equilibrium constant. Yet, accurate and precise determination of the equilibrium constant can still be achieved. The findings shed light on the importance of optimal experimental design for accurately determining thermodynamic parameters and binding affinities in complex chemical systems. [ABSTRACT FROM AUTHOR]

Bayesian inference typically focuses upon two issues. The first is estimating the parameters of some model from data, and the second is quantifying the evidence for alternative hypotheses—formulated as alternative models. This paper focuses upon a third issue. Our interest is in the selection of data—either through sampling subsets of data from a large dataset or through optimising experimental design—based upon the models we have of how those data are generated. Optimising data-selection ensures we can achieve good inference with fewer data, saving on computational and experimental costs. This paper aims to unpack the principles of active sampling of data by drawing from neurobiological research on animal exploration and from the theory of optimal experimental design. We offer an overview of the salient points from these fields and illustrate their application in simple toy examples, ranging from function approximation with basis sets to inference about processes that evolve over time. Finally, we consider how this approach to data selection could be applied to the design of (Bayes-adaptive) clinical trials. [ABSTRACT FROM AUTHOR]

In recent years, more attention has been paid prominently to accelerated degradation testing (ADT) in order to characterize accurate estimation of reliability properties for systems that are designed to work properly for years or even decades. In this paper, we propose optimal experimental designs for repeated measures ADTs with competing failure modes that correspond to multiple response components. The marginal degradation paths are expressed using linear mixed effects models. The optimal design is obtained by minimizing the asymptotic variance of the estimator of some quantile of the failure time distribution at the normal use conditions. Numerical examples are introduced to ensure the robustness of the proposed optimal designs and compare their efficiency with standard experimental designs. [ABSTRACT FROM AUTHOR]

Bernoulli gripper is a fluid-driven vacuum suction device, widely used in automated production lines, with suction force regarded as its key mechanical indicator. Previous design methods for optimizing the structure and parameters showed very limited enhancement in suction force due to the compressibility of air, which limits the supply pressure and supply power. Therefore, this study proposes an innovative design for replacing the fluid medium from compressible gas to incompressible liquid (e.g., water). It was experimentally validated that the gripper using water can increase the supply power several times and the suction force dozens of times compared with that using air. Then, the pressure distribution and flow field of the gripper were investigated experimentally, implying that the flow field exists in the water-film zone and the bubble zone, with radial lines of water flow of equal cross section formed in the bubble zone. Furthermore, a model of pressure distribution was established, including the water-film zone, the bubble zone, and the boundary between the two. The theoretical results were basically consistent with the experimental results. This study provides an innovative idea, theoretical model, and experimental data for the optimal design and application of the Bernoulli gripper. [ABSTRACT FROM AUTHOR]

During the initial design phases of complex multi-disciplinary systems such as urban tunnelling, the appraisal of different design alternatives can ensure optimal designs in terms of costs, construction time, and safety. To enable the evaluation of a large number of design scenarios and to find an optimal solution that minimises impact of tunnelling on existing structures, the design and assessment process must be efficient, yet provide a holistic view of soil-structure interaction effects. This paper proposes an integrated tunnel design tool for the initial design phases to predict the ground settlements induced by tunnelling and building damage using empirical and analytical solutions as well as simulation-based meta models. Furthermore, visualisation of ground settlements and building damage risk is enabled by integrating empirical and analytical models within our Building Information Modelling (BIM) framework for tunnelling. This approach allows for near real-time assessment of structural damage induced by settlements with consideration of soil-structure interaction and non-linear material behaviour. Furthermore, because this approach is implemented on a BIM platform for tunnelling, first, the design can be optimised directly in the design environment, thus eliminating errors in data exchange between designers and computational analysts. Secondly, the effect of tunnelling on existing structures can be effectively visualised within the BIM by producing risk-maps and visualising the scaled deformation field, which allows for a more intuitive understanding of design actions and for collaborative design. Having a fully parametric design model and real-time predictions therefore enables the assessment and visualisation of tunneling-induced damage for large tunnel sections and multiple structures in an effective and computationally efficient way. [ABSTRACT FROM AUTHOR]

In many industrial applications, obtaining labeled observations is not straightforward as it often requires the intervention of human experts or the use of expensive testing equipment. In these circumstances, active learning can be highly beneficial in suggesting the most informative data points to be used when fitting a model. Reducing the number of observations needed for model development alleviates both the computational burden required for training and the operational expenses related to labeling. Online active learning, in particular, is useful in high‐volume production processes where the decision about the acquisition of the label for a data point needs to be taken within an extremely short time frame. However, despite the recent efforts to develop online active learning strategies, the behavior of these methods in the presence of outliers has not been thoroughly examined. In this work, we investigate the performance of online active linear regression in contaminated data streams. Our study shows that the currently available query strategies are prone to sample outliers, whose inclusion in the training set eventually degrades the predictive performance of the models. To address this issue, we propose a solution that bounds the search area of a conditional D‐optimal algorithm and uses a robust estimator. Our approach strikes a balance between exploring unseen regions of the input space and protecting against outliers. Through numerical simulations, we show that the proposed method is effective in improving the performance of online active learning in the presence of outliers, thus expanding the potential applications of this powerful tool. [ABSTRACT FROM AUTHOR]

Computational models are powerful tools for understanding human cognition and behavior. They let us express our theories clearly and precisely and offer predictions that can be subtle and often counter-intuitive. However, this same richness and ability to surprise means our scientific intuitions and traditional tools are ill-suited to designing experiments to test and compare these models. To avoid these pitfalls and realize the full potential of computational modeling, we require tools to design experiments that provide clear answers about what models explain human behavior and the auxiliary assumptions those models must make. Bayesian optimal experimental design (BOED) formalizes the search for optimal experimental designs by identifying experiments that are expected to yield informative data. In this work, we provide a tutorial on leveraging recent advances in BOED and machine learning to find optimal experiments for any kind of model that we can simulate data from, and show how by-products of this procedure allow for quick and straightforward evaluation of models and their parameters against real experimental data. As a case study, we consider theories of how people balance exploration and exploitation in multi-armed bandit decision-making tasks. We validate the presented approach using simulations and a real-world experiment. As compared to experimental designs commonly used in the literature, we show that our optimal designs more efficiently determine which of a set of models best account for individual human behavior, and more efficiently characterize behavior given a preferred model. At the same time, formalizing a scientific question such that it can be adequately addressed with BOED can be challenging and we discuss several potential caveats and pitfalls that practitioners should be aware of. We provide code to replicate all analyses as well as tutorial notebooks and pointers to adapt the methodology to different experimental settings. [ABSTRACT FROM AUTHOR]

Green chemistry emphasizes the isolation of biologically active compounds from plants and biomass to produce renewable, bio-based products and materials through sustainability and circularity-driven innovation processes. In this work, we have investigated the extraction of rosmarinic acid (RA), a phenolic acid with several biological properties, from aromatic herbs using ultrasounds and low environmental risk natural deep eutectic solvents (NADES). Various solvent mixtures have been investigated, and the parameters influencing the process have been studied by a mixture-process experimental design to identify the optimal RA extraction conditions. The extraction yield has been calculated by HPLC-diode array analysis. The lactic acid:ethylene glycol mixture using an ultrasound-assisted process has been found to be the most versatile solvent system, giving RA yields 127–160% higher than hydroalcoholic extraction (70% ethanol). The deep eutectic solvent nature of lactic acid:ethylene glycol has been demonstrated for the first time by multi-technique characterization (1H-NMR and 13C-NMR, DSC, and W absorption properties). The aqueous raw extract has been directly incorporated into poly(vinyl alcohol) to obtain films with potential antibacterial properties for applications in the field of food and pharmaceutical packaging. [ABSTRACT FROM AUTHOR]

Mathematical models are increasingly being developed and calibrated in tandem with data collection, empowering scientists to intervene in real time based on quantitative model predictions. Well-designed experiments can help augment the predictive power of a mathematical model but the question of when to collect data to maximize its utility for a model is non-trivial. Here we define data as model-informative if it results in a unique parametrization, assessed through the lens of practical identifiability. The framework we propose identifies an optimal experimental design (how much data to collect and when to collect it) that ensures parameter identifiability (permitting confidence in model predictions), while minimizing experimental time and costs. We demonstrate the power of the method by applying it to a modified version of a classic site-of-action pharmacokinetic/pharmacodynamic model that describes distribution of a drug into the tumor microenvironment (TME), where its efficacy is dependent on the level of target occupancy in the TME. In this context, we identify a minimal set of time points when data needs to be collected that robustly ensures practical identifiability of model parameters. The proposed methodology can be applied broadly to any mathematical model, allowing for the identification of a minimally sufficient experimental design that collects the most informative data. [ABSTRACT FROM AUTHOR]

The optimal experimental design (OED) provides informative experimental resources and reduces the inherent uncertainty of the experiment. The OED is a framework to maximize performed experiments by controlling design variables. This paper proposes the OED framework combined with deep neural network (DNN) surrogate model to find the optimal design over large and complex design space. The OED has the optimization process to maximize the conditional mutual information (CMI) between hidden properties and composite structural performances. Approximate coordinate exchange (ACE) algorithm was applied to find the optimal design over a high and complicated design space. For fast optimization, surrogate DNN model was used instead of finite element analysis. Two examples were executed for the demonstration of the OED frameworks. From the results of each demonstration, OED provided the results that are consistent with heuristic knowledge. [ABSTRACT FROM AUTHOR]

Renewable energy has recently attracted a lot of interest due to its low cost and long-term sustainability. Integration of a photovoltaic (PV) system into an existing power system on a large scale is a challenging task. Due to irregular irradiance and an inertia-less system, frequency regulation is a cumbersome task. So, load frequency control is recommended in this article for multi-area power systems that include PV and thermal plant sources. The performance of four competing controllers, namely proportional integral derivative (PID), fractional-order PID, tilt-integral-derivative (TID), and fractional-order TID (FOTID), is investigated and compared. For obtaining the best-performing parameters, the honey badger algorithm (HBA) is implemented. Furthermore, uncertainty is taken into account by varying the system parameters from -20% to 20% in steps of 10%. The suggested competing controllers' performance is evaluated using a dynamic load. The performance of the proposed HBA-based FOTID controller is also studied against a randomly varying load applied in area-1. The work is further extended to a four-area system. Finally, it is observed that the HBA-based FOTID controller exhibits superior performance in maintaining the system's frequency and tie-line power when it is subjected to a disturbance. [ABSTRACT FROM AUTHOR]

Valid simulation models play a critical role in enhancing efficiency in development processes and minimizing experimental efforts. As a result, ensuring accurate predictions through model discrimination, Mo del Ca libration, and Val idation (MoCaVal) has become increasingly important and is a necessary step to analyze the behavior and evaluate the effectiveness of systems during their initial design stages. Additionally, to achieve the climate objectives, it is imperative that heat pumps predominantly perform the building sector's heating. The effectiveness of heat pumps hinges on the performance of the compressor. Therefore, there is a requirement for valid compressor models. However, currently, there is no universal compressor model that applies to all refrigerants and designs. Therefore, every refrigerant or design modification requires the development or adaptation of models. Since creating new models is a time-consuming procedure, automated techniques are advantageous. This paper compares two automated model development methods, Optimal Experimental Design (OED) and Machine Learning (ML), to create valid simulation models within the MoCaVal framework. OED is used to calibrate existing simulation models, while ML is employed to develop new models. Our comparison of the two methods focuses on a fixed-speed scroll compressor of 4 kW nominal power, using R410A and featuring 51 measurement points. Using the Full Factorial Plan (FFP) and the D-Optimal Experimental Design (D-OED), predefined models for mass flow rate, electrical power, isentropic and volumetric efficiency were calibrated. Employing the FFP, Machine Learning (ML) was also applied through support vector regression. The OED reduces experimental effort by 75–90 % compared to the FFP while only slightly increasing the average uncertainty by 0.30–0.79 %. For the FFP, MoCaVal achieved uncertainties of 0.52–3.9 % for calibrated models and 0.34–0.56 % with ML models. Therefore, within the design space, ML outperforms calibrated models based on the FFP and OED regarding uncertainty. However, since machine learning models were trained on the FFP, additional research is needed to demonstrate their potential for reducing test time and extrapolating results. • Experimental investigation of a scroll compressor. • Model calibration and validation using full factorial design and optimal experimental design. • Comparison of automated model development by Optimal Experimental Design and Machine Learning. • Test time reduction up to 80 % while still keeping reasonable accuracy. [ABSTRACT FROM AUTHOR]

Mixture experimental designs have been in widespread use in agricultural, pharmaceutical, and other industrial research for many years. Much of the previous work mainly focuses on optimal design for mixture experiments when observations are uncorrelated, in large part because of the intractability of the optimal mixture experimental design on correlated case. When observations have certain correlation structures within each block, the order of the observations in each block matters and this order impacts the optimality of the design. Thus, there is need of research into construction of these useful designs when correlation structures might exist within blocks. In this paper, we propose D-optimal minimum support designs for Scheffe's quadratic mixture model with odd number of components when observations in blocks are circulant correlated and hub correlated respectively, and Scheffe's mixture model with any components when observations are in block-structured correlation. [ABSTRACT FROM AUTHOR]

Cross-Over Designs or Repeated Measurements Designs are experimental designs in which treatments (e.g., medicines, fertilizers, diets) are applied to experimental units (usually humans) in different time periods. A common problem is to find the distribution of n experimental units in order to find the optimal experimental design for the well-known criteria of optimality (A, D, E optimality, etc.). If there is only one parameter of interest, the criterion is the minimization of the variance of the parameter estimator. In this case, a Repeated Measurements Design with one parameter of interest (the direct effect of the treatment) is examined and the distribution of n which minimizes the variance of that parameter is found. The objective of the research is the estimation of the variance of the Ordinal Least-Squares estimators of the Repeated Measurements Design model for two treatments and five periods. Heydayat and Afsarinejad introduced the basic model which is used. The optimal Repeated Measurements Designs are derived for n experimental units. Optimality criterion is the minimization of the variance of the estimated direct effects. [ABSTRACT FROM AUTHOR]

This study presents experimental investigations on the optimal design and operating conditions of pulsating heat pipe (PHP) cooling systems for cylindrical 18650 cells in electric vehicles with a special top heating mode. The research explores the effects of various parameters, including the number of turns, working fluid, filling ratio, coolant temperature, and condenser length, on the PHP system's performance. A cylindrical 3D PHP module was designed based on the flat-type PHP experimental results and evaluated as a cooling system for 18650 cells. The study highlights that the number of turns and working fluid significantly influence the PHP system's performance, with methanol identified as the most suitable working fluid. The optimal number of turns for continuous operation was determined to be 8 to 9. The condenser length was found to play a crucial role in enhancing heat transfer efficiency. The PHP system's continuous flow of working fluid ensured stable temperature profiles and prevented overheating. The research recommends a 9-turn PHP system with methanol as the working fluid and 10 %–15 % filling ratio for cooling two 18650 cylindrical cells. Maintaining cooling water temperature below 25 °C and considering specific orientation angles further improved the PHP system's cooling performance. Overall, these findings provide valuable insights into the optimal design and operating conditions of PHP cooling systems for electric vehicle batteries, leading to stable and efficient cooling, prolonged battery life, and improved electric vehicle efficiency. These results are vital for researchers and engineers working in the field of electric vehicle battery cooling systems and can serve as a foundation for future studies in this area. [ABSTRACT FROM AUTHOR]

S‐N curve parameter determination is a time‐ and cost‐intensive procedure. A standardized method for simultaneously determining all S‐N curve parameters with minimum testing effort is still missing. The Bayesian optimal experimental design (BOED) approach can reduce testing effort and accelerates uncertainty reduction during fatigue testing for S‐N curve parameters. The concept is applicable to all S‐N curve models and is exemplary illustrated for a bilinear S‐N curve model. We demonstrate the fatigue testing workflow for the bilinear S‐N curve in detail while discussing steps and challenges when generalizing to other S‐N curve models. Applying the BOED to the bilinear S‐N curve models, minor errors and uncertainties for all S‐N curve parameters are obtained after only 10 experiments for data scatter values below 1.1. For such, the relative error in fatigue limit estimation was less than 1% after five tests. When S‐N data scatter higher than 1.2 is concerned, 17 tests were required for robust analysis. The BOED methodology should be applied to other S‐N curve models in the future. The high computational effort and the approximation of the posterior distribution with a normal distribution are the limitations of the presented BOED approach. [ABSTRACT FROM AUTHOR]

Parentage exclusion probability is usually calculated to evaluate the informativeness of a set of markers for, and the statistical power of, a parentage analysis. Equations for parentage exclusion probability have been derived in various scenarios such as paternity exclusion when maternity is known or unknown or when candidate males are unrelated or loosely related (being from the same subpopulation) to the father. All previous work assumes a diploid species. Although marker-based parentage analyses have been conducted in haploidiploid species (such as ants, bees and wasps) for diploid offspring at the individual level or haploid offspring at the class level, rigorously derived formulations of parentage exclusion probability for haploid offspring at the individual level are lacking, which prevents the precise evaluation of the informativeness for and the statistical power of a parentage analysis. In this study we derive equations for the exclusion probability of maternity of a haploid male when multiple mother candidates (workers or queens) are unrelated or fullsibs to the mother. The usefulness of the equations is exemplified by numerical examples, and the results are discussed in the context of the study of worker reproductivity in eusocial haplodiploid species. The results are especially valuable for an optimal experimental design in determining sampling intensities (e.g. number of markers and number of individuals) to achieve satisfactory statistical power of a parentage analysis in investigating workers' reproductivity in eusocial haplodiploid species. [ABSTRACT FROM AUTHOR]

There are multiple possible cluster randomised trial designs that vary in when the clusters cross between control and intervention states, when observations are made within clusters, and how many observations are made at each time point. Identifying the most efficient study design is complex though, owing to the correlation between observations within clusters and over time. In this article, we present a review of statistical and computational methods for identifying optimal cluster randomised trial designs. We also adapt methods from the experimental design literature for experimental designs with correlated observations to the cluster trial context. We identify three broad classes of methods: using exact formulae for the treatment effect estimator variance for specific models to derive algorithms or weights for cluster sequences; generalised methods for estimating weights for experimental units; and, combinatorial optimisation algorithms to select an optimal subset of experimental units. We also discuss methods for rounding experimental weights, extensions to non-Gaussian models, and robust optimality. We present results from multiple cluster trial examples that compare the different methods, including determination of the optimal allocation of clusters across a set of cluster sequences and selecting the optimal number of single observations to make in each cluster-period for both Gaussian and non-Gaussian models, and including exchangeable and exponential decay covariance structures. [ABSTRACT FROM AUTHOR]

A key parameter of interest recovered from hyperpolarized (HP) MRI measurements is the apparent pyruvate-to-lactate exchange rate, k P L , for measuring tumor metabolism. This manuscript presents an information-theory-based optimal experimental design approach that minimizes the uncertainty in the rate parameter, k P L , recovered from HP-MRI measurements. Mutual information is employed to measure the information content of the HP measurements with respect to the first-order exchange kinetics of the pyruvate conversion to lactate. Flip angles of the pulse sequence acquisition are optimized with respect to the mutual information. A time-varying flip angle scheme leads to a higher parameter optimization that can further improve the quantitative value of mutual information over a constant flip angle scheme. However, the constant flip angle scheme, 35 and 28 degrees for pyruvate and lactate measurements, leads to an accuracy and precision comparable to the variable flip angle schemes obtained from our method. Combining the comparable performance and practical implementation, optimized pyruvate and lactate flip angles of 35 and 28 degrees, respectively, are recommended. [ABSTRACT FROM AUTHOR]

Airflow velocity uniformity in material drying room used in steam blanching and hot-air vacuum drying equipment is crucial for obtaining a good drying quality of fruits and vegetables. To analyze the airflow velocity uniformity and give the optimization design for improving the airflow uniformity, CFD method is used for the flow field analysis of material drying room and the velocity non-uniformity coefficient is used as the evaluation indicator for the velocity uniformity. Results show that the airflow velocity distribution in the material drying room at the original inlet airflow distribution chamber is relatively uneven and the airflow velocity non-uniformity coefficient in drying area is 34 %. For reducing the airflow velocity non-uniformity, the airflow velocity distribution of three structural modification directions with 12 designs in total are analyzed and compared. The minimum airflow velocity non-uniformity coefficient for adding a spoiler model, reducing the closed end width and adding nozzle baffles are 17.1 %, 30.2 %, 10.4 %, respectively. Through comparison, adding the nozzle baffle with a width of 11 mm at the rear end of each nozzle outlet is considered as the optimal designs to conduct the experimental validation. Experimental validation results shows the the relative error between the simulated value and the measured value is within 10 %, proving that the CFD prediction results is reliable and the research results can give a theoretical reference for the designers to evaluate and optimize the structure of inlet airflow distribution chamber for improving the airflow uniformity in drying area. [ABSTRACT FROM AUTHOR]

Much of classical optimal design theory relies on specifying a model with only a small number of parameters. In many applications, such models will give reasonable approximations. However, they will often be found not to be entirely correct when enough data are at hand. A property of classical optimal design methodology is that the amount of data does not influence the design when a fixed model is used. However, it is reasonable that a low dimensional model is satisfactory only if limited data is available. With more data available, more aspects of the underlying relationship can be assessed. We consider a simple model that is not thought to be fully correct. The model misspecification, that is, the difference between the true mean and the simple model, is explicitly modeled with a stochastic process. This gives a unified approach to handle situations with both limited and rich data. Our objective is to estimate the combined model, which is the sum of the simple model and the assumed misspecification process. In our situation, the low-dimensional model can be viewed as a fixed effect and the misspecification term as a random effect in a mixed-effects model. Our aim is to predict within this model. We describe how we minimize the prediction error using an optimal design. We compute optimal designs for the full model in different cases. The results confirm that the optimal design depends strongly on the sample size. In low-information situations, traditional optimal designs for models with a small number of parameters are sufficient, while the inclusion of the misspecification term lead to very different designs in data-rich cases. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive review of the Active Simultaneous Localization and Mapping (A-SLAM) research conducted over the past decade. It explores the formulation, applications, and methodologies employed in A-SLAM, particularly in trajectory generation and control-action selection, drawing on concepts from Information Theory (IT) and the Theory of Optimal Experimental Design (TOED). This review includes both qualitative and quantitative analyses of various approaches, deployment scenarios, configurations, path-planning methods, and utility functions within A-SLAM research. Furthermore, this article introduces a novel analysis of Active Collaborative SLAM (AC-SLAM), focusing on collaborative aspects within SLAM systems. It includes a thorough examination of collaborative parameters and approaches, supported by both qualitative and statistical assessments. This study also identifies limitations in the existing literature and suggests potential avenues for future research. This survey serves as a valuable resource for researchers seeking insights into A-SLAM methods and techniques, offering a current overview of A-SLAM formulation. [ABSTRACT FROM AUTHOR]

The 'big' seismic data not only acquired by seismometers but also acquired by vibrometers installed in buildings and infrastructure and accelerometers installed in smartphones will be certainly utilized for seismic research in the near future. Since it is impractical to utilize all the seismic big data in terms of the computational cost, methods which can select observation sites depending on the purpose are indispensable. We propose an observation site selection method for the accurate reconstruction of the seismic wavefield by process-driven approaches. The proposed method selects observation sites suitable for accurately estimating physical model parameters such as subsurface structures and source information to be input into a numerical simulation of the seismic wavefield. The seismic wavefield is reconstructed by the numerical simulation using the parameters estimated based on the observed signals at only observation sites selected by the proposed method. The observation site selection in the proposed method is based on the sensitivity of each observation site candidate to the physical model parameters; the matrix corresponding to the sensitivity is constructed by approximately calculating the derivatives based on the simulations, and then, observation sites are selected by evaluating the quantity of the sensitivity matrix based on the D-optimality criterion proposed in the optimal design of experiments. In this study, physical knowledge on the sensitivity to the parameters such as seismic velocity, layer thickness, and hypocentre location was obtained by investigating the characteristics of the sensitivity matrix. Furthermore, the effectiveness of the proposed method was shown by verifying the accuracy of seismic wavefield reconstruction using the observation sites selected by the proposed method. [ABSTRACT FROM AUTHOR]

Flucloxacillin is prescribed to treat skin infections but its highly bitter taste is poorly tolerated in children. This work describes the application of the D-optimal mixture experimental design to identify the optimal component ratio of flucloxacillin, Eudragit EPO and palmitic acid to prepare flucloxacillin taste-masked microparticles that would be stable to storage and would inhibit flucloxacillin release in the oral cavity while facilitating the total release of the flucloxacillin load in the lower gastrointestinal tract (GIT). The model predicted ratio was found to be very close to the stoichiometric equimolar component ratio, which supported our hypothesis that the ionic interactions among flucloxacillin, Eudragit EPO and palmitic acid underscore the polyelectrolyte complex formation in the flucloxacillin taste-masked microparticles. The excipient–drug interactions showed protective effects on the microparticle storage stability and minimised flucloxacillin release at 2 min in dissolution medium. These interactions had less influence on flucloxacillin release in the dissolution medium at 60 min. Storage temperature and relative humidity significantly affected the chemical stability of the microparticles. At the preferred storage conditions of ambient temperature under reduced RH of 23%, over 90% of the baseline drug load was retained in the microparticles at 12 months of storage. [ABSTRACT FROM AUTHOR]

The present study employed a modified QuEChERS method to systematically analyze the presence of fifteen quinolone and seven tetracycline antibiotic residues in local animal food. Additionally, a multi-level four-factor Box–Behnken design (BBD) within the framework of response surface methodology (RSM) was utilized to evaluate the factors impacting the detection efficiency of the sample pretreatment procedure. Optimization was performed via Design Expert® 10.0.3, and the factors, including the volume of the acetonitrile, the addition of formic acid, the duration of the extraction, and the addition of EDTA, were combined with experimental design until an optimal solution was reached. Finally, the sample was tested via ultra-high performance liquid chromatography-quadrupole-linear ion trap mass spectrometry (UPLC/MS/MS) in both multiple reaction monitoring (MRM) and enhanced product ion (EPI) scan modes on a QTRAP® 5500 instrument (AB SCIEX instruments, Framingham, MA, USA). The overall average recoveries from actual samples fortified with 22 antibiotics at three levels ranged from 73.8% to 98.5% based on the use of matrix-fortified calibration, with variations ranging from 5.80 to 12.4% (n = 6). The limits of detection and quantification were 0.3 μg kg−1 and 1.0 μg kg−1, respectively. Lastly, the modified method was applied to practical sample analysis in the daily risk monitoring and assessment of food safety with satisfactory stability and robustness. [ABSTRACT FROM AUTHOR]

The human immune system is a distributed system of specialized cell populations with unique functions that collectively give rise to immune responses to infections and during immune-mediated diseases. Cell composition, plasma proteins, and functional responses vary among individuals, making the system difficult to interpret, but this variation is nonrandom. With careful analyses using novel experimental and computational tools, human immune system composition and function carry interpretable information. Here, we propose that systems-level analyses offer an opportunity to make human immune responses more interpretable in the future, and we discuss herein important considerations and lessons learned to this end. Predictable human immunology holds implications for better diagnostic and curative precision in patients with infectious and immune-associated diseases. Systems immunology is the study of cell–cell interactions in the immune system, their regulatory functions, and the emergent properties of immune responses. We posit that, despite large interindividual variation, cell composition is nonrandom, and understanding its rules will make human immune systems more predictable in the future. A growing number of computational tools exists for the inference of cell–cell relationships, but no 'gold standard' methods exist, and statistical power is often limiting. Minimizing the technical sources of variance by optimal experimental design and sampling procedures allows for more biological 'signals' to be discovered. A systems approach is necessary to comprehend the complexity of an organism's immune system since there is an underlying shared mechanism of cell–cell relationships. Better inferences of cell–cell interactions are essential to increase our understanding of immune responses for various health conditions. A well-planned longitudinal sampling of whole blood is vital in translating in vitro findings to in vivo biology, allowing the systematic tracking of immune system responses over time, and facilitating the development of more accurate models with improved predictability for immune system behaviors. [ABSTRACT FROM AUTHOR]

In photolithographic processes, nanometer-level-precision wavefront-aberration models enable the machine to be able to meet the overlay (OVL) drift and critical dimension (CD) specifications. Software control algorithms take as input these models and correct any expected wavefront imperfections before reaching the wafer. In such way, a near-optimal image is exposed on the wafer surface. Optimizing the parameters of these models, however, involves several time costly sensor measurements which reduce the throughput performance of the machine in terms of exposed wafers per hour. In that case, photolithography machines come across the trade-off between throughput and quality. Therefore, one of the most common optimal experimental design (OED) problems in photolithography machines (and not only) is how to choose the minimum amount of sensor measurements that will provide the maximum amount of information. Additionally, each sensor measurement corresponds to a point on the wafer surface and therefore we must measure uniformly around the wafer surface as well. In order to solve this problem, we propose a sensor mark selection algorithm which exploits genetic algorithms. The proposed solution first selects a pool of points that qualify as candidates to be selected in order to meet the uniformity constraint. Then, the point that provides the maximum amount of information, quantified by the Fisher-based criteria of G-, D-, and A-optimality, is selected and added to the measurement scheme. This process, however, is considered "greedy", and for this reason, genetic algorithms (GA) are exploited to further improve the solution. By repeating in parallel the "greedy" part several times, we obtain an initial population that will be the input to our GA. This meta-heuristic approach outperforms the "greedy" approach significantly. The proposed solution is applied in a real life semiconductors industry use case and achieves interesting industry as well as academical results. [ABSTRACT FROM AUTHOR]

A multitude of industries, including energy and process engineering, as well as academia are researching and utilizing new fluid substances to further the aim of sustainability. Knowledge of the thermodynamic properties of these substances is a prerequisite, if they are to be utilized to their fullest potential. To date, the way to acquire reliable knowledge of the thermodynamic behavior is through measurements. The ensuing experimental data are then used to develop equations of state, which efficiently embody the gained knowledge of the behavior of the fluid substance, allow for interpolation and, to some extent, extrapolation. However, the acquisition of low-uncertainty experimental data, and thus the development of accurate equations of state, is often time-consuming and expensive. For substances for which suitable force field models exist, molecular modeling and simulation are well-suited to generate thermodynamic data or to augment experimental data, however, at the expense of larger uncertainties. The major goal of this work is to present a new approach for the development of equations of state using (1) symbolic regression, which is a machine learning based model development approach, (2) optimal experimental design, and (3) efficient data acquisition. We demonstrate this approach using the example of density data of an air-like binary mixture ( 0.2094 O 2 + 0.7906 N 2 ) over the temperature range from 100 K to 300 K at pressures of up to 8 MPa , which covers the gaseous, liquid, and supercritical regions. For this purpose, an experimental data set published by von Preetzmann et al. (Int. J. Thermophys. 42, 2021) and molecular simulation data sampled in this work are used. The two data sets are compared in terms of acquisition time, cost, and uncertainty, showing that an optimized combination of experimental and simulation data leads to lower cost while maintaining low uncertainties. [ABSTRACT FROM AUTHOR]

The research is interested in studying the thermal property of the composite material because of its importance in the thermal uses of composite materials. The composite material was prepared with different amounts of kaolin (0, 0.2, 0.5, 0.6, and 0.8 g) in (polyacrylamide -kaolin) composite. Its thermal conductivity and specific heat capacity were studied. Taguchi Array L25(5^2), factors 2, Runs 25, columns 25(5^6) Array 12, was used as an experimental design and 25 samples were prepared using this array. The results improved that the thermal conductivity of the composite material decreases with a high kaolin amount toward temperature increase. Moreover, a case with the composite material's specific heat capacity. The current research describes using Taguchi experimental design to select the optimal factor level from the process to get a maximum thermal conductivity of composite material. The basis for choosing the orthogonal matrix is to take each of the different quantities of kaolin (0,0.2, 0.5, 0.6, and 0.8g) at different temperatures. Taguchi program is applied to the combination of factors and their levels. Besides, the output parameter representing thermal conductivity of (polyacrylamide – kaolin) composite with repetition to the given input parameters using smaller the better. The test results show the factor that influences the amount of kaolin (0.2g), and temperature(70°C). By using the factor and its level, thermal conductivity is (0.428 w· m-1· °C-1). [ABSTRACT FROM AUTHOR]

The key to ensure the reliability of the cloud detonation in high-drop-velocity double-event fuel-air explosives (DEFAEs) is to cause the twice-detonating device (TDD) to detonate in the dispersed fuel. Here, an ejection mechanism for a TDD is designed and the ejection process is analyzed through an outfield ejection test. Accordingly, a simulation model for the description of the ejection process is established and verified to be reliable by comparing it with the experimental results. Based on the model, two extended ensamples for design optimization of the ejection mechanism are developed. The factors influencing the ejection characteristics of the TDD are further analyzed, including the ejection charge mass and screw (for baffle fixing) parameter. The research carried out here provides theoretical and experimental support for the optimal design of the ejection mechanism in high-drop-velocity DEFAEs. [ABSTRACT FROM AUTHOR]

TRIMER, Transcription Regulation Integrated with MEtabolic Regulation, is a genome-scale modeling pipeline targeting at metabolic engineering applications. Using TRIMER, regulated metabolic reactions can be effectively predicted by integrative modeling of metabolic reactions with a transcription factor-gene regulatory network (TRN), which is modeled through a Bayesian network (BN). In this article, we focus on sensitivity analysis of metabolic flux prediction for uncertainty quantification of BN structures for TRN modeling in TRIMER. We propose a computational strategy to construct the uncertainty class of TRN models based on the inferred regulatory order uncertainty given transcriptomic expression data. With that, we analyze the prediction sensitivity of the TRIMER pipeline for the metabolite yields of interest. The obtained sensitivity analyses can guide optimal experimental design (OED) to help acquire new data that can enhance TRN modeling and achieve specific metabolic engineering objectives, including metabolite yield alterations. We have performed small- and large-scale simulated experiments, demonstrating the effectiveness of our developed sensitivity analysis strategy for BN structure learning to quantify the edge importance in terms of metabolic flux prediction uncertainty reduction and its potential to effectively guide OED. [ABSTRACT FROM AUTHOR]

The dynamic modeling of multibody systems has been recognized as a key aid in the analysis, design, optimization, and control of mechanical systems. During the past three decades, many software tools, whether commercial or in scientific research, have been introduced that enable the engineers to construct accurate models of multibody systems. These tools introduced powerful methods of the numerical solution, simulation and mathematical transformation that facilitated the process of modeling complex systems. One of the most challenging topics in this context, is the parameter estimation of multibody systems based on laboratory and experimental data and optimal design of such systems. This paper proposes an approach of utilizing optimization methods in the design of multibody systems in which the coordinates-dependent sensitivity of the system is at a definite level with respect to the parameter-dependent sensitivity. In order to attain this condition, the desired dynamic characteristics are introduced instead of experimental data on the estimation process. In the case of gyroscopic systems, these characteristics are either constant or limited in motion, and thus, the sensitivity is highly depended on the system parameters. Consequently, the design of the necessary system parameters can be accomplished with minimal computational effort. The experimental verification of the proposed approach was carried out on the Gyroscopic laboratory system. Eventually, the experimental data of the designed system was tested and compared with the referenced dynamic behavior, which showed acceptable performance in terms of accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Multi-gravity separator (MGS) is a centrifugal gravity separator deployed for beneficiation of fine particles with relatively low concentration. It is for the first time that a statistical tool was engaged to evaluate effects of the most influencing process variables and their actual impact upon the performance of MGS with respect to its potential to clean the difficult-to-wash low volatile coking (LVC) coal fines. Characteristics of LVC coal sample were analyzed and discussed in terms of physical properties, petrographic composition, washability, XRD and SEM analysis. Three different feed sizes such as – 500 μm, – 250 μm and – 150 μm were used for assessing the separation mechanism and efficacy of MGS along with three different process variables such as drum speed, shaking amplitude and wash water rate to study and ascertain the most efficient experimental design for obtaining optimal result. Results revealed that drum speed and feed size turned out to be most significant parameters for reduction of ash concentration. In design of experiments, clean coal ash, combustible recovery and separation efficiency were considered as response functions. Material balance for MGS unraveled that about 74% clean coal produced with 10% ash reduction from the feed ash of 32.8% could be achieved in single stage, in optimized process conditions. [ABSTRACT FROM AUTHOR]

Oaks (Quercus spp.) are widespread hardwood trees in the Northern Hemisphere and of high ecological, economic, and social values. Optimal experimental design of genetic trials is essential for accurate estimates of genetic parameters and improving the genetic merit of breeding stock. Here, we evaluate the use of post hoc row-column factors combined with spatial adjustment to improve genetic analyses of parents and individual trees in field progeny tests of plantation hardwoods, using cherrybark oak (Quercus pagoda Raf.) as an example. For tree height, post hoc incomplete blocking reduced ~14% more of the within-block environmental variance compared to the randomized complete block design (RCBD) model. Incomplete blocking also improved the heritability estimates for height by 7% to 14% compared to the original RCBD model. No clinal trend for growth breeding values was identified due to provenances. Our approach warrants the initial selection for height as early as age ~10 based on its moderate narrow-sense heritability of 0.2; however, diameter and volume need longer evaluation times. The post hoc incomplete blocking is more efficient and promising to improve the genetic analysis of Q. pagoda to minimize the environmental heterogeneity influences. Adjusting competition and spatial effects, including the distance principal components and autoregressive residual structure notably improves the model fit based on the observed reductions in AICs and BICs. Employing our approach is promising for hardwood genetic improvement in the southern USA. [ABSTRACT FROM AUTHOR]

D-optimality is a well-known concept in experimental design that seeks to select an optimal set of design points to estimate the unknown parameters of a statistical model with a minimum variance. In this paper, we focus on proving a conjecture made by Ford, Torsney and Wu regarding the existence of a class of D-optimal designs for binary and weighted linear regression models. Our concentration is on models with one design variable. The conjecture states that, for any given level of precision, there exists a two-level factorial design that is D-optimal for these models. To prove this conjecture, we use an intuitive approach that explores various link functions in the generalised linear model context to establish the veracity of the conjecture. We also present explicit and clear plots of various functions wherever deemed necessary and appropriate to further strengthen the proofs. Our results establish the existence of D-optimal designs for binary and weighted linear regression models with one design variable, which have important implications for the efficient design of experiments in various fields. These findings contribute to the development of optimal experimental designs for studying binary and weighted linear regression models and provide a foundation for future research in this area. [ABSTRACT FROM AUTHOR]

We present a basic algorithm for optimal experimental design in distributed fibre-optic sensing. It is based on the fast random generation of fibre-optic cable layouts that can be tested for their cost-benefit ratio. The algorithm accounts for the maximum available cable length, lets the cable pass through pre-defined points of interest, avoids obstacles that the cable must not traverse, permits the adaptation of geometric complexity of different cable segments and allows for the incorporation of topography. Furthermore, the algorithm can be combined with arbitrary measures of the cost-benefit ratio, and its simplicity enables easy adaptations to the needs of specific applications. In addition to a description of the basic concept, we provide examples that illustrate the circumnavigation of obstacles, the steering of geometric complexity and the cable layout optimization in the presence of topographic variations. [ABSTRACT FROM AUTHOR]

Background: In pre-post designs, analysis of covariance (ANCOVA) is a standard technique to detect the treatment effect with a continuous variable measured at baseline and follow-up. For measurements subject to a high degree of variability, it may be advisable to repeat the pre-treatment and/or follow-up assessments. In general, repeating the follow-up measurements is more advantageous than repeating the pre-treatment measurements, while the latter can still be valuable and improve efficiency in clinical trials. Methods: In this article, we report investigations of using multiple pre-treatment and post-treatment measurements in randomized clinical trials. We consider the sample size formula for ANCOVA under general correlation structures with the pre-treatment mean included as the covariate and the mean follow-up value included as the response. We propose an optimal experimental design of multiple pre-post allocations under a specified constraint, that is, given the total number of pre-post treatment visits. The optimal number of the pre-treatment measurements is derived. For non-linear models, closed-form formulas for sample size/power calculations are generally unavailable, but we conduct Monte Carlo simulation studies instead. Results: Theoretical formulas and simulation studies show the benefits of repeating the pre-treatment measurements in pre-post randomized studies. The optimal pre-post allocation derived from the ANCOVA extends well to binary measurements in simulation studies, using logistic regression and generalized estimating equations (GEE). Conclusions: Repeating baselines and follow-up assessments is a valuable and efficient technique in pre-post design. The proposed optimal pre-post allocation designs can minimize the sample size, i.e., achieve maximum power. [ABSTRACT FROM AUTHOR]

We consider balanced one-way, two-way, and three-way ANOVA models to test the hypothesis that the fixed factor A has no effect. The other factors are fixed or random. We determine the noncentrality parameter for the exact F-test, describe its minimal value by a sharp lower bound, and thus we can guarantee the worst-case power for the F-test. These results allow us to compute the minimal sample size, i.e. the minimal number of experiments needed. We also provide a structural result for the minimum sample size, proving a conjecture on the optimal experimental design. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to investigate the resultant optimal ultimate tensile strength, elongation, flexural strength and modulus, compression strength and impact strength of fabricated alkali-treated Lagenaria siceraria fiber (LSF)-reinforced polymer matrix composite by optimizing input factors and microstructural characterization by influencing fiber length, fiber concentration and treatment condition of LSF. Design/methodology/approach: The fabrication of LSF-reinforced composite specimens involved surface treatment followed by custom experimental design using a simple hand layup process. The wear analysis was performed by a multi-tribotester TR25 machine, and the developed model was validated by using statistical software Design Expert V.8 and analysis of variance (ANOVA). The surface morphology of the sample was also analyzed by field emission scanning electron microscopy. Findings: The alkali treatment for LSFs had reduced the hemicellulose, and enhanced mechanical performance was observed for 30 wt.% concentration of L. siceraria in epoxy resin. Thermogravimetric analysis revealed thermal stability up to 245°C; microstructure revealed fiber entanglements in case of longer fiber length and compression strength reduction; and the surface-treated fiber composites exhibited reduced occurrences of defects and enhanced matrix–fiber bonding. Enhanced mechanical performances were observed, namely, ultimate tensile strength of 17.072 MPa, elongation of 1.847%, flexural strength of 50.4 MPa, flexural modulus of 3,376.31 GPa, compression strength of 52.154 MPa and impact strength of 0.53 joules. Originality/value: The novel approach of optimizing and characterizing alkali surface-treated LSF-reinforced epoxy matrix composite was explored, varying fiber length and concentrations for specimens by empirical relations and experimental design to obtain optimal performance validated by ANOVA. Enhanced properties were obtained for: 7 mm fiber length and 30 wt.% concentration of fiber in the composite for alkali-treated fiber. [ABSTRACT FROM AUTHOR]

Control of nonlinear dynamical systems is a complex and multifaceted process. Essential elements of many engineering systems include high-fidelity physics-based modeling, offline trajectory planning, feedback control design, and data acquisition strategies to reduce uncertainties. This article proposes an optimization-centric perspective which couples these elements in a cohesive framework. We introduce a novel use of hyper-differential sensitivity analysis to understand the sensitivity of feedback controllers to parametric uncertainty in physics-based models used for trajectory planning. These sensitivities provide a foundation to define an optimal experimental design which seeks to acquire data most relevant in reducing demand on the feedback controller. Our proposed framework is illustrated on the Zermelo navigation problem and a hypersonic trajectory control problem using data from NASA's X-43 hypersonic flight tests. [ABSTRACT FROM AUTHOR]