Experimental Design Procedure Research Articles

An optimal experimental design framework for fast kinetic model identification based on artificial neural networks

The development of mathematical models to describe reaction kinetics is crucial in process design, control, and optimization. However, distinguishing between different candidate kinetic models presents a non-trivial challenge. Recent works on this topic introduced an approach that employs artificial neural networks (ANNs) to identify kinetic models. In this paper, the ANNs-based model identification approach is expanded by introducing an optimal experimental design procedure. The performance of the method is evaluated through a case study related to the identification of kinetics in a batch reaction system, where different combinations of experimental design variables and noise level on the measurements are compared to assess their impact on kinetic model identification. The proposed experimental design methodology effectively reduces the number of required experiments while enhancing the artificial neural network's ability to accurately identify the appropriate set of equations defining the kinetic model structure.

Experimental, predictive and RSM studies of H2 production using Ag-La-CaTiO3 for water-splitting under visible light

Ag-La-CaTiO3 was used in place of sacrificial agents to assess the influence of operational factors on hydrogen generation in a photocatalytic water splitting system. After being synthesized, the physicochemical features of this substance were accurately described. Several characterization techniques including UV–Vis spectroscopy, FTIR, XRD, XPS, EDX, SEM, TGA, DRS and BET were applied to study the prepared Ag-La-CaTiO3 photocatalyst. Ag-La-CaTiO3 shows a band in the visible wavelength between 400 and 800 nm at < 560 nm compared to the main CaTiO3 band at 350 nm. Ag 4d5s electrons transition to the conduction band (CB), which is responsible for the absorption band at ~ 560 nm (> 2.21 eV). The effects of catalyst concentration, light intensity, and beginning solution pH on the H2 generation rate may all be evaluated simultaneously using experimental design procedures. Up to a maximum threshold, where a drop in the rate of gas evolution occurs, it was confirmed that the increase in catalyst dose positively affects system productivity. The initial solution pH plays a crucial role in H2 production, and pH = 4 and 10 are the optimum pH with a higher yield of H2 production. The highest total H2 production rate, 6246.09 μmol, was obtained using a catalyst concentration of 700 mg and solution pH equal to 10 under 1200 W Vis lamp for 3 h. For prediction and optimization, a D-Optimal design was applied and the optimal results were pH 4, the catalyst dose of 645.578 mg and 1200 W with H2 production of 6031.11 μmol.

Experimental Evaluation and Design Procedure for All-Steel Tube-in-Tube Buckling Restrained Braces

Experimental Evaluation and Design Procedure for All-Steel Tube-in-Tube Buckling Restrained Braces

Identifying patient preferences for diabetes care: A protocol for implementing a discrete choice experiment in Samoa.

In Samoa, adult Type 2 diabetes prevalence has increased within the past 30 years. Patient preferences for care are factors known to influence treatment adherence and are associated with reduced disease progression and severity. However, patient preferences for diabetes care, generally, are understudied, and other patient-centered factors such as willingness-to-pay (WTP) for diabetes treatment have never been explored in this setting. Discrete Choice Experiments (DCE) are useful tools to elicit preferences and WTP for healthcare. DCEs present patients with hypothetical scenarios composed of a series of multi-alternative choice profiles made up of attributes and levels. Patients choose a profile based on which attributes and levels may be preferable for them, thereby quantifying and identifying locally relevant patient-centered preferences. This paper presents the protocol for the design, piloting, and implementation of a DCE identifying patient preferences for diabetes care, in Samoa. Using an exploratory sequential mixed methods design, formative data from a literature review and semi-structured interviews with n = 20 Samoan adults living with Type 2 diabetes was used to design a Best-Best DCE instrument. Experimental design procedures were used to reduce the number of choice-sets and balance the instrument. Following pilot testing, the DCE is being administered to n = 450 Samoan adults living with diabetes, along with associated questionnaires, and anthropometrics. Subsequently, we will also be assessing longitudinally how preferences for care change over time. Data will be analyzed using progressive mixed Rank Order Logit models. The results will identify which diabetes care attributes are important to patients (p < 0.05), examine associations between participant characteristics and preference, illuminate the trade-offs participants are willing to make, and the probability of uptake, and WTP for specific attributes and levels. The results from this study will provide integral data useful for designing and adapting efficacious diabetes intervention and treatment approaches in this setting.

A new framework of piezoelectric smart tiles based on magnetic plucking, mechanical impact, and mechanical vibration force mechanisms for electrical energy harvesting

In this paper, a new framework based on combining three different mechanisms is proposed to improve electrical energy harvesting from piezoelectric smart tiles. These mechanisms are named magnetic plucking (MP), mechanical impact (MI), and mechanical vibration force (MVF). The MP mechanism uses the magnetic interaction between an iron rod and a magnet to excite the consoles used in the tile. In the MI mechanism, the piezoelectric cells are stimulated by the mechanical function of the cantilevers. Also, in the MVF mechanism, piezoelectric cells are stimulated by the cantilever oscillations of MP and MI. In the experimental design procedure of the proposed smart tile structure, at first, the smart tile based on consoles made of MP, MI, and MVF mechanisms are designed. Then, to compare the electrical energy harvesting of the proposed structure with other tiles, the proposed smart tile with 36 piezoelectric consoles, which is a combination of three mechanisms, is implemented. To protect the piezoelectric cells against excessive bending during the operation of the consoles, the swing of the consoles is limited by using a plastic stopper. The proper air gap between the magnet and the iron rod as well as the air gap between the console and the stoper are determined by actual tests. In the energy harvesting evaluations of the proposed structure, the energy and average power of 71.0 mJ and 13.6 mW were obtained at the optimal resistance load of 100 kΩ. Experiments on energy harvesting from tiles made of MP, MI, and MVF mechanisms were also conducted. When comparing the electrical energy harvested from the proposed framework, utilizing rectification with the diode bridge and capacitor filter, with that generated by the MP, MI, and MVF mechanisms, a substantial increase in energy yield was observed. Also, the average energy harvesting time of the proposed structure has increased by 59.4 % compared to the other three mechanisms.

Plasma-Induced Surface Modification of Sapphire and Its Influence on Graphene Grown by Plasma-Enhanced Chemical Vapour Deposition.

In this work, we study the influence of the different surface terminations of c-plane sapphire substrates on the synthesis of graphene via plasma-enhanced chemical vapor deposition. The different terminations of the sapphire surface are controlled by a plasma process. A design of experiments procedure was carried out to evaluate the major effects governing the plasma process of four different parameters: i.e., discharge power, time, pressure and gas employed. In the characterization of the substrate, two sapphire surface terminations were identified and characterized by means of contact angle measurements, being a hydrophilic (hydrophobic) surface and the fingerprint of an Al- (OH-) terminated surface, respectively. The defects within the synthesized graphene were analyzed by Raman spectroscopy. Notably, we found that the ID/IG ratio decreases for graphene grown on OH-terminated surfaces. Furthermore, two different regimes related to the nature of graphene defects were identified and, depending on the sapphire terminated surface, are bound either to vacancy or boundary-like defects. Finally, studying the density of defects and the crystallite area, as well as their relationship with the sapphire surface termination, paves the way for increasing the crystallinity of the synthesized graphene.

Developing a Biomechanical Testing Setup of the Pelvis-Part I: Computational Design of Experiments.

Biomechanics of the human pelvis and the associated implants are still a medical and engineering debated topic. Today, no biomechanical testing setup is dedicated to pelvis testing and associated reconstructive implants with accepted clinical relevance. This paper uses the computational experiment design procedure to numerically design a biomechanical test stand that emulates the pelvis physiological gait loading. The numerically designed test stand reduces the 57 muscles and joints' contact forces iteratively to only four force actuators. Two hip joints' contact forces and two equivalent muscle forces with a maximum magnitude of 2.3 kN are applied in a bilateral reciprocating action. The stress distribution of the numerical model of the developed test stand is very similar to that of the numerical model of the pelvis with all 57 muscles and joint forces. For instance, at the right arcuate line, the state of stress is identical. However, at the location of superior rami, there is a deviation ranging from 2% to 20% between the two models. The boundary conditions and the nature of loading adopted in this study are more realistic regarding the clinical relevance than state-of-the-art. The numerically developed biomechanical testing setup of the pelvis in this numerical study (Part I) was found to be valid for the experimental testing of the pelvis. The construct of the testing setup and the experimental testing of an intact pelvis under gait loading are discussed in detail in Part II: Experimental Testing.

Rapid dynamic analysis for structures with variable system parameters through multi-fidelity model

Dynamic analysis for structures with variable parameters, such as structural dynamic reliability analysis, optimization and uncertainty quantification, usually requires a large number of samples, while high-fidelity experiments or simulations are usually too time-consuming to repeat many times. To address the above issue, this paper proposes a new approach called multi-fidelity based dynamic analysis (MF-DA) methodology for efficiently approximating dynamic responses with available multiple computational models. The responses from dynamic analyses with different fidelities can be integrated concurrently through MF-DA, thus improving the modeling efficiency and approximation accuracy simultaneously. The MF-DA method begins with the design of experiment procedure through optimized and inherit Latin hypercube sampling. Then, the obtained high-dimensional dynamic responses are transformed into several low-dimensional projections according to principal component analysis. Finally, the relationship between projections from two dynamic analyses with different fidelities is constructed based on radial basis function. Two numerical examples are provided to demonstrate the accuracy and robustness of MF-DA. Throughout the modeling process, only a small number of HF dynamic analyses are performed, thus the proposed method provides an efficient tool for dynamic analysis with variable system parameters under insufficient high-fidelity samples.

Comparison of Preferences and Data Quality between Discrete Choice Experiments Conducted in Online and Face-to-Face Respondents.

Discrete choice experiments (DCE) are increasingly being conducted using online panels. However, the comparability of such DCE-based preferences to traditional modes of data collection (e.g., in-person) is not well established. In this study, supervised, face-to-face DCE was compared with its unsupervised, online facsimile on face validity, respondent behavior, and modeled preferences. Data from face-to-face and online EQ-5D-5L health state valuation studies were compared, in which each used the same experimental design and quota sampling procedure. Respondents completed 7 binary DCE tasks comparing 2 EQ-5D-5L health states presented side by side (health states A and B). Data face validity was assessed by comparing preference patterns as a function of the severity difference between 2 health states within a task. The prevalence of potentially suspicious choice patterns (i.e., all As, all Bs, and alternating As/Bs) was compared between studies. Preference data were modeled using multinomial logit regression and compared based on dimensional contribution to overall scale and importance ranking of dimension-levels. One thousand five Online respondents and 1,099 face-to-face screened (F2FS) respondents were included in the main comparison of DCE tasks. Online respondents reported more problems on all EQ-5D dimensions except for Mobility. The face validity of the data was similar between comparators. Online respondents had a greater prevalence of potentially suspicious DCE choice patterns ([Online]: 5.3% [F2FS] 2.9%, P = 0.005). When modeled, the relative contribution of each EQ-5D dimension differed between modes of administration. Online respondents weighed Mobility more importantly and Anxiety/Depression less importantly. Although assessments of face validity were similar between Online and F2FS, modeled preferences differed. Future analyses are needed to clarify whether differences are attributable to preference or data quality variation between modes of data collection.

Музыка и язык: мышление–восприятие–практика

The article aims to shed light on some recent empirical neuroscience and psychology researches, which target music perception and cognition. The explosive growth of data collected and the measurement preciseness should correlate with the clarity of conclusions made on their basis. However, for the skilled music practitioners, such correlation is not obvious. The author propose a definition of the term musician, based on the concept of musical cognitive status. The purpose is to provide the adequate support and refinement of the experiment design procedures for the cross-disciplinary researches, dealing with selection of music stimuli material, conceptual theoretical foundations, and cultural-dependent cognitive backgrounds of the participants. The author considers that music cognition of performers is framed by the specific music idea, represented in the acoustically certain sound image, transformed to the appropriate gesture. This clear image is generated successfully when the performer has a clear artistic idea of musical sense and content. The specific types of perception determine the content-consuming habits. The degree of perceptual accessibility of sounding musical phenomena is also determined by the peculiarities of both musical and linguistic perception matrix, rooted in culture. In order to avoid the waste of research resources, author considers several investigation paths, coming from the methodology of philosophic anthropology, focused on the cultural and language-dependent dimension of music discourse.

Synergic effect of recycled paper sludge and expanded perlite on the engineering properties of porous clay bricks: A new mathematical modelling approach

Lightweight bricks produced with addition of different additive materials such as expanded perlite (EP) and recycling paper sludge (RPS) waste have been accomplished by a pug mill. Porous fired bricks were produced by using organic and inorganic additives such as paper waste and expanded perlite as pore-makers. In the experimental study, the seventeen mixture sets were prepared with Box-Behnken experimental design procedure of two additive ratios and firing temperatures. The brick samples were produced with shaping by extrusion method and firing at 850, 950 and 1050 °C for two hours after drying. Physical characteristics such as bulk density, apparent porosity and water absorption, and also, compressive strength, thermal conductivity and microstructural properties of brick samples were studied. Influences of process parameters on brick properties were investigated with a detailed neuro-regression analysis. In conclusion, the best brick composition (10 % EP + 10 %RPS by weight) in terms of thermal conductivity corresponding to acceptable mechanical properties was optimized. Accordingly, the compressive strength values of all samples are around 10 to 27 MPa. Also, the samples with high additive content (especially 10 %RPS + 5 %EP sample) exhibited the lowest thermal conductivity value (0.432 W/mK) due to the low firing temperature.

A compendium of single extracellular vesicle flow cytometry.

Flow cytometry (FCM) offers a multiparametric technology capable of characterizing single extracellular vesicles (EVs). However, most flow cytometers are designed to detect cells, which are larger than EVs. Whereas cells exceed the background noise, signals originating from EVs partly overlap with the background noise, thereby making EVs more difficult to detect than cells. This technical mismatch together with complexity of EV-containing fluids causes limitations and challenges with conducting, interpreting and reproducing EV FCM experiments. To address and overcome these challenges, researchers from the International Society for Extracellular Vesicles (ISEV), International Society for Advancement of Cytometry (ISAC), and the International Society on Thrombosis and Haemostasis (ISTH) joined forces and initiated the EV FCM working group. To improve the interpretation, reporting, and reproducibility of future EV FCM data, the EV FCM working group published an ISEV position manuscript outlining a framework of minimum information that should be reported about an FCM experiment on single EVs (MIFlowCyt-EV). However, the framework contains limited background information. Therefore, the goal of this compendium is to provide the background information necessary to design and conduct reproducible EV FCM experiments. This compendium contains background information on EVs, the interaction between light and EVs, FCM hardware, experimental design and preanalytical procedures, sample preparation, assay controls, instrument data acquisition and calibration, EV characterization, and data reporting. Although this compendium focuses on EVs, many concepts and explanations could also be applied to FCM detection of other particles within the EV size range, such as bacteria, lipoprotein particles, milk fat globules, and viruses.

An adaptive information-theoretic experimental design procedure for high-to-low fidelity calibration of prostate cancer models.

The use of mathematical models to make predictions about tumor growth and response to treatment has become increasingly prevalent in the clinical setting. The level of complexity within these models ranges broadly, and the calibration of more complex models requires detailed clinical data. This raises questions about the type and quantity of data that should be collected and when, in order to maximize the information gain about the model behavior while still minimizing the total amount of data used and the time until a model can be calibrated accurately. To address these questions, we propose a Bayesian information-theoretic procedure, using an adaptive score function to determine the optimal data collection times and measurement types. The novel score function introduced in this work eliminates the need for a penalization parameter used in a previous study, while yielding model predictions that are superior to those obtained using two potential pre-determined data collection protocols for two different prostate cancer model scenarios: one in which we fit a simple ODE system to synthetic data generated from a cellular automaton model using radiotherapy as the imposed treatment, and a second scenario in which a more complex ODE system is fit to clinical patient data for patients undergoing intermittent androgen suppression therapy. We also conduct a robust analysis of the calibration results, using both error and uncertainty metrics in combination to determine when additional data acquisition may be terminated.

Efficient Sensor Calibration Via Machine Learning-Based Resampling Methods

It remains a challenge to accurately and precisely calibrate a sensor in an experimentally-efficient manner. The calibration task at hand is to quantify the functional relationship between the sensor’s response (denoted as y) and the target analyte concentration (denoted as x) based on laboratory experiments. In practical use, the sensor’s mathematical calibration model needs to be coupled with the device to make inference regarding the concentration of the analyte in an environment based on the observed sensor response. It is critical to provide a single-value estimate for the analyte concentration, as well as to quantify the uncertainty of the target estimate (i.e., to provide the possible range of the underlying concentration level).However, classic statistical methods are not able to provide valid inference on the target quantity of interest due to the following difficulties in sensor calibration. First, not only the typical forward modeling but also the inverse estimation is involved. The former refers to the generation of a calibration model functionally relating the analyte concentration to the sensor response (x→y) based on experimental data; The latter concerns the use of the calibration model in practical setting to obtain from the observed response the estimation of the unknown concentration (y→x). Second, the variability of the sensor responses tends to be heterogeneous as opposed to homogeneous, which is an assumption that classic statistical methods rely on.In light of the above, a computational resampling method was adapted in this work to address the machine learning (ML)-based inference issues (i.e., the uncertainty quantification of the estimated analyte concentrations). Built on the inference capability, a two-stage experimental design procedure was developed to guide the efficient sampling of calibration data via laboratory experiments.The ML-based inference and experimental design methods are integrated in the chemometric calibration procedure, which was applied on a simulated biosensor to demonstrate its efficiency over traditional methods. The sensor simulator is developed from the real experimental data for a paper-based lateral flow strip (PLFS) designed to measure in blood the diagnosis biomarkers for traumatic brain injury. PLFS is finding increasing applications in detection of various diseases in point-of-care testing settings. Figure 1

Technique Used to Perform Rocket-Plane Free-Flight Tests

Abstract This paper includes description of the technique that was applied for free-flight (drop) tests of the rocket-plane scaled model. The main aim of the experiment was to validate the numerical approach to be used to simulate the gliding flight of the rocket-plane, especially the transition between high to low angles of attack and the rocket-plane response to control. The primary goal of this paper is to show what kind of challenges must be addressed when planning the flight test campaign. This paper includes description of how the rocket-plane model was scaled and built, the model preparation, experimental design and flight procedure. This paper shows an overview of how the experiment can be planned for different scenarios and the lessons learned during the deep stall free-flight tests.

Optimization of joining HDPE rods by continuous drive friction welding

Abstract Continuous drive friction welding (CDFW) is a solid-state joining procedure that can be used to join various similar and dissimilar materials. High-density polyethylene (HDPE) is a thermoplastic that can replace many traditional materials. Utilizing experimental design procedures such as response surface method (RSM) is a reliable approach for determining the most significant process parameters and optimizing the desired responses. The current study employed an RSM experimental design to investigate the effects of the process parameters for welding HDPE rods using CDFW. The design evaluated the process parameters and three outcome responses: the maximum welding temperature, the axial shortening, and the tensile strength (TS). The combination of the three responses can allow achieving high-efficiency welds. The results showed that it was possible to achieve high-efficiency welds while maintaining axial shortening and controlling temperature. A TS &gt;65% of the parent material's strength with an axial shortening of &lt;3 mm was achieved.

An Experimental Design Approach for Regret Minimization in Logistic Bandits

In this work we consider the problem of regret minimization for logistic bandits. The main challenge of logistic bandits is reducing the dependence on a potentially large problem dependent constant that can at worst scale exponentially with the norm of the unknown parameter vector. Previous works have applied self-concordance of the logistic function to remove this worst-case dependence providing regret guarantees that move the reduce the dependence on this worst case parameter to lower order terms with only polylogarithmic dependence on the main term and as well as linear dependence on the dimension of the unknown parameter. This work improves upon the prior art by 1) removing all scaling of the worst case term on the main term and 2) reducing the dependence on the dependence to scale with the square root of dimension in the fixed arm setting by employing an experimental design procedure. Our regret bound in fact takes a tighter instance (i.e., gap) dependent regret bound for the first time in logistic bandits. We also propose a new warmup sampling algorithm that can dramatically reduce the lower order term in the regret in general and prove that it can exponentially reduce the lower order term's dependency on the worst case parameter in some instances. Finally, we discuss the impact of the bias of the MLE on the logistic bandit problem in d dimensions, providing an example where d^2 lower order regret (cf., it is d for linear bandits) may not be improved as long as the MLE is used and how bias-corrected estimators may be used to make it closer to d.

DeltaPCA: A statistically robust method for analysing surface-enhanced Raman spectra for quantitative analyte detection

Surface-enhanced Raman spectroscopy using functionalised nanoparticles or nano-engineered surfaces provides a highly selective and sensitive technique for detecting and quantifying analyte binding. However, practical diagnostic and sensing applications are hindered by technical variability due to hot-spotting and analyte drift, as well as variability in sample preparation and substrate uniformity. In this work, we introduce a novel joint experimental design and computational analysis procedure to minimize and/or control for these sources of error. Sample variability is minimized by preparing functionalised nanoparticles with and without analyte under the same conditions, and then recording and analysing difference spectra. To account for technical variability, multiple spectra are recorded from each sample. The key novelty of our analysis procedure is that all information about sample and technical variability is retained through to the final comparative analysis step, and we apply principal component analysis twice - once to extract “variance-minimized” spectra as principal loading vectors and again to distinguish between samples with and without the target analyte. Proof of principle experiments using thiolated aptamers to detect CoV-SARS-2 spike protein reveal that analyte binding show that analyte binding primarily shows up as a depletion of free covalent stretching bands, coupled to appearance of corresponding hydrogen-bonded stretching bands. This is quite different from previous work which largely focusses on changes in the “fingerprint” region where we find that the signal may be obscured by greater technical variability. Our computational analysis code can be freely downloaded from https://github.com/dlc62/DeltaPCA.

Study of the effect of mechanical uncertainties parameters on performance of PEMFC by coupling a 3D numerical multiphysics model and design of experiment

As the PEMFC is a complex multi-physics device whose reliability and durability depend on the thermal-mechanical-electrical and chemical parameters. In this paper, theoretical and numerical studies is proposed to optimize the fuel cell performance using multiphysics model and design of experiments. 3D finite element analysis including a fully coupling of thermal-electrical-mechanical model is proposed to predict the electrical resistance of fuel cell. As the mechanical parameters (bending radius of the bipolar plate, thickness of the GDL and clamping pressure) remain uncertain, the design of experiments procedure is used to optimize the fuel cell behavior under several conditions.

Scanning the landscape of axion dark matter detectors: Applying gradient descent to experimental design

The hunt for dark matter remains one of the principal objectives of modern physics and cosmology. Searches for dark matter in the form of axions are proposed or underway across a range of experimental collaborations. As we look to the next generation of detectors, a natural question to ask is whether there are new experimental designs waiting to be discovered and how we might find them. Here we take a new approach to the experimental design procedure by using gradient descent techniques to search for optimal detector designs. We provide a proof of principle for this technique by searching 1D detectors varying the bulk properties of the detector until the optimal detector design is obtained. Remarkably, we find the detector is capable of out-performing a human designed experiment on which the search was initiated. This opens the door to further gradient descent searches of more complex 2D and 3D designs across a wider variety of materials and boundary geometries of the detector. There is also an opportunity to use more sophisticated gradient descent algorithms to complete a more exhaustive scan of the landscape of designs.