Reduction Of Experimental Effort Research Articles

In data science we trust: Machine learning for stable halide perovskites

The exploration of the compositional space of halide perovskites is prohibitively costly using traditional experimental research strategies. Sun and colleagues demonstrated a physics-informed, machine-learning-guided, experimental approach that overturns this limitation enabling a rapid and efficient maximization of stability in mixed-cation perovskites.

Reduction of experimental effort in conventional engine calibration process by using reduced order model

The calibration process of the internal combustion engine becomes more costly and time-consuming than ever due to complexity of the engines and stringent emission regulations. The accurate and fast calibration of the engine requires a lot of performance tests which demands time, cost, and expert manpower as well as test facilities that must be taken into considerations. This process needs a large number of test points which should be examined to get an optimum map of the engine. Although some works have been done on reducing test time for the engine calibration, still the process timing remains one of the challenges for the automotive industry. The present study aims to decrease the calibration cost and time by employing the proper orthogonal decomposition (POD) method. The POD-based reduced order model is applied to reduce the number of experimental engine tests to regenerate the entire calibration table by using only a few operating points accurately. The low, mid-range and high RPM test points of a small single-cylinder, two-stroke engine are chosen as the input to the mathematical model to regenerate the missing calibration data. The comparison between the generated and the corresponding experimental data indicates that they are in good agreement (<10% difference) which implies the POD model is capable to decrease the number of test points and consequently the run time of dynamometer. Furthermore, although this approach is implemented for a conventional calibration process, however, the methodology can be extended to complex calibration procedures.

Using Machine Learning to Predict Synergistic Antimalarial Compound Combinations With Novel Structures.

The parasite Plasmodium falciparum is the most lethal species of Plasmodium to cause serious malaria infection in humans, and with resistance developing rapidly novel treatment modalities are currently being sought, one of which being combinations of existing compounds. The discovery of combinations of antimalarial drugs that act synergistically with one another is hence of great importance; however an exhaustive experimental screen of large drug space in a pairwise manner is not an option. In this study we apply our machine learning approach, Combination Synergy Estimation (CoSynE), which can predict novel synergistic drug interactions using only prior experimental combination screening data and knowledge of compound molecular structures, to a dataset of 1,540 antimalarial drug combinations in which 22.2% were synergistic. Cross validation of our model showed that synergistic CoSynE predictions are enriched 2.74 × compared to random selection when both compounds in a predicted combination are known from other combinations among the training data, 2.36 × when only one compound is known from the training data, and 1.5 × for entirely novel combinations. We prospectively validated our model by making predictions for 185 combinations of 23 entirely novel compounds. CoSynE predicted 20 combinations to be synergistic, which was experimentally validated for nine of them (45%), corresponding to an enrichment of 1.70 × compared to random selection from this prospective data set. Such enrichment corresponds to a 41% reduction in experimental effort. Interestingly, we found that pairwise screening of the compounds CoSynE individually predicted to be synergistic would result in an enrichment of 1.36 × compared to random selection, indicating that synergy among compound combinations is not a random event. The nine novel and correctly predicted synergistic compound combinations mainly (where sufficient bioactivity information is available) consist of efflux or transporter inhibitors (such as hydroxyzine), combined with compounds exhibiting antimalarial activity alone (such as sorafenib, apicidin, or dihydroergotamine). However, not all compound synergies could be rationalized easily in this way. Overall, this study highlights the potential for predictive modeling to expedite the discovery of novel drug combinations in fight against antimalarial resistance, while the underlying approach is also generally applicable.

Using Data Sampling and Inverse Optimization for the Reduction of the Experimental Effort in the Characterization of Hot Working Behaviour for a Case Hardening Steel

For the full characterization of the hot working behaviour of a given material a large number of laboratory experiments have to be performed. The experiments themselves are time consuming and the required specimen material can be quite expensive. With the increasing versatility of the testing machines, like dilatometry with easily variable temperatures, overthinking the classical approaches for materials characterization becomes expedient.In this paper a new technique for the reduction of the experimental effort is presented at the example of a 25MoCrS4 case hardening steel. To analyse the potential for the reduction of the experimental effort the classical approach of a full experimental test matrix is chosen. Here 55 flow curves with temperatures between 700 and 1200°C and strain rates from 0.01 to 100/s are experimentally determined. Then a semi-empirical model for strain hardening and dynamic recrystallization is fitted using an automated routine for parameter determination, taking all available flow curves into account. Subsequently, the number of flow curves used to fit the model parameters is gradually reduced. The model accuracy obtained with the reduced experimental data is compared to the initial fit. The natural decrease in accuracy with the use of less data compared to the gain due to the reduction of experimental effort is analysed. In addition optimal distribution of the sampling points in the experimental matrix for a reduced number of experiments is discussed. It is shown that less than a quarter of the full matrix is sufficient to reach accuracies comparable to using the full matrix. Using the vertices and symmetrical distribution of the data within the full experimental matrix allows a drastic reduction of experimental effort while maintaining the initial accuracy. The results suggest that it might be possible to reduce the costs and effort for material characterization by 50-80%.

A Bayesian compressed-sensing approach for reconstructing neural connectivity from subsampled anatomical data

In recent years, the problem of reconstructing the connectivity in large neural circuits ("connectomics") has re-emerged as one of the main objectives of neuroscience. Classically, reconstructions of neural connectivity have been approached anatomically, using electron or light microscopy and histological tracing methods. This paper describes a statistical approach for connectivity reconstruction that relies on relatively easy-to-obtain measurements using fluorescent probes such as synaptic markers, cytoplasmic dyes, transsynaptic tracers, or activity-dependent dyes. We describe the possible design of these experiments and develop a Bayesian framework for extracting synaptic neural connectivity from such data. We show that the statistical reconstruction problem can be formulated naturally as a tractable L₁-regularized quadratic optimization. As a concrete example, we consider a realistic hypothetical connectivity reconstruction experiment in C. elegans, a popular neuroscience model where a complete wiring diagram has been previously obtained based on long-term electron microscopy work. We show that the new statistical approach could lead to an orders of magnitude reduction in experimental effort in reconstructing the connectivity in this circuit. We further demonstrate that the spatial heterogeneity and biological variability in the connectivity matrix--not just the "average" connectivity--can also be estimated using the same method.

Multilayer test method for water vapor transmission testing of construction materials

Water vapor transmission (WVT) measurements conducted with highly permeable materials are complicated by the instability of boundary conditions, more specifically by changes in vapor pressure and air velocity on either or both sides of the specimen. Such effects pose a greater challenge in determining the water vapor transport characteristics of thin, flexible, and highly permeable construction materials. This article presents a novel approach to WVT testing that improves some of these issues. The approach denoted as ‘multilayer test’ involves testing simultaneously several vertically stacked material layers each separated by air gap with equal thickness. Wireless relative humidity and temperature sensors mounted on the opposing surfaces of each layer provide continuous temperatures and relative humidity monitoring near the specimen surfaces. The test is conducted in a controlled environment with the set-up kept on an analytical balance. Change in the weight of the system is determined automatically at predetermined time intervals, and fluxes are calculated. Two approaches are used in determining the permeance of each material layer. The results of both approaches are compared. The multilayer WVT tests provide several benefits over traditional ‘Dry’ or ‘Wet’ cup WVT test method in that; the boundary layer effects due to moving air above the specimen surface can be accounted for, and simultaneous testing of multiple specimens provides greater statistical confidence. The method is particularly advantageous in cases when the transport coefficient has a strong dependence on moisture content. Using data from a single multilayer test, a continuous transport function can be derived. This article highlights that the multilayer test approach leads to significant reduction in experimental effort, resources required for testing, and test durations, and improves the precision of the material property data.

Effect of plot size on measured soil loss for two Italian experimental sites

The objective of this investigation was to determine empirically the plot width and length effects on runoff volume, Ve, soil loss, Ae, and sediment concentration, Ce, by using data collected, at the temporal scale of the erosive event, on bare plots differing in width (2–8 m) and length (11–22 m) for two Italian stations (Masse, Umbria; Sparacia, Sicily). Mean results differed by a maximum factor of 1.6 for Ve, 1.8 for Ae and 1.2 for Ce when plots differing in width were compared and by a maximum factor of 1.4 for Ve, 1.2 for Ae and 1.3 for Ce when comparison between plot lengths was conducted. Differences between two plot widths or two plot lengths were not statistically significant (P = 0.01). Soil loss was the variable most sensitive to plot size (maximum factor of discrepancy equal to 11.6 for plot width and 25.0 for plot length). Plot width and length effects varied with event erosivity, being low for the most erosive events. Relatively short and narrow plots may give long term results representative of longer and wider plots within the sampled sizes. An appropriate sampling scheme may be based on relatively narrow and short plots instead of wide and long plots, with a reduction of experimental efforts, at least when the events of interest are the most erosive ones. Reducing plot length from 22 m to 11 m is not expected to reduce soil loss per unit area at the two considered locations.

Multi‐response unreplicated‐saturated Taguchi designs and super‐ranking in food formulation improvement

PurposeThe aim of this paper is to examine product formulation screening at the industrial level in terms of multi‐trait improvement by considering several pertinent controlling factors.Design/methodology/approachThe study adopts Taguchi's orthogonal arrays (OAs) for sufficient and economical sampling in a mixture problem. Robustness of testing data is instilled in this method by employing a two‐stage analysis where controlling components are investigated together while the slack variable is tested independently. Multi‐responses collapse to a single master response has been incurred according to the Super Ranking concept. Order statistics are employed to provide statistical significance. The slack variable influence is tested by regression and nonparametric correlation.FindingsSynergy among Taguchi methodology, super ranking and nonparametric testing was seamless to offer practical resolution to product component activeness. The concurrent modulation of two key product traits due to five constituents in the industrial production of muffin‐cake is invoked. The slack variable, rich cream, is strongly active while the influence of added amount of water is barely evident.Research limitations/implicationsThe method presented is suitable only for situations where industrial mixtures are investigated. The case study demonstrates prediction capabilities up to quadratic effects for five nominated effects. However, the statistical processor selected here may be adapted to any number of factor settings dictated by the OA sampling plan.Practical implicationsBy using a case study from food engineering, the industrial production of a muffin‐cake is examined focusing on a total of five controlling mixture components and two responses. This demonstration emphasizes the dramatic savings in time and effort that are gained by the proposed method due to reduction of experimental effort while gaining on analysis robustness.Originality/valueThis work interconnects Taguchi methodology with powerful nonparametric tests of Kruskal‐Wallis for the difficult problem of non‐linear analysis of mixtures for saturated, unreplicated fractional factorial designs in search of multi‐factor activeness in multi‐response cases employing simple and practical tools.

Multichannel quench-flow microreactor chip for parallel reaction monitoring

This paper describes a multichannel silicon-glass microreactor which has been utilized to investigate the kinetics of a Knoevenagel condensation reaction under different reaction conditions. The reaction is performed on the chip in four parallel channels under identical conditions but with different residence times. A special topology of the reaction coils overcomes the common problem arising from the difference in pressure drop of parallel channels having different length. The parallelization of reaction coils combined with chemical quenching at specific locations results in a considerable reduction in experimental effort and cost. The system was tested and showed good reproducibility in flow properties and reaction kinetic data generation.

Compound Noise: Evaluation as a Robust Design Method

AbstractThis paper evaluates compound noise as a robust design method. Application of compound noise as a robust design method leads to a reduction in experimental effort. The compound noise strategy was applied to two types of situation: the first type has been described with active effects up to two‐factor interactions and the second type has been described with effects up to three‐factor interactions. These two situations are illustrated with help of case studies. The paper provides theoretical justification for the effectiveness of the compound noise strategy as formulated by Taguchi and Phadke. For example, we found that the compound noise strategy is very effective for systems which exhibit effect sparsity. This paper gives an alternative procedure to formulate a compound noise, distinctly different from Taguchi's formulation. The alternative method requires less information to formulate compound noise as compared to Taguchi's formulation. Overall, the paper studies the effectiveness of such an alternative formulation, outlines scenarios where compound noise as a robust design method can be effectively used and gives alternative strategies for the systems on which compound noise cannot be effective. Copyright © 2006 John Wiley & Sons, Ltd.

Molecular thermodynamics of adsorption from gas mixtures: composition of adsorbed phase from gravimetric data

Abstract A gravimetric procedure is proposed for measuring equilibrium adsorption from gas mixtures. The total mass adsorbed is measured with a microbalance as a function of temperature, pressure, and composition of the gas phase. The composition of the adsorbed phase is then calculated from the gravimetric data using the principles of adsorption thermodynamics. The increased computational work is more than compensated for by a reduction in experimental effort. The model proposed here, a Bragg—Williams approximation for the local isotherm and a beta distribution of adsorptive energies, provides a consistent framework for the rigorous thermodynamic equations that govern adsorption equilibrium for gas mixtures. The beta distribution is distinguished by its flexibility and its finite range of energies, and generates adsorption isotherms with continuous derivatives at the limit of zero pressure. The BWB equation for adsorption of mixtures is thermodynamically consistent and therefore suitable for calculating the composition of the adsorbed phase from measurements of the total amount adsorbed. The reliability of the procedure is tested by comparing it with both experimental and Monte Carlo data for which the composition of the adsorbed phase is already known. It is found that deviations between experimental and calculated mole fractions lie within the estimated error of the experimental data.

Ion Exchange Equilibria for Ternary Systems from Binary Exchange Data

Ternary ion exchange equilibria have been predicted from measured binary equilibrium data, using the Wilson equation for solid phase activity coefficients, with a significant reduction in experimental effort. Using the reciprocal relationship and the Hála constraint, predictions have been made from data measured for only two binary pairs with similar accuracy to those made with data from all three binary systems, provided the binary pairs selected both include the most favourably selected ion. The difference between ternary equilibrium predictions and experimental data was within the accuracy of the experimental data itself.