Set Of Experimental Data Research Articles

Exploration and exploitation: Which research strategy are you better at?

Abstract This study quantifies and analyzes the individual-level abilities of scientists utilizing either an exploration or an exploitation strategy. Specifically, we present a Research Strategy Q model, which untangles the coupling effect of scientists’ research ability (Qα) and research strategy ability (Eαπ) on research performance. Qα indicates scientists’ fundamental ability to publish high-quality papers, while Eαπ indicates scientists’ proficiency in terms of exploration and exploitation strategies. Five research strategies proposed by our previous study are employed. We generate synthetic data and collect empirical data as our experimental data set. We show that these research strategies present different benefit and risk characteristics. Adopting some exploitation strategies tends to stifle research performance, while exploration strategies are high risk and high yield. Qα and Eαπ have predictive power for research performance. Moreover, we find that, first, scholars who prefer to execute a research strategy, π, may not necessarily be better at executing π. Second, some scholars have differences in their abilities towards different strategies, while other scholars have differences in their abilities towards the same strategy. Third, exploration and exploitation are not contradictory but complementary from the perspective of proficiency, while they are mutually exclusive from the perspective of selection preference.

Universal Phase Identification of Block Copolymers From Physics‐Informed Machine Learning

ABSTRACTBlock copolymers play a vital role in materials science due to their diverse self‐assembly behavior. Traditionally, exploring the block copolymer self‐assembly and associated structure–property relationships involve iterative synthesis, characterization, and theory, which is labor‐intensive both experimentally and computationally. Here, we introduce a versatile, high‐throughput workflow toward materials discovery that integrates controlled polymerization and automated chromatographic separation with a novel physics‐informed machine‐learning algorithm for the rapid analysis of small‐angle X‐ray scattering data. Leveraging the expansive and high‐quality experimental data sets generated by fractionating polymers using automated chromatography, this machine‐learning method effectively reduces data dimensionality by extracting chemical‐independent features from SAXS data. This new approach allows for the rapid and accurate prediction of morphologies without repetitive and time‐consuming manual analysis, achieving out‐of‐sample predictive accuracy of around 95% for both novel and existing materials in the training data set. By focusing on a subset of samples with large predictive uncertainty, only a small fraction of the samples needs to be inspected to further improve accuracy. Collectively, the synergistic combination of controlled synthesis, automated chromatography, and data‐driven analysis creates a powerful workflow that markedly expedites the discovery of structure–property relationships in advanced soft materials.

Separating edges from microstructure in X-ray dark-field imaging: evolving and devolving perspectives via the X-ray Fokker-Planck equation.

A key contribution to X-ray dark-field (XDF) contrast is the diffusion of X-rays by sample structures smaller than the imaging system's spatial resolution; this is related to position-dependent small-angle X-ray scattering. However, some experimental XDF techniques have reported that XDF contrast is also generated by resolvable sample edges. Speckle-based X-ray imaging (SBXI) extracts the XDF by analyzing sample-imposed changes to a reference speckle pattern's visibility. We present an algorithm for SBXI (a variant of our previously developed multimodal intrinsic speckle-tracking (MIST) algorithm) capable of separating these two physically different XDF contrast mechanisms. The algorithm uses what we call the devolving Fokker-Planck equation for paraxial X-ray imaging as its forward model and then solves the associated multimodal inverse problem to retrieve the attenuation, phase, and XDF properties of the sample. Previous MIST variants were based on the evolving Fokker-Planck equation, which considers how a reference-speckle image is modified by the introduction of a sample. The devolving perspective instead considers how the image collected in the presence of the sample and the speckle membrane optically flows in reverse to generate the reference-speckle image when the sample is removed from the system. We compare single- and multiple-exposure multimodal retrieval algorithms from the two Fokker-Planck perspectives. We demonstrate that the devolving perspective can distinguish between two physically different XDF contrast mechanisms, namely, unresolved microstructure- and sharp-edge-induced XDF. This was verified by applying the different retrieval algorithms to two experimental data sets - one phantom sample and one organic sample. We anticipate that this work will be useful in (1) yielding a pair of complementary XDF images that separate sharp-edge diffuse scatter from diffuse scatter due to spatially random unresolved microstructure, (2) XDF computed tomography, where the strong edge XDF signal can lead to strong contaminating streaking artefacts, and (3) sample preparation, as samples will not need to be embedded since the strong XDF edge signal seen between the sample and air can be separated out.

A Data-Informed Mathematical Model of Microglial Cell Dynamics During Ischemic Stroke in the Middle Cerebral Artery.

Neuroinflammation immediately follows the onset of ischemic stroke in the middle cerebral artery. During this process, microglial cells are activated in and recruited to the penumbra. Microglial cells can be activated into two different phenotypes: M1, which can worsen brain injury; or M2, which can aid in long-term recovery. In this study, we contribute a summary of experimental data on microglial cell counts in the penumbra following ischemic stroke induced by middle cerebral artery occlusion (MCAO) in mice and compile available data sets into a single set suitable for time series analysis. Further, we formulate a mathematical model of microglial cells in the penumbra during ischemic stroke due to MCAO. Through use of global sensitivity analysis and Markov Chain Monte Carlo (MCMC)-based parameter estimation, we analyze the effects of the model parameters on the number of M1 and M2 cells in the penumbra and fit identifiable parameters to the compiled experimental data set. We utilize results from MCMC parameter estimation to ascertain uncertainty bounds and forward predictions for the number of M1 and M2 microglial cells over time. Results demonstrate the significance of parameters related to M1 and M2 activation on the number of M1 and M2 microglial cells. Simulations further suggest that potential outliers in the observed data may be omitted and forecast predictions suggest a lingering inflammatory response.

Representation of Dense Shelf Water formation by global oceanic reanalyses

This study evaluates the representation of Dense Shelf Water (DSW) formation in the Southern Ocean by two global oceanic reanalyses: GLORYS2v4 and ORAS5. We found that GLORYS2v4 generally represents larger areas and more consistent temporal patterns of DSW compared to ORAS5. This latter reanalysis significantly underestimates the extent and frequency of DSW formation. Although both reanalyses show good statistical agreement with the World Ocean Circulation Experiment data set, with a root mean squared error of approximately 0.7, a mean absolute percentage error of 1.16% and an r value of 0.99, ORAS5 fails to reproduce the expected sea-surface salinity trend, exhibiting a negative trend across most of the Southern Ocean. This may lead to a weak representation of the water-mass formation processes in the region, thereby affecting its ability to satisfactorily represent the Meridional Overturning Circulation and the global thermohaline circulation. These discrepancies are attributed to the nudging process used for the sea-surface temperature in the ORAS5 model. This study highlights the importance of oceanic reanalyses to validate climate modelling results, in particular in high-latitude regions, where observations are scarce and crucial for understanding global climatic changes.

Development of an Active Cerebrovascular Autoregulation Model Using Representation Learning: A Proof of Concept Study With Experimental Data.

It remains a challenge to monitor cerebrovascular autoregulation (CA) reliably and dynamically in an intensive care unit. The objective was to build a proof-of-concept active CA model exploiting advances in representation learning and the full complexity of the arterial blood pressure (ABP) and intracranial pressure (ICP) signal and outperform the pressure reactivity index (PRx). A porcine cranial window CA data set (n = 20) was used. ABP and ICP signals were preprocessed and downsampled to 20 Hz. Quadriphasic CA state labels were assigned to each piglet's CA curve and projected on their preprocessed ABP and ICP time series. Windowed ABP and ICP segments of 300 seconds, reflecting active CA, were used to optimize a neural network to reconstruct its own input. Reconstruction error of ABP and ICP were compared between active CA and inactive CA, and assessed together with PRx over quadriphasic CA states. The study confirmed that the optimized model achieved stellar reconstruction quality of ABP and ICP segments that derived from active CA while reconstruction quality deteriorated for segments that came from inactive CA. ABP and ICP reconstruction errors steadily increased concurrently with cerebral blood flow deviation from baseline. A significant interaction between variable and CA state showed that the model captured the differential behavior of CA with increasing vs decreasing cerebral perfusion pressures and offered improved discriminative ability regarding PRx. The present work showed that an active CA model can be built using advanced representation learning and the full complexity of 300-second ABP and ICP segments. On assessment in an experimental data set, relevant CA state information was present in both lower and higher frequencies of ABP and ICP. Improved discriminative ability between CA states was attained regarding PRx, which focuses only on slow-wave ABP and ICP information.

Using machine learning to improve the hard modeling of NMR time series.

Modeling time series of NMR spectra is a useful method to accurately extract information such as temporal concentration profiles from complex processes, e.g. reactions. Modeling these time series by using nonlinear optimization often suffers from high runtimes. On the other hand, using deep learning solves the modeling problem quickly, especially for single spectra with separated peaks. However, the accuracy decreases significantly when peaks overlap or cross. We propose a hybrid approach combining the strengths of both methods while mitigating their drawbacks. This hybrid methods improves on a previous work (Meinhardt et al., 2022) and employs neural networks to predict initial parameters for the optimization algorithm, which only needs to fine-tune the parameters afterwards. We present results for both constructed and experimental data sets and achieve improvements in both runtime and accuracy.

Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review.

Multiple sclerosis (MS) is an autoimmune disease of the brain and spinal cord with both inflammatory and neurodegenerative features. Although advances in imaging techniques, particularly magnetic resonance imaging (MRI), have improved the process of diagnosis, its cause is unknown, a cure remains elusive and the evidence base to guide treatment is lacking. Computational techniques like machine learning (ML) have started to be used to understand MS. Published MS MRI-based computational studies can be divided into five categories: automated diagnosis; differentiation between lesion types and/or MS stages; differential diagnosis; monitoring and predicting disease progression; and synthetic MRI dataset generation. Collectively, these approaches show promise in assisting with MS diagnosis, monitoring of disease activity and prediction of future progression, all potentially contributing to disease management. Analysis quality using ML is highly dependent on the dataset size and variability used for training. Wider public access would mean larger datasets for experimentation, resulting in higher-quality analysis, permitting for more conclusive research. This narrative review provides an outline of the fundamentals of MS pathology and pathogenesis, diagnostic techniques and data types in computational analysis, as well as collating literature pertaining to the application of computational techniques to MRI towards developing a better understanding of MS.

Advancing Enhanced Weathering Modeling in Soils: Critical Comparison With Experimental Data

AbstractEnhanced weathering (EW) is a promising strategy to remove atmospheric by amending agricultural and forestry soils with ground silicate rocks. However, current model‐based EW assessments face large uncertainties stemming from the intricate interplay among soil processes, compounded by the absence of a detailed comparison with available observational data. Here, we address this critical gap by first advancing a dynamic, ecohydrological, and biogeochemical Soil Model for Enhanced Weathering (SMEW). We then conduct a hierarchical model‐experiment comparison with four experimental data sets of increasing complexity, from simple closed incubation systems to open mesocosm experiments. The comparison demonstrates SMEW's ability to capture the dynamics of primary variables, including soil moisture, alkalinity, and inorganic carbon. The comparison also reveals that weathering rates are consistently lower than traditionally assumed by up to two orders of magnitude. We finally discuss the implications for carbon removal scenarios and avenues for further theoretical and experimental explorations.

SCREEN: A Graph-based Contrastive Learning Tool to Infer Catalytic Residues and Assess Enzyme Mutations.

when applied to inferred enzyme structures; and (iii) generalizes well to enzymes dissimilar from those in the training set. We also show that the putative catalytic residues predicted by SCREEN mimic key structural and biophysical characteristics of native catalytic residues. Moreover, using experimental data sets, we show that SCREEN's predictions can be used to distinguish residues with a high mutation tolerance from those likely to cause functional loss when mutated, indicating that this tool might be used to infer disease-associated mutations. SCREEN is publicly available at https://github.com/BioColLab/SCREEN and https://ngdc.cncb.ac.cn/biocode/tool/7580.

Combining Group Contribution Method and Semisupervised Learning to Build Machine Learning Models for Predicting Hydroxyl Radical Rate Constants of Water Contaminants.

Machine learning is an effective tool for predicting reaction rate constants for many organic compounds with the hydroxyl radical (HO•). Previously reported models have achieved relatively good performance, but due to scarce data (<1400 records), the applicability domain (AD) has been significantly limited. To address this limitation, we curated a much larger experimental data set (Primary data set), which contains 2358 kinetic records. We then employed both the group contribution method (GCM) and a semisupervised learning (SSL) strategy to add new data points, aiming to effectively expand the model's AD while improving model performance. The results indicated that GCM improved the model's performance for chemicals outside the AD, while SSL expanded the model's AD. The final model, after incorporating 147,168 new data points, achieved an R2 = 0.77, root-mean-square-error = 0.32, and mean-absolute-error = 0.24 on the test set. Importantly, the AD was expanded by 117% compared to the model developed solely based on the Primary data set, and the final model can be reliably applied to more than 560,000 chemicals from the DSSTox database. Further model interpretation results indicated that the model made predictions based on a correct "understanding" of the impact of key substituents and reactive sites toward HO•. This research provides an effective method for augmenting data sets, which is important in improving ML model performance and expanding AD. The final model has been made widely accessible through a free online predictor.

Effect of Drug-to-Protein Reaction Kinetics on the Results of Thermal Proteome Profiling.

In this Letter, a two-term formalism for constructing protein solubility curves in thermal proteome profiling (TPP) is considered, which takes into account the efficiency of the drug-protein binding reaction. When the reaction is incomplete, this results in distortion of the otherwise sigmoidal shape of the curve after drug treatment, which is often observed in experiments. This distortion may be significant enough to disqualify the corresponding protein from the list of drug target candidates, thus negatively affecting the results of TPP data analysis. To further assist this analysis, we also developed the solubility curve simulation software to visualize the discussed effect. Several experimental data sets from recent TPP studies have been reprocessed, and we demonstrate in a few examples that the proposed two-term equation fits correctly the observed protein solubility curves with distorted shapes, also highlighting the previously unrecognized targets.

Validation of spatial accuracy with high-speed imaging to support in situ flaw detection for laser directed energy deposition processes

The application of in situ monitoring systems for part quality verification or process qualification of the laser based directed energy deposition process will require the ability to confidently detect (or estimate) the type, size, and location of defects in an additive manufactured component. These developments are requested by the industry to supplement the expensive and time-consuming ex situ inspections for part certification to further support the number of viable business cases. This confidence level of the in situ monitoring solutions should be comparable to that of the ex situ inspection techniques (such as microcomputed tomography). These developments will also require very large experimental data sets with spatially resolved in situ monitoring signals which must be correlated to ex situ inspection data. A crucial element in this correlation is the spatial absolute accuracy of these signals from both in situ and ex situ solutions. The in situ solutions should, preferably, at least match with ex situ accuracy. Currently, there is no consensus framework for validation of the spatial accuracy of the in situ monitoring signals. Therefore, a method will be presented in this paper to evaluate the spatial accuracy obtainable with the typical available location information on ethernet field bus implemented in the laser based directed energy deposition process. The accuracy is verified by the implementation of a high-speed imaging setup that additionally enables to verify the synchronization accuracy of the imaging solutions. Cameras act often as a source of data for the in situ monitoring solutions of the melt pool.

Machine Learning Prediction of the Corrosion Rate of Zinc-Based Alloys Containing Copper, Lithium, Magnesium, and Silver.

Implementation of machine learning (ML) techniques in materials science often requires large data sets. However, a proper choice of features and regression methods allows the construction of accurate ML models able to work with a relatively small data set. In this work, an extensive, although still limited, experimental data set of corrosion-related properties of Zn-based alloys used in biomedicine was created. On the basis of this data set, a robust and accurate model was built to predict the corrosion behavior of Zn-based alloys. This work highlights the effectiveness of ML methods for assessing the corrosion behavior of Zn-based alloys, which can facilitate their application in bioimplants.

Analysis of the Tensile Properties of Composite Material Added Carbonisate Based on the Change of Strain Dynamics.

This paper presents the application of Kolmogorov-Sinai (EK-S) metric entropy calculations performed on experimental data sets (relative elongations ε) recorded during static tensile testing of a composite material with carbonisate. The EK-S calculation method makes it possible to represent the dynamics of strain change occurring during the endurance test. The depiction of the change in the dynamics of elongation compared to the course of the tensile curve makes it possible to analyse the strength properties of the tested specimens. The material used for the study is a layered epoxy composite with the addition of carbonisate obtained by pyrolysis from organic waste from MDF (Medium Density Fibreboard) furniture boards. For the tested material, two variants were prepared without the addition of carbonisate (samples designated as I and III) and two variants differing in the percentage of carbonisate: 5% by mass (sample IV) and 7.5% by mass (sample II), with a constant fraction of 0.5 mm. Analyses showed a slight deterioration in the tensile properties of composites containing carbonisate. SEM (Scanning Electron Microscope) studies of the carbonisate samples revealed the presence of cracks, pores and local delamination, which correlates with a reduction in strength parameters. For sample II, the tensile strength (Rm) was 9.032% lower compared to the base sample I and the tensile strain decreased by 0.098%. For sample IV, a decrease in parameters was also observed compared to base sample III-the strength decreased by 13.29%, and the tensile strain decreased by 10.64%. The results obtained in this study were additionally decided to be analysed using metric entropy calculations, which makes it possible to capture significant qualitative changes occurring in the structure of the tested samples not depending on the results of the static tensile test. In the context of epoxy composites with the addition of carbonisate, this analysis can contribute to a better understanding of the influence of the carbonisate obtained in the pyrolysis process on the structure of the composite and its performance properties.

Unmixing Autoencoder for Image Reconstruction from Hyperspectral Data.

Due to the complexity of samples and the limitations in spatial resolution, the spectra in hyperspectral imaging (HSI) are generally contributed to by multiple components, making univariate analysis ineffective. Although feature extraction methods have been applied, the chemical meaning of the compressed variables is difficult to interpret, limiting their further applications. An unmixing autoencoder (UAE) was developed in this work for the separation of the mixed spectra in HSI. The proposed model is composed of an encoder and a fully connected (FC) layer. The former is used to compress the input spectrum into several variables, and the latter is employed to reconstruct the spectrum. Combining reconstruction loss and sparse regularization, the weights and the spectral profiles of the components will be encoded in the compressed variables and the connection weights of FC, respectively. A simulated and three experimental HSI data sets were adopted to investigate the performance of the UAE model. The spectral components were successfully obtained, from which the handwriting under papers was revealed from the image of near-infrared (NIR) diffusive reflectance spectroscopy, and the images of lipids, proteins, and nucleic acids were reconstructed from the Raman and stimulated Raman scattering (SRS) images.

Thermoelectric Material Performance (zT) Predictions with Machine Learning.

Research efforts using the tools in machine- and deep learning models have begun to show success in predicting target properties such as thermoelectric (TE) properties, including the figure of merit (zT). These models were trained on various data sources that used experimental, crystallographic, and density functional theory (DFT) data. We developed an interpretable model on a huge experimental data set of ∼160,000 data points to predict the performance of thermoelectric materials. The model predicts the results of three different test sets with high accuracy, such as the root-mean-square error (RMSE) ranging from 0.15 to 0.20 and the evaluation coefficients (R2) ranging from 0.80 to 0.67. Furthermore, we highlight probable reasons such as literature error, varied synthesis routes for the same material, different forms of crystallinity and morphology, and different particle sizes and densities for the deviation of predicted zT from the experimental zT results of the test sets. Lastly, using an experimental data set, our study is one of the few examples that predict a complex zT property directly across the entire gamut of TE materials.

Machine Learning Models for Predicting Polymer Solubility in Solvents across Concentrations and Temperatures.

Artificial intelligence and machine learning have become essential tools in predicting material properties to aid in the accelerated design of new materials. Polymer solubility, critical for new formulations and solution processing, is one such property. However, current models are limited by inadequate experimental data sets that cannot capture the complexity and detail for many features contributing to polymer solubility. Here, we provide a data set for polymer solution behavior based on Crystal16 turbidity measurements that includes high quality percent transmission data for polymer solutions for a variety of polymers, solvents, concentrations and temperatures. We use this data set to train a model that predicts the experimental transmission data at many temperatures and multiple concentrations. From this, we are able to classify the polymer/solvent pairs into three solubility categories providing a level of granularity to predictions beyond prior binary classification models considering only solvent/nonsolvent classes. The inclusion of multiple concentrations, temperatures and partially soluble data expands solubility prediction capability beyond prior work into predictions more attractive for use by formulators and process designers working with industrial polymer solutions.

A Bayesian-interference approach to quantify degradation parameters in a water-cooled variable speed screw compressor chiller

Abstract Variable-speed screw chillers can provide effective capacity modulations in commercial buildings to reduce energy consumption, match the load requirements, and contribute to lower equivalent carbon emissions. However, variable-speed operation can also yield to degradation phenomena inside the compressor. Assessing the real-time health status of chillers and detecting anomalies are important aspects to ensure long-term operation and improve reliability. To this end, an automated accelerated life test (ALT) procedure was developed and applied to a 513 kW (145.9 RT) water-cooled variable-speed screw chiller in a laboratory test facility to experimental assess performance degradation over time. After every 1,000 operating hours of ALT, steady-state performance tests at 30%, 50%, 75% and 100% load were conducted according to AHRI Standard 550/590. Additional operating conditions were included while conducting the tests to provide a more complete assessment of the degradation trends compared to the initially measured baseline. The experimental data set was then used to quantify the performance degradation based on the impact on the screw compressor operation. A Bayesian-inference identification approach has been developed to identify changes of sensitive parameters that statistically provide evidence for the likelihood of a performance degradation of the compressor. The information was used to implement the parameters in a dynamic model which was used to perform studies to predict the degradations of a chiller. The outcomes of this study help to improve the maintenance schedule of a compressor and even the whole chiller so that the system can be operated more economically and unplanned downtime can be minimized.

Research on marine fish classification and recognition based on an optimized ResNet50 model

AbstractObjectiveIn order to solve the problems of low accuracy and limited generalization ability in traditional marine fish species identification methods, the optimized ResNet 50 model is proposed in this paper.MethodsFirst, a data set of marine fish images was constructed, targeting 30 common marine fish species (e.g., Japanese Eel Anguilla japonica, Japanese Horsehead Branchiostegus japonicus, Black Sea Sprat Clupeonella cultriventris, and Atlantic Cutlassfish Trichiurus lepturus). The marine fish images were pre‐processed to increase the sample size of the data set. Second, the ResNet50 model was optimized by introducing a Dual Multi‐Scale Attention Network (DMSANet) module to improve the model's attention to subtle features. A dropout regularization mechanism and dense layer were added to improve the model's generalization ability and prevent overfitting. The triplet loss function was adopted as the optimization objective of the model to reduce errors. Third, species identification was conducted on 30 species of marine fish to test the comprehensive performance of the optimized ResNet50 model.ResultThe test results showed that the optimized model had a recognition accuracy of 98.75% in complex situations, which was 3.05% higher than that of the standard ResNet50 model. A confusion matrix of the visual analysis results showed that the optimized ResNet50 model had a high accuracy rate for marine fish species recognition in many cases. To further validate and evaluate the generalization ability of the optimized ResNet50 model, partial fish data from the ImageNet database and the Queensland University of Technology (QUT) Fish Dataset were used as data sets for performance experiments. The results showed that the optimized ResNet50 model achieved accuracies of 97.65% and 98.75% on the two benchmark data sets (ImageNet and the QUT Fish Dataset, respectively).ConclusionThe optimized ResNet50 model integrates the DMSANet module, effectively capturing subtle features in images and improving the accuracy of fish classification tasks. This model has good recognition and generalization abilities in complex scenes, and can be applied to marine fish recognition tasks in different situations.