Experimental Data Acquisition Research Articles

High-speed 4-dimensional scanning transmission electron microscopy using compressive sensing techniques.

Here we show that compressive sensing allows 4-dimensional (4-D) STEM data to be obtained and accurately reconstructed with both high-speed and reduced electron fluence. The methodology needed to achieve these results compared to conventional 4-D approaches requires only that a random subset of probe locations is acquired from the typical regular scanning grid, which immediately generates both higher speed and the lower fluence experimentally. We also consider downsampling of the detector, showing that oversampling is inherent within convergent beam electron diffraction (CBED) patterns and that detector downsampling does not reduce precision but allows faster experimental data acquisition. Analysis of an experimental atomic resolution yttrium silicide dataset shows that it is possible to recover over 25 dB peak signal-to-noise ratio in the recovered phase using 0.3% of the totaldata. Lay abstract: Four-dimensional scanning transmission electron microscopy (4-D STEM) is a powerful technique for characterizing complex nanoscale structures. In this method, a convergent beam electron diffraction pattern (CBED) is acquired at each probe location during the scan of the sample. This means that a 2-dimensional signal is acquired at each 2-D probe location, equating to a 4-D dataset. Despite the recent development of fast direct electron detectors, some capable of 100kHz frame rates, the limiting factor for 4-D STEM is acquisition times in the majority of cases, where cameras will typically operate on the order of 2kHz. This means that a raster scan containing 256^2 probe locations can take on the order of 30s, approximately 100-1000 times longer than a conventional STEM imaging technique using monolithic radial detectors. As a result, 4-D STEM acquisitions can be subject to adverse effects such as drift, beam damage, and sample contamination. Recent advances in computational imaging techniques for STEM have allowed for faster acquisition speeds by way of acquiring only a random subset of probe locations from the field of view. By doing this, the acquisition time is significantly reduced, in some cases by a factor of 10-100 times. The acquired data is then processed to fill-in or inpaint the missing data, taking advantage of the inherently low-complex signals which can be linearly combined to recover the information. In this work, similar methods are demonstrated for the acquisition of 4-D STEM data, where only a random subset of CBED patterns are acquired over the raster scan. We simulate the compressive sensing acquisition method for 4-D STEM and present our findings for a variety of analysis techniques such as ptychography and differential phase contrast. Our results show that acquisition times can be significantly reduced on the order of 100-300 times, therefore improving existing frame rates, as well as further reducing the electron fluence beyond just using a faster camera.

A sonication-filtration method for acquisition of integrated bacterial communities from phycosphere

Bacteria in phycosphere engage in intricate interactions with microalgae by exchanging organic and inorganic matter. However, elucidating the primary roles of bacteria in phycosphere has been a big challenge, due to the lack of adequate methods for separating tightly associated bacteria from microalgal cells. In this study we evaluated several isolation methods including centrifugation, filtration, sonication combined with filtration, and tween lysis followed by sonication and filtration, aiming to efficiently acquire complete bacterial communities from phycosphere. The results demonstrated that the sonication-filtration approach maximally preserves the original characteristics of the bacterial communities. This method will facilitate the acquisition and further analysis of future experimental data.

Shedding Light on the Failure Factors of Subsea Critical Fastener Bolts

Abstract Offshore facilities, such as oil and gas rigs, wind turbines, and related subsea equipment, typically use flanges fastened using bolts and nuts as the main connectors. In this study, multidisciplinary parameters, namely the preload torque used to tighten bolts, simulated subsea water currents, water temperature, and impressed current cathodic protection, were applied to ASTM A193 B7 bolts. An experimental supervisory control and data acquisition system was designed to obtain measurements every 5 min throughout a 21-day experiment. Finite element analysis was performed to predict the structurally vulnerable areas of the bolts. A strong correlation was found between the reference electrode readings and the measured electrical current, tightening torque, and water temperature. As the water temperature rises during the day, the reference electrode reading becomes less negative and the electrical current decreases. Subsea water currents cause about a four-time increase in the bolt corrosion rate, with unprotected bolts suffering a nine-time-higher corrosion rate than protected bolts. A unique supply–demand interaction is observed; less protection is supplied to areas with lower corrosion rates (lower demand for protection). Finally, scanning electron microscopy examination reveals new insights into the failure mechanisms of subsea bolts.

Experimental study on the use of the ARM Cortex M7 processor for measuring far-field blast waves

AbstractThe ongoing study of blast waves and blast wave mitigation continues to play an essential role in protecting structures and personnel. The methodology, however, for capturing far-field blast waves in large-scale tests has remained largely unchanged for three decades, relying on large arrays of pressure transducers connected by hundreds of meters of cabling and requiring a considerable amount of time to set up. This paper evaluates the use of a modern low-cost microprocessor with high computational power to capture blast waves with sufficient fidelity to provide scientists and engineers with credible data. The system utilizes an ARM Cortex M7 processor as an experimental data acquisition (DAQ) system for measuring far-field blast waves in an open-air blast arena at sampling speeds of up to 1.8 Msps (megasamples per second). The experimental system’s performance was evaluated by comparing it to a traditional commercial system used for measuring blast waves. The comparison showed an average Spearman correlation coefficient r of 0.928 between the two systems, suggesting a low variance between the commercial and experimental DAQ systems. This suggests that, despite its simplicity, the experimental system is an effective and low-cost alternative for accurately measuring blast waves.

Distributed neural representations of conditioned threat in the human brain

Detecting and responding to threat engages several neural nodes including the amygdala, hippocampus, insular cortex, and medial prefrontal cortices. Recent propositions call for the integration of more distributed neural nodes that process sensory and cognitive facets related to threat. Integrative, sensitive, and reproducible distributed neural decoders for the detection and response to threat and safety have yet to be established. We combine functional MRI data across varying threat conditioning and negative affect paradigms from 1465 participants with multivariate pattern analysis to investigate distributed neural representations of threat and safety. The trained decoders sensitively and specifically distinguish between threat and safety cues across multiple datasets. We further show that many neural nodes dynamically shift representations between threat and safety. Our results establish reproducible decoders that integrate neural circuits, merging the well-characterized ‘threat circuit’ with sensory and cognitive nodes, discriminating threat from safety regardless of experimental designs or data acquisition parameters.

A Three-Dimensional Deformation Monitoring Method: Combining Optical Deformation Monitoring Based on Regression Models and GB-SAR Interferometry.

This paper introduces a method for quantifying the three-dimensional deformation of ground targets and outlines the associated process. Initially, ground-based synthetic aperture radar was employed to monitor the radial deformation of targets, and optical equipment monitored pixel-level deformation in the vertical plane of the line of sight. Subsequently, a regression model was established to transform pixel-level deformation into two-dimensional deformation based on a fundamental length unit, and the radar deformation monitoring data were merged with the optical deformation monitoring data. Finally, the fused data underwent deformation, resulting in a comprehensive three-dimensional deformation profile of the target. Through physical data acquisition experiments, the comprehensive three-dimensional deformation of targets was obtained and compared with the actual deformations. The experimental results show that the method has a relative error of less than 10%, and monitoring accuracy is achieved at the millimeter level.

Analysis of particle dispersion and cavity formation during bulk particle water entry using fully coupled CFD-DEM-VOF approach

This paper presents the development and validation of a fully coupled computational fluid dynamics—discrete element method—volume of fluid (CFD-DEM-VOF) model to simulate the complex behavior of particle-laden flows with free surfaces. The coupling between the fluid and particle phases is established through the implemented continuity, momentum, and alpha transport equation. The coupled particle forces such as drag, pressure gradient, dense virtual mass, viscous, and interface forces are also integrated, with drag and dense virtual mass forces being dependent on local porosity. The integrated conservative alpha transport equation ensures phase volume conservation during interactions between particles and water. Additionally, we have implemented a trilinear interpolation method designed to operate on unstructured hexahedral meshes. This method has been tested for its ability to properly resolve the coupling effects in the numerical simulations, particularly in cases with a relatively low cell-size ratio. The model is validated through three distinct test cases: single particle water entry, dam break with particles, and water entry of a group of particles case. The experimental setup is built to study the dynamics of the water entry of a group of particles, where three key flow features are analyzed: the evolution of average particle velocity, cavity shape, and particle dispersion cloud profiles in water. The tests involve four different scenarios, including two different water levels (16.1 and 20.1 cm) and two different particle densities (2650 and 4000 kg/m3). High-speed videometry and particle tracking velocimetry (using ImageJ/TrackMate) methods are employed for experimental data acquisition. It is demonstrated that numerical results are in excellent agreement with theoretical predictions and experimental data. The study highlights the significance of vortices in cavity shaping and particle dispersion. The validated CFD-DEM-VOF model constitutes a robust tool for simulating particle-laden flows, contributing valuable insights into the complex interplay between particles and fluids.

Development of a cost-effective compact diode-laser-based photoacoustic sensing instrument for breast tissue diagnosis.

The photoacoustic (PA) technique, a noninvasive pump-probe technique, has found interesting applications in biomedical tissue diagnosis over the last decade. To take it a step further to clinical applications, the PA technique needs to be designed as an instrument focusing on a compact design, reducing the cost, and quickly providing a quantitative diagnosis. This work presents a design and characterization of a cost-effective, compact PA sensing instrument for biomedical tissue diagnosis. A compact laser diode case design is developed to house several laser diodes for PA excitation, and a pulsed current supply unit is also developed in-house to power the laser diodes to generate a 25ns current pulse at a frequency of 20kHz. After PA experimental data acquisition, the signal's frequency spectra were calculated to characterize the tissue quantitatively and correlated with their mechanobiological properties. The corresponding dominant frequency peak in the PA spectral response (PASR) study was low in the fibrofatty normal breast tissue , compared to the dominant frequency peak of in the fibrocystic disease tissue, which had increased glandular and stromal elements, thereby increased tissue density. The histopathological findings correlated with the PASR results, and the fibrocystic breast disease tissue exhibited a higher dominant frequency peak and energy compared to the normal breast tissue. We experimented with an in vitro PASR study of fibrocystic human breast tissues and successfully differentiated different tissue types using quantitative spectral parameters peak frequency, mean frequency, and spectral energy. This gives the potential to take this technique further for cost-effective and quick clinical applications.

Simulating the cellular context in synthetic datasets for cryo-electron tomography.

Cryo-electron tomography (cryo-ET) allows to visualize the cellular context at macromolecular level. To date, the impossibility of obtaining a reliable ground truth is limiting the application of deep learning-based image processing algorithms in this field. As a consequence, there is a growing demand of realistic synthetic datasets for training deep learning algorithms. In addition, besides assisting the acquisition and interpretation of experimental data, synthetic tomograms are used as reference models for cellular organization analysis from cellular tomograms. Current simulators in cryo-ET focus on reproducing distortions from image acquisition and tomogram reconstruction, however, they can not generate many of the low order features present in cellular tomograms. Here we propose several geometric and organization models to simulate low order cellular structures imaged by cryo-ET. Specifically, clusters of any known cytosolic or membrane bound macromolecules, membranes with different geometries as well as different filamentous structures such as microtubules or actin-like networks. Moreover, we use parametrizable stochastic models to generate a high diversity of geometries and organizations to simulate representative and generalized datasets, including very crowded environments like those observed in native cells. These models have been implemented in a multiplatform open-source Python package, including scripts to generate cryo-tomograms with adjustable sizes and resolutions. In addition, these scripts provide also distortion-free density maps besides the ground truth in different file formats for efficient access and advanced visualization. We show that such a realistic synthetic dataset can be readily used to train generalizable deep learning algorithms.

Treatment of multiple-beam X-ray diffraction in energy-dependent measurements.

During X-ray diffraction experiments on single crystals, the diffracted beam intensities may be affected by multiple-beam X-ray diffraction (MBD). This effect is particularly frequent at higher X-ray energies and for larger unit cells. The appearance of this so-called Renninger effect often impairs the interpretation of diffracted intensities. This applies in particular to energy spectra analysed in resonant experiments, since during scans of the incident photon energy these conditions are necessarily met for specific X-ray energies. This effect can be addressed by carefully avoiding multiple-beam reflection conditions at a given X-ray energy and a given position in reciprocal space. However, areas which are (nearly) free of MBD are not always available. This article presents a universal concept of data acquisition and post-processing for resonant X-ray diffraction experiments. Our concept facilitates the reliable determination of kinematic (MBD-free) resonant diffraction intensities even at relatively high energies which, in turn, enables the study of higher absorption edges. This way, the applicability of resonant diffraction, e.g. to reveal the local atomic and electronic structure or chemical environment, is extended for a vast majority of crystalline materials. The potential of this approach compared with conventional data reduction is demonstrated by the measurements of the Ta L3 edge of well studied lithium tantalate LiTaO3.

Synthetic IMU Datasets and Protocols Can Simplify Fall Detection Experiments and Optimize Sensor Configuration.

Falls represent a significant cause of injury among the elderly population. Extensive research has been devoted to the utilization of wearable IMU sensors in conjunction with machine learning techniques for fall detection. To address the challenge of acquiring costly training data, this paper presents a novel method that generates a substantial volume of synthetic IMU data with minimal actual fall experiments. First, unmarked 3D motion capture technology is employed to reconstruct human movements. Subsequently, utilizing the biomechanical simulation platform Opensim and forward kinematic methods, an ample amount of training data from various body segments can be custom generated. Synthetic IMU data was then used to train a machine learning model, achieving testing accuracies of 91.99% and 86.62% on two distinct datasets of actual fall-related IMU data. Building upon the simulation framework, this paper further optimized the single IMU attachment position and multiple IMU combinations on fall detection. The proposed method simplifies fall detection data acquisition experiments, provides novel venue for generating low cost synthetic data in scenario where acquiring data for machine learning is challenging and paves the way for customizing machine learning configurations.

Integration of Automatic Procedures for Acquisition, Processing, Storage, Analysis and Monitoring of Experimental Data in Physics Education

Integration of Automatic Procedures for Acquisition, Processing, Storage, Analysis and Monitoring of Experimental Data in Physics Education

Experimental study and comprehensive analysis of leakage characteristics of the casing flange of an aero-engine

Leakage at the casing flange in aero-engines can reduce the engine performance and endanger the normal operation of the whole aircraft. An experimental system is designed in this paper to investigate the influence of loaded parameters and structural parameters on the sealing characteristics of the casing flange in aero-engines. The system includes an experimental table subsystem, a hydraulic subsystem, an inflation and leakage measurement subsystem and an experimental data acquisition subsystem. The results show that the gas pressure has a greater influence compared to the axial force. When the gas pressure rises from 0.2 MPa to 0.5 MPa, the leakage volume increases from about 9 L/min to about 34 L/min. Among the structural parameters, the number of bolts has the greatest influence, followed by the number of layers, and the surface roughness has the least influence, with the three influencing parameters not being mutually exclusive. The benefits of improving the structural parameters to reduce the leakage volume all increase with the rise of gas pressure. The leakage volume of experiment parts with 48 bolts arranged on the 2-layer casing flange with surface roughness 1.6 and 3.2 are taken as two sets of the contract type data and fitted by the nested Genetic Algorithm. The fitted results indicate that increase of surface roughness from 1.6 to 3.2 will result in an increase of 26 L/min in the leakage volume. The conclusions from the paper can provide guidance suggestions for the sealing design of the aero-engine casing.

Modular Open-Source Design of Pyrolysis Reactor Monitoring and Control Electronics

Industrial pilot projects often rely on proprietary and expensive electronic hardware to control and monitor experiments. This raises costs and retards innovation. Open-source hardware tools exist for implementing these processes individually; however, they are not easily integrated with other designs. The Broadly Reconfigurable and Expandable Automation Device (BREAD) is a framework that provides many open-source devices which can be connected to create more complex data acquisition and control systems. This article explores the feasibility of using BREAD plug-and-play open hardware to quickly design and test monitoring and control electronics for an industrial materials processing prototype pyrolysis reactor. Generally, pilot-scale pyrolysis plants are expensive custom designed systems. The plug-and-play prototype approach was first tested by connecting it to the pyrolysis reactor and ensuring that it can measure temperature and actuate heaters and a stirring motor. Next, a single circuit board system was created and tested using the designs from the BREAD prototype to reduce the number of microcontrollers required. Both open-source control systems were capable of reliably running the pyrolysis reactor continuously, achieving equivalent performance to a state-of-the-art commercial controller with a ten-fold reduction in the overall cost of control. Open-source, plug-and-play hardware provides a reliable avenue for researchers to quickly develop data acquisition and control electronics for industrial-scale experiments.

Predicting moisture penetration dynamics in paper with machine learning approach

In this work, we predicted the gradient of the deformational moisture dynamics in a sized commercial paper by observing the curl deformation in response to the one-sided water application. The deformational moisture is a part of the applied liquid which ends up in the fibers causing swelling and subsequent mechanical response of the entire fiber network structure. The adapted approach combines traditional experimental procedures, advanced machine learning techniques and continuum modeling to provide insights into the complex phenomenon relevant to ink-jet digital printing in which the sized and coated paper is often used, meaning that not all the applied moisture will reach the fibers. Key material properties including elasticity, plastic parameters, viscoelasticity, creep, moisture dependent behavior, along with hygroexpansion coefficients are identified through extensive testing, providing vital data for subsequent simulation using a continuum model. Two machine learning models, a Feedforward Neural Network (FNN) and a Recurrent Neural Network (RNN), are probed in this study. Both models are trained using exclusively numerically generated moisture profile histories, showcasing the value of such data in contexts where experimental data acquisition is challenging. These two models are subsequently utilized to predict moisture profile history based on curl experimental measurements, with the RNN demonstrating superior accuracy due to its ability to account for temporal dependencies. The predicted moisture profiles are used as inputs for the continuum model to simulate the associated curl response comparing it to the experiment representing “never seen” data. The result of comparison shows highly predictive capability of the RNN. This study melds traditional experimental methods and innovative machine learning techniques, providing a robust technique for predicting moisture gradient dynamics that can be used for both optimizing the ink solution and paper structure to achieve desirable printing quality with lowest curl propensities during printing.

Analytical estimation of the signal to noise ratio efficiency in axion dark matter searches using a Savitzky-Golay filter

The signal to noise ratio efficiency ϵSNR in axion dark matter searches has been estimated using large-statistic simulation data reflecting the background information and the expected axion signal power obtained from a real experiment. This usually requires a lot of computing time even with the assistance of powerful computing resources. Employing a Savitzky-Golay filter for background subtraction, in this work, we estimated a fully analytical ϵSNR without relying on large-statistic simulation data, but only with an arbitrary axion mass and the relevant signal shape information. Hence, our work can provide ϵSNR using minimal computing time and resources prior to the acquisition of experimental data, without the detailed information that has to be obtained from real experiments. Axion haloscope searches have been observing the coincidence that the frequency independent scale factor ξ is approximately consistent with the ϵSNR. This was confirmed analytically in this work, when the window length of the Savitzky-Golay filter is reasonably wide enough, i.e., at least 5 times the signal window.

Artificial intelligence modelling human mental fatigue: A comprehensive survey

Mental fatigue refers to the decline in cognitive abilities that can occur as a result of prolonged mental exertion. Neuroscientists have been studying mental fatigue for a while. They clearly understand some underlying mechanisms of mental fatigue, such as brain chemistry and neural activity changes. However, defining mental fatigue is still an open research question. Despite this, neuroscience and cognitive psychology has made significant progress in understanding the causes and consequences of mental fatigue. In contrast, computer scientists presumably have a limited understanding of mental fatigue. This lack of understanding leads to inadequate models of mental fatigue in computer science. However, the ever evolving field of artificial intelligence (AI) shows a great potential to answer the open challenges in mental fatigue modelling. For instance, AI with fuzzy rules, machine learning or deep learning algorithms, as well as the methods of model explanation can be a valuable tool for creating accurate models of mental fatigue. Artificial intelligence models can learn from large amounts of data and accurately predict mental fatigue. However, modelling efforts are often more focused on acquiring parameters than studying and validating them within the model. Models developed in this way suffer problems with reliability making it challenging to understand the underlying causes of mental fatigue. Therefore, it is essential balance between these parameters’ correct acquisition, validation and interaction to create more accurate models of mental fatigue. In our survey, we have observed that an unspecified modelling impact the model at four scale : experimental design, data acquisition and processing, choice of indicators or parameters and reasoning. We provide some useful guidelines through criticisms for modelling, which would be closer to reality.

Computational investigation of molecular structure, spectral analysis, PES study and molecular docking studies of 4-(butan-2-ylideneamino) benzenesulfonamide

The compound 4-(butan-2-ylideneamino)benzenesulfonamide (EMKNI) was subjected to spectroscopic investigations, both experimental and theoretical in nature. The research study was conducted at two distinct levels. The initial stage involved the acquisition of experimental data, including FT-IR, NMR (1H, 13C), absorption (UV) and fluorescence spectral data. The subsequent stage involved the execution of theoretical calculations (UV) at the TD-DFT/WB97XD/cc-pVDZ basis set. The vibrational analysis was done for each vibrational modes using potential energy vibrational analysis (PED). A comparison was made between the theoretical NMR and the corresponding experimental data. In NLO study the water solvent showed highest NLO property, due to solvent effect. The emission study was done and showed two different wavelengths. A TD-DFT was made between water, DMSO and gas phase and experimental absorption wavelengths (λ) made in DMSO solvent. Furthermore, the FMO and MEP study were made using the different solvents such as water, DMSO and gas phase. The topological analysis studies were done such as ELF, LOL and RDG. The docking study was done and identify the highest binding energy score.

Establishment and validation of a relationship model between nozzle experiments and CFD results based on convolutional neural network

The acquisition of experimental data in a supersonic wind tunnel often faces challenges of complexity and high costs. Furthermore, there are limitations in the control of experimental conditions and measurement techniques. In this study, experiments were conducted on the start-up and shutdown processes of a single expansion ramp nozzle (SERN), and a dataset was established in conjunction with computational fluid dynamics (CFD). By utilizing experimental data and CFD simulation results, we employed a deep learning-based approach, utilizing one-dimensional convolutional neural networks (CNN), to establish a relationship model between the experimental and CFD results, with the aim of using CFD results to predict experimental outcomes. We provide detailed descriptions of the experimental methods, numerical simulation methods, and CNN model, and conduct training and testing of the model. The results demonstrate that the CNN model is capable of accurately predicting the wall pressure distribution of the SERN, with low prediction errors. In interpolation and extrapolation cases, the model exhibits good accuracy and generalization ability, with coefficients of determination above 96%. Compared to CFD calculations, the CNN model proves to be more reliable and accurate in predicting SERN performance. Additionally, we establish a comparative model that does not utilize CFD data to verify the superiority of the proposed CNN model. The results show that the CNN model with CFD data improves performance on average by 14%. This approach reduces reliance on experiments to a certain extent, lowering experimental costs and complexity. It not only saves the resources and time required for experiments but also compensates for the limitations in controlling experimental conditions and measurement techniques. Therefore, this research provides a viable solution to overcome the challenges and limitations of acquiring experimental data.

Modeling of diesel particulate filter temperature dynamics during exotherm using neural networks

Diesel Particulate Filter (DPF) in the diesel engine exhaust stream needs frequent regeneration (exotherm) to remove captured particulate matter (PM, or soot) without damaging to the porous DPF structure by controlling the peak temperatures and temperature gradients across the DPF. In this study, temperature distribution in a DPF is measured at 42 strategic locations in the test DPF under various regeneration conditions of exhaust flow rates, regeneration temperatures and soot loads. Then a data-based model with feed-forward neural network architecture is designed to model the thermal gradients and temperature dynamics of the DPF during the regeneration process. The neural network feature vector selection, network architecture, hyperparameter calibration process, measured data preprocessing, and experimental data acquisition procedure are evaluated. Over 7,400 experimental data points at various regeneration temperatures, flow rates and soot loads are used in training and validating the neural network model. It is found that the neural network model can accurately predict the 42 DPF bed temperatures simultaneously at different locations, and the time series analysis of both model-predicted and experimentally measured temperatures shows a good correlation. This indicates that the currently developed neural network model can provide spatial distribution of temperature in the DPF, and comprehend the nonlinearity of the temperature dynamics due to DPF soot load at exothermic conditions. These results demonstrate that the data-based model has capability in predicting thermal gradients within a DPF, aiding in determining a safer DPF regeneration strategy, onboard diagnostics and DPF development.