Multiple Experimental Datasets Research Articles

Experimental measurement of the size distribution of microbubbles prepared by reciprocating syringe technique

Microbubbles have captured the attention of scholars due to their exceptional physical and chemical properties that exhibit practical applications. Transcranial Doppler ultrasound is a diagnostic tool used in clinical medicine to identify patent foramen ovale (PFO) heart disease caused by incomplete fusion of primary and secondary septum after birth, using microbubbles. The volume of gas and size of the bubbles determine the consequences of air entering the bloodstream. Therefore, it is imperative to investigate stable and efficient microbubble generation technology. The team designed and developed a new type of bubble generator and performed basic research on the flow and mixing rules in the gas-liquid two-phase system in the syringe. The shadow imaging method was used to explore the influence of the equipment operation parameters on the experimental system's microbubble flow process. After analyzing multiple sets of experimental data, it was found that the diameter of bubbles decreases as the frequency increases. After ten times of high-frequency reciprocating movement of the equipment, the microbubble size distribution is mainly between 10–100 μm, the mean diameter is between 23–26 μm, and the relative standard deviation is less than 4.5 %. Compared to manual methods, the device exhibits good accuracy and repeatability.

Modeling of textile composite using analytical network-averaging and gradient damage approach

In this contribution, we present a gradient damage model for anisotropic textile reinforcements including fiber inextensibility and fiber sliding. In contrast to previous works, the gradient damage formulation stems not from a numerical regularization basis but from the thermodynamics of internal variables. It results in a nonlocal term as the internal energy of fiber bending with measurable nonlocal parameter. Furthermore, to guarantee a priori that rotations and reflections determined by orthogonal tensors among the symmetry group do not affect the response function of the anisotropic constitutive law, a novel mesoscopic kinematic measure for the representative volume element of the fabric is defined on the basis of the analytical network-averaging concept. Such kinematic measure is of crucial importance for material modeling of damage-elastoplasticity in anisotropic textile reinforcements, and allows for analytical descriptions of inter- and intra-ply sliding of fibers. A mixed finite element formulation is then presented for textile reinforcements taking into account fiber inextensibility. The predictive capability of the computational model is demonstrated by comparing with multiple experimental datasets of dry textile fabrics.

Accurate neuron segmentation method for one-photon calcium imaging videos combining convolutional neural networks and clustering

One-photon fluorescent calcium imaging helps understand brain functions by recording large-scale neural activities in freely moving animals. Automatic, fast, and accurate active neuron segmentation algorithms are essential to extract and interpret information from these videos. One-photon imaging videos’ low resolution, high noise, and high background fluctuation pose significant challenges. Here, we develop a software pipeline to address the challenges of processing one-photon calcium imaging videos. We extend our previous two-photon active neuron segmentation algorithm, Shallow U-Net Neuron Segmentation (SUNS), to better suppress background fluctuations in one-photon videos. We also develop additional neuron extraction (ANE) to locate small or dim neurons missed by SUNS. To train our segmentation method, we create ground truth neurons by developing a manual labeling pipeline assisted with semi-automatic refinement. Our method is more accurate and faster than state-of-the-art techniques when processing simulated videos and multiple experimental datasets acquired over various brain regions with different imaging conditions.

A memory-dependent three-dimensional creep model for concrete

The study of creep models for concrete is of great significance in analyzing and predicting the long-term stability of concrete structures. Based on the theory of memory-dependent derivatives, memory-dependent viscous dashpots that can describe the characteristics of decaying creep and accelerating creep are constructed based on the varying characteristics of the creep process in different stages. The Kelvin body was replaced with a stable memory-dependent dashpot and the Newtonian dashpot in the visco-plastic body was transformed into an unstable memory-dependent dashpot by drawing on the component combination form of the Nishihara model. A three-dimensional concrete creep model based on memory-dependent derivatives was established. By adjusting the model parameters of stable and unstable memory-dependent viscous dashpots, the model can simulate the creep process of concrete with different decaying creep characteristics and accelerating creep characteristics, and it has good universality. Multiple sets of experimental data on concrete creep verify that the memory-dependent concrete creep model corresponds well to the experimental data. Compared with five other concrete creep models, this model has fewer parameters and higher fitting accuracy, especially in characterizing the accelerating creep characteristics of concrete.

Enhancing genome-wide populus trait prediction through deep convolutional neural networks.

As a promising model, genome-based plant breeding has greatly promoted the improvement of agronomic traits. Traditional methods typically adopt linear regression models with clear assumptions, neither obtaining the linkage between phenotype and genotype nor providing good ideas for modification. Nonlinear models are well characterized in capturing complex nonadditive effects, filling this gap under traditional methods. Taking populus as the research object, this paper constructs a deep learning method, DCNGP, which can effectively predict the traits including 65 phenotypes. The method was trained on three datasets, and compared with other four classic models-Bayesian ridge regression (BRR), Elastic Net, support vector regression, and dualCNN. The results show that DCNGP has five typical advantages in performance: strong prediction ability on multiple experimental datasets; the incorporation of batch normalization layers and Early-Stopping technology enhancing the generalization capabilities and prediction stability on test data; learning potent features from the data and thus circumventing the tedious steps of manual production; the introduction of a Gaussian Noise layer enhancing predictive capabilities in the case of inherent uncertainties or perturbations; fewer hyperparameters aiding to reduce tuning time across datasets and improve auto-search efficiency. In this way, DCNGP shows powerful predictive ability from genotype to phenotype, which provide an important theoretical reference for building more robust populus breeding programs.

Improved Equivalent Strain Method for Fatigue Life of Automobile Aluminum Alloy

Improved Equivalent Strain Method for Fatigue Life of Automobile Aluminum Alloy

SifiNet: a robust and accurate method to identify feature gene sets and annotate cells.

SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods. SifiNet can analyze both single-cell RNA and ATAC sequencing data, thereby rendering comprehensive multi-omic cellular profiles. It is conveniently available as an open-source R package.

Improved hierarchical Bayesian modeling framework with arbitrary polynomial chaos for probabilistic model updating

Bayesian finite element model updating techniques have found widespread application in the structural health monitoring. The conventional Bayesian modeling framework (CBMF) identifies the posterior distribution of structural parameters using data from a single experiment. However, structural parameters exhibit variability due to changes in environmental or experimental conditions, an aspect overlooked by CBMF. In recent years, the hierarchical Bayesian modeling framework (HBMF) has gradually attracted attention. This framework incorporates an additional layer of hyperparameters and utilizes multiple sets of experimental data to describe the variability in structural parameters caused by changes in experimental conditions. Unfortunately, the increase in hyperparameters and data size leads the HBMF to face the dilemma of exceedingly high computational costs. To address this issue, this study proposes an improved hierarchical Bayesian modeling framework (IHBMF). To enhance the computational efficiency of IHBMF, the importance sampling method is employed to simplify the computation of the likelihood function for hyperparameters. Subsequently, a surrogate model based on arbitrary polynomial chaos expansion is introduced to further reduce the computational cost of IHBMF. The framework's accuracy and efficiency were validated through analysis of a simply supported beam and a steel pedestrian bridge. The results demonstrate that IHBMF not only delivers a more accurate quantification of structural parameter uncertainty than CBMF but also exhibits higher computational efficiency compared to HBMF, showcasing its superior capability in structural health monitoring applications.

Analysis of wheel-rail adhesion redundancy considering the third-body medium on the rail surface

PurposeIn response to the problem of insufficient traction/braking adhesion force caused by the existence of the third-body medium on the rail surface, this study aims to analyze the utilization of wheel-rail adhesion coefficient under different medium conditions and propose relevant measures for reasonable and optimized utilization of adhesion to ensure the traction/braking performance and operation safety of trains.Design/methodology/approachBased on the PLS-160 wheel-rail adhesion simulation test rig, the study investigates the variation patterns of maximum utilized adhesion characteristics on the rail surface under different conditions of small creepage and large slip. Through statistical analysis of multiple sets of experimental data, the statistical distribution patterns of maximum utilized adhesion on the rail surface are obtained, and a method for analyzing wheel-rail adhesion redundancy based on normal distribution is proposed. The study analyzes the utilization of traction/braking adhesion, as well as adhesion redundancy, for different medium under small creepage and large slip conditions. Based on these findings, relevant measures for the reasonable and optimized utilization of adhesion are derived.FindingsWhen the third-body medium exists on the rail surface, the train should adopt the low-level service braking to avoid the braking skidding by extending the braking distance. Compared with the current adhesion control strategy of small creepage, adopting appropriate strategies to control the train’s adhesion coefficient near the second peak point of the adhesion coefficient-slip ratio curve in large slip can effectively improve the traction/braking adhesion redundancy and the upper limit of adhesion utilization, thereby ensuring the traction/braking performance and operation safety of the train.Originality/valueMost existing studies focus on the wheel-rail adhesion coefficient values and variation patterns under different medium conditions, without considering whether the rail surface with different medium can provide sufficient traction/braking utilized adhesion coefficient for the train. Therefore, there is a risk of traction overspeeding/braking skidding. This study analyzes whether the rail surface with different medium can provide sufficient traction/braking utilized adhesion coefficient for the train and whether there is redundancy. Based on these findings, relevant measures for the reasonable and optimized utilization of adhesion are derived to further ensure operation safety of the train.

A fuzzy track-to-track association algorithm with dynamic time warping for trajectory-level vehicle detection

Multi-source track-to-track association (TTTA), which identifies trajectories from multiple sensors or data sources of the same dynamic vehicle, is an important data fusion technique widely applied to vehicle detection in the fields of road, marine, and aviation transportation. However, issues such as time asynchrony, heterogeneous sampling intervals, and random sensing errors have posed considerable challenges to the accuracy and robustness of TTTA. Aiming to address these issues in an integrated manner, this paper proposes a TTTA algorithm that comprehensively calculates the similarity between trajectories using multiple trajectory features through dynamic time warping and Cauchy distribution degree of membership function. Multiple experimental datasets were generated by randomly sampling real AIS trajectory data into two trajectory data sources and adding random errors. The average association accuracy of all scenarios and error levels of the proposed method reached 97.33%, far higher than other benchmark methods. Experimental results demonstrated the advantage of the proposed algorithm in various TTTA scenarios, especially its robustness in intricate trajectory situations. The results also indicated that more features can maintain the stability of associations in the presence of larger random errors, and dynamic time warping can improve association accuracy in intricate scenarios. This study provides a practical solution for the problem of time asynchrony, heterogeneous sampling intervals, and random errors in multi-source trajectory data fusion, showcasing promising applications across diverse domains.

System model and performance analysis of a solid oxide fuel cell system self-humidified with anode off-gas recycling

Recycling the anode off-gas of the solid oxide fuel cells can effectively improve the system fuel utilization (Uf,sys) under a low stack fuel utilization (Uf,1), prolonging the service life and reducing the power generation cost. A compact self-humidified anode off-gas recycle (SAOR) system fueled by natural gas is proposed and analyzed. Clear mathematical relationships for key physical quantities in all system components are derived. Analytical expressions concerning the controllable parameters, Uf,sys and the recycling ratio (λ), and the controlling targets, Uf,1 and the oxygen to carbon ratio, are presented to facilitate the accurate system control for high efficiency and moderate Uf,1 and free of carbon deposition. The core system model is found to agree very well with multiple sets of available experimental data. The SAOR system performance for representative fuel flow rates and Uf,1 and for all λ are presented. The results show the SAOR system can achieve a DC efficiency similar to, but with Uf,1 10% lower than the best known non-SAOR system, demonstrating the presumed superiority of SAOR system. The SAOR system is a promising low cost design capable of achieving high efficiency and durability and should be pursuit for commercial development.

Compact and accurate chemical mechanism for methane pyrolysis with PAH growth

A reliable and compact chemical mechanism of gas-phase methane pyrolysis leading to formation of large polycyclic aromatic hydrocarbon (PAH) molecules has been developed. This model is designed for studies of carbon nanostructure synthesis such as carbon black and graphene flakes, including soot growth kinetics. Methane pyrolysis with carbon nanostructure synthesis is a two-stage process where conversion of CH4 to C2H2 precedes the growth of PAH molecules from acetylene. We present a single chemical mechanism that accurately describes both stages. We have constructed a compact and accurate chemical mechanism capable of modeling both stages of methane pyrolysis based on the ABF [1] mechanism which was expanded with the most prominent reaction pathways from the mechanism by Tao [2] for small PAH molecules and HACA pathways for larger PAH molecules, up to 37 aromatic rings. The resulting mechanism was validated through comparison to multiple available sets of experimental data. Good agreement with the experimental data for both processes was obtained. Performance of the mechanism was tested for pyrolysis of methane-rich mixtures under long residence times leading to abundant formation of PAH molecules. It is shown that the inclusion of larger PAH species (up to A37) in the chemical mechanism is important for accurate prediction of the fraction of carbon converted to PAH molecules and, correspondingly, the residual fraction of acetylene in the mixture. The mechanism file is available upon request.

Anomaly Detection in Machining Centers Based on Graph Diffusion-Hierarchical Neighbor Aggregation Networks

Inlight of the extensive utilization of automated machining centers, the operation and maintenance level and efficiency of machining centers require further enhancement. In our work, an anomaly detection model is proposed to detect the operation execution process by using the anomaly detection method of graph diffusion and graph neighbor hierarchical aggregation. In this paper, six machining center equipment states are defined and modeled, the monitoring sensors are referred to as nodes, and the connections between the sensors are represented as edges. First, we employed the graph diffusion model to enhance data quality within the sensor network model. Then, the node features were extracted using the hierarchical aggregation of neighboring nodes. Finally, after attentional connectivity, the ability of the model to learn global information was further improved. The performance of our model has been rigorously assessed using multiple experimental datasets and benchmarked against various anomaly detection techniques. The empirical findings unequivocally demonstrate the superior performance of our model, in terms of accuracy (96%) and F1 score (94), when compared to baseline models (MLP, GCN, GAT, GraphSAGE, GraphSAINT, GDC, and DiffusAL). The demonstrated effectiveness of the model underscores its versatility for a myriad of application prospects within the realm of manufacturing maintenance management.

Calibration methods to fit parameters within complex biological models.

Mathematical and computational models of biological systems are increasingly complex, typically comprised of hybrid multi-scale methods such as ordinary differential equations, partial differential equations, agent-based and rule-based models, etc. These mechanistic models concurrently simulate detail at resolutions of whole host, multi-organ, organ, tissue, cellular, molecular, and genomic dynamics. Lacking analytical and numerical methods, solving complex biological models requires iterative parameter sampling-based approaches to establish appropriate ranges of model parameters that capture corresponding experimental datasets. However, these models typically comprise large numbers of parameters and therefore large degrees of freedom. Thus, fitting these models to multiple experimental datasets over time and space presents significant challenges. In this work we undertake the task of reviewing, testing, and advancing calibration practices across models and dataset types to compare methodologies for model calibration. Evaluating the process of calibrating models includes weighing strengths and applicability of each approach as well as standardizing calibration methods. Our work compares the performance of our model agnostic Calibration Protocol (CaliPro) with approximate Bayesian computing (ABC) to highlight strengths, weaknesses, synergies, and differences among these methods. We also present next-generation updates to CaliPro. We explore several model implementations and suggest a decision tree for selecting calibration approaches to match dataset types and modeling constraints.

Revisiting the reaction scheme of slow pyrolysis of woody biomass

Existing lumped kinetic models have limited accuracy in predicting the pyrolysis behavior of different materials. There is currently no universally accepted model capable of accurately predicting pyrolysis rates and final product yields for various materials under different experimental conditions. This study aims to address this limitation by assessing the sensitivity of a widely used wood pyrolysis kinetic model across multiple sets of experimental data. The analysis reveals that the existing model falls short in accurately predicting the yields of woody biomass at higher temperatures. To overcome this, two new kinetic models were proposed that incorporate additional reactions not accounted for in conventional models. These additional reactions have impact on the formation of secondary pyrolysis phases. The first proposed model introduces a term for secondary tar formation, which takes into account the production of more stable cracked, dehydrogenated, and deoxygenated tars that typically occur at elevated pyrolysis temperatures, possibly influenced by catalysts. The second proposed model expands on this concept by incorporating terms that represent the formation of secondary gases and chars arising from the primary chars. By including these additional reactions, the model enhances its accuracy and predictive capacity for determining the pyrolytic products of various types of woody biomass.

Developing a Hybrid Algorithm Based on an Equilibrium Optimizer and an Improved Backpropagation Neural Network for Fault Warning

In today’s rapidly evolving manufacturing landscape with the advent of intelligent technologies, ensuring smooth equipment operation and fostering stable business growth rely heavily on accurate early fault detection and timely maintenance. Machine learning techniques have proven to be effective in detecting faults in modern production processes. Among various machine learning algorithms, the Backpropagation (BP) neural network is a commonly used model for fault detection. However, due to the intricacies of the BP neural network training process and the challenges posed by local minima, it has certain limitations in practical applications, which hinder its ability to meet efficiency and accuracy requirements in real-world scenarios. This paper aims to optimize BP networks and develop more effective fault warning methods. The primary contribution of this research is the proposal of a novel hybrid algorithm that combines a random wandering strategy within the main loop of an equilibrium optimizer (EO), a local search operator inspired by simulated annealing, and an adaptive learning strategy within the BP neural network. Through analysis and comparison of multiple sets of experimental data, the algorithm demonstrates exceptional accuracy and stability in fault warning tasks, effectively predicting the future operation of equipment and systems. This innovative approach not only overcomes the limitations of traditional BP neural networks, but also provides an efficient and reliable solution for fault detection and early warning in practical applications.

Improving Accuracy of Real-Time Positioning and Path Tracking by Using an Error Compensation Algorithm against Walking Modes.

Wide-range application scenarios, such as industrial, medical, rescue, etc., are in various demand for human spatial positioning technology. However, the existing MEMS-based sensor positioning methods have many problems, such as large accuracy errors, poor real-time performance and a single scene. We focused on improving the accuracy of IMU-based both feet localization and path tracing, and analyzed three traditional methods. In this paper, a planar spatial human positioning method based on high-resolution pressure insoles and IMU sensors was improved, and a real-time position compensation method for walking modes was proposed. To validate the improved method, we added two high-resolution pressure insoles to our self-developed motion capture system with a wireless sensor network (WSN) system consisting of 12 IMUs. By multi-sensor data fusion, we implemented dynamic recognition and automatic matching of compensation values for five walking modes, with real-time spatial-position calculation of the touchdown foot, enhancing the 3D accuracy of its practical positioning. Finally, we compared the proposed algorithm with three old methods by statistical analysis of multiple sets of experimental data. The experimental results show that this method has higher positioning accuracy in real-time indoor positioning and path-tracking tasks. The methodology can have more extensive and effective applications in the future.

Constitutive modeling of non-ideal isotropic materials based on a novel theoretical framework

If a plastic constitutive relation is used to simulate a metal forming process, the main concern should be its ability to predict the plastic strain parameters. At present, constitutive modelling basically relies on the properties of stress parameters, and emphasizes on how to improve the prediction accuracy of initial yield and subsequent hardening stresses. Although, corresponding to the anisotropic materials, the experimental data of plastic strain ratios are also used, the experimental plastic strains data obtained from all related experiments, in addition to the one as the equivalent plastic strain, are not employed at all. The lack of the most important experimental data in the constitutive model will inevitably weaken its ability to predict the plastic strain parameters which will determine the final shape of formed parts.To make a constructed constitutive relation that can fully reflect the experimental characteristics of materials, a new theoretical framework of constitutive model is proposed, and the application of experimental plastic strain data can be achieved with the introduction of a plastic strain-increment function. To illustrate the importance of the application of plastic strain-increment function to improve the predictive accuracy of plastic strain-increments, a new constitutive model is constructed and combined with a large amount of experimental data to verify it. Experimental verifications were carried out by some sheet metals with different anisotropic properties under uniaxial and equi-biaxial tension stress states, and also deal with multiple sets of experimental data obtained under complex tension-tension and tension-compression stress states. Discussions also include the results predicted by the models constructed based on the classical theory. Calculation examples show, the stronger the anisotropic properties of material, the greater the prediction deviation of plastic strains generated by the classical-theory-based models. Moreover, such prediction deviations will gradually increase with the accumulation of predicted plastic strain-increments. Predictive ability of the new constitutive relation is completely unimpacted by the increased degree of anisotropic features of materials. Predicted results are in good agreement with all experimental data.

Data-Driven and Machine Learning-Based Framework for Image-Guided Single-Cell Mass Spectrometry.

Improved throughput of analysis and lowered limits of detection have allowed single-cell chemical analysis to go beyond the detection of a few molecules in such volume-limited samples, enabling researchers to characterize different functional states of individual cells. Image-guided single-cell mass spectrometry leverages optical and fluorescence microscopy in the high-throughput analysis of cellular and subcellular targets. In this work, we propose DATSIGMA (DAta-driven Tools for Single-cell analysis using Image-Guided MAss spectrometry), a workflow based on data-driven and machine learning approaches for feature extraction and enhanced interpretability of complex single-cell mass spectrometry data. Here, we implemented our toolset with user-friendly programs and tested it on multiple experimental data sets that cover a wide range of biological applications, including classifying various brain cell types. Because it is open-source, it offers a high level of customization and can be easily adapted to other types of single-cell mass spectrometry data.

Detailed analysis of nucleation and subsequent δ to γ phase transformation of a high N super austenitic stainless steel

Super austenitic stainless steel (SASS) is widely used in extreme environments due to its excellent corrosion resistance and mechanical properties, and a feature of this grade is its relatively high nitrogen content. As valuable tools to study the solidification process and phase transformation, high temperature confocal microscopy (HTCM) and thermal analysis methods, such as differential thermal analysis (DTA) and differential scanning calorimetry (DSC), have been widely reported in the investigation of SASS. However, the inherent limitations of these two techniques make it difficult to explain the complex solidification behaviors of SASS, which involves multiple phases. Here, a unique design of HTCM-DTA was employed to investigate the nucleation and subsequent phase transformation of SASS. This in-house build apparatus enables contemporaneous in-situ observation of the surface and thermal analysis of the bulk thermal events. Using the novel HTCM-DTA device, we systematically revealed the four solidification modes of SASS and analyzed them in detail through thermodynamic arguments and multiple sets of experimental data under different conditions: (a) bimodal solidification with solid-state phase transformation, (b) bimodal solidification with liquid-state phase transformation, (c) pseudo-bimodal solidification, and (d) unimodal solidification. By comparing with the findings of earlier studies, the limitation of spatiotemporal separation employing HTCM and thermal analysis methods was pointed out. The results suggest that both the shielding gas and the time of retaining in the liquid state affect the N content in the melt, leading to a change of the primary nucleation phase. The cooling rate not only affects the undercooling and microstructure but also the transformation stage of δ-ferrite to γ-austenite, and when the phase transformation occurs in the liquid state it is usually accompanied by recalescence. These insights contribute understanding required for adjusting the continuous casting process of high nitrogen alloys. • In-situ observations on surface and thermal analysis of the bulk in parallel. • Solidification mode and phase transformation highly depend on shielding gas. • Four different solidification modes of S31254 were found and analyzed in detail. • The recalescence phenomenon was observed and analyzed frame by frame. • C.R. and t L influence the solidification behavior.