Time-dependent Data Research Articles

ICLOTS: open-source, artificial intelligence-enabled software for analyses of blood cells in microfluidic and microscopy-based assays

While microscopy-based cellular assays, including microfluidics, have significantly advanced over the last several decades, there has not been concurrent development of widely-accessible techniques to analyze time-dependent microscopy data incorporating phenomena such as fluid flow and dynamic cell adhesion. As such, experimentalists typically rely on error-prone and time-consuming manual analysis, resulting in lost resolution and missed opportunities for innovative metrics. We present a user-adaptable toolkit packaged into the open-source, standalone Interactive Cellular assay Labeled Observation and Tracking Software (iCLOTS). We benchmark cell adhesion, single-cell tracking, velocity profile, and multiscale microfluidic-centric applications with blood samples, the prototypical biofluid specimen. Moreover, machine learning algorithms characterize previously imperceptible data groupings from numerical outputs. Free to download/use, iCLOTS addresses a need for a field stymied by a lack of analytical tools for innovative, physiologically-relevant assays of any design, democratizing use of well-validated algorithms for all end-user biomedical researchers who would benefit from advanced computational methods.

Real-time monitoring of the thermooxidative degradation behavior of poly(acrylonitrile-butadiene-styrene) using isothermal in-situ Fourier transform infrared spectroscopy combined with principal component analysis

Real-time monitoring of the thermooxidative degradation behavior of poly(acrylonitrile-butadiene-styrene) (ABS) was enabled by isothermal in-situ Fourier transform infrared (FTIR) measurements combined with principal component analysis (PCA). By applying PCA to the complex time-dependent spectral data obtained during the aging process of ABS, containing multiple components, crucial information to draw meaningful conclusions was effectively extracted. The in-situ characterization unveiled the aging behavior of ABS at 120, 150, and 180 °C under air, which involved oxidation, chain scission, and volatilization of polybutadiene, in addition to consumption of the brominated flame retardant. The aging behavior varied notably depending on the aging temperature. No variations in the spectral intensities derived from styrene and acrylonitrile reveal that these compounds were roughly constant under the aging conditions. This newly developed technique for probing polymer aging behavior can provide a better understanding of the thermooxidative degradation mechanisms of polymers.

Classification of Human Motion Data Based on Inertial Measurement Units in Sports: A Scoping Review

Inertial measurement units (IMU) are widely used in sports applications to digitise human motion by measuring acceleration and rotational velocity in three-dimensional space. A common machine learning problem is the classification of human motion primitives from IMU data. In order to investigate the classification methods used in the existing literature and to analyse whether and how the time-dependent data structure is considered in the classification process of motion data analysis in sports, a scoping review was conducted. Based on a keyword search, articles from 2010 to 2021 were extracted, and 93 articles were relevant for data extraction. Over- and undersampling of data and data augmentation techniques were rarely used. The classification methods applied can be divided into three main branches: classic machine learning and deep learning models, threshold-based approaches, and dynamic time warping. The most often applied algorithms were support vector machines (SVM), followed by neural networks and k-nearest neighbours. In comparative works, when more than one classifier was applied, random forests, neural networks, boosting models and SVM were found to be the methods that achieved the highest accuracy. If the time-dependent data structure was taken into account, it was incorporated either within the models, for example, by using long-short-term memory models or within the feature calculation step by using rolling windows with an overlap, which was the most common method of considering the time dependency of the IMU data.

Combined Excitation and System Parameter Identification of Dynamic Systems by an Inverse Meta-Model

In the inverse problem, it is common that either the corresponding component parameters or the corresponding input signals are obtained for a given output or response. Most model-based solutions to the inverse problem involve optimization using the so-called forward model. The forward model typically comprises the mechanistic model in some form. Most commonly, inverse problems are formulated in a static setting where a wealth of theoretical results and numerical methods are available. However, there are many important dynamic applications wherein time-dependent information needs to be discerned from time-dependent data. Recently, data-based approaches, or model-free methods, have been invoked whereby feature extraction methods such as Support vector machines (SVM) and artificial neural networks (ANN) are used. Herein we develop an inverse solution for dynamic systems through easy-to-understand least-squares meta-model mathematics. The input and output training data are interchanged, so that a mixed input comprising both component parameters and discrete-time excitations can be found for a given discrete-time output. Single-value decomposition (SVD) makes any matrix inversion tractable. The inverse meta-model is compared to the optimization method and ANN using mechanistic models for fidelity, and is shown to have better accuracy and much increased speed.

1D and 2D Chaotic Time Series Prediction Using Hierarchical Reservoir Computing System

Reservoir Computing (RC) is a type of machine learning inspired by neural processes, which excels at handling complex and time-dependent data while maintaining low training costs. RC systems generate diverse reservoir states by extracting features from raw input and projecting them into a high-dimensional space. One key advantage of RC networks is that only the readout layer needs training, reducing overall training expenses. Memristors have gained popularity due to their similarities to biological synapses and compatibility with hardware implementation using various devices and systems. Chaotic events, which are highly sensitive to initial conditions, undergo drastic changes with minor adjustments. Cascade chaotic maps, in particular, possess greater chaotic properties, making them difficult to predict with memoryless devices. This study aims to predict 1D and 2D cascade chaotic time series using a memristor-based hierarchical RC system.

A novel method of determining the active drag profile in swimming via data manipulation of multiple tension force collection methods

A novel method aimed at evaluating the active drag profile during front-crawl swimming is proposed. Fourteen full trials were conducted with each trial using a stationary load cell set-up and a commercial resistance trainer to record the tension force in a rope, caused by an athlete swimming. Seven different stroke cycles in each experiment were identified for resampling time dependent data into position dependent data. Active drag was then calculated by subtracting resistance trainer force data away from the stationary load cell force data. Mean active drag values across the stroke cycle were calculated for comparison with existing methods, with mean active drag values calculated between 76 and 140 N depending on the trial. Comparing results with established active drag methods, such as the Velocity Perturbation Method (VPM), shows agreement in the magnitude of the mean active drag forces. Repeatability was investigated using one athlete, repeating the load cell set-up experiment, indicating results collected could range by 88 N depending on stroke cycle position. Variation in results is likely due to inconsistencies in swimmer technique and power output, although further investigation is required. The method outlined is proposed as a representation of the active drag profile over a full stroke cycle.

Theoretical Analysis of Exciton Wave Packet Dynamics in Polaritonic Wires.

We present a comprehensive study of the exciton wave packet evolution in disordered lossless polaritonic wires. Our simulations reveal signatures of ballistic, diffusive, and subdiffusive exciton dynamics under strong light-matter coupling and identify the typical time scales associated with the transitions between these qualitatively distinct transport phenomena. We determine optimal truncations of the matter and radiation subsystems required for generating reliable time-dependent data from computational simulations at an affordable cost. The time evolution of the photonic part of the wave function reveals that many cavity modes contribute to the dynamics in a nontrivial fashion. Hence, a sizable number of photon modes is needed to describe exciton propagation with a reasonable accuracy. We find and discuss an intriguingly common lack of dominance of the photon mode on resonance with matter in both the presence and absence of disorder. We discuss the implications of our investigations for the development of theoretical models and analysis of experiments where coherent intermolecular energy transport and static disorder play an important role.

Availability, outage, and capacity of spatially correlated, Australasian free-space optical networks

Network capacity and reliability for free space optical communication (FSOC) is strongly driven by ground station availability, which is dominated by local cloud cover causing an outage. Here, we combine remote sensing data and novel methods to provide a generalized framework for assessing and optimizing optical ground station networks. This work is guided by an example network of eight Australian and New Zealand optical communication ground stations that span approximately 60° in longitude and 20° in latitude. Utilizing time-dependent cloud cover data from five satellites, we present a detailed analysis that determines the network availability and diversity, which showed that the Australasian region is well-suited for an optical network with a 69% average site availability and low spatial cloud cover correlations. Employing methods from computational neuroscience, we provide a Monte Carlo method for sampling the joint probability distribution of site availabilities for an arbitrarily sized and point-wise correlated network of ground stations. Furthermore, we develop a general heuristic for site selection under availability and correlation optimizations and combine it with orbital propagation simulations to compare the data capacity between optimized networks and the example network. We show that the example network may be capable of providing tens of terabits per day to a low Earth orbit satellite and up to 99.97% reliability to geostationary satellites. We therefore used the Australasian region to demonstrate, to the best of our knowledge, novel, generalized tools for assessing and optimizing FSOC ground station networks, as well as the suitability of the region for hosting such a network.

Fitting Force Field Parameters to NMR Relaxation Data.

We present an approach to optimize force field parameters using time-dependent data from NMR relaxation experiments. To do so, we scan parameters in the dihedral angle potential energy terms describing the rotation of the methyl groups in proteins and compare NMR relaxation rates calculated from molecular dynamics simulations with the modified force fields to deuterium relaxation measurements of T4 lysozyme. We find that a small modification of Cγ methyl groups improves the agreement with experiments both for the protein used to optimize the force field and when validating using simulations of CI2 and ubiquitin. We also show that these improvements enable a more effective a posteriori reweighting of the MD trajectories. The resulting force field thus enables more direct comparison between simulations and side-chain NMR relaxation data and makes it possible to construct ensembles that better represent the dynamics of proteins in solution.

A survey of visualization techniques for comparing event sequences

Event sequence data is a special type of time-dependent data that captures information about the order in which discrete events occur. The time-dimension is one of the factors that makes event sequence data hard to understand. Other factors that contribute to this complexity are the dimensionality in terms of the amount of events and attributes, the frequency of events in terms of consistency and density, varying durations, and parallel occurrences. When end-users need to compare event sequences, all these characteristics need to be considered and justified. In this state-of-the-art report we review visualization techniques for the event sequence comparison task. We focus specifically on comparison, in contrast to general event sequence visualization, and review how different aspects, such as the data attributes or granularity, affect the comparison. We define a taxonomy based on five dimensions: Scale, Comparison Type, Size, Data, and Visualization & Interaction. Based on these dimensions, we provide an overview of the literature of the past 17 years. This overview is accompanied with an analysis of the strengths and weaknesses of the proposed techniques. Furthermore, we identify current gaps and research challenges in the literature.

A Multistep Sequence-to-Sequence Model With Attention LSTM Neural Networks for Industrial Soft Sensor Application

Soft sensor technology is widely used in the industries to handle highly nonlinear, dynamic, time-dependent sequence data of industrial processes for predicting the key variables associated with auxiliary process variables. Many existing soft sensor algorithms based on deep-learning are able to build complex nonlinear models but ignore the dynamic characteristics of processes. The long short-term memory (LSTM) neural network is exploited to solve the modeling issue which is related to strong time-varying features. In this paper, a novel multi-step sequence-to-sequence model based on attention LSTM (MA-LSTM) neural networks is proposed to improve the soft sensor modeling performance of industrial processes with strong dynamics and nonlinearity. The LSTM-based encoder-decoder architecture with the attention mechanism is applied to extract inherent characteristics relevant to the quality variables and capture both the long-term and short-term dependences of sequence data. Instead of the traditional sequence-to-point quality prediction architecture, the improved architecture is designed to predict multi-step quality variables, and the intermediate results are fed into an additional one-dimensional weighted convolution module to obtain accurate prediction results. The superiority of the proposed framework is demonstrated through a debutanizer column case and a sulfur recovery process.

The Role of Mobility in the Dynamics of the COVID-19 Epidemic in Andalusia

Metapopulation models have been a popular tool for the study of epidemic spread over a network of highly populated nodes (cities, provinces, countries) and have been extensively used in the context of the ongoing COVID-19 pandemic. In the present work, we revisit such a model, bearing a particular case example in mind, namely that of the region of Andalusia in Spain during the period of the summer-fall of 2020 (i.e., between the first and second pandemic waves). Our aim is to consider the possibility of incorporation of mobility across the province nodes focusing on mobile-phone time-dependent data, but also discussing the comparison for our case example with a gravity model, as well as with the dynamics in the absence of mobility. Our main finding is that mobility is key toward a quantitative understanding of the emergence of the second wave of the pandemic and that the most accurate way to capture it involves dynamic (rather than static) inclusion of time-dependent mobility matrices based on cell-phone data. Alternatives bearing no mobility are unable to capture the trends revealed by the data in the context of the metapopulation model considered herein.

Orientational Coupling of Molecules at Interfaces Revealed by Two-Dimensional Electronic-Vibrational Sum Frequency Generation (2D-EVSFG).

Photoinduced relaxation processes at interfaces are intimately related to many fields such as solar energy conversion, photocatalysis, and photosynthesis. Vibronic coupling plays a key role in the fundamental steps of the interface-related photoinduced relaxation processes. Vibronic coupling at interfaces is expected to be different from that in bulk due to the unique environment. However, vibronic coupling at interfaces has not been well understood due to the lack of experimental tools. We have recently developed a two-dimensional electronic-vibrational sum frequency generation (2D-EVSFG) for vibronic coupling at interfaces. In this work, we present orientational correlations in vibronic couplings of electronic and vibrational transition dipoles as well as the structural evolution of photoinduced excited states of molecules at interfaces with the 2D-EVSFG technique. We used malachite green molecules at the air/water interface as an example, to be compared with those in bulk revealed by 2D-EV. Together with polarized VSFG and ESHG experiments, polarized 2D-EVSFG spectra were used to extract relative orientations of an electronic transition dipole and vibrational transition dipoles at the interface. Combined with molecular dynamics calculations, time-dependent 2D-EVSFG data have demonstrated that structural evolutions of photoinduced excited states at the interface have different behaviors than those in bulk. Our results showed that photoexcitation leads to intramolecular charge transfer but no conical interactions in 25 ps. Restricted environment and orientational orderings of molecules at the interface are responsible for the unique features of vibronic coupling.

Minimizing Product Inhibition in DNA Self-Replication: Insights for Prebiotic Replication from the Role of the Enzyme in Lesion-Induced DNA Amplification.

Self-replication of nucleic acids in the absence of enzymes represents an important and poorly understood step in the origin of life as such reportedsystems are strongly hindered by product inhibition. Studying one of the few successful examples of enzymatic DNA self-replication based on a simple ligation chain reaction, lesion-induced DNA amplification (LIDA), can shed light on how this fundamental process may have originally evolved. To identify the unknown factors that lead LIDA to overcome product inhibitionwe have employed isothermal titration calorimetryand global fitting of time-dependent ligation data to characterize the individual steps of the amplification process. We find that incorporating the abasic lesion into one of the four primers substantially decreases the stability difference between the product and intermediate complexes compared with complexes without the abasic group. In the presence of T4 DNA ligase this stability gap is further reduced by two orders of magnitude revealing thatthe ligase also helps overcome product inhibition. Kinetic simulations revealthat the intermediate complex stability and the magnitude of the ligation rate constant significantly impact the rate of self-replication, suggesting that catalysts that both facilitate ligation and stabilize the intermediate complex might be a route to efficient nonenzymatic replication.

Investigating time-dependent COVID-19 pandemic mental health data: Challenges and opportunities of using panel data analysis.

Mariana Pinto da Costa and Robert Stewart provide commentary on a large prospective panel survey of mental health during the pandemic and consider the implications of such data science initiatives.

GANSim-surrogate: An integrated framework for stochastic conditional geomodelling

Stochastic conditional geomodelling requires effective integration of geological patterns and various types of data, which is crucial but challenging. To address this, we propose a deep-learning framework (GANSim-surrogate) for conditioning geomodels to static well facies data, facies probability maps, and non-spatial global features, as well as dynamic time-dependent pressure or flow rate data observed at wells. The framework consists of a Convolutional Neural Network (CNN) generator trained from GANSim (a Generative Adversarial Network-based geomodelling simulation approach), a CNN-based surrogate, and options for searching appropriate input latent vectors for the generator. The four search methods investigated are Markov Chain Monte Carlo, Iterative Ensemble Smoother, gradient descent, and gradual deformation. The framework is validated with channelized reservoirs. First, a generator is trained using GANSim to generate geological facies models; in addition, a flow simulation surrogate is trained using a physics-informed approach. Then, given well facies data, facies probability maps, global facies proportions, and dynamic bottomhole pressure data (BHP), the trained generator takes the first three static conditioning data and a latent vector as inputs and produces a random realistic facies model conditioned to the three static data. To condition to the dynamic data, the produced facies model is converted to permeability property and mapped to BHP data by the trained surrogate. Finally, the mismatch between the surrogate-produced and the observed BHP data is minimized to obtain appropriate input latent vectors which are further mapped into appropriate facies models through the generator. These facies models prove to be realistic and consistent with all of the conditioning data, and the framework is computationally efficient.

Integration of the finite complex Toda lattice with a self-consistent source

In the paper, we derive a finite complex Toda lattice with a self-consistent source. We discuss the complete integrability of the constructed systems that is based on the transformation to the spectral data of an associated finite Jacobi matrix. We show that the finite complex Toda lattice with a self-consistent source is also an important theoretical model as it is a completely integrable system. Namely, we have determined the time evolution of the spectral data for the Jacobi matrix associated with the solution of a finite complex Toda chain with a self-consistent source. Then, using the solution of the inverse spectral problem with respect to the time-dependent spectral data, we recover the time-dependent Jacobi matrix and hence the desired solution of the finite complex Toda chain with a self-consistent source. For the case N=2, explicit formulas for the solution of the problem under consideration are obtained.

Time-dependent SOLPS-ITER simulations of the tokamak plasma boundary for model predictive control using SINDy *

Time-dependent SOLPS-ITER simulations have been used to identify reduced models with the sparse identification of nonlinear dynamics (SINDy) method and develop model-predictive control of the boundary plasma state using main ion gas puff actuation. A series of gas actuation sequences are input into SOLPS-ITER to produce a dynamic response in upstream and divertor plasma quantities. The SINDy method is applied to identify reduced linear and nonlinear models for the electron density at the outboard midplane and the electron temperature at the outer divertor . Note that is not necessarily the peak value of T e along the divertor. The identified reduced models are interpretable by construction (i.e. not black box), and have the form of coupled ordinary differential equations. Despite significant noise in , the reduced models can be used to predict the response over a range of actuation levels to a maximum deviation of 0.5% in and 5%–10% in for the cases considered. Model retraining using time history data triggered by a preset error threshold is also demonstrated. A model predictive control strategy for nonlinear models is developed and used to perform feedback control of a SOLPS-ITER simulation to produce a setpoint trajectory in using the integrated plasma simulator framework. The developed techniques are general and can be applied to time-dependent data from other boundary simulations or experimental data. Ongoing work is extending the approach to model identification and control for divertor detachment, which will present transient nonlinear behavior from impurity seeding, including realistic latency and synthetic diagnostic signals derived from the full SOLPS-ITER output.

Early warning of stator winding overheating fault of water-cooled turbogenerator based on SAE-LSTM and sliding window method

Aiming at the early warning of overheating defects in the stator winding of water-cooled turbogenerators, this paper proposes a novel method based on SAE-LSTM and sliding window method by combining the Sparse Auto-Encoder (SAE) and the Long–Short Term Memory network (LSTM) with highly time dependent time series data characteristics. Firstly, the sparse auto-encoder is used to reconstruct the operation data collected by the Distributed Control System (DCS) installed on the turbogenerator to extract the data characteristics; Secondly, the LSTM prediction model optimized by attention mechanism is used to predict the outlet temperature of each slot of the stator winding of the turbogenerator under normal working condition; Then, the sliding window method is adopted to detect the stator winding overheating defect, and the alarm threshold is defined based on both the maximum mean value and maximum standard deviation of the predicted residual within the window. Finally, the proposed method is validated by using the historical DCS data of a turbo generator with stator winding overheating defect before failure,and the results show that compared with the traditional threshold warning method, the proposed method can warn the defects 85 h in advance, which provides strong support for the stable operation of the turbogenerator.

A novel in situ sample environment setup for combined small angle x-ray scattering (SAXS), wide-angle x-ray scattering (WAXS), and Fourier transform infrared spectrometer (FTIR) simultaneous measurement.

Developing the synchrotron radiation experiment method based on combined technology offers more information on the formation mechanism of new materials and their physical and chemical properties. In this study, a new small-angle x-ray scattering/ wide-angle x-ray scattering/ Fourier-transform infrared spectroscopy (SAXS/WAXS/FTIR) combined setup was established. Using this combined SAXS/WAXS/FTIR setup, x-ray and FTIR signals can be obtained simultaneously from the same sample. The in situ sample cell was designed to couple two FTIR optical paths for the attenuated total reflection and transmission modes, which greatly saved the time of adjusting and aligning the external infrared light path when switching between the two modes with good accuracy. A transistor-transistor logic circuit was used to trigger the synchronous acquisition from the IR and x-ray detectors. A special sample stage is designed, allowing access by the IR and x-ray with temperature and pressure control. The newly developed, combined setup can be used to observe the evolution of the microstructure during the synthesis of composite materials in real-time at both the atomic and molecular levels. The crystallization of polyvinylidene fluoride (PVDF) at different temperatures was observed. The time-dependent experimental data demonstrated the success of the in situ SAXS, WAXS, and FTIR study of the structural evolution, which is feasible to track the dynamic processes.