Time-dependent Data Research Articles

Parametric optimization of Cu(II) removal process by a metakaolin-based geopolymer: Batch and continuous process design

In the comprehensive research, the uptake of Cu(II) ions from contaminated water was explored using metakaolin-based geopolymer (MKG). The effects of contact time, adsorbent amount, initial pH, temperature, stirring speed, flow rate, column diameter, and adsorbate volume on the Cu(II) removal yield of MKG in the batch and column treatment systems were examined by employing Box-Behnken Design (BBD) approach. Cu(II) ions uptake was achieved at the optimum batch conditions of agitation speed: 160 rpm, temperature: ∼20 °C, initial pH: 5.3, contact time: 50 min, adsorbent amount: 0.04 g, with 95.02% removal yield. ANOVA results indicated that pH and adsorbent dosage were the most efficient Cu(II) adsorption parameters in the batch system. The PSO kinetic model could better describe the time-dependent data. Equilibrium adsorption data of Cu(II) showed a good adaptation with Langmuir isotherm with the maximum monolayer adsorption capacity of 1.36 × 10−3 mol g−1 (86.6 mg g−1). Breakthrough capacity at the exhausted point was computed as 42.2 mg g–1. The interaction mechanism between MKG and Cu(II) ions was tried to examine via zeta potential analysis, SEM/EDX, IR, XRD, and BET techniques. All the results have proven that MKG is a recoverable, cost-effective, and environment-friendly adsorbent for the removal of Cu(II) ions from aqueous solutions.

ANN-Based Dynamic Prediction of Daily Ground Settlement of Foundation Pit Considering Time-Dependent Influence Factors

Accurately and dynamically predicting ground settlements during the construction of foundation pits is pivotal to the understanding of the potential risk of foundation pits and, therefore, enables constructors to take timely and effective actions to ensure the construction safety of foundation pits. Existing settlement prediction methods mainly focus on the prediction of the maximum ground settlements based on static influence factors, such as soil properties and the geometry of foundation pits. However, these methods are unable to be applied to the prediction of daily ground settlements in a direct way because daily ground settlements can be affected by many time-dependent influence factors, and an accurate prediction of daily ground settlements should take into consideration such factors. To address this problem, this paper proposes an artificial neural network-based daily ground settlement prediction method, where both static and time-dependent influence factors, as well as previous settlement monitoring data, are considered in the optimum artificial neural network. The proposed method is validated using data collected from a real cut-and-cover highway tunnel project in western Hangzhou, China. The results demonstrate that time-dependent influence factors and previous settlement monitoring data play vital roles in establishing an optimum artificial neural network for the accurate prediction of daily ground settlement.

Ffowcs Williams – Hawkings analogy for near-field acoustic sources analysis

The paper expands the scope of applying the Ffowcs Williams – Hawkings integration method. We propose using the acoustic field generated from time-dependent data stored on the FW-H control surface as the same common field for computational acoustic beamforming and dynamic mode decomposition methods to analyze the aerodynamic noise sources. We exemplify that it leads to obtaining mutually consistent and complementary information for reliable prediction of acoustic sources characteristics in the process of inverting data produced by a CFD simulation. Moreover, as the results of applying computational acoustic beamforming and dynamic mode decomposition methods depend on many geometric and algorithmic inputs, the proposed approach makes it possible to use various sets of the latter for a comprehensive analysis of obtained inversions and to form the final answer by an averaging procedure. We illustrate this by taking advantage of fast generating the examined acoustic field snapshots in any required region by the FW-H integration method for the recently developed new inverse computational acoustic beamforming algorithm and the standard dynamic mode decomposition method when carrying out a sensitivity study of the predicted acoustic source. The capabilities of the developed approach are demonstrated on the data of CFD scale-resolving simulation of turbulent flow over the 30P30N high-lift configuration.

Mass Balance and Compositional Analysis of Biomass Outputs from Cacao Fruits.

The global chocolate value chain is based exclusively on cacao beans (CBs). With few exceptions, most CBs traded worldwide are produced under a linear economy model, where only 8 to 10% of the biomass ends up in chocolate-related products. This contribution reports the mass balance and composition dynamics of cacao fruit biomass outputs throughout one full year of the crop cycle. This information is relevant because future biorefinery developments and the efficient use of cacao fruits will depend on reliable, robust, and time-dependent compositional and mass balance data. Cacao husk (CH), beans (CBs), and placenta (CP) constitute, as dry weight, 8.92 ± 0.90 wt %, 8.87 ± 0.52 wt %, and 0.57 ± 0.05 wt % of the cacao fruit, respectively, while moisture makes up most of the biomass weight (71.6 ± 2.29 wt %). CH and CP are solid lignocellulosic outputs. Interestingly, the highest cellulose and lignin contents in CH coincide with cacao’s primary harvest season (October to January). CB contains carbohydrates, fats, protein, ash, and phenolic compounds. The total polyphenol content in CBs is time-dependent, reaching maxima values during the harvest seasons. In addition, the fruit contains 4.13 ± 0.80 wt % of CME, a sugar- and nutrient-rich liquid output, with an average of 20 wt % of simple sugars (glucose, fructose, and sucrose), in addition to minerals (mainly K and Ca) and proteins. The total carbohydrate content in CME changes dramatically throughout the year, with a minimum of 10 wt % from August to January and a maximum of 29 wt % in March.

"Book Music" Representation for Temporal Data, as a Part of the Feature Extraction Process: A Novel Approach to Improve the Handling of Time-Dependent Data in Secondary Use of Healthcare Structured Data.

Book music is extensively used in street organs. It consists of thick cardboard, containing perforated holes specifying the musical notes. We propose to represent clinical time-dependent data in a tabular form inspired from this principle. The sheet represents a statistical individual, each row represents a binary time-dependent variable, and each hole denotes the "true" value. Data from electronic health records or nationwide medical-administrative databases can then be represented: demographics, patient flow, drugs, laboratory results, diagnoses, and procedures. This data representation is suitable for survival analysis (e.g., Cox model with repeated outcomes and changing covariates) and different types of temporal association rules. Quantitative continuous variables can be discretized, as in clinical studies. The "book music" approach could become an intermediary step in feature extraction from structured data. It would enable to better account for time in analyses, notably for historical cohort analyses based on healthcare data reuse.

Nonparametric estimation of functional dynamic factor model

Data can be assumed to be continuous functions defined on an infinite-dimensional space for many phenomena. However, the infinite-dimensional data might be driven by a small number of latent variables. Hence, factor models are relevant for functional data. In this paper, we study functional factor models for time-dependent functional data. We propose nonparametric estimators under stationary and nonstationary processes. We obtain estimators that consider the time-dependence property. Specifically, we use the information contained in the covariances at different lags. We show that the proposed estimators are consistent. Through Monte Carlo simulations, we find that our methodology outperforms estimators based on functional principal components. We also apply our methodology to monthly yield curves. In general, the suitable integration of time-dependent information improves the estimation of the latent factors.

Fluorescence signatures of SARS-CoV-2 spike S1 proteins and a human ACE-2: excitation-emission maps and fluorescence lifetimes.

.Significance: Fast and reliable detection of infectious SARS-CoV-2 virus loads is an important issue. Fluorescence spectroscopy is a sensitive tool to do so in clean environments. This presumes a comprehensive knowledge of fluorescence data.Aim: We aim at providing fully featured information on wavelength and time-dependent data of the fluorescence of the SARS-CoV-2 spike protein S1 subunit, its receptor-binding domain (RBD), and the human angiotensin-converting enzyme 2, especially with respect to possible optical detection schemes.Approach: Spectrally resolved excitation-emission maps of the involved proteins and measurements of fluorescence lifetimes were recorded for excitations from 220 to 295 nm. The fluorescence decay times were extracted by using a biexponential kinetic approach. The binding process in the SARS-CoV-2 RBD was likewise examined for spectroscopic changes.Results: Distinct spectral features for each protein are pointed out in relevant spectra extracted from the excitation-emission maps. We also identify minor spectroscopic changes under the binding process. The decay times in the biexponential model are found to be and .Conclusions: Specific material data serve as an important background information for the design of optical detection and testing methods for SARS-CoV-2 loaded media.

Co-clustering of evolving count matrices with the dynamic latent block model: application to pharmacovigilance

The simultaneous clustering of observations and features of datasets (known as co-clustering) has recently emerged as a central topic in machine learning applications. However, most models focus on continuous data in stationary scenarios, where cluster assignments do not evolve over time. We propose in this paper the dynamic latent block model (dLBM), which extends the classical binary latent block model, making amenable such analysis to dynamic cases where data are counts. Our approach operates on temporal count matrices allowing to detect abrupt changes in the way existing clusters interact with each other. The time breaks detection is performed through clustering of time instants that allows for better model parsimony. The time-dependent counting data are modeled via non-homogeneous Poisson processes (HHPPs), conditionally to the latent variables. In order to handle the model inference, we rely on a SEM-Gibbs algorithm and the ICL criterion is used for model selection. Numerical experiments on simulated data highlight the main features of the proposed approach and show the interest of dLBM with respect to related works. An application to adverse drug reaction in pharmacovigilance is also proposed, where dLBM was able to recognize clusters in a meaningful way that identified safety events that were consistent with retrospective knowledge. Hence, our aim is to propose this dynamic co-clustering method as a tool for automatic safety signal detection, to support medical authorities.

Inflated Cone Experiment for High-Throughput Characterization of Time-Dependent Polymer Membranes

Abstract Long-term behavior of polymer membranes in the nonlinear regime is probed by strain histories under various stress levels. Current characterization methods for polymer membranes impose a uniform stress field and hence require a series of long-duration tests to be conducted, which poses a significant experimental challenge. Here, we present the inflated cone method to generate a continuous spectrum of strain histories under various stresses in a single experiment. By imposing a known stress gradient and utilizing a full-field strain measurement technique, the inflated cone method provides a high-throughput approach for extracting time-dependent data of polymer membranes. The method is suitable for studying nonlinear time-dependent deformations under a biaxial stress state. The stress range and ratio can be easily modulated by cone geometry design. We demonstrate the utility of the method through creep-recovery tests carried out on a polyethylene thin film. The proposed experimental method is highly beneficial for the development of nonlinear viscoelastic and viscoplastic models.

A Simplified Approach to Understanding Body Cooling Behavior and Estimating the Postmortem Interval

Ascertaining the postmortem interval or PMI has been an item of interest over many decades in forensic science for crime scene investigations. The challenge revolves around establishing the postmortem interval or PMI with a single temperature measurement, given the known initial and the final boundary condition of a human body and room temperature. Despite the advent of a succession of single, double, and triple-exponential analytical models, and more recently, the 3-D heat-transfer modeling, the uncertainty remains in the PMI estimation. This study presents a pragmatic way to solve this problem in a two-step approach. First, we attempted to understand the cooling rate in various body parts. Second, we proposed a hyperbolic modeling approach to fit the time-dependent temperature data to estimate the PMI. The latest digital data of Wilk et al.’s study provided the platform for validating our solution approach. Overall, the use of 20 subsets of three bodies involving Wilk et al. and five from one body of Bartgis et al. provided the required data. Although body imaging and 3-D modeling greatly facilitate our understanding of overall body-cooling behavior in the modern era in real-time, a simple semi-analytical tool can corroborate the model results for PMI.

Simplified Method for Estimation of Mean Residual Life of Rubble-mound Breakwaters

A simplified model using the lifetime distribution has been presented to estimate the Mean Residual Life (MRL) of rubble-mound breakwaters, which is not like a stochastic process model based on time-dependent history data to the cumulative damage progress of rubble-mound breakwaters. The parameters involved in the lifetime distribution can be easily estimated by using the upper and lower limits of lifetime and their likelihood that made a judgement by several experts taking account of the initial design lifetime, the past sequences of loads, and others. The simplified model presented in this paper has been applied to the rubble-mound breakwater with TTP armor layer. Wiener Process (WP)-based stochastic model also has been applied together with Monte-Carlo Simulation (MCS) technique to the breakwater of the same condition having time-dependent cumulative damage to TTP armor layer. From the comparison of lifetime distribution obtained from each models including Mean Time To Failure (MTTF), it has found that the lifetime distributions of rubble-mound breakwater can be very satisfactorily fitted by log-normal distribution for all types of cumulative damage progresses, such as exponential, linear, and logarithmic deterioration which are feasible in the real situations. Finally, the MRL of rubble-mound breakwaters estimated by the simplified model presented in this paper have been compared with those by WP stochastic process. It can be shown that results of the presented simplified model have been identical with those of WP stochastic process until any ages in the range of MTT F regardless of the deterioration types. However, a little of differences have been seen at the ages in the neighborhood of MTTF, specially, for the linear and logarithmic deterioration of cumulative damages. For the accurate estimation of MRL of harbor structures, it may be desirable that the stochastic processes should be used to consider properly time-dependent uncertainties of damage deterioration. Nevertheless, the simplified model presented in this paper can be useful in the building of the MRL-based preventive maintenance planning for several kinds of harbor structures, because of which is not needed timedependent history data about the damage deterioration of structures as mentioned above.

Asymptotic stability of solutions to the Navier–Stokes–Fourier system driven by inhomogeneous Dirichlet boundary conditions

We consider global in time solutions of the Navier–Stokes–Fourier system describing the motion of a general compressible, viscous and heat conducting fluid far from equilibirum. Using a new concept of weak solution suitable to accommodate the inhomogeneous Dirichlet time dependent data we find sufficient conditions for the global in time weak solutions to be ultimately bounded.

A Multi-mode Power Management Method for Aircraft Convolution

In order to realize the management of the aircraft, it is suitable for the long-term management operation of the aircraft. In this paper, a multi-mode management method for aircraft based on convolution prediction is proposed. Firstly, the historical consumption data of the system over time is collected, and its idle time is sorted into time-dependent historical input data. Secondly, the consumption prediction model based on convolution is established. The historical idle time series of the systemt is input, and the system is output to predict the idle time series. Finally, according to the difference between idle time and mode switching balance time, the corresponding management multi-mode switching is performed.

Estimation of plasma properties using an extended Kalman filter with plasma global models

A physically-constrained extended Kalman filter (EKF) is applied to various zero-dimensional global models for the estimation of plasma properties using time-dependent experimental data such as the plasma density or ion flux. The capability of the EKF is demonstrated to estimate unknown system states simultaneously, such as reaction rate coefficients and the absorbed electron input power, which can be difficult, if not impossible, to measure experimentally. Global models accounting for pure argon reactions and argon-oxygen reactions are used in this work to demonstrate the ability of the filter to estimate dynamic and complex systems. The results obtained from the EKF plasma global model illustrate that model-data fusion techniques can be used to estimate plasma properties and processes for time-varying systems, such as pulsed discharges.

Dynamic analysis of dielectric elastomer membrane for actuation in soft fish robots

Soft robots often show overwhelming performances. In the present work, mathematical formulations have been derived for nonlinear dynamic analysis of axial stretch in convex tapered dielectric elastomer considering temperature dependent material properties. The present formulation is based on the Gent model of hyperelasticity and the standard rheological model. The fish tail is modeled as bimorph dielectric elastomer actuator (DEA) for propulsion of soft fish robot. Based on the obtained axial stretch of the active layer, flapping movements of fish tail have been determined. The dependency of frequency, temperature, viscoelasticity, and taper effects on the dynamic performance in the active layer of bimorph DEA is studied. Investigation of time-dependent actuation, phase portraits, Poincare maps, bifurcation diagrams, and hysteresis loop associated in this system is carried out. The results reveal that at lower temperature more actuation is attained. At low frequency, time-dependent data show very small amplitude. With the increase in height taper, it is declared that the actuation increased. Thus, it is also observed that taper effects have significant role on the axial stretch. Subsequently, with the obtained stretch, deflection of bimorph DEA is determined through mathematical formulation in order to predict the bending actuation of fish tail.

Long-Term Graft Survival and Decline in Endothelial Cell Density Following Penetrating Keratoplasty with Organ-Cultured Corneas.

IntroductionEndothelial cell density (ECD) changes long after penetrating keratoplasty (PKP) of organ-cultured corneas have been little studied. We aim to calculate the point when ECD decline stabilises following PKP with organ culture stored corneas.MethodsThis is an observational study of first-ever PKPs and first-ever re-grafts, performed over 17 years under a single surgeon. ECDs were acquired at 3 and 6 months, 1 year post-graft and annually thereafter by specular microscopy. Time-dependent ECD data was fitted to a log-biexponential model.ResultsWe studied 465 first-ever grafts and 128 re-grafts. Mean recipient age was 59 years (range 0–96 years; SD 22). Median follow-up was 5.7 (range 0.2–17.1) years. Probability of ED at 5 years in first grafts and re-grafts was 4.4% (2.6–7.1%) and 14.8% (8.3–23.2%). In first grafts, ECD loss reached 0.6% per annum at 7.9 (6.2–9.6) years post-operatively. The half-lives of ECD loss during the immediate post-operative period for first grafts, re-grafts, dystrophies, ectasias, and previous ocular surgery are 20.1 (14.9–30.9), 12.8 (6.9–79.4), 19.5 (13.1–37.7), 26.2 (16.2–68), and 11.6 (6.7–41.3) months, respectively. The half-life during this rapid phase of ECD loss has an inverse correlation with graft survival at 10 years (r = − 0.89, p = 0.02).ConclusionsRate of endothelial decompensation is higher in first grafts than re-grafts. ECD decline stabilises 7.9 years post-operatively in first grafts but then becomes lower than the physiological loss expected. Further work is needed to verify whether organ-cultured grafts reach physiological levels of ECD loss faster than hypothermically stored grafts.

NetGAM: Using generalized additive models to improve the predictive power of ecological network analyses constructed using time-series data

Ecological network analyses are used to identify potential biotic interactions between microorganisms from species abundance data. These analyses are often carried out using time-series data; however, time-series networks have unique statistical challenges. Time-dependent species abundance data can lead to species co-occurrence patterns that are not a result of direct, biotic associations and may therefore result in inaccurate network predictions. Here, we describe a generalize additive model (GAM)-based data transformation that removes time-series signals from species abundance data prior to running network analyses. Validation of the transformation was carried out by generating mock, time-series datasets, with an underlying covariance structure, running network analyses on these datasets with and without our GAM transformation, and comparing the network outputs to the known covariance structure of the simulated data. The results revealed that seasonal abundance patterns substantially decreased the accuracy of the inferred networks. In addition, the GAM transformation increased the predictive power (F1 score) of inferred ecological networks on average and improved the ability of network inference methods to capture important features of network structure. This study underscores the importance of considering temporal features when carrying out network analyses and describes a simple, effective tool that can be used to improve results.

Time-Dependent Diffusion MRI for Quantitative Microstructural Mapping of Prostate Cancer.

Background Recently developed time-dependent diffusion MRI has potential in characterizing cellular tissue microstructures; however, its value in imaging prostate cancer (PCa) remains unknown. Purpose To investigate the feasibility of time-dependent diffusion MRI-based microstructural mapping for noninvasively characterizing cellular properties of PCa and for discriminating between clinically significant PCa and clinically insignificant disease. Materials and Methods Men with a clinical suspicion of PCa were enrolled prospectively between October 2019 and August 2020. Time-dependent diffusion MRI data were acquired with pulsed and oscillating gradient diffusion MRI sequences at an equivalent diffusion time of 7.5-30 msec on a 3.0-T scanner. Time-dependent diffusion MRI-based microstructural parameters, including cell diameter, intracellular volume fraction, cellularity, and diffusivities, were estimated with a two-compartment model. These were compared for different International Society of Urological Pathology grade groups (GGs), and their performance in discriminating clinically significant PCa (GG >1) from clinically insignificant disease (benign and GG 1) was determined with a linear discriminant analysis. The fitted microstructural parameters were validated by means of correlation with histopathologic measurements. Results In the 48 enrolled men, the time-dependent diffusion MRI measurements showed that higher GG was correlated with higher intracellular volume fraction and higher cellularity (intracellular volume fraction = 0.22, 0.36, 0.34, 0.37, and 0.40 in GGs 1-5, respectively; P < .001 at one-way analysis of variance), while lower cell diameter was found at higher GGs (diameter = 23.4, 18.3, 19.2, 17.9, and 18.5 μm in GGs 1-5, respectively; P = .002). Among all measurements derived from time-dependent diffusion MRI, cellularity achieved the highest diagnostic performance, with an accuracy of 92% (44 of 48 participants) and area under the receiver operating characteristic curve of 0.96 (95% CI: 0.87, 0.99) in discriminating clinically significant PCa from clinically insignificant disease. Microstructural mapping was supported by positive correlations between time-dependent diffusion MRI-based and pathologic examination-based intracellular volume fraction (r = 0.83; P < .001). Conclusion Time-dependent diffusion MRI-based microstructural mapping correlates with pathologic findings and demonstrates promise for characterizing prostate cancer. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Chatterjee and Oto in this issue.

321 Mesenchymal Stem Cell-derived Exosomes Improve Functional Recovery in Rats After Traumatic Brain Injury with a Broad Therapeutic Window

INTRODUCTION: Mesenchymal stem cell (MSC)-derived exosomes are emerging as an important therapy in regenerative medicine with broad applications. METHODS: Male rats were injured with controlled cortical impact. In the dose-response study, a single intravenous injection of exosomes (50, 100, 200 µg/rat) or vehicle (n = 8/group) was administered 1 day after TBI. In the therapeutic window study, an intravenous injection of 100 µg exosomes or vehicle was administered on days 1, 4, or 7 after injury (n = 8/group). Neurological function was assessed using Neurological Severity Scores (NSSs), Footfault and Morris Water Maze (MWM) tests. Animals were sacrificed 35 days after TBI and tissues were analyzed using immunohistological studies. RESULTS: Compared to the vehicle, regardless of the dose and delay in treatment, exosome treatment significantly improved sensorimotor and cognitive function (NSS, MWM &amp; Footfault tests), reduced hippocampal cell loss, promoted angiogenesis and neurogenesis, and reduced neuroinflammation. Maximum therapeutic benefit was seen with 100 µg dose. The time-dependent treatment data demonstrated that although 1-day post TBI treatment provided greatest improvement, significant functional and histological benefit was also seen with the other two delayed treatments (4 &amp; 7 days post TBI). CONCLUSION: These results indicate that exosomes have a wide range of effective doses for treatment of TBI with a therapeutic window of at least 7 days post-injury. This efficacy of delayed treatment enhances the promise of exosome treatment as a potential therapeutic intervention for TBI.

Protein Dynamics to Define and Refine Disordered Protein Ensembles.

Intrinsically disordered proteins and unfolded proteins have fluctuating conformational ensembles that are fundamental to their biological function and impact protein folding, stability, and misfolding. Despite the importance of protein dynamics and conformational sampling, time-dependent data types are not fully exploited when defining and refining disordered protein ensembles. Here we introduce a computational framework using an elastic network model and normal-mode displacements to generate a dynamic disordered ensemble consistent with NMR-derived dynamics parameters, including transverse R2 relaxation rates and Lipari-Szabo order parameters (S2 values). We illustrate our approach using the unfolded state of the drkN SH3 domain to show that the dynamical ensembles give better agreement than a static ensemble for a wide range of experimental validation data including NMR chemical shifts, J-couplings, nuclear Overhauser effects, paramagnetic relaxation enhancements, residual dipolar couplings, hydrodynamic radii, single-molecule fluorescence Förster resonance energy transfer, and small-angle X-ray scattering.