High Temporal Accuracy Research Articles

Initial studies into camera-based modal force reconstruction

Much work has been done toward the reconstruction of an unknown force using measured response. Modal Force Reconstruction (MFR) attempts a solution by using measured modes and vibrations at some number of degrees of freedom. While developed by way of traditional sensors (e.g., accelerometers), the full-field nature of cameras may be exploited for such tasks. In particular, the spatial density of an image can re-establish the traditionally underdetermined problem into one that is overdetermined. Not only does this make the reconstruction well-posed, but the modal matrix being that which needs inversion establishes a niche solution in certain force reconstruction scenarios. This talk will explore the proposed theory behind camera-based MFR and present proofs of concept using analytical models. Limitations and points of error will also be discussed, helping to further establish credibility and understanding. Initial results suggest camera-based MFR to be a viable reconstruction technique with high spatial and temporal accuracy.

An enforced non-negative matrix factorization based approach towards community detection in dynamic networks

Identifying community structures within network dynamics is important for analysing the latent structure of the network, understanding the functions of the network, predicting the evolution of the network as well as detecting unusual events of the network. From various perspectives, a diversity of approaches towards dynamic community detection has been advised. However, owing to the difficulty in parameter adjustment, high temporal complexity and detection accuracy is diminishing as time slice rises; and recognizing the community composition in dynamic networks gets extremely complex. The basic models, principles, qualities, and techniques of latent factor models, as well as their various modifications, generalizations and extensions, are summed up systematically in this study which focuses on both theoretical and experimental research into latent factor models across the latest ten years. Latent factor model like non-negative matrix factorization is considered one of the most successful models for community identification which aims to uncover distributed lower dimension representation so as to reveal community node membership. These models are mostly centred on reconstructing the network from node representations while requiring the representation to have special desirable qualities (non-negativity). The purpose of this work is to provide an experimental as well as theoretical comparative analysis of the latent factor approaches employed to detect communities within dynamic networks. Parallelly we have devised the generic and improved non-negative matrix factorization-based model which will help in producing robust community detection results in dynamic networks. The results have been calculated from the experiments done in Python. Moreover our models methodology focuses on information dynamics so as to quantify the information propagation among the involved nodes unlike existing methods that considers networks first-order topological information described by its adjacency matrix without considering the information propagation between the nodes. In addition, this paper intends to create a unified, state of the art framework meant for non-negative matrix factorization conception which could be useful for future study.

RETRACTED: Monitoring system of agricultural performance based on multisensor data and cross‐validation technique in merging temporal images

AbstractSeveral approaches to forecasting agricultural production have been used across the country, but they have focused on information widely detected with technology that was not very successful. Unfortunately, because of many difficulties such as climate variables (50% global fog cover) with limited temporal accuracy, the remotely detected information necessary to predict crop production was often insufficient. As a result of these problems, existing methods of estimating agricultural production are ineffective or out of date. Several efforts have been made to overcome these challenges by combining images with high temporal accuracy but poor geographic detail. On the other hand, it is this kind of situation that is most suitable for extremely large and homogeneous agricultural areas. An innovative theoretical framework has developed that explains this absence of high‐quality satellite images. This intelligent method was built around this new theoretical framework, which incorporates its use of something like the energy equation to improve the predictions of multiple cultures. Many producers were contacted and data regarding agricultural production were obtained to validate the results of the smart technology. The excellent reliability of this intelligent method has been shown by a comparative contrast between projected crop yields and actual output in various areas.

High temporal accuracy elastic wave simulation with new time–space domain implicit staggered‐grid finite‐difference schemes

Implicit staggered-grid finite-difference methods are attractive for elastic wave modelling due to significantly enhanced spatial accuracy compared to explicit ones. However, the central-grid finite-difference operators used to approximate the temporal derivatives result in a limited accuracy in time. Temporal high-order finite-difference methods have the ability to weaken the temporal dispersion and improve the modelling stability. It is noted that the previous temporal high-order and spatial implicit finite-difference methods are all designed in the space domain for performing acoustic wave propagation. To implement 2-D elastic wave simulation with high-order accuracy both in space and time, we propose two time–space domain implicit staggered-grid finite-difference schemes, in which the spatial derivatives are approximated by the weighted average of a few extra off-axial nodes and axial nodes of the conventional cross-stencil. We derive the P- and S-wave dispersion relations of the whole elastic wave equation and estimate the finite-difference coefficients via a variable substitution-based Taylor-series expansion. Our Taylor-series expansion-based new scheme yields high-order temporal and spatial accuracy. Besides, the spatial accuracy can be further enhanced by our newly proposed linear optimization strategy, which benefits from easy implementation since we only optimize the axial spatial coefficients via a least-squares strategy and set the off-axial temporal coefficients the same as the solution of the Taylor-series expansion method. Besides, the P- and S-wave separation approach is adopted to propagate the P- and S-wavefields with the P- and S-wave dispersion relation-based finite-difference operators, respectively. Our two new schemes are more capable of suppressing the numerical dispersion and exhibit better stability performance compared to conventional one, as we will illustrate via a detailed analysis of dispersion, stability and numerical experiments. In addition, a comparison of computation times demonstrates the efficiency advantage of two new schemes since small operator lengths and large time steps are allowed.

Spatiotemporal changes in environmental DNA concentrations caused by fish spawning activity

Determining the timing and location of spawning is critical for the conservation and management of aquatic species. Environmental DNA (eDNA) analysis can be used to monitor fish spawning activity by detecting peaks in eDNA concentrations and nuclear DNA (nuDNA)/mitochondrial DNA (mtDNA) ratios; however, the duration and diffusion distance of high concentrations of eDNA produced by spawning activities in water bodies are still unknown, preventing us from making accurate and effective survey plans to monitor spawning activities. Using common carp as a target species, we conducted artificial spawning experiments to investigate the spatiotemporal changes in eDNA concentrations during spawning. The results showed that the nuDNA concentration, mtDNA concentration, and nuDNA/mtDNA ratio that were increased by spawning activity decreased at rates of 7.78 %, 5.22 %, and 2.56 %, respectively, per 0.1 h after reaching the peak. Data simulations were performed to estimate the probability of successful monitoring of carp spawning activity during repeated sampling at different times and distance intervals. The results showed that carp spawning activity over the past 24 h could be monitored by eDNA analysis. Additionally, when carp spawning activity occurs once, spawning activity can be successfully monitored by measuring the nuDNA concentration or the nuDNA/mtDNA ratio with a probability of approximately 50–75 % based on a sampling plan of sample collection every 100 m and every 24 h. Thus, the spawning activity of aquatic species can be estimated with high spatial and temporal accuracy using eDNA analysis.

Mice are Near Optimal Timers

Abstract Many conventional interval timing tasks do not contain asymmetric cost (loss) functions and thereby favor high temporal accuracy. On the other hand, asymmetric cost functions that differentially penalize/reinforce the early or late responses result in adaptive biases (shift) in timed responses due to timing uncertainty. Consequently, optimal performance in these tasks entails the normative parametrization of adaptive timing biases by the level of timing uncertainty. Differential reinforcement of response duration (DRRD) is one of these tasks that require mice to actively respond (e.g., continuously depressing a lever) for a minimum amount of time to be reinforced. The active production of a time interval by mice in DRRD differentiates this task from the differential reinforcement of low rates of responding (DRL) task as a passive waiting task that was used in earlier studies to investigate the optimality of adaptive biases in timing behavior. We tested 21 Th-Cre male mice (9 weeks old) in a DRRD task with a minimum requirement of 2 s. Mean response durations were positively biased (longer than the minimum requirement), and the extent of bias was predicted by the level of endogenous timing uncertainty. Mice nearly maximized the reward rate in this task. These results contribute to the accumulating evidence supporting optimal temporal risk assessment in non-human animals.

A generalized Eulerian-Lagrangian discontinuous Galerkin method for transport problems

We propose a generalized Eulerian-Lagrangian (GEL) discontinuous Galerkin (DG) method. The method is a generalization of the Eulerian-Lagrangian (EL) DG method for transport problems proposed in Cai et al. (2021) [5], which tracks solution along approximations to characteristics in the DG framework, allowing extra large time stepping size with stability. The newly proposed GEL DG method in this paper is motivated for solving linear hyperbolic systems with variable coefficients, where the velocity field for adjoint problems of the test functions is frozen to constant. In this paper, in a simplified scalar setting, we propose the GEL DG methodology by freezing the velocity field of adjoint problems, and by formulating the semi-discrete scheme over the space-time region partitioned by linear lines approximating characteristics. The fully-discrete schemes are obtained by method-of-lines Runge-Kutta methods. We further design flux limiters for the schemes to satisfy the discrete geometric conservation law (DGCL) and maximum principle preserving (MPP) properties. Numerical results on 1D and 2D linear transport problems are presented to demonstrate great properties of the GEL DG method. These include the high order spatial and temporal accuracy, stability with extra large time stepping size, and satisfaction of DGCL and MPP properties.

DESIGN OF GNSS NETWORKS FOR MONITORING EARTH DAMS DEFORMATIONS

The paper presents a series of results regarding the application of GNSS technology for measuring displacements to earth dams. An appropriate horizontal and vertical deformation-tracking network must be established for the rapid monitoring of earthmoving movements. Data obtained from the monitoring network can be used in the earth dam stability analysis program. Periodic tracking of earth dams using GNSS technology provides high accuracy of spatial distortions. The results of the paper show the application of GNSS technology to monitor the deformations of the Podisu earth dam located on the Sheep Valley River in Iasi County. In the first stage of research (2019 and 2020), the local geodetic tracking network was created. To monitor the dam, four control landmarks were placed on the crest of dam and seven landmarks on the downstream slope. In the second stage of the research (year 2021) the measurements of the displacements at the earth dam were performed. The GNSS monitoring method has the advantage of positioning the target quickly, statically, and with high accuracy. The measurements used fixed stations located at two points in the local geodetic network. Four Trimble GNSS receivers were used for the measurements. The resulting data was processed using the Trimble Business Centre program. The obtained results were analysed in comparison with those existing in the design documentation and technical expertise of the dam. The results highlight the presence of displacements on the crest of a dam and slopes of the dam. The final findings highlight the performance of GNSS technology in the monitoring process, where it provides millimetre-accurate accuracy with high spatial and temporal accuracy.

Design of a Hybrid Split-Delay Line for Hard X-ray Free-Electron Lasers

High repetition-rate X-ray free-electron lasers (XFELs) enable the study of fast dynamics on microsecond time scales. Split-delay lines (SDLs) further bring the time scale down to femtoseconds by splitting and delaying the XFEL pulses. Crystals and multilayers are two common types of optical elements in SDLs, offering either long delay ranges or high temporal accuracy. In this work, we introduce the design of a hybrid SDL for the coherent diffraction endstation of Shanghai High Repetition Rate XFEL and Extreme Light Facility (SHINE). It uses crystals for the first branch and multilayers for the second one, thus simultaneously offering a relatively long delay range and high temporal accuracy. Moreover, a third branch can be installed to switch the SDL to the all-crystal configuration for longer delay ranges.

REPP: A robust cross-platform solution for online sensorimotor synchronization experiments

Sensorimotor synchronization (SMS), the rhythmic coordination of perception and action, is a fundamental human skill that supports many behaviors, including music and dance (Repp, 2005; Repp & Su, 2013). Traditionally, SMS experiments have been performed in the laboratory using finger tapping paradigms, and have required equipment with high temporal fidelity to capture the asynchronies between the time of the tap and the corresponding cue event. Thus, SMS is particularly challenging to study with online research, where variability in participants’ hardware and software can introduce uncontrolled latency and jitter into recordings. Here we present REPP (Rhythm ExPeriment Platform), a novel technology for measuring SMS in online experiments that can work efficiently using the built-in microphone and speakers of standard laptop computers. In a series of calibration and behavioral experiments, we demonstrate that REPP achieves high temporal accuracy (latency and jitter within 2 ms on average), high test-retest reliability both in the laboratory (r = .87) and online (r = .80), and high concurrent validity (r = .94). We also show that REPP is fully automated and customizable, enabling researchers to monitor experiments in real time and to implement a wide variety of SMS paradigms. We discuss online methods for ensuring high recruiting efficiency and data quality, including pre-screening tests and automatic procedures for quality monitoring. REPP can therefore open new avenues for research on SMS that would be nearly impossible in the laboratory, reducing experimental costs while massively increasing the reach, scalability, and speed of data collection.

Application of neural-network-based deep-learning analysis to reveal changes of the movement parameters in rat neuromuscular disorder models

Convolutional neural networks (CNNs) used for solving computer vision tasks are now able to detect objects and features with human-level accuracy. Deep learning algorithms often require large training datasets, which limit their use in everyday laboratory applications, however recently a new generation of CNNs emerged. For assessing the movement of the selected model animals in the 3D space with high spatial and temporal accuracy, we developed a method with combining the accurate pose estimation of the DeepLabCut CNN with RGB-D depth sensing device.

Live-Cell Superresolution Imaging of Retrograde Axonal Trafficking Using Pulse-Chase Labeling in Cultured Hippocampal Neurons.

The entanglement of long axons found in cultured dissociated hippocampal neurons restricts the analysis of the machinery underlying directed axonal trafficking. Further, hippocampal neurons exhibit "en passant" presynapses that may confound the analysis of long-range retrograde axonal transport. To solve these issues, we and others have developed microfluid-based methods to specifically follow the fates of the retrograde axonal cargoes following pulse-chase labeling by super-resolution live-cell imaging, and automatically tracking their directed transport and analyzing their kinetical properties. These methods have allowed us to visualize the trafficking of fluorescently tagged signaling endosomes and autophagosomes derived from axonal terminals and resolve their localizations and movements with high spatial and temporal accuracy. In this chapter, we describe how to use a commercially available microfluidic device to enable the labeling and tracking of retrograde axonal carriers, including (1) how to culture and transfect rat hippocampal neurons in the microfluidic device; (2) how to perform pulse-chase to label specific populations of retrograde axonal carriers; and (3) how to conduct the automatic tracking and data analysis using open-source software.

Foot angular kinematics measured with inertial measurement units: A reliable criterion for real-time gait event detection

Accurate and reliable real-time detection of gait events using inertial measurement units (IMUs) is crucial for (1) developing clinically meaningful gait parameters to differentiate normal and impaired gait or (2) creating patient-tailored gait rehabilitation strategies or control of prosthetic devices using feedback from gait phases. However, most previous studies focused only on algorithms with high temporal accuracy and neglected the importance of (1) high reliability, i.e., detecting only and all true gait events, and (2) real-time implementation. Thus, in this study, we presented a novel approach for initial contact (IC) and terminal contact (TC) detection in real-time based on the measurement of the foot orientation. Unlike foot/shank angular velocity and acceleration, foot orientation provides physiologically meaningful kinematic features corresponding to our observational recognition of IC and TC, regardless of the walking modality. We conducted an experimental study to validate our algorithm, including seven participants performing four walking/running activities. By analyzing 5,555 ICs/TCs recorded during the tests, only our algorithm achieved a sensitivity and precision of 100%. Our obtained temporal accuracy (mean ± standard deviation of errors ranging from 0 ± 3 to 6 ± 5 time samples; sampling frequency: 100 Hz) was better than or comparable to those reported in the literature. Our algorithm’s performance does not depend on thresholds and gait speed/modality, and it can be used for feedback-based therapeutic gait training or real-time control of assistive or prosthetic technologies. Nevertheless, its performance for pathological gait must be validated in the future. Finally, we shared the codes and sample data on https://www.ncbl.ualberta.ca/codes.

Magnetic Microdimer as Mobile Meter for Measuring Plasma Glucose and Lipids.

With the development of designed materials and structures, a wide array of micro/nanomachines with versatile functionalities are employed for specific sensing applications. Here, we demonstrated a magnetic propelled microdimer-based point-of-care testing system, which can be used to provide the real-time data of plasma glucose and lipids relying on the motion feedback of mechanical properties. On-demand and programmable speed and direction of the microdimers can be achieved with the judicious adjustment of the external magnetic field, while their velocity and instantaneous postures provide estimation of glucose, cholesterol, and triglycerides concentrations with high temporal accuracy. Numerical simulations reveal the relationship between motility performance and surrounding liquid properties. Such technology presents a point-of-care testing (POCT) approach to adapt to biofluid measurement, which advances the development of microrobotic system in biomedical fields.

Modeling of wastewater treatment processes with hydrosludge.

The pressure for Water Resource Recovery Facilities (WRRF) operators to efficiently treat wastewater is greater than ever because of the water crisis, produced by the climate change effects and more restrictive regulations. Technicians and researchers need to evaluate WRRF performance to ensure maximum efficiency. For this purpose, numerical techniques, such as CFD, have been widely applied to the wastewater sector to model biological reactors and secondary settling tanks with high spatial and temporal accuracy. However, limitations such as complexity and learning curve prevent extending CFD usage among wastewater modeling experts. This paper presents HydroSludge, a framework that provides a series of tools that simplify the implementation of the processes and workflows in a WRRF. This work leverages HydroSludge to preprocess existing data, aid the meshing process, and perform CFD simulations. Its intuitive interface proves itself as an effective tool to increase the efficiency of wastewater treatment. PRACTITIONER POINTS: This paper introduces a software platform specifically oriented to WRRF, named HydroSludge, which provides easy access to the most widespread and leading CFD simulation software, OpenFOAM. Hydrosludge is intended to be used by WRRF operators, bringing a more wizard-like, automatic, and intuitive usage. Meshing assistance, submersible mixers, biological models, and distributed parallel computing are the most remarkable features included in HydroSludge. With the provided study cases, HydroSludge has proven to be a crucial tool for operators, managers, and researchers in WRRF.

Accuracy Assessment of UAV-Photogrammetric-Derived Products Using PPK and GCPs in Challenging Terrains: In Search of Optimized Rockfall Mapping

Unmanned aerial photogrammetric surveys are increasingly being used for mapping and studying natural hazards, such as rockfalls. Surveys using unmanned aerial vehicles (UAVs) can be performed in remote, hardly accessible, and dangerous areas, while the photogrammetric-derived products, with high spatial and temporal accuracy, can provide us with detailed information about phenomena under consideration. However, as photogrammetry commonly uses indirect georeferencing through bundle block adjustment (BBA) with ground control points (GCPs), data acquisition in the field is not only time-consuming and labor-intensive, but also extremely dangerous. Therefore, the main goal of this study was to investigate how accurate photogrammetric products can be produced by using BBA without GCPs and auxiliary data, namely using the coordinates X0, Y0 and Z0 of the camera perspective centers computed with PPK (Post-Processing Kinematic). To this end, orthomosaics and digital surface models (DSMs) were produced for three rockfall sites by using images acquired with a DJI Phantom 4 RTK and the two different BBA methods mentioned above (hereafter referred to as BBA_traditional and BBA_PPK). The accuracy of the products, in terms of the Root Mean Square Error (RMSE), was computed by using verification points (VPs). The accuracy of both BBA methods was also assessed. To test the differences between the georeferencing methods, two statistical test were used, namely a paired Student’s t-test, and a non-parametric Wilcoxon signed-rank. The results show that the accuracy of the BBA_PPK is inferior to that of BBA_traditional, with the total RMSE values for the three sites being 0.056, 0.066, and 0.305 m, respectively, compared to 0.019, 0.036 and 0.014 m obtained with BBA_traditional. The accuracies of the BBA methods are reflected in the accuracy of the orthomosaics, whose values for the BBA_PPK are 0.039, 0.043 and 0.157 m, respectively, against 0.029, 0.036 and 0.020 m obtained with the BBA_traditional. Concerning the DSM, those produced with the BBA_PPK method present accuracy values of 0.065, 0.072 and 0.261 m, respectively, against 0.038, 0.060 and 0.030 m obtained with the BBA_traditional. Even though that there are statistically significant differences between the georeferencing methods, one can state that the BBA_PPK presents a viable solution to map dangerous and exposed areas, such as rockfall transit and deposit areas, especially for applications at a regional level.

Use of multispectral and thermal satellite imagery to determine crop water requirements using SEBAL, METRIC, and SWAP models in hot and hyper-arid Oman

Hyper-arid regions, such as the Sultanate of Oman, require precise information regarding actual water application to develop efficient irrigation management. Remote sensing techniques with imagery models can be used as practical tools in irrigation practices by estimating the actual evapotranspiration (ETa) at local and regional scales. In this study, spatial and temporal changes in the ETa were evaluated by investigating the performance of three different models, including two surface energy balance models: surface energy balance algorithm for land (SEBAL) and mapping evapotranspiration at high-resolution internalized calibration (METRIC), as well as an agro-hydrological model: soil, water, atmosphere, and plant (SWAP), for the hyper-arid conditions of Oman. This study is unique in its field application, as very few studies have conducted ETa estimations using satellite imagery in the hot and hyper-arid regions of Oman. In situ meteorological data were integrated with Landsat-8 operational land multispectral imagery and thermal infrared sensor satellite images. Further, a sensitivity analysis comparing the three models' estimated ETa with in situ sap flow field measurements (SFM) and the Penman-Monteith (PM) model. The results showed that the SEBAL model overestimated the sensible heat flux compared with the METRIC model. Although the PM model showed a higher correlation (R2 = 0.87) with the SFM, the METRIC model, among all the investigated models, showed the highest correlation of R2 = 0.83. Further, the SEBAL and METRIC models overestimated the ETa, especially in the summer. Conversely, SWAP underestimated the ETa, as compared with the SFM. These results show that the METRIC model estimates the ETa with high spatial and temporal accuracy. Meanwhile, the SWAP model was the least suitable model for estimating the ETa under the hyper-arid condition of Oman. These findings will act as a reference for accurate water requirements, thereby linking water resource management and future irrigation scheduling in local hot and hyper-arid conditions.

Measurements of the unsteady flow field around beating cilia

Abstract

Competitive state of movements during planning predicts sequence performance.

Humans can learn and produce skilled movement sequences from memory, yet the nature of sequence planning is not well understood. Previous computational and neurophysiological work suggests that movements in a sequence are planned as parallel graded activations and selected for output through competition. However, the relevance of this planning pattern to sequence production fluency and accuracy, as opposed to the temporal structure of sequences, is unclear. To resolve this question, we assessed the relative availability of constituent movements behaviorally during the preparation of motor sequences from memory. In three separate multisession experiments, healthy participants were trained to retrieve and produce four-element finger press sequences with particular timing according to an abstract sequence cue. We evaluated reaction time (RT) and error rate as markers of movement availability to constituent movement probes. Our results demonstrate that longer preparation time produces more pronounced differences in availability between adjacent sequence elements, whereas no effect was found for sequence speed or temporal grouping. Further, participants with larger position-dependent differences in movement availability tended to initiate correct sequences faster and with a higher temporal accuracy. Our results suggest that competitive preactivation is established gradually during sequence planning and predicts sequence skill, rather than the temporal structure of the motor sequence.NEW & NOTEWORTHY Sequence planning is an integral part of motor sequence control. Here, we demonstrate that the competitive state of sequential movements during sequence planning can be read out behaviorally through movement probes. We show that position-dependent differences in movement availability during planning reflect sequence preparedness and skill but not the timing of the planned sequence. Behavioral access to the preparatory state of movements may serve as a marker of sequence planning capacity.

Piezoelectric nanocomposites for sonodynamic bacterial elimination and wound healing

• Au@BTO nanocomposites are used as novel sonosensitizers to generate considerable ROS under US irradiation. • Au@BTO nanocomposites have significant antibacterial efficiency under US irradiation in vitro and in vivo . • Au@BTO nanocomposites also promote the migration of fibroblasts to promote wound healing. Sonodynamic therapy is considered as a minimally invasive method for pathogen elimination and cancer therapy with high spatial and temporal accuracy. However, sonosensitizers with good biocompatibility and high efficiency are still urgently needed. Herein, we have developed a piezoelectric nanocomposite, barium titanate (BaTiO 3 , BTO) nanocubes with Schottky junction modified by Au nanoparticles (Au@BTO) as a new kind of sonosensitizer for high-efficient sonodynamic therapy. Ultrasound as an exogenous mechanical wave can trigger the piezotronic effect of Au@BTO to facilitate the separation and migration of charge carriers at the piezoelectric/metal interface, which further effectively increases reactive oxygen species (ROS) generation via redox reaction. Due to the high ROS generation efficiency, Au@BTO as the sonosensitizer shows a high antibacterial efficiency against representative Gram-negative and Gram-positive bacteria. Furthermore, the in vitro and in vivo results illustrate that the sonodynamic process also promotes fibroblast migration, which contributes to dermal wound healing in mice. The proposed piezoelectric nanocomposite as a new kind of sonosensitizer has great potential for sonodynamic therapy.