Dynamic Datasets Research Articles

Effect of soil management practice on soil phosphorus dynamics: A meta‐analysis

AbstractSoil phosphorus (P) is pivotal for crop health and growth. We collated a dataset spanning 65 studies from 19 countries to quantify the effects of both natural and anthropogenic factors on P dynamics (dataset provided). Our meta‐analysis encompassed five variables: the type of fertilizer (chemical or organic fertilizer), tillage practices (tillage or no‐till), land use (cultivated or forest), soil texture (either loam soil or sandy loam soil) and soil depth (surface or subsurface). The interplay between soil management strategies, especially those encompassing fertilizer application and tillage, and soil phosphorus dynamics is well established. Our quantitative data suggests the use of organic fertilizers relates to a significant 82% increase in soil Olsen‐P content. Additionally, while the no‐till method has resulted in only a slight 1.6% increase in Total‐P, it has brought about a remarkable 60% increase in organic phosphorus, especially NaHCO3‐Po, in contrast to conventionally tilled soils. Further, we found that the concentrations of Total‐P and Olsen‐P in surface loam soils is elevated, being 24.8% and 1023.6% higher, respectively, compared with subsurface loam soils. In sandy loam soils, these values are 35.3% and 872.2% higher, respectively. These meta‐analysis data highlight how soil management practices can cause variation in P species in soil. They not only underscore potential strategies for refined phosphorus use in diverse farming contexts but also pave the way for further research focusing on agricultural P management.

Accuracy improvement of a simulation model of a radiation portal monitor by correcting dead-time effect

Thousands of radiation portal monitors (RPMs) have been deployed around the world to detect radioactive materials that are outside of regulatory control. The detection capability of the RPMs can be evaluated by conducting a field test with various radioactive materials. However, field tests are limited because the RPMs are operated 24 h a day. The Monte Carlo N-Particle (MCNP) transport code provides an alternative. To evaluate the performance of the RPM with MCNP-based simulation model, the simulation results should be experimentally validated and the measurement result for the validation should be corrected for dead time effect. In this study, an MCNP-based RPM simulation model was developed and the accuracy of the RPM simulation model was improved by correcting dead-time effect. Static and dynamic measurements were conducted and the dead-time correction for the measurement data was factored in. With the static measurement data, the simulation model was developed and the comparing results with the static measurement data show that it satisfied an average relative difference of less than 10%. The simulation model was validated with 17 dynamic measurement datasets and the validation results show that the average relative differences are also within 10% even though the experimental conditions of the static and dynamic measurements were different.

A fast trans-lasso algorithm with penalized weighted score function

An efficient transfer learning algorithm for high-dimensional sparse logistic regression models is proposed using penalized weighted score function based on square root Lasso, which intends to prespecify the tuning parameter. Three different choices of the tuning parameter are considered in the case of fixed design matrix. With a novel weight construction, the estimator of the regression vector is showed to be consistent when the inequality with respect to the Karush-Kuhn-Tucker (KKT) optimality conditions holds with high probability and the sparsity assumption for the regression vectors is required. There is information from source data to fit target data such that with high probability tending to 1-α, which is sharper than the corresponding probability bound without using the auxiliary samples, the KKT optimality conditions hold for the asymptotic choice. To detect which sources are transferable, an efficient data-driven method is proposed, which helps avoid negative transfer in practice. Simulation studies are carried out to demonstrate the numerical performance of the proposed procedure and their superiority over some existing methods. The procedures are also illustrated by analyzing the China Migrants Dynamic Survey dataset with binary outcomes concerning the associations among different provinces.

A seismologically consistent surface rupture length model for unbounded and width-limited events

A new surface-rupture-length (SRL) relationship as a function of magnitude [Formula: see text], fault thickness, and fault dip angle is presented in this article. The objective of this study is to model the change in scaling between unbounded and width-limited ruptures. This is achieved through the use of seismological-theory-based relationships for the average displacement scaling and the aid of dynamic fault rupture simulations to constrain the rupture width scaling. The empirical data set used in the development of this relationship is composed of [Formula: see text] events ranging from [Formula: see text] 5 to [Formula: see text] and SRL 1.1 to 432 km. The dynamic rupture simulations data set includes [Formula: see text] events ranging from [Formula: see text] 4.9 to 8.2 and [Formula: see text] 1 to 655 km. For the average displacement [Formula: see text] scaling, three simple models and two composite models were evaluated. The simple average displacement models are a square root of the rupture area [Formula: see text], a down-dip width [Formula: see text], and a rupture length [Formula: see text] proportional model. The two composite models have a [Formula: see text] scaling for unbounded ruptures and transition to [Formula: see text] and [Formula: see text] scaling for width-limited events, respectively. The empirical data favors a [Formula: see text] scaling for the entire regime (unbounded and width-limited ruptures) followed by a [Formula: see text] scaling for unbounded that changes to [Formula: see text] scaling for width-limited ruptures. The selected models exhibit better predictive performance compared to linear [Formula: see text] type models, especially in the large magnitude range, which is dominated by width-limited events. A comparison with published SRL models shows consistent scaling for different fault types and tectonic environments.

Probabilistic lithium-ion battery state-of-health prediction using convolutional neural networks and Gaussian process regression

As the world accelerates its renewable energy transition, lithium-ion batteries are increasingly being used for large scale energy storage. Due to the high cost of production and replacement of large lithium-ion battery packs, accurate estimation of battery state-of-health (SOH) has become important. Accurate estimation is especially important for microgrid and transportation industries where operational batteries will be charged intermittently and incompletely. Incomplete charging scenarios provide models with inconsistent inputs, hence, cause inconsistent output accuracies, which are important to quantify in the context of dispatch controllers and predictive maintenance. In this paper, we present a model that provides a probabilistic prediction of battery SOH from periodic charging events as would be encountered in real world operations. The proposed model combines the feature recognition and many-to-one mapping abilities of the classic convolutional neural network (CNN) with the probabilistic characteristics of Gaussian Process Regression (GPR) models. The combined CNN-GPR model predicts battery SOH to less than 1% mean absolute error using random partial charge segments as input and displays its confidence in the prediction using the variance from the GPR. Additionally, the CNN-GPR is able to predict battery SOH irrespective of battery usage history; this is demonstrated by validation on dynamic data sets where the model achieves a mean absolute error of less than 1% despite only being trained on standardized degradation cycles.

Real-Time Dynamic Gesture Recognition Algorithm Based on Adaptive Information Fusion and Multi-Scale Optimization Transformer

Gesture recognition is a popular technology in the field of computer vision and an important technical mean of achieving human-computer interaction. To address problems such as the limited long-range feature extraction capability of existing dynamic gesture recognition networks based on convolutional operators, we propose a dynamic gesture recognition algorithm based on spatial pyramid pooling Transformer and optical flow information fusion. We take advantage of Transformer’s large receptive field to reduce model computation while improving the model’s ability to extract features at different scales by embedding spatial pyramid pooling. We use the optical flow algorithm with the global motion aggregation module to obtain an optical flow map of hand motion, and to extract the key frames based on the similarity minimization principle. We also design an adaptive feature fusion method to fuse the spatial and temporal features of the dual channels. Finally, we demonstrate the effectiveness of model components on model recognition enhancement through ablation experiments. We conduct training and validation on the SCUT-DHGA dynamic gesture dataset and on a dataset we collected, and we perform real-time dynamic gesture recognition tests using the trained model. The results show that our algorithm achieves high accuracy even while keeping the parameters balanced. It also achieves fast and accurate recognition of dynamic gestures in real-time tests.

A Lightweight Visual Simultaneous Localization and Mapping Method with a High Precision in Dynamic Scenes.

Currently, in most traditional VSLAM (visual SLAM) systems, static assumptions result in a low accuracy in dynamic environments, or result in a new and higher level of accuracy but at the cost of sacrificing the real-time property. In highly dynamic scenes, balancing a high accuracy and a low computational cost has become a pivotal requirement for VSLAM systems. This paper proposes a new VSLAM system, balancing the competitive demands between positioning accuracy and computational complexity and thereby further improving the overall system properties. From the perspective of accuracy, the system applies an improved lightweight target detection network to quickly detect dynamic feature points while extracting feature points at the front end of the system, and only feature points of static targets are applied for frame matching. Meanwhile, the attention mechanism is integrated into the target detection network to continuously and accurately capture dynamic factors to cope with more complex dynamic environments. From the perspective of computational expense, the lightweight network Ghostnet module is applied as the backbone network of the target detection network YOLOv5s, significantly reducing the number of model parameters and improving the overall inference speed of the algorithm. Experimental results on the TUM dynamic dataset indicate that in contrast with the ORB-SLAM3 system, the pose estimation accuracy of the system improved by 84.04%. In contrast with dynamic SLAM systems such as DS-SLAM and DVO SLAM, the system has a significantly improved positioning accuracy. In contrast with other VSLAM algorithms based on deep learning, the system has superior real-time properties while maintaining a similar accuracy index.

Explainable deep learning for tumor dynamic modeling and overall survival prediction using Neural-ODE

While tumor dynamic modeling has been widely applied to support the development of oncology drugs, there remains a need to increase predictivity, enable personalized therapy, and improve decision-making. We propose the use of Tumor Dynamic Neural-ODE (TDNODE) as a pharmacology-informed neural network to enable model discovery from longitudinal tumor size data. We show that TDNODE overcomes a key limitation of existing models in its ability to make unbiased predictions from truncated data. The encoder-decoder architecture is designed to express an underlying dynamical law that possesses the fundamental property of generalized homogeneity with respect to time. Thus, the modeling formalism enables the encoder output to be interpreted as kinetic rate metrics, with inverse time as the physical unit. We show that the generated metrics can be used to predict patients’ overall survival (OS) with high accuracy. The proposed modeling formalism provides a principled way to integrate multimodal dynamical datasets in oncology disease modeling.

Inferring free-energy barriers and kinetic rates from molecular dynamics via underdamped Langevin models.

Rare events include many of the most interesting transformation processes in condensed matter, from phase transitions to biomolecular conformational changes to chemical reactions. Access to the corresponding mechanisms, free-energy landscapes and kinetic rates can in principle be obtained by different techniques after projecting the high-dimensional atomic dynamics on one (or a few) collective variable. Even though it is well-known that the projected dynamics approximately follows - in a statistical sense - the generalized, underdamped or overdamped Langevin equations (depending on the time resolution), to date it is nontrivial to parameterize such equations starting from a limited, practically accessible amount of non-ergodic trajectories. In this work we focus on Markovian, underdamped Langevin equations, that arise naturally when considering, e.g., numerous water-solution processes at sub-picosecond resolution. After contrasting the advantages and pitfalls of different numerical approaches, we present an efficient parametrization strategy based on a limited set of molecular dynamics data, including equilibrium trajectories confined to minima and few hundreds transition path sampling-like trajectories. Employing velocity autocorrelation or memory kernel information for learning the friction and likelihood maximization for learning the free-energy landscape, we demonstrate the possibility to reconstruct accurate barriers and rates both for a benchmark system and for the interaction of carbon nanoparticles in water.

Optimization of Q.Clear reconstruction for dynamic 18F PET imaging

BackgroundQ.Clear, a Bayesian penalized likelihood reconstruction algorithm, has shown high potential in improving quantitation accuracy in PET systems. The Q.Clear algorithm controls noise during the iterative reconstruction through a β penalization factor. This study aimed to determine the optimal β-factor for accurate quantitation of dynamic PET scans.MethodsA Flangeless Esser PET Phantom with eight hollow spheres (4–25 mm) was scanned on a GE Discovery MI PET/CT system. Data were reconstructed into five sets of variable acquisition times using Q.Clear with 18 different β-factors ranging from 100 to 3500. The recovery coefficient (RC), coefficient of variation (CVRC) and root-mean-square error (RMSERC) were evaluated for the phantom data. Two male patients with recurrent glioblastoma were scanned on the same scanner using 18F-PSMA-1007. Using an irreversible two-tissue compartment model, the area under curve (AUC) and the net influx rate Ki were calculated to assess the impact of different β-factors on the pharmacokinetic analysis of clinical PET brain data.ResultsIn general, RC and CVRC decreased with increasing β-factor in the phantom data. For small spheres (< 10 mm), and in particular for short acquisition times, low β-factors resulted in high variability and an overestimation of measured activity. Increasing the β-factor improves the variability, however at a cost of underestimating the measured activity. For the clinical data, AUC decreased and Ki increased with increased β-factor; a change in β-factor from 300 to 1000 resulted in a 25.5% increase in the Ki.ConclusionIn a complex dynamic dataset with variable acquisition times, the optimal β-factor provides a balance between accuracy and precision. Based on our results, we suggest a β-factor of 300–500 for quantitation of small structures with dynamic PET imaging, while large structures may benefit from higher β-factors.Trial registrationClinicaltrials.gov, NCT03951142. Registered 5 October 2019, https://clinicaltrials.gov/ct2/show/NCT03951142. EudraCT no 2018-003229-27. Registered 26 February 2019, https://www.clinicaltrialsregister.eu/ctr-search/trial/2018-003229-27/NO.

Authentication by Keystroke Dynamics: The Influence of Typing Language

Keystroke dynamics is a biometric method that uses a subject’s typing patterns for authentication or identification. In this paper we investigate typing language as a factor influencing an individual’s keystroke dynamics. Specifically, we discern whether keystroke dynamics is contingent on the spatial arrangement of letters on the keyboard, or alternatively, whether it is influenced by the linguistic characteristics inherent to the language being used. For this purpose, we construct a new dataset called the Bilingual Keystroke Dynamics Dataset in two languages: English and Arabic. The results show that the authentication system is not contingent on the spatial arrangement of the letters, and is primarily influenced by the language being used, and a system that is used by bilingual users must take into account that each user should have two profiles created, one for each language. An average equal error rate of 0.486% was achieved when enrolling in English and testing on Arabic, and 0.475% when enrolling in Arabic and testing on English.

Dynamic displacement estimation of structures using one-dimensional convolutional neural network

For large infrastructures, dynamic displacement measurement in structures is an essential topic. However, limitations imposed by the installation location of the displacement sensor can lead to measurement difficulties. Accelerometers are characterized by easy installation, good stability and high sensitivity. For this regard, this paper proposes a structural dynamic displacement estimation method based on a one-dimensional convolutional neural network and acceleration data. It models the complex relationship between acceleration signals and dynamic displacement information. In order to verify the reliability of the proposed method, a finite element-based frame structure was created. Accelerations and displacements were collected for each node of the frame model under seismic response. Then, a dynamic displacement estimation dataset is constructed using the acceleration time series signal as features and the displacement signal at a certain moment as target. In addition, a typical neural network was used for a comparative study. The results indicated that the error of the neural network model in the dynamic displacement estimation task was 9.52 times higher than that of the one-dimensional convolutional neural network model. Meanwhile, the proposed modelling scheme has stronger noise immunity. In order to validate the utility of the proposed method, data from a real frame structure was collected. The test results showed that the proposed method has a mean square error of only 5.097 in the real dynamic displacement estimation task, which meets the engineering needs. Afterwards, the outputs of each layer in the dynamic displacement estimation model are visualized to emphasize the displacement calculation process of the convolutional neural network.

Large-scale response-aware online ANN search in dynamic datasets

Large-scale response-aware online ANN search in dynamic datasets

A Review of Data Mining, Big Data Analytics and Machine Learning Approaches

The phenomenon of economic globalization has led to the swift advancement of industries across diverse domains. Consequently, big data technology has garnered increasing interest. The generation of network data is occurring at an unparalleled pace, necessitating the intelligent processing of vast amounts of data. To fully leverage the value inherent in this data, the implementation of machine learning techniques is imperative. The objective of machine learning in a vast data setting is to identify particular rules that are concealed within dynamic, variable, multi-origin heterogeneous data, with the ultimate aim of maximizing the value of the data. The integration of big data technology and machine learning algorithms is imperative in order to identify pertinent correlations within intricate and dynamic datasets. Subsequently, computer-based data mining can be utilized to extract valuable research insights. The present study undertakes an analysis of deep learning in comparison to conventional data mining and machine learning techniques. It conducts a comparative assessment of the strengths and limitations of the traditional methods. Additionally, the study introduces the requirements of enterprises, their systems and data, the IT challenges they face, and the role of Big Data in an extended service infrastructure. This study presents an analysis of the probability and issues associated with the utilization of deep learning, including machine learning and traditional data mining techniques, in the big data analytics context.

Digitization of weather records ofSeungjeongwon Ilgi: A historical weather dynamics dataset of the Korean Peninsula in 1623–1910

AbstractHistorical weather records from Europe indicate that the Earth experienced substantial climate variability, which caused, for instance, the Little Ice Age and the global crisis in the period between the 14th and 19th centuries. However, it is still unclear how global this climate variability was because of the scarce meteorological data availability in other regions including East Asia, especially around the 17th century. In this context,Seungjeongwon Ilgi, a daily record of the Royal Secretariat of the Joseon Dynasty of Korea, is a precious source of historical meteorological records for the Korean Peninsula, as it covers 288 years of weather observations made during 1623–1910. We used the digital database ofSeungjeongwon Ilgito construct a machine‐readable weather condition dataset. To this end, we extracted valid weather information from the original weather description text and compiled them into pre‐defined weather categories. Additionally, we attempted to improve the usability of dataset by converting the reported dates in the traditional calendar system to those in the Gregorian calendar. Finally, we outlined promising implications of this dataset for meteorological and climatological studies, while describing the limitations of the dataset. Overall, future studies focusing on the climate and weather of the past could use this meteorological database for investigating long‐term climate variability. Our datasets are publicly available at10.5281/zenodo.8382243.

Post-educator relaxation in the U-shaped curve: Evidence from a panel study of Tyneside (ing)

AbstractAge-grading—a cornerstone of sociolinguistic theorizing—is hypothesized to follow a U-shaped pattern. Vernacular forms peak in adolescence, abate in middle age, and increase again in retirement, forming a vernacular tail. A complete understanding of age-grading has been hampered by a lack of empirical evidence across the entire adult trajectory and a relatively narrow understanding of speakers’ motivations to change. This paper presents data from a dynamic panel dataset of Tyneside English speakers, covering successive cohorts over the entire adult lifespan. An analysis of (ing) reveals that the U-shaped curve is occupationally niched; only professional educators demonstrate clear retrenchment followed by a tail. Drawing on educational policy research, we argue this effect is largely driven by institutional (and heavily policed) expectations of UK educational policies. We are the first to demonstrate the occupationally niched nature of the U-shaped curve and provide quantitative evidence of the effect of educational policy on linguistic production.

Saliency-Induced Moving Object Detection for Robust RGB-D Vision Navigation Under Complex Dynamic Environments

Localization in unknown environments is an essential requirement for vision navigation of robotic vehicles in intelligent transportation systems. However, moving objects in dynamic scenarios usually bring about great difficulty for robot localization, because motion estimation of robotic vehicles is disturbed by increasing feature outliers caused by moving objects. In order to improve the accuracy and robustness of robot localization, a novel saliency-induced moving object detection (SMOD) approach is proposed to filter out feature outliers for RGB-D-based simultaneous localization and mapping (SLAM) in complex dynamic workspaces. Firstly, three complementary motion saliency potentials, including motion energy (ME), spatiotemporal objectness (STO), and dynamic superpixels (DS), are modeled by fully analyzing spatial, temporal, appearance and depth cues in RGB-D inputs. They can be used to identify the dynamic objects effectively from diversely changing backgrounds. Then, a superpixel-level graph-based motion saliency (MS) measure is proposed to generate the MS map for reliable localization of the moving objects. The edge weights and background nodes on the graph are determined reasonably by fusing ME, STO, and DS, which is not vulnerable to background interferences. Furthermore, the SMOD approach is embedded into the front-end of ORB-SLAM3 as a pre-processing stage, in order to filter out feature outliers associated with the moving objects. Finally, the extensive experiments are performed to verify the accuracy and robustness of the proposed approach on the public dynamic datasets. The experimental results show that the SMOD method can detect the moving objects effectively in a variety of challenging dynamic environments, and separate the dynamic regions reliably from the irrelevant background. The data comparison demonstrates that the SMOD-SLAM navigation system can outperform other state-of-the-art dynamic visual SLAM (vSLAM) systems.

BP-SRM: A directly training algorithm for spiking neural network constructed by spike response model

Spiking neural networks (SNNs) have attracted widespread attention due to their unique bio-interpretability and low-power properties, but the non-differentiability of discrete spike sequences fired by spiking neurons brings difficulties to the learning of SNNs. Recently, surrogate gradient (SG) and back-propagation through time (BPTT) have provided an excellent idea for training SNNs constructed by leaky integrate-and-fire (LIF) neuron models. The LIF neuron model has been widely used in previous SNNs due to its simplicity and low computational cost, however, this also limits its simulation of biological neuron dynamics, reducing the biological interpretability of SNNs. In this paper, we generalize SG and BPTT to SNNs constructed by spike response model (SRM) and propose the BP-SRM algorithm. Specifically, we address why BPTT succeeded in LIF neurons-based SNNs but failed in SRM neurons-based SNNs in previous researches. Then we establish an iterative form of the SRM neuron model by selecting different state variables. Based on iterable SRM, we get the spatiotemporal dependencies between the state variables of SRM neurons-based SNNs, which allows us to derive the gradient and update weights in SNN by BP-SRM. Then we design temporal channel normalization for BP-SRM and verify the performance of the SNNs on static image dataset, dynamic image dataset and engineering dataset, including Fashion-MNIST, Weather Dataset, N-MNIST, American Sign Language Dataset and bearing fault diagnosis dataset. The experiment results indicate that BP-SRM achieves the state-of-the-art performance of SNNs.

Low-rank plus sparse joint smoothing model based on tensor singular value decomposition for dynamic MRI reconstruction

Dynamic magnetic resonance imaging (DMRI) is an important medical imaging modality, but the long imaging time limits its practical applications. This paper proposes a low-rank plus sparse joint smoothing model based on tensor singular value decomposition (T-SVD) to reconstruct DMR images from highly under-sampled k-t space data. The low-rank plus sparse tensor (ℒ+S) model decomposes the DMR data into a low-rank and sparse tensor, which naturally fits the dynamic MR images characteristics and exploits the spatiotemporal correlation of DMRI data to improve reconstruction effect. T-SVD is utilized in the ℒ+S model to maintain the intrinsic structure of the low-rank tensor and further enhance the low-rank property. In addition, considering the global multi-dimensional smoothness of the DMR images, the proposed method joint tensor total variation (TTV) constraints to utilize the smoothness of DMR images to obtain more reconstruction details while protecting the global structure. We conducted experiments on the dynamic cardiac datasets, and the experiment results show that the proposed method has superior performance to several state-of-the-art imaging methods.

Construction of a feedback control system based on CFD simulations for the 64-element Canadian SCWR

The knowledge of the dynamic behaviors of the supercritical water in the fuel bundle of the nuclear reactor is necessary for the design of the control system. For this purpose, the transient full-scale computational fluid dynamics (CFD) simulations are used to obtain the dynamic input and output data sets of the 64-element Canadian supercritical water-cooled reactor (SCWR) in this study. Subsequently, the linear dynamic models of the reactor are obtained by the system identification technique and validated by the comparison with the results from CFD simulations. Based on the linear dynamic models, a feedback control system consists of three PI/PID controllers is constructed. The objective is to regulate the system back to the design point when the reactor is subjected to disturbances. Therefore, the performance of the designed feedback control system is also evaluated in this work. It shows that the designed control system can timely minimize the deviations and return the reactor back to the desired operating condition.