Dynamic Time Research Articles

The accuracy of forecasted hospital admission for respiratory tract infections in children aged 0-5 years for 2017/2023.

Healthcare services are in need of tools that can help to ensure a sufficient capacity in periods with high prevalence of respiratory tract infections (RTIs). During the COVID-19 pandemic, we forecasted the number of hospital admissions for RTIs among children aged 0-5 years. Now, in 2024, we aim to examine the accuracy and usefulness of our forecast models. We conducted a retrospective analysis using data from 753,070 children aged 0-5 years, plotting the observed monthly number of RTI admissions, including influenza coded RTI, respiratory syncytial virus (RSV) coded RTI, COVID-19 coded RTI, and other upper and lower RTI, from January 1st, 2017, until May 31st, 2023. We determined the accuracy of four different forecast models, all based on monthly hospital admissions and different assumptions regarding the pattern of virus transmission, computed with ordinary least squares regression adjusting for seasonal trends. We compared the observed vs. forecasted numbers of RTIs between October 31st, 2021, and May 31st, 2023, using metrics such as mean absolute error (MAE), mean absolute percentage error (MAPE) and dynamic time warping (DTW). In our most accurate prediction, we assumed that the proportion of children who remained uninfected and non-hospitalized during the lockdown would be prone to hospitalization in the subsequent season, resulting in increased numbers when lockdown measures were eased. In this prediction, the difference between observed and forecasted numbers at the peak of hospitalizations requiring vs. not requiring respiratory support in November 2021 to January 2022 was 26 (394 vs. 420) vs. 48 (1810 vs. 1762). In scenarios similar to the COVID-19 pandemic, when the transmission of respiratory viruses is suppressed for an extended period, a simple regression model, assuming that non-hospitalized children would be hospitalized the following season, most accurately forecasted hospital admission numbers. These simple forecasts may be useful for capacity planning activities in hospitals.

Weak dependence and optimal quantitative self-normalized central limit theorems

Consider a stationary, weakly dependent sequence of random variables. Given only mild conditions, allowing for polynomial decay of the autocovariance function, we show a Berry–Esseen bound of optimal order n^{-1/2} for studentized (self-normalized) partial sums, both for the Kolmogorov and Wasserstein (and L^{p} ) distances. The results show that in general, (minimax) optimal estimators of the long-run variance lead to suboptimal bounds in the central limit theorem, that is, the rate n^{-1/2} cannot be reached, refuting a popular belief in the literature. This can be salvaged by simple methods. We reveal that in order to maintain the optimal speed of convergence n^{-1/2} , simple over-smoothing within a certain range is necessary and sufficient. The setup contains many prominent dynamical systems and time series models, including random walks on the general linear group, products of positive random matrices, functionals of GARCH models of any order, functionals of dynamical systems arising from stochastic differential equations, iterated random functions and many more.

Dynamical response and time correlation functions in random quantum systems

Dynamical response and time correlation functions in random quantum systems

Design of in-line-of-sight dynamic wireless time comparison system based on pseudo-code ranging

Design of in-line-of-sight dynamic wireless time comparison system based on pseudo-code ranging

Adaptive parameters identification for nonlinear dynamics using deep permutation invariant networks

The promising outcomes of dynamical system identification techniques, such as SINDy (Brunton et al. in Proc Natl Acad Sci 113(15):3932–3937, 2016), highlight their advantages in providing qualitative interpretability and extrapolation compared to non-interpretable deep neural networks (Rudin in Nat Mach Intell 1(5):206–215, 2019). These techniques suffer from parameter updating in real-time use cases, especially when the system parameters are likely to change during or between processes. Recently, the OASIS (Bhadriraju et al. in AIChE J 66(11):16980, 2020) framework introduced a data-driven technique to address the limitations of real-time dynamical system parameters updating, yielding interesting results. Nevertheless, we show in this work that superior performance can be achieved using more advanced model architectures. We present an innovative encoding approach, based mainly on the use of Set Encoding methods of sequence data, which give accurate adaptive model identification for complex dynamic systems, with variable input time series length. Two Set Encoding methods are used: the first is Deep Set (Zaheer et al. in Adv Neural Inf Process Syst 30, 2017), and the second is Set Transformer (Lee et al. in: International conference on machine learning, PMLR, pp 3744–3753 2019). Comparing Set Transformer to OASIS framework on Lotka–Volterra for real-time local dynamical system identification and time series forecasting, we find that the Set Transformer architecture is well adapted to learning relationships within data sets. We then compare the two Set Encoding methods based on the Lorenz system for online global dynamical system identification. Finally, we trained a Deep Set model to perform identification and characterization of abnormalities for 1D heat-transfer problem.

Cross-Timescale Interaction Analysis Between Current Control and Rotor Speed Control Timescale Dynamics in a High-Proportion DFIG-WT System

Cross-Timescale Interaction Analysis Between Current Control and Rotor Speed Control Timescale Dynamics in a High-Proportion DFIG-WT System

Hydrological drivers of flooding in Niamey (Niger): the role of the Sirba River

Abstract In recent decades, floods have become a major global concern. In Niamey (Niger) in West Africa, flooding is primarily caused by the significant increase in surface runoff resulting from heavy rainfall occuring between July and September in the upstream river basins of the three major tributaries of the Middle Niger River (Sirba, Gorouol and Dargol catchments). While the Sirba is empirically considered as the largest driver to flooding in Niamey, its contribution have not been precisely established. This study analyzes the influence of these tributaries on the Niger River discharges at Niamey during the rainy season, with a particular focus on the Sirba River basin. Daily annual maximum discharge (AMAX) data from 1990 to 2022 timeseries are used as inputs to various statistical analyses, including trend analyses, change point detection, concordance analysis and flood dependency assessment. The results reveal a significant change point in 2009 and increasing trends between the Sirba and Niger River stations. The flood propagation time delay varies from 1 to 4 days between the upstream river basins tributaries and Niamey station, with a strong concordance in peak discharges, particularly dominant with the Sirba River. The Dynamic Time Warping (DTW) and the Gumbel copula analyses highlighted the significant control of the Sirba River basin on flooding in Niamey, while also highlighting the important roles played by other tributaries. These findings are crucial for improving flood prevention and further refine urban flood management strategies in Niamey and other cities globally, affected by fluvial floods.

Tennis Assistance Technology Based on Dynamic Time Warping Algorithm

Tennis Assistance Technology Based on Dynamic Time Warping Algorithm

Articulatory correlates of consonantal length contrasts: The case of Japanese mimetic geminates.

This study investigates the articulatory correlates of consonantal length contrasts in Japanese mimetic words using electromagnetic articulography data. Regression and dynamic time warping analyses applied to intragestural timing, kinematic properties, and intergestural timing reveal that Japanese geminates are characterized by longer closure phases, longer gestural plateaus, higher tongue tip positions, larger movements, and lower stiffness. Geminates also exhibit distinct timing relationships with adjacent vowels, specifically, longer times to target that allow for longer preceding vowels. These findings shed light on the articulatory mechanisms underlying Japanese geminate production, their relationship to acoustics, and their characterization in a broader cross-linguistic perspective.

A novel gait quality measure for characterizing pathological gait based on Hidden Markov Models.

This study addresses the characterization of normal gait and pathological deviations caused by neurological diseases. We focus on the angular knee kinematics in the sagittal plane and we propose to exploit Hidden Markov Models to build a statistical model of normal gait. Such model provides a log-likelihood score that quantifies gait quality. Hence allowing to assess deviations of pathological cycles from normal gait. Our approach allows a refined characterization of motor impairments of three different patients' groups. In particular, it detects the affected lower limb in Hemiparetic patients. Comparatively to the Gait Variable Score and a Dynamic Time Warping-based metric, our results show that our statistical method is more effective for finely quantifying pathological deviations. Finally, we show the potential use of our methodology to assess therapeutic impacts during gait rehabilitation, which represents a promising avenue for improving patient care.

The development of deep convolutional generative adversarial network to synthesize odontocetes' clicks.

Odontocetes are capable of dynamically changing their echolocation clicks to efficiently detect targets, and learning their clicking strategy can facilitate the design of man-made detecting signals. In this study, we developed deep convolutional generative adversarial networks guided by an acoustic feature vector (AF-DCGANs) to synthesize narrowband clicks of the finless porpoise (Neophocaena phocaenoides sunameri) and broadband clicks of the bottlenose dolphins (Tursiops truncatus). The average short-time objective intelligibility (STOI), spectral correlation coefficient (Spe-CORR), waveform correlation coefficient (Wave-CORR), and dynamic time warping distance (DTW-Distance) of the synthetic clicks were 0.975, 0.968, 0.877, and 0.992, respectively. AF-DCGAN outperformed the minimum phase signal reconstruction (MPSR) method and variational quantized variational autoencoders (VQ-VAE) by 5.9% and 3.7% in STOI, 5.2% and 3.5% in Spe-CORR, and 5.8% and 2.8% in Wave-CORR, respectively. In addition, AF-DCGAN reduced DTW-Distances by 29.9% and 9.4% compared to MPSR and VQ-VAE, respectively. Results showed that AF-DCGAN was robust in synthesizing both narrowband and broadband clicks that can produce a substantial number of high-fidelity odontocetes' clicks with flexibility in modulating parameters. Employing AF-DCGAN to synthesize odontocete-like clicks could advance the development of a click database, offering promising applications in the research of biomimetic target detection and recognition.

Motion-Based Visual Encoding Can Improve Performance on Perceptual Tasks with Dynamic Time Series.

Dynamic data visualizations can convey large amounts of information over time, such as using motion to depict changes in data values for multiple entities. Such dynamic displays put a demand on our visual processing capacities, yet our perception of motion is limited. Several techniques have been shown to improve the processing of dynamic displays. Staging the animation to sequentially show steps in a transition and tracing object movement by displaying trajectory histories can improve processing by reducing the cognitive load. In this paper, We examine the effectiveness of staging and tracing in dynamic displays. We showed participants animated line charts depicting the movements of lines and asked them to identify the line with the highest mean and variance. We manipulated the animation to display the lines with or without staging, tracing and history, and compared the results to a static chart as a control. Results showed that tracing and staging are preferred by participants, and improve their performance in mean and variance tasks respectively. They also preferred display time 3 times shorter when staging is used. Also, encoding animation speed with mean and variance in congruent tasks is associated with higher accuracy. These findings help inform real-world best practices for building dynamic displays.

The distance and entropy measures-based intuitionistic fuzzy C-means and similarity matrix clustering algorithms and their applications

Determining the similarity between datasets by distance/similarity measures is important in most clustering algorithms. Therefore, this paper characterizes two clustering algorithms with improved distance measures. In the similarity matrix clustering algorithm, the mixed correlation coefficient and two probability parameters are suggested into the weighted probabilistic Euclidean distance (WPED) measure, which expresses the randomness and fuzziness of intuitionistic fuzzy sets. Second, based on the characteristics of dynamic time warping (DTW), a DTW-based probabilistic Euclidean distance (PED-DTW) method is introduced. The attribute ordering rule is developed with the help of entropy, which is beneficial for PED-DTW in achieving optimal path matching. Furthermore, a DTW-based intuitionistic fuzzy C-means algorithm is constructed. Finally, we performed experiments using car datasets and 7 UCI datasets. Compared with the existing method, the similarity matrix clustering algorithm is feasible. The DTW-based intuitionistic fuzzy C-means algorithms is compared with six clustering methods on four performance metrics, and the performance of our algorithm is analyzed. Our algorithm performs well on four performance metrics. Experimental results indicate that the clustering algorithms proposed in this paper are practical and outperform their existing counterparts. Also, the proposed algorithm gives good results in image segmentation.

Overcoming Shifting Baselines: Paleo-Behaviour Reveals Industrial Revolution as Tipping Point.

Human activities have significantly altered coastal ecosystems worldwide. The phenomenon of shifting baselines syndrome (SBS) complicates our understanding of these changes, masking the true scale of human impacts. This study investigates the long-term ecological effects of anthropogenic activities on New Zealand's coastal ecosystems over 800 years using fish otolith microchemical profiling and dynamic time warping across an entire stock unit. Results reveal a shift in snapper (Chrysophrys auratus; Sparidae) habitat-use behaviour, transitioning from low-salinity estuarine environments to higher-salinity habitats, correlating with ongoing land-use changes. This shift coincided with New Zealand's localised Industrial Revolution, which served as a tipping point for widespread ecosystem transformation. By comparing current coastal fish movement profiles with historical baselines, we provide evidence to address SBS and guide conservation strategies. Re-establishing pre-industrial habitat-use behaviours in snapper will indicate successful habitat restoration, promoting overall ecosystem connectivity and resilience. Our findings enable more effective habitat restoration measures and sustainable management practices, informing policies for maintaining coastal biodiversity and ecosystem function.

Quantifying spatial patterns of urban building morphology in the China’s Guangdong-Hong Kong-Marco greater bay area

ABSTRACT Understanding the spatial patterns of urban building morphology is crucial for revealing the interplay between the built and social environments. Previous research has predominantly concentrated on the computation of building-level metrics which makes it challenging to quantify and compare spatial patterns and variations across different cities. Using the newly available world settlement footprint 3D (WSF3D) data, this study examines the spatial patterns of urban building morphology across various cities within the Guangdong-Hong Kong-Macao Greater Bay Area, a rapidly urbanizing region in China. Specifically, we applied a concentric ring approach to delineate the spatial gradients of the building fraction, area, height, and volume from the urban center to the suburban fringes. Subsequently, we utilized dynamic time warping and a multi-dimensional scaling technique facilitating a comparative analysis of these gradients across cities. Developed cities demonstrated more homogenous distributions of building morphologies; however, notable differences were observed among the spatial patterns of distinct building metrics. Furthermore, a correlation between the spatial patterns of urban building morphology and degree of urban development was revealed, suggesting that developed cities exhibit significantly smaller declines in building morphology from the urban core to the rural periphery.

Verification of normalized online signatures without calculating dynamic features

Objectives. Study of the method of verification of the authenticity of a human signature made on a tablet with a stylus and given three parameters: coordinates X, Y and pressure on the tablet P.Methods. N genuine dynamic human signatures are given. Data describing different signatures made by one person always have a different number of points. The main variants of normalization of the original signature data are investigated. A model of an individual image of human signatures is built without calculating dynamic features. The method of dynamic time transformation (DTW) is used to compare similar data of different signatures. The results of this transformation are DTW-distances between the data of pairs of signatures. These distances serve as coordinates of a point in the feature space describing the similarity of a pair of signatures. A set of such points represents a model describing the similarity of genuine human signatures. The parameters of the signature being verified are compared with each of the N authentic signatures used to build the model for their proximity to the model. If more than half of these pairs are further from the model than a certain threshold T, the signature is considered fake.Results. As a result of comparative experiments, a variant of normalization of initial data of dynamic human signatures was found, which allows verification of such signatures without calculating additional features usually calculated from the initial data X, Y, P.Conclusion. Experiments to generate individual signature models for each of 230 people from the publicly available MCYT dynamic signature database (a subset of the DeepSignDB database) and verify the authenticity of 11,500 signatures made on behalf of these people showed a verification accuracy of 99.82 %. Half of them were genuine, half were fake.

Human motion similarity evaluation based on deep metric learning

In order to eliminate the impact of camera viewpoint factors and human skeleton differences on the action similarity evaluation and to address the issue of human action similarity evaluation under different viewpoints, a method based on deep metric learning is proposed in this article. The method trains an automatic encoder-decoder deep neural network model by means of a homemade synthetic dataset, which maps the 2D human skeletal key point sequence samples extracted from motion videos into three potential low-dimensional dense spaces. Action feature vectors independent of camera viewpoint and human skeleton structure are extracted in the low-dimensional dense spaces, and motion similarity metrics are performed based on these features, thereby effectively eliminating the effects of camera viewpoint and human skeleton size differences on motion similarity evaluation. Specifically, when extracting the action information feature vectors using the automatic encoder-decoder network model, a sliding window method is used to divide the key point sequences of each limb part into sequence patches, and the action information feature vectors independent of the camera viewpoint and skeleton structure are extracted in a smaller time unit, so as to obtain a more refined action similarity evaluation result. In addition, the dynamic time warping (DWT) algorithm is exploited to align the sequence of action information feature vectors temporally, which solves the problem of temporal axis discrepancies in realizing similarity metrics based on action information feature vectors. More accurate and reliable human action similarity evaluation results were achieved by the loss function composed of three components, namely, cross-reconstruction loss, reconstruction loss and triplet loss. Finally, the performance of the algorithm is evaluated in a homemade dataset, and the experimental results show that the method could effectively eliminate the influence of the differences in camera viewpoints and human skeleton sizes on the similarity evaluation of actions, and generate more reliable and closer to the human subjective perception of similarity evaluation results for human actions captured from different viewpoints or with varying skeleton sizes.

A Software Tool for Rapid and Automated Preprocessing of Large-Scale Serum Metabolomic Data by Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry.

Mass spectrometry (MS)-based metabolomics often rely on separation techniques when analyzing complex biological specimens to improve method resolution, metabolome coverage, quantitative performance, and/or unknown identification. However, low sample throughput and complicated data preprocessing procedures remain major barriers to affordable metabolomic studies that are scalable to large populations. Herein, we introduce PeakMeister as a new software tool in the R statistical environment to enable standardized processing of serum metabolomic data acquired by multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS), a high-throughput separation platform (<4 min/sample) which takes advantage of a serial injection format of 13 samples within a single analytical run. We performed a rigorous validation of PeakMeister by analyzing 47 cationic metabolites consistently measured in 5,000 serum and 420 quality control samples from the Brazilian National Survey on Child Nutrition (ENANI-2019) comprising a total of 224,983 metabolite peaks acquired in 40 days across three batches over an eight-month period. A migration time index using a panel of 11 internal standards was introduced to correct for large variations in migration times, which allowed for reliable peak annotation, peak integration, and sample position assignment for serum metabolites having two flanking internal standards or a single comigrating stable-isotope internal standard. PeakMeister accelerated data preprocessing times by 30-fold compared to manual processing of MSI-CE-MS data by an experienced analyst using vendor software, while also achieving excellent peak annotation fidelity (median accuracy >99.9%), acceptable intermediate precision (median CV = 16.0%), consistent metabolite peak integration (mean bias = -2.1%), and good mutual agreement when quantifying 16 plasma metabolites from NIST SRM-1950 (mean bias = -1.3%). Reference ranges are also reported for 40 serum metabolites in a national nutritional survey of Brazilian children under 5 years of age from the ENANI-2019 study. MSI-CE-MS in conjunction with PeakMeister allows for rapid and automated processing of large-scale metabolomic studies that tolerate nonlinear migration time shifts without complicated dynamic time warping or effective mobility scale transformations.

Effects of Thermodynamics on the Concurrent Accretion and Migration of Gas Giants in Protoplanetary Disks

Accretion and migration usually proceeds concurrently for giant planet formation in the natal protoplanetary disks. Recent works indicate that the concurrent accretion onto a giant planet imposes significant impact on the planetary migration dynamics in the isothermal regime. In this work, we carry out a series of 2D global hydrodynamical simulations with Athena++ to explore the effect of thermodynamics on the concurrent accretion and migration process of the planets in a self-consistent manner. The thermodynamics effect is modeled with a thermal relaxation timescale using a β-cooling prescription. Our results indicate that radiative cooling has a substantial effect on the accretion and migration processes of the planet. As cooling timescales increase, we observe a slight decrease in the planetary accretion rate, and a transition from the outward migrating into inward migration. This transition occurs approximately when the cooling timescale is comparable to the local dynamical timescale (β∼1), which is closely linked to the asymmetric structures from the circumplanetary disk (CPD) region. The asymmetric structures in the CPD region which appear with an efficient cooling provide a strong positive torque driving the planet migrate outward. However, such a positive torque is strongly suppressed, when the CPD structures tend to disappear with a relatively long cooling timescale (β≳10). Our findings may also be relevant to the dynamical evolution of accreting stellar-mass objects embedded in disks around active galactic nuclei.

An Exploration of Spiral of Silence in Online News Comments: Characteristics of Four Flow Patterns of Users’ Comments

Abstract To address prior methodological, contextual, and conceptual gaps in research on the spiral of silence (SOS), we investigated whether SOS manifests in online news comments where public opinions naturally emerge and examined the impact of these comments on public opinion. To analyze the progression of comments, we adopted a novel approach to time-series analysis—previously confined to social media—by implementing the dynamic time warp (DTW) technique. Data were collected from 47,845 comments on 281 online news articles related to three controversial topics published on a Korean portal site. Using DTW, we categorized comment patterns into four clusters across all topics: Contentious, Parallel, Overwhelming, and Gradual gap. A key observation was that minority opinions did not consistently follow a predictable path but rather displayed varied patterns. This indicates that SOS is selectively relevant in online discussions, with various factors influencing how minority opinions are expressed and shape public opinion. However, over a longer timeframe, the Contentious type, where minority views remained strong, significantly decreased in later discussions compared with earlier ones. Moreover, the current study corroborates previous research on the influence of initial comments. The pattern of comment flows is also influenced by factors such as click speech, neutral comments, and age.