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This research introduces a method for seismic-well tie using a modified Dynamic Time Warping, DTW algorithm with fuzzy features. The Seismic-Fuzzy DTW technique aligns synthetic seismograms to seismic traces by considering waveform similarity and geological features. It uses acoustic impedance models and membership results from fuzzy model-based inversion. Traditional seismic-well tie methods frequently prioritize amplitude matching above geological consistency. The proposed approach rethinks the well tie target by stressing the high correlation of fuzzy acoustic impedance features. The results show improvements over the traditional DTW-based technique. The result’s correctness, however, depends on the accuracy of the fuzzy seismic inversion data. It is proposed that more research be conducted into potential mismatches, noise effects, and complex geological structures. The algorithm’s effectiveness could be improved by incorporating more data types and optimizing its behavior under different geological settings. Overall, this unique approach yields promising results by combining seismic and well data to improve seismic interpretation outcomes. [ABSTRACT FROM AUTHOR]

The process of energy transformation is one of the crucial elements of the process of improvement of the quality of consumed energy. The aim of the research is to assess the European Union countries in terms of the quality of their consumed energy and the speed of adaptation of this aspect of the energy transformation process. We assess the quality of consumed energy by means of the synthetic measure obtained by means of the dynamic version of the COmplex PRoportional ASsessment (COPRAS) method. We compare the countries with the dynamics of the energy transformation process by means of the dynamic time warping method and the hierarchical clustering. Obtained results indicate the best countries with respect to the quality of consumed energy were Malta, Austria, and Germany, and the worst ones—Poland, Czechia, and Slovakia. The process of energy transformation was evolving in the right direction—the quality of consumed energy increased. This increase was the fastest in Malta, Luxembourg, and Poland. The direction for future research is extending the set of variables to also consider other aspects of the energy transformation. [ABSTRACT FROM AUTHOR]

Currently, deep learning-based traffic flow prediction methods have deficiencies. Firstly, the prediction model based on graph convolutional network uses a simplified road network topology, ignores the actual traffic organization information, and affects the prediction accuracy. Secondly, the clustering-based prediction model does not consider the regional and temporal similarity of traffic flow and fails to effectively utilize spatio-temporal patterns, resulting in limited enhancement of prediction by clustering results. In addition, overly large training samples increases the training and prediction time, affecting real-time performance. In order to solve the above problems, this paper proposed a deep temporal clustering traffic flow prediction(DTCTFP) model based on deep temporal clustering for short-term traffic flow prediction at urban chokepoints. Firstly, the method constructed the road network topology containing actual traffic organization information and used a graph convolutional network to mine the spatio-temporal characteristics between chokepoints. Secondly, it introduced improved dynamic temporal regularization and shortest path analysis methods to classify similar traffic flow objects into the same cluster, allowing the model to make full use of feature information such as flow rate, time, and location to improve prediction accuracy. Finally, it used a cluster-based recurrent neural network for prediction to enhance the real-time and computational efficiency of the model. Using Chongqing Dadukou traffic data, experiments show that the model reduces MAE, RMSE, and MAPE by 15.02%, 10.72%, and 10.98% on average compared to the latest benchmark. The ablation test also confirms a 14.5% improvement in prediction accuracy with the proposed clustering method. [ABSTRACT FROM AUTHOR]

The study performed in this article aimed to reproduce the penetrometric profile of the soil from the perforation parameters of deep foundation and ground improvement. This could allow for more easily interpretable information on the soil strength during execution as well as validate the design hypotheses. To achieve this goal, a series of Machine Learning algorithms have been used and compared with traditionally applied analytical formulas. Dynamic time warping is used to measure the likeness of the results with the expected shape. The results show that the algorithms are capable of better fitting the penetrometric profiles of the soil. Tree ensemble methods stand out with the best results. [ABSTRACT FROM AUTHOR]

A fundamental problem in the field of protein evolutionary biology is determining the degree and nature of evolutionary relatedness among homologous proteins that have diverged to a point where they share less than 30% amino acid identity yet retain similar structures and/or functions. Such proteins are said to lie within the "Twilight Zone" of amino acid identity. Many researchers have leveraged experimentally determined structures in the quest to classify proteins in the Twilight Zone. Such endeavors can be highly time consuming and prohibitively expensive for large‐scale analyses. Motivated by this problem, here we use molecular weight–hydrophobicity physicochemical dynamic time warping (MWHP DTW) to quantify similarity of simulated and real‐world homologous protein domains. MWHP DTW is a physicochemical method requiring only the amino acid sequence to quantify similarity of related proteins and is particularly useful in determining similarity within the Twilight Zone due to its resilience to primary sequence substitution saturation. This is a step forward in determination of the relatedness among Twilight Zone proteins and most notably allows for the discrimination of random similarity and true homology in the 0%–20% identity range. This method was previously presented expeditiously just after the outbreak of COVID‐19 because it was able to functionally cluster ACE2‐binding betacoronavirus receptor binding domains (RBDs), a task that has been elusive using standard techniques. Here we show that one reason that MWHP DTW is an effective technique for comparisons within the Twilight Zone is because it can uncover hidden homology by exploiting physicochemical conservation, a problem that protein sequence alignment algorithms are inherently incapable of addressing within the Twilight Zone. Further, we present an extended definition of the Twilight Zone that incorporates the dynamic relationship between structural, physicochemical, and sequence‐based metrics. [ABSTRACT FROM AUTHOR]

Online signature verification considers signatures as time sequences of different measurements of the signing instrument. These signals are captured on digital devices and therefore consist of a discrete number of samples. To enrich or simplify this information, several verifiers employ resampling and interpolation as a preprocessing step to improve their results; however, their design decisions may be difficult to generalize. This study investigates the direct effect of the sampling rate of the input signals on the accuracy of online signature verification systems without using interpolation techniques and proposes a novel online signature verification system based on a signer-dependent sampling frequency. Twenty verifier configurations were created for five different public signature databases and a variety of popular preprocessing approaches and evaluated for 20–40 different sampling rates. Our results show that there is an optimal range for the sampling frequency and the number of sample points that minimizes the error rate of a verifier. A sampling frequency range of 15–50 Hz and a signature point count of 60–240 provided the best accuracies in our experiments. As expected, lower ranges showed inaccurate results; interestingly, however, higher frequencies often decreased the verification accuracy. The results show that one can achieve better or at least the same verification accuracies faster by down-sampling the online signatures before further processing. The proposed system achieved competitive results to state-of-the-art systems for different databases by using the optimal sampling frequency. We also studied the effect of choosing individual sampling frequencies for each signer and proposed a signature verification system based on signer-dependent sampling frequency. The proposed system was tested using 500 different verification methods and improved the accuracy in 92% of the test cases compared to the usage of the original frequency. [ABSTRACT FROM AUTHOR]

Effective water resource management relies on understanding hydrological processes and accurately predicting hydrological variables. This study highlights the critical importance of estimating suspended sediment load (SSL) for water quality assessment. Traditional forecasting techniques often struggle to capture the complexities of natural processes. In this context, a Fuzzy Cerebellar Model Articulation Controller (FCMAC) was proposed for SSL estimation, demonstrating its superior performance compared to Adaptive Neuro-fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) models. The findings indicate that FCMAC significantly outperforms both ANFIS and SVR in capturing data variance and accurately predicting SSL, as evidenced by key performance metrics such as R², RMSE, and NSE. An R2 value of approximately 0.9 and an NRMSE of less than 5% across both study areas indicate the promising performance of this modeling approach for predicting sediment load. The adaptive learning capabilities of FCMAC not only enhance prediction accuracy but also offer a promising solution for managing intricate hydrological challenges, ultimately contributing to improved water resource management and policy-making. [ABSTRACT FROM AUTHOR]

In query-by-example spoken term detection (QbE-STD), reference utterances are matched with an audio query. A matching matrix-based approach to QbE-STD needs to compute a matching matrix between a query and reference utterance using an appropriate similarity metric. Recent approaches use kernel-based matching to compute this matching matrix. The matching matrices are converted to grayscale images and given to a CNN-based classifier. In this work, we propose to speed up QbE-STD by computing the matching matrix in parallel using a coarse-grained data parallelism approach. We explore two approaches to coarse-grained data parallelism: In the first approach, we compute parts of the matching matrix in parallel and then combine them to form a matching matrix, while in the second one, we propose to compute matrices in parallel. We also propose to convert the matching matrices into two-colored images using the threshold and use these images for QbE-STD. The efficacy of the proposed parallel computation approach is explored using the TIMIT dataset. [ABSTRACT FROM AUTHOR]

The research on electricity consumption pattern recognition generally encounters some prominent problems such as poor similarity, poor accuracy, and low efficiency of existing clustering algorithms. Therefore, this paper utilizes elbow judgment (EJ), gap statistic (GS), and DTW (dynamic time warping) to develop a DTW-based adaptive K-means (DAKM) clustering algorithm for electricity consumption pattern recognition. The algorithm includes three main aspects. First, the DTW distance with the Sakoe-Chiba band global constraint is used to find the optimal alignment between the two load curves by matching the shapes with local stretching or compression sequences. Second, gap statistic and elbow are used to obtain the optimal number of clusters for high clustering efficiency automatically. Third, a max-min DTW distance (MMDD) method is presented to optimize the initial cluster centers of the K-means algorithm. The comparative experimental results demonstrate that the proposed DAKM algorithm achieved best evaluation values of 0.7055 for DBI, 0.0237 for SSE, 132.0435 for CHI, 0.6649 for SC, and 1.1670 for DI, respectively, which proves that the proposed DAKM algorithm is far superior to other clustering algorithms. [ABSTRACT FROM AUTHOR]

Food sustainability has become a major global concern in recent years. Multiple complimentary strategies to deal with this issue have been developed; one of these approaches is regenerative farming. The identification and analysis of crop type phenology are required to achieve sustainable regenerative faming. Earth Observation (EO) data have been widely applied to crop type identification using supervised Machine Learning (ML) and Deep Learning (DL) classifications, but these methods commonly rely on large amounts of ground truth data, which usually prevent historical analysis and may be impractical in very remote, very extensive or politically unstable regions. Thus, the development of a robust but intelligent unsupervised classification model is attractive for the long-term and sustainable prediction of agricultural yields. Here, we propose FastDTW-HC, a combination of Fast Dynamic Time Warping (DTW) and Hierarchical Clustering (HC), as a significantly improved method that requires no ground truth input for the classification of winter food crop varieties of barley, wheat and rapeseed, in Norfolk, UK. A series of variables is first derived from the EO products, and these include spectral indices from Sentinel-2 multispectral data and backscattered amplitude values at dual polarisations from Sentinel-1 Synthetic Aperture Radar (SAR) data. Then, the phenological patterns of winter barley, winter wheat and winter rapeseed are analysed using the FastDTW-HC applied to the time-series created for each variable, between Nov 2019 and June 2020. Future research will extend this winter food crop mapping analysis using FastDTW-HC modelling to a regional scale. [ABSTRACT FROM AUTHOR]

Energy stock price prediction is a pivotal challenge in financial forecasting, characterized by high volatility and complexity influenced by geopolitical factors, regulatory shifts, and sector-specific issues. Traditional methods often struggle to account for the intricate dependencies and temporal patterns present in energy stock data. To address these limitations, this study introduces a hybrid model that integrates a Graph Convolutional Network (GCN) with an attention-enhanced Long Short-Term Memory (LSTM) architecture. By employing a graph structure derived from Dynamic Time Warping (DTW), the GCN captures inter-stock relationships, while the attention mechanism within the LSTM component refines the modelling of temporal dynamics, allowing the model to focus on the most relevant historical information. Experimental evaluations across multiple energy stocks show that this combined LSTMGC model significantly outperforms conventional approaches- including Linear Regression, GRU, MLP, and standalone LSTMs- when assessed using Mean Squared Error (MSE) and R-squared (R2) metrics. By jointly leveraging spatial and temporal dependencies, as well as the selective attention mechanism, the proposed framework enhances predictive accuracy and reliability, offering valuable insights for investors and policymakers navigating the evolving energy market.

With the surge in energy demand worldwide, renewable energy is becoming increasingly important. Solar power, in particular, is positioning itself as a sustainable and environmentally friendly alternative, and is increasingly playing a role not only in large-scale power plants but also in small-scale home power generation systems. However, small-scale power generation systems face challenges in the development of efficient prediction models because of the lack of data and variability in power generation owing to weather conditions. In this study, we propose a novel forecasting framework that combines transfer learning and dynamic time warping (DTW) to address these issues. We present a transfer learning-based prediction system design that can maintain high prediction performance even in data-poor environments. In the process of developing a prediction model suitable for the target domain by utilizing multi-source data, we propose a data similarity evaluation method using DTW, which demonstrates excellent performance with low error rates in the MSE and MAE metrics compared with conventional long short-term memory (LSTM) and Transformer models. This research not only contributes to maximizing the energy efficiency of small-scale PV power generation systems and improving energy independence but also provides a methodology that can maintain high reliability in data-poor environments.

Relating continuously collected inertial data to the activities or postures performed by the sensor wearer requires pattern recognition or machine-learning-based algorithms, accounting for the temporal and scale variability present in human movements. The objective of this study was to develop a sensor-based framework for the detection and measurement of high-flexion postures frequently adopted in occupational settings. IMU-based joint angle estimates for the ankle, knee, and hip were time and scale normalized prior to being input to a multi-dimensional Dynamic Time Warping (mDTW) distance-based Nearest Neighbour algorithm for the identification of twelve postures. Data from 50 participants were divided to develop and evaluate the mDTW model. Overall accuracies of 82.3% and 55.6% were reached when classifying movements from the testing and validation datasets, respectively, which increased to 86% and 74.6% when adjusting for imbalances between classification groups. The highest misclassification rates occurred between flatfoot squatting, heels-up squatting, and stooping, while the model was incapable of identifying sequences of walking based on a single stride template. The developed mDTW model proved robust in identifying high-flexion postures performed by participants both included and precluded from algorithm development, indicating its strong potential for the quantitative measurement of postural adoption in real-world settings.

The quantification of collision risk in the water area is crucial for marine traffic safety. However, most existing studies primarily adopt a statistical perspective or only consider collision risks at discrete time points, neglecting the dynamic nature and temporal characteristics of collision risk. To overcome the problem, this paper proposed a novel time-frame regional collision risk model based on dynamic time warping (DTW). The time series of the ships in the water area were generated by modeling the collision risk from the perspectives of geometric encounters and traffic situation pressure. Dynamic time warping was used to mine the eigenvalue of each sequence in representing collision risk. The time-frame regional collision risk can be obtained by synthesizing the collision risk eigenvalues through considering the contribution of each ship to the entire collision risk. To validate the proposed model, some experiments were carried out by using the real AIS data in the Bohai Strait. The results show the capability of the proposed model in reasonably and effectively identifying the time-frame regional collision risk and prove its merits compared with the traditional methods. Therefore, it can better assist the supervision and analysis of regional collision risk so as to further enhance marine traffic safety.

The study performed in this article aimed to reproduce the penetrometric profile of the soil from the perforation parameters of deep foundation and ground improvement. This could allow for more easily interpretable information on the soil strength during execution as well as validate the design hypotheses. To achieve this goal, a series of Machine Learning algorithms have been used and compared with traditionally applied analytical formulas. Dynamic time warping is used to measure the likeness of the results with the expected shape. The results show that the algorithms are capable of better fitting the penetrometric profiles of the soil. Tree ensemble methods stand out with the best results.

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 ad 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.

Food sustainability has become a major global concern in recent years. Multiple complimentary strategies to deal with this issue have been developed; one of these approaches is regenerative farming. The identification and analysis of crop type phenology are required to achieve sustainable regenerative faming. Earth Observation (EO) data have been widely applied to crop type identification using supervised Machine Learning (ML) and Deep Learning (DL) classifications, but these methods commonly rely on large amounts of ground truth data, which usually prevent historical analysis and may be impractical in very remote, very extensive or politically unstable regions. Thus, the development of a robust but intelligent unsupervised classification model is attractive for the long-term and sustainable prediction of agricultural yields. Here, we propose FastDTW-HC, a combination of Fast Dynamic Time Warping (DTW) and Hierarchical Clustering (HC), as a significantly improved method that requires no ground truth input for the classification of winter food crop varieties of barley, wheat and rapeseed, in Norfolk, UK. A series of variables is first derived from the EO products, and these include spectral indices from Sentinel-2 multispectral data and backscattered amplitude values at dual polarisations from Sentinel-1 Synthetic Aperture Radar (SAR) data. Then, the phenological patterns of winter barley, winter wheat and winter rapeseed are analysed using the FastDTW-HC applied to the time-series created for each variable, between Nov 2019 and June 2020. Future research will extend this winter food crop mapping analysis using FastDTW-HC modelling to a regional scale.

• Using nonlinear reduced order model VAE to study the nonlinear collective dynamics behaviors of coupled flame oscillators made of unsteady laminar flickering diffusion flames. • Providing a robust and comprehensive framework for mode recognition in coupled flickering flames. • Proposing VAE-WDC, a supervised mode recognition method based on VAE and Wasserstein distance. • Proposing VAE-GMM-DTWC, an unsupervised mode recognition method based on VAE and GMM-DTW cluster. Combustion instability in gas turbines and rocket engines, as one of the most challenging problems in combustion research, arises from the complex interactions among flames influenced by chemical reactions, heat and mass transfer, and acoustics. Identifying and understanding combustion instability is essential for ensuring the safe and reliable operation of many combustion systems, where exploring and classifying the dynamical behaviors of complex flame systems is a core task. To facilitate fundamental studies, the present work concerned dynamical mode recognition of coupled flame oscillators made of flickering buoyant diffusion flames, which have gained increasing attention in recent years but are not sufficiently understood. The time series data of flame oscillators were generated through fully validated reacting flow simulations. Due to the limitations of expertise-based models, a data-driven approach was adopted. In this study, a nonlinear dimensional reduction model of variational autoencoder (VAE) was used to project the high dimensional data onto a 2-dimensional latent space. Based on phase trajectories in the latent space, both supervised and unsupervised classifiers were proposed for datasets with and without well-known labeling, respectively. For labeled datasets, we established the Wasserstein-distance-based classifier (WDC) for mode recognition; for unlabeled datasets, we developed a novel unsupervised classifier (GMM-DTW) combining dynamic time warping (DTW) and Gaussian mixture model (GMM). Through comparing with conventional approaches for dimensionality reduction and classification, the proposed supervised and unsupervised VAE-based approaches exhibit a prominent performance across seven assessment metrics for distinguishing dynamical modes, implying their potential extension to dynamical mode recognition in complex combustion problems. [ABSTRACT FROM AUTHOR]

Background: Subacromial pain syndrome is the most common cause of shoulder pain and is associated with altered humeral and scapular kinematics. Symptoms can be improved by rehabilitation. Accurate tools to analyze shoulder kinematic curves are lacking., Methods: A single-center prospective pilot study using inertial measurement units located on both arms and scapulae to assess bilateral arm elevation in the sagittal, scapular and frontal planes. Reparameterization and signal registration algorithms compared similarity of global shoulder and scapular kinematic curves from participants with subacromial pain syndrome before and after a short rehabilitation program, with a control template combining the curves of asymptomatic participants. A similarity score used curve comparisons; the more closely the curve shapes matched, the closer the score was to zero. We used a paired Wilcoxon test to compare the scores., Findings: We included 9 right-handed symptomatic participants (10 shoulders): 2 males (22 %), mean (SD) age 53.8 (13.7) years, symptom duration 29 (23) months, pain (Numeric Rating Scale) 61.1 (22.4)/100, activity limitation (Quick-Dash): 48.3 (26.6)/100 points, and 10 asymptomatic age-matched right-handed participants (20 shoulders): 4 males (40 %), 54.2 (5.4) years old. Post-rehabilitation similarity scores decreased non-significantly for shoulder elevation (scapular and frontal planes), scapular lateral rotation (sagittal and scapular planes) and anterior-posterior tilt (scapular plane) and significantly for shoulder sagittal elevation (P = 0.004). Participant heterogeneity was high., Interpretation: The similarity methodology, used for the first time in the context of subacromial pain syndrome, offers a new quantitative tool to assess kinematic changes, measure movement-related impairments and monitor patient progress., Competing Interests: Declaration of competing interest The authors declare that they have no competing interests., (Copyright © 2025. Published by Elsevier Ltd.)

Context: The timing of a woman's final menstrual period (FMP) in relation to her age is considered a valuable indicator of overall health, being associated with cardiovascular, bone health, reproductive, and general mortality outcomes., Objective: This work aimed to evaluate the relationship between hormones and the "time to FMP" when daily hormone trajectories are characterized by their 1) entropy, and 2) deviation from premenopausal/stable cycle patterns (representing a textbook "gold standard"; GS)., Methods: As part of the Study of Women's Health Across the Nation, urinary luteinizing hormone (LH), follicle-stimulating hormone (FSH), estrogen conjugates (E1C), and pregnanediol glucuronide (PDG) were measured daily from a multiracial sample of 549 mid-life women for the duration of one menstrual cycle. Hormone trajectories were mapped onto a plane with axes representing Fuzzy entropy (FuzzEn) and the normalized dynamic time warping distance (DTW) from the GS., Results: Viewing FSH, E1C, PDG, and LH through this lens reveals that, contrary to existing wisdom, PDG stands out as a powerful predictor/descriptor of "time to FMP." Using cluster analyses to discretize PDG on the DTW/FuzzEn plane yields statistically different survival curves, and Cox proportional hazards analyses confirm that this separation persists in the presence of known covariates of FSH, antimüllerian hormone, age, body mass index, financial hardship, smoking status, and cycle length., Conclusion: Since PDG is generally not considered a predictor/descriptor of ovarian aging, this work validates the DTW/FuzzEn analytical framework and introduces another metric/hormone to be used in FMP-related preventive care., (© The Author(s) 2025. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com. See the journal About page for additional terms.)