Time Warping Research Articles

Seismic data extrapolation based on multi-scale dynamic time warping

Seismic data reconstruction can provide high-density sampling and regular input data for inversion and imaging, playing a crucial role in seismic data processing. In seismic data reconstruction, a common scenario involves a significant distance between the source and the first receiver, which makes it unattainable to acquire near-offset data. A new workflow for seismic data extrapolation is proposed to address this issue, which is based on a multi-scale dynamic time warping (MS-DTW) algorithm. MS-DTW can accurately calculate the time-shift between two time series and is a robust method for predicting time-offset (t−x) domain data. Using the time-shift calculated by the MS-DTW as the basic input, predict the two-way traveltime (TWT) of other traces based on the TWT of the reference trace. Perform autoregressive polynomial fitting on TWT and extrapolate TWT based on the fitted polynomial coefficients. Extract amplitude information from the TWT curve, fit the amplitude curve, and extrapolate the amplitude using polynomial coefficients. The proposed workflow does not necessitate data conversion to other domains and does not require prior knowledge of underground geological information. It applies to both isotropic and anisotropic media. The effectiveness of the workflow was verified through synthetic data and field data. The results show that compared with the method of predictive painting based on local slope, this approach can accurately predict missing near-offset seismic signals and demonstrates good robustness to noise.

Time Series Clustering Analysis Using Dynamic Time Warping Technique of Daily Rainfall in Bengkulu Province

Time Series Clustering Analysis Using Dynamic Time Warping Technique of Daily Rainfall in Bengkulu Province

A manifesto for coastal landscape governance: Reframing the relationship between coastal and landscape governance

There is an urgent demand for substantial reforms in the governance of coastal regions. Recent research advocates for a transformative shift in European coastal governance system towards a landscape governance approach. This perspective, informed by a narrative literature review on coastal and landscape governance, explores the potential value of coastal landscape governance, drawing on the Council of Europe Landscape Convention. Our results, presented in the form of a manifesto, underscore the need to move beyond political administrative boundaries and address all coastal landscapes as socio-ecological systems. It emphasises the necessity for the State to recognise them as a public and common good, establishing a specific governance arena with dedicated actors and institutions. The manifesto also advocates for landscape justice through knowledge co production, urging transformative change and landscape based regional design to envision alternative futures. Additionally, it calls for regionalising coastal landscape governance and invites scholars from other transdisciplinary and interdisciplinary perspectives to contribute to this research agenda.

Exploring a method to reduce inconsistency in the group Analytic Hierarchy Process using the Delphi method and Nudge theory

This paper reports a new method based on a Delphi process to nudge responses of participants toward consistency in an Analytic Hierarchy Process (AHP) study. We hypothesized that data collected during an early round of an AHP study could establish ranges of answers for later rounds that would improve consistency of responses. In our design, the Delphi method provided an effective framework for feedback of the bounds calculated between rounds. Using Delphi within the AHP to nudge answers of experts toward answers that promote consistency was the focus of our method. We propose an application using four mini scenarios depicting alternate futures for the management of genetic modification technologies. We found that in most instances our nudges improved the consistency of responses in sequential Delphi rounds; however, in some instances, the Delphi suggestions of bounds were not followed by a sufficient number of participants and a small number of inconsistencies remained. The value of this work may be in its warning to other researchers who attempt to achieve consistency by nudging responses in a paired comparison AHP study, to be quite explicit in instructions about remaining within the given bounds and perhaps creating incentives for respondents to do so.

Reframing Ports with Ecological Concrete

Lukas Ley demonstrates how the use of “ecological concrete” taps the liveliness of shore ecologies in ways that contribute to recreating nearshore habitats and alternative ecological futures.

“The clock is ticking”: (dis)orientations to ageing and end-of-life care in advanced capitalism and care directives

This article engages Italian migrant experiences and enactments of futurity to problematize neoliberal anticipatory approaches to ageing and care. Stepping beyond the focus on atomized and agentic individuals and a singular imagined future defined by notions of advancement and progress, sistemazione (home, future, and security) offers ways of building alternative and relational futures within times and spaces of shared precarity. We draw on ethnographic fieldwork and interviews with Italian migrant families living in Adelaide, and a critical analysis of objects as “orienting devices,” to consider how a family heirloom, a 26-face handmade Italian clock made from the physical remnants of World War II, offers new ways of imagining care within spaces of ruin.

Experience sampling of suicidality, religiosity and spirituality in depression: Network analyses using dynamic time warping

BackgroundSuicidality is a clinically important and multifaceted phenomenon, frequently present in depressed subjects. Religiosity and spirituality (R/S) can have an attenuating as well as a reinforcing effect on suicidality. MethodsFrom two Dutch mental health care settings, a sample of 31 depressed and in- and outpatients with suicidal ideation, self-identifying as being religious or spiritual, was selected by convenience sampling. Using an experience sampling method (ESM) mobile application, during six days (mean of 42 assessments per subject), the association between symptoms of depression, suicidality, and specific positive-supportive affective R/S and positive psychology variables. For 28 participants symptom network plots on a group level, and on an individual level, were analyzed using dynamic time warping (DTW). ResultsParticipants were on average 35.7 years old, and 65 % were women. In the group-level undirected network, R/S variables were linked to positive psychology variables via a bridge function of inner peace. Changes in the experience of inner peace and enjoying a physical activity preceded changes of several other symptoms. A network dynamic appeared with a dense cluster of ‘positive psychology’ items. LimitationsOnly a limited number of R/S variables were included. ConclusionThe results of this study suggest that religiosity and spirituality function as meaningful factors in depression and suicidality in religiously or spiritually engaged persons. Experienced inner peace has a positive association with reasons to live. Experience sampling method data can be effectively analyzed using dynamic time warping. Exploring individual religious or spiritual engagement can prove important in treating suicidality and depression.

The Influence of Fatigue, Recovery, and Environmental Factors on the Body Stability of Construction Workers.

In the construction industry, falls, slips, and trips (FST) account for 42.3% of all accidents. The primary cause of FST incidents is directly related to the deterioration of workers' body stability. To prevent FST-related accidents, it is crucial to understand the interaction between physical fatigue and body stability in construction workers. Therefore, this study investigates the impact of fatigue on body stability in various construction site environments using Dynamic Time Warping (DTW) analysis. We conducted experiments reflecting six different fatigue levels and four environmental conditions. The analysis process involves comparing changes in DTW values derived from acceleration data obtained through wearable sensors across varying fatigue levels and construction environments. The results reveal the following changes in DTW values across different environments and fatigue levels: for non-obstacle, obstacle, water, and oil conditions, DTW values tend to increase as fatigue levels rise. In our experiments, we observed a significant decrease in body stability against external environments starting from fatigue Levels 3 or 4 (30% and 40% of the maximum failure point). In the non-obstacle condition, the DTW values were 9.4 at Level 0, 12.8 at Level 3, and 23.1 at Level 5. In contrast, for the oil condition, which exhibited the highest DTW values, the values were 10.5 at Level 0, 19.1 at Level 3, and 34.5 at Level 5. These experimental results confirm that the body stability of construction workers is influenced by both fatigue levels and external environmental conditions. Further analysis of recovery time, defined as the time it takes for body stability to return to its original level, revealed an increasing trend in recovery time as fatigue levels increased. This study quantitatively demonstrates through wearable sensor data that, as fatigue levels increase, workers experience decreased body stability and longer recovery times. The findings of this study can inform individual worker fatigue management in the future.

AlgoRitmo Literacies In Gaming: Leveraging Chicanx Praxis To Reimagine AI Systems

ABSTRACTThis article reports on findings from a social design‐based study conducted with an intergenerational group of youth, educators and researchers participating in the Learning to Transform (LiTT) Gaming Lab. We advance the notion of AlgoRitmo Literacies, to highlight the ingenuity of youth and educators as they used a tool called Character AI to author lore emerging within a virtual city called LiTT City. We conceptualize AlgoRitmo—a play on the word algorithm—as part inquiry and reflection (the algo or “something” of AI tools), and part action and future‐oriented (ritmo as in movement). Inspired by cosmogonies influenced by Coyolxauhqui, the fragmented Aztec moon goddess, this paper illustrates how young people reconfigure AI artifacts, reshape relationships with AI‐governed non‐playable characters, and repurpose AI tools to envision alternative futures and identities. In identifying AlgoRitmo Literacies, we provide examples of how ChicanX communities subvert ideologies embedded in AI through creative and ingenious interventions in video games and the construction of cyborg Chicanx subjectivities. This paper offers implications for how educators across content areas can leverage gaming, and AI tools, toward consequential literacy development.

Refined simulation and verification of blast loads on curved shells

Terrorist attacks and accidental explosions pose significant challenges to the security of public buildings. This study combines detailed test data from three different curved shells to simulate different blast scenarios, effectively validating the simulation of blast loads and blast waves. Based on dynamic time warping and blast damage criteria, a similarity algorithm for blast loads based on overpressure histories of multi-gauge is proposed, and the similarity of blast loads on curved shells under four different scenarios is systematically evaluated. The spatiotemporal distribution of blast pressure around curved shells is analyzed based on simulation results. This paper provides a convenient tool for predicting blast loads on complex building shapes and optimizing blast-resistant designs.

ANALYSIS OF RAINFALL IN INDONESIA USING A TIME SERIES-BASED CLUSTERING APPROACH

Indonesia has a tropical climate and has two seasons: dry and rainy. Prolonged drought can cause drought disasters, and rain can cause floods and landslides. According to information from the Meteorology, Climatology, and Geophysics Agency (BMKG), natural disasters such as floods and landslides due to heavy rains have been a severe problem in Indonesia for the past five years. Different regional characteristics can affect the intensity of rain that falls in every province in Indonesia. It can be grouped to determine which provinces have similar characteristics to natural disasters due to rainfall. Later, it can provide information to the government and the public so that they are more aware of natural disasters. So, it is necessary to research and classify provinces in Indonesia for rainfall with cluster analysis. The data used is secondary rainfall data taken from the official BMKG website. Cluster analysis of rainfall in 34 provinces in Indonesia used hierarchical and non-hierarchical methods in this study. The approach that is used in this research limits our clustering of the data. Further research with a machine learning approach is recommended. For the clustering method, the agglomerative hierarchical method includes single, average, and complete linkage. The non-hierarchical method includes k-medoids and fuzzy c-means. The cluster analysis results show that the dynamic time warping (DTW) distance measurement method with the average linkage method has the most optimal cluster results with a silhouette coefficient value of 0.813.

Resolving the Amino Acid Sequence of Aβ1‐42 at the Single‐Residue Level Using Subnanopores in Ultrathin Films

AbstractProteoforms are proteins derived from highly related genes or post translational modifications (PTMs) of the same protein. They share extremely similar primary structures but have varying functions. Unfortunately, protein de novo sequencing including specific PTM/mutation detection is still challenging. Herein, a nanopore‐based technique is reported to resolve the amino acid order of amyloid‐β (Aβ1‐42) with site specificity. Subnanopores are sputtered in 5 nm‐thick inorganic membranes with a sensing depth of 0.66 nm inferred by finite element analysis. Denatured molecules at 0.45 ng mL−1 translocate through subnanopores while the current traces are sampled at 500 kHz with rms noise <15 pA. Hundreds of blockades are clustered using machine learning, and multiple blockades are averaged to establish current consensus. Consensus traces strongly correlate with a linear model of amino acid volume of Aβ1‐42 at single residue resolution, with Pearson Correlation Coefficients (PCCs) of 0.81 ± 0.03 and 0.92 ± 0.03 before and after dynamic time warping (DTW). A scrambled version of Aβ1‐42 is tested for validation purposes. Deep learning classification reveals that different polypeptides generate distinct translocation fluctuating patterns, but variations become imperceptible for the same species measured across nanopores (Area Under the Curve, AUC 0.93 ± 0.05 vs 0.64 ± 0.12). Lastly, important PTMs and mutations are site‐specifically located along the primary structure, implying new potential clinical applications.

Spatial and temporal variation of biomass density beneath drifting fish‐aggregating devices in the western and central Pacific Ocean

AbstractWe analyzed three‐dimensional (3‐D) spatial and temporal variation of biomass density associated with drifting fish aggregating devices (dFADs) in the western and central Pacific Ocean. Detection depth of dFAD echosounder buoys was divided into three layers, for estimation of biomass density in each layer based on detected water volume. Temporal variation and spatial distribution of biomass density in each layer were compared. Similarity of biomass density gravity center shifts in each layer were assessed using the dynamic time warping (DTW) regularization algorithm. Biomass density varied regularly over ~1 month, with the 2nd and 3rd layers delayed ~14 days compared to the 1st layer. Biomass distribution range and density values were higher in the 1st layer than the 2nd and 3rd layers, but locations of maximum biomass density were close. The distribution of biomass density gravity centers between adjacent water layers were similar, with gravity centers of the 2nd and 3rd layers shifted over time in relationd to the 1st layer. Our study demonstrated that the aggregation behavior of species from different water layers attracted by dFADs were related, and emphasized the necessity for ecosystem‐based fisheries management.

The making(s) of more-than-human design: introduction to the special issue on more-than-human design and HCI

ABSTRACT Human activities have drastically altered the planet, with design playing a significant role. While design may intend to do good, its consequences are not always positive: from climate change to resource depletion to unforeseen social dynamics. These transformations also include ourselves, as our relationships with new technologies blur and complicate previous human and machine agency distinctions. Increasingly, design has become a matter of defining what it means to be human. This special issue explores the proposition that conventional human-centered design approaches may not adequately address the complex challenges we face, and that there is instead a need to ground design in more-than-human perspectives. This introduction outlines the evolving landscape of more-than-human design in the context of HCI. Articulating a series of emerging research trajectories, we aim to illuminate the transformative potential of more-than-human orientations to design, including how they both extend and depart from familiar lines of inquiry in HCI – for example, how designers are redefining data, interfaces, and responsibility, and reshaping posthuman knowledge through design. Ultimately, this special issue aims to explore new pathways for designing in the era of the more-than-human, challenging the perceived divide between practice and theory to imagine alternative futures for HCI.

Symptoms of a feather flock together? An exploratory secondary dynamic time warp analysis of 11 single case time series of suicidal ideation and related symptoms

Suicidal ideation fluctuates over time, as does its related risk factors. Little is known about the difference or similarities of the temporal patterns. The current exploratory secondary analysis examines which risk symptoms have similar time dynamics using a mathematical algorithm called dynamic time warping (DTW). Ecological momentary assessment data was used of 11 depressed psychiatric outpatients with suicidal ideation who answered three daytime surveys at semi-random sampling points for a period of three to six months. Patients with 45 assessments or more were included. Results revealed significant inter-individual variability in symptom dynamics and clustering, with certain symptoms often clustering due to similar temporal patterns, notably feeling sad, hopelessness, feeling stuck, and worrying.The directed network analyses shed light on the temporal order, highlighting entrapment and worrying as symptoms strongly related to suicide ideation. Still, all patients also showed unique directed networks. While for some patients changes in entrapment directly preceded change in suicide ideation, the reverse temporal ordering was also found. Relatedly, within some patients, perceived burdensomeness played a pivotal role, whereas in others it was unconnected to other symptoms. The study underscores the individualized nature of symptom dynamics and challenges linear models of progression, advocating for personalized treatment strategies.

Transient Attention Gates Access Consciousness: Coupling N2pc and P3 Latencies Using Dynamic Time Warping.

The N2pc and P3 event-related potentials (ERPs), used to index selective attention and access to working memory and conscious awareness, respectively, have been important tools in cognitive sciences. Although it is likely that these two components and the underlying cognitive processes are temporally and functionally linked, such links have not yet been convincingly demonstrated. Adopting a novel methodological approach based on dynamic time warping (DTW), we provide evidence that the N2pc and P3 ERP components are temporally linked. We analyzed data from an experiment where 23 participants (16 women) monitored bilateral rapid serial streams of letters and digits in order to report a target digit indicated by a shape cue, separately for trials with correct responses and trials where a temporally proximal distractor was reported instead (distractor intrusion). DTW analyses revealed that N2pc and P3 latencies were correlated in time, both when the target or a distractor was reported. Notably, this link was weaker on distractor intrusion trials. This N2pc-P3 association is discussed with respect to the relationship between attention and access consciousness. Our results demonstrate that our novel method provides a valuable approach for assessing temporal links between two cognitive processes and their underlying modulating factors. This method allows to establish links and their modulator for any two time-series across all domains of the field (general-purpose MATLAB functions and a Python module are provided alongside this paper).

Effects of rainfall pattern classification methods on the probability estimation of typhoon-induced debris-flow occurrence

ABSTRACT The frequent occurrence of typhoons causes geological disasters, such as debris flow and landslide, by bringing extreme rainfall events. Due to the lack of data collection on extreme rainfall events caused by typhoons, the relationship between rainfall patterns and debris flow has not been deeply studied. Therefore, based on hourly rainfall data during typhoons in Wenzhou from 1980 to 2017, this study used a variety of methods to classify the rainfall events and analyze the characteristics of typhoon-induced rainfall events and their impacts on the probability of debris-flow occurrence. Three classification techniques, including dynamic time warping, K-Means cluster, and self-organizing maps, are applied with two ways to normalize rainfall records, including dimensionless rainfall density curves and dimensionless rainfall cumulation curves, for extracting rainfall patterns from recorded 1 h rainfall data. The rainfall patterns are then used for the estimation of typhoon-induced debris-flow occurrence probability. Results show that different methods present different rainfall patterns. The probability of debris flows varies with different patterns of rainfall events. The research results help deepen the understanding of typhoon rainfall events and debris-flow disaster prevention in the region and contribute to regional flood control and disaster reduction.

Forecasting daily PM2.5 concentrations in Wuhan with a spatial-autocorrelation-based long short-term memory model

Accurate daily air pollution forecasts play a pivotal role in enabling government to implement timely emergency responses and helping alert individuals sensitive to air pollution to take preventive measures. The atmospheric continuity fosters spatial correlations among air pollutants at various locations, which is a factor frequently overlooked in contemporary research focused on harnessing data-driven models for air quality prediction. Therefore, this study proposed a Spatial-Autocorrelation-based Long Short-Term Memory (SALSTM) model for the daily forecasting in Wuhan, Hubei Province, China. Using a multivariate prediction approach with daily air pollution data and meteorological data from Wuhan, as well as air pollution data from surrounding cities, from 2021 to 2022 as input, the model was applied for projecting the daily PM2.5 for Wuhan during the year 2023 and conducting accuracy cross-validation. The results were compared with a univariate prediction approach utilizing the Autoregressive Integrated Moving Average (ARIMA) model and the original Long Short-Term Memory (LSTM) model. Furthermore, this study utilized Dynamic Time Warping (DTW) for feature selection in multivariate prediction, comparing the accuracy of prediction results before and after feature selection. Experimental results indicated that the SALSTM model, incorporating the DTW method, achieved a Root Mean Squared Error (RMSE) of 6.92 μg/m3, a Mean Absolute Error (MAE) of 4.07 μg/m3 and a coefficient of determination (R2) of 0.95. Compared to the univariate forecasting method, the three accuracy metrics RMSE, MAE, and R2 have improved by 54.74%, 58.68%, and 37.68%, respectively. In comparison with the original LSTM, the improvement is 23.79%, 30.90%, and 4.40%. In conclusion, the SALSTM model established in this study demonstrates accurate daily forecasting of PM2.5 concentrations.

Improvement and Application of Hale’s Dynamic Time Warping Algorithm

Due to the different generation and propagation mechanisms of P- and S-waves, there may be significant differences in the seismic data collected by the two, which poses a great obstacle to the time domain matching of P- and S-waves in multiwave exploration. Furthermore, the quality and accuracy of the matching effect will directly affect the subsequent multiwave joint inversion and interpretation effect. Therefore, the study of P and S-wave-matching methods plays a crucial role in seismic exploration. In 2013, Hale improved the classical Dynamic Time Warping (DTW) algorithm applied to solve the problem of speech recognition, and obtained the DTW algorithm suitable for solving the matching of P-waves and S-waves. The seismic wave-matching results generated by this algorithm are horizontal discontinuous (different trajectories) and need further processing. This study analyses the algorithm based on simulations of seismic waves using Ricker wavelets. In response to existing problems, this paper proposes strategies to improve the DTW algorithm. The algorithm in this study significantly improved the continuity of the registration results of the actual seismic wave data in the horizontal direction (different traces).

The invisible pandemic of antimicrobial resistance and its possible endings.

Antimicrobial resistance (AMR) is often framed as a 'silent pandemic,' an invisible crisis unfolding beyond the public gaze. This unseen emergency narrative fuels policy responses aimed at re-establishing human control over antimicrobial use and benefits. In this commentary, we critique the reduction of AMR to a homogenising framework - a product of long-standing paradigms for disease control and elimination. We argue that AMR stems not merely from microbial exposure to drugs, but also involves broader anthropocentric practices. We assert that merely extending AMR concerns to encompass environmental factors is insufficient. Instead, we advocate for a paradigm shift towards a holistic understanding that respects the evolutionary adaptability and survival strategies of microbial life itself. Consequently, a fundamental re-evaluation of large-scale antibiotic use and production is necessary. Rather than seeking to control AMR as a pandemic, we propose exploring the inherent complexity and interdependence of AMR issues. Our proposition advocates for alternative futures that foster collaborations between human and non-human actors, ultimately envisioning a shift in human-microbial relationships towards more integrative health strategies.