Time Warping Research Articles

Kansas agriculture in 2050: a pathway for climate-resilient crop production

IntroductionAdvances in farming technologies and practices have helped farmers to increase crop yields, but continued production increases – while needed to feed a growing global population – are being hindered by climate change and other environmental challenges. Kansas, a globally important region for wheat production, is already experiencing threats to staple crop production. This study explores one possible alternative future for Kansas crop production in which farmers could, by crop switching, continue to grow nutrient-rich crops while responding to hotter temperatures and increasing water demand.MethodsWe used a combination of climate and crop modeling with simple water budgets to identify optimal crop mixes under anticipated climatic and water constraints. Interviews with Kansas farmers helped identify feasible crop switching options: sorghum instead of corn (maize), winter rye and winter oats instead of winter wheat, and millet instead of soybeans.ResultsOur analysis suggests that a sizeable proportion of current Kansas cropland would need to shift to these alternative crops by 2050 to meet anticipated water constraints and produce equivalent nutritional value under projected climate conditions. Alternative crops could increase from 16% of Kansas' crop area in 2021 to 43% of the area in 2050, resulting in a reduction in 2050 crop water demand of 12% relative to that of the current crop mix. This crop water demand reduction would be concentrated in parts of the state that will experience the greatest change in water needs between today and mid-century due to changing climate conditions.DiscussionOur analysis shows that, by changing (diversifying) the mix of crops grown, it is biophysically possible for crop production in Kansas to be both sustainable and resilient under future climate conditions. However, achieving a more climate-resilient crop mix on the ground, in Kansas and elsewhere, will require major shifts in the broader agricultural system. Food companies, agricultural lenders, and policymakers can play a key role in enabling farmers to adapt cropping systems in the face of climate and environmental challenges.

Mikä aiheuttaa ilmastonmuutoksen?

This article discusses the transformation of climate change science from scientific knowledge to Finnish upper secondary school geography curricula and textbooks from 1985 to 2023. Scientific knowledge transforms into teachable knowledge through the transformation process at the societal, institutional, and classroom levels. In our analysis, we apply the idea of powerful knowledge and use three levels of geographical powerful knowledge, including geographical knowledge, critical and conceptual knowledge, and propensity to imagine alternative futures. The research material consists of five framework curricula from 1985 to 2019 and a total of thirteen upper secondary school geography textbooks that are based on the curricula. Our findings suggest that climate science knowledge has transformed slowly into societal curricula. However, in textbooks, scientific knowledge has transformed more rapidly. According to our results, the transformation may not necessarily occur linearly, first at the societal level and finally in classrooms.

Exploring Alternative Futures Focusing on the Science and Technology Policy of Kim Jong‐un's Regime*

Science and technology policy of Kim Jong‐un's regime in North Korea is more strategic and aggressive than ever before. Under the tight economic sanctions led by the United States and its partners, the young leader makes a desperate effort to boost science and technology (S&T) for maintaining North Korea's political stability and achieving economic development. Despite growing concerns over nuclear and security issues, studies addressing the alternative futures of North Korea are mainly focused on the economy, nuclear weapons, and stability of the regime. This study explores various alternative futures for North Korea's S&T policy, which are different from the viewpoints of most existing studies. To envision and propose alternative futures for North Korea's S&T policy in 2050, we try to link uncertainty levels and types of futures based on Voros' future types and utilize Dator's alternative future model. Based on alternative futures, this study suggests a new perspective to better understand the plausible futures of S&T policies and strategies for a preferable future of the country.

Uncovering urban water consumption patterns through time series clustering and entropy analysis

Sustainable urban water management is crucial for meeting the growing demands of urban populations. This study presents a novel approach that combines time series clustering, seasonal analysis, and entropy analysis to uncover residential water consumption patterns and their drivers. Using a three-year dataset from the SmartH2o project, encompassing 374 households, we identify nine distinct water consumption patterns through time series clustering, leveraging Dynamic Time Warping (DTW) as the optimal similarity measure. Multiple linear regression reveals key household characteristics influencing water usage behaviors, such as the number of bathrooms and appliance efficiency ratings. Seasonal analysis uncovers temporal dynamics, highlighting shifts towards lower consumption during summer months and increased variability in transitional seasons. Entropy analysis quantifies the diversity and complexity of water consumption at both cluster and household levels, informing targeted interventions. This comprehensive, granular approach enables the development of personalized water conservation strategies and policies, empowering water utilities to optimize resource management and contribute to sustainable urban water practices.

Network dynamics of self-compassion, anxiety, and depression during eating disorder therapy.

Recovery processes during residential treatment for eating disorders, especially in patients with a history of maltreatment, are insufficiently understood. This study aimed to explore the temporal relationships among comorbid factors, including depression, anxiety, and self-compassion, with the influence of childhood maltreatment. Using Dynamic Time Warp (DTW), weekly scores from the Symptom Checklist-5, Eating Disorder Examination, and Self-Compassion Scale were analysed over 12weeks. The study generated undirected and directed networks to identify influential symptoms in a transdiagnostic sample, comparing patients with and without childhood maltreatment. The study included 124 patients with eating disorders (ED) (97% women), mean age of 30.9years (SD=9.7, range 18-61years). Diagnoses included anorexia nervosa (26%), bulimia nervosa (38%), and other specified feeding and eating disorders (36%). The directed DTW network showed that hopelessness, worrying, and restlessness had the highest out-strength, predicting changes in self-compassion and ED behaviour. In maltreatment cases, hopelessness and low acceptance predicted changes, while worry, restlessness, and nervousness were predictive in non-maltreatment cases. Temporal network analyses suggest that a change in hopelessness, worrying, and restlessness drives symptom improvement in ED behaviour and the development of self-compassion during residential treatment. These processes vary between patients with and without a history of childhood maltreatment separately, indicating the need for further analyses.

Using torsional wave elastography to evaluate spring pot parameters in skin tumor mimicking phantoms

Estimating the tissue parameters of skin tumors is crucial for diagnosis and effective therapy in dermatology and related fields. However, identifying the most sensitive biomarkers require an optimal rheological model for simulating skin behavior this remains an ongoing research endeavor. Additionally, the multi-layered structure of the skin introduces further complexity to this task. In order to surmount these challenges, an inverse problem methodology, in conjunction with signal analysis techniques, is being employed. In this study, a fractional rheological model is presented to enhance the precision of skin tissue parameter estimation from the acquired signal from torsional wave elastography technique (TWE) on skin tumor-mimicking phantoms for lab validation and the estimation of the thickness of the cancerous layer. An exhaustive analysis of the spring-pot model (SP) solved by the finite difference time domain (FDTD) is conducted. The results of experiments performed using a TWE probe designed and prototyped in the laboratory were validated against ultrafast imaging carried out by the Verasonics Research System. Twelve tissue-mimicking phantoms, which precisely simulated the characteristics of skin tissue, were prepared for our experimental setting. The experimental data from these bi-layer phantoms were measured using a TWE probe, and the parameters of the skin tissue were estimated using inverse problem-solving. The agreement between the two datasets was evaluated by comparing the experimental data obtained from the TWE technique with simulated data from the SP- FDTD model using Pearson correlation, dynamic time warping (DTW), and time-frequency representation. Our findings show that the SP-FDTD model and TWE are capable of determining the mechanical properties of both layers in a bilayer phantom, using a single signal and an inverse problem approach. The ultrafast imaging and the validation of TWE results further demonstrate the robustness and reliability of our technology for a realistic range of phantoms. This fusion of the SP-FDTD model and TWE, as well as inverse problem-solving methods has the potential to have a considerable impact on diagnoses and treatments in dermatology and related fields.

ChinaSoyArea10m: a dataset of soybean-planting areas with a spatial resolution of 10 m across China from 2017 to 2021

Abstract. Soybean, an essential food crop, has witnessed a steady rise in demand in recent years. There is a lack of high-resolution annual maps depicting soybean-planting areas in China, despite China being the world's largest consumer and fourth-largest producer of soybean. To address this gap, we developed the novel Regional Adaptation Spectra-Phenology Integration method (RASP) based on Sentinel-2 remote sensing images from the Google Earth Engine (GEE) platform. We utilized various auxiliary data (e.g., cropland layer, detailed phenology observations) to select the specific spectra and indices that differentiate soybeans most effectively from other crops across various regions. These features were then input for an unsupervised classifier (K-means), and the most likely type was determined by a cluster assignment method based on dynamic time warping (DTW). For the first time, we generated a dataset of soybean-planting areas across China, with a high spatial resolution of 10 m, spanning from 2017 to 2021 (ChinaSoyArea10m). The R2 values between the mapping results and the census data at both the county and prefecture levels were consistently around 0.85 in 2017–2020. Moreover, the overall accuracy of the mapping results at the field level in 2017, 2018, and 2019 was 77.08 %, 85.16 %, and 86.77 %, respectively. Consistency with census data was improved at the county level (R2 increased from 0.53 to 0.84) compared to the existing 10 m crop-type maps in Northeast China (Crop Data Layer, CDL) based on field samples and supervised classification methods. ChinaSoyArea10m is very spatially consistent with the two existing datasets (CDL and GLAD (Global Land Analysis and Discovery) maize–soybean map). ChinaSoyArea10m provides important information for sustainable soybean production and management as well as agricultural system modeling and optimization. ChinaSoyArea10m can be downloaded from an open-data repository (DOI: https://doi.org/10.5281/zenodo.10071427, Mei et al., 2023).

Enhancing participatory governance in biosphere reserves through co-creation of transdisciplinary and intergenerational knowledge

In the context of the current global climate and biodiversity crisis, urgent action is needed to improve participatory and co-productive governance in territories under sustainability directives, such as biosphere reserves. These territories comprise a global network with the potential to apply and replicate sustainability actions, improve livelihoods, and boost climate change resilience while reducing impacts on the environment and the biodiversity in all continents. In the biosphere reserves network’s 50 years of existence, progress and setbacks have been reported in different regions around the world, and there is an urgent need to envision alternative futures. In this contribution, we describe the results and reflections of our ‘Open Academy’ that enhanced the participatory governance in La Campana–Peñuelas Biosphere Reserve in Central Chile. We crossed the traditional assessment with the principles of transdisciplinary and intergenerational knowledge co-creation. The results show that the traditional performance assessment shows a generally poor performance and reveals the weaknesses of the governance system of the reserve’s management. The extraction of water by mining and agroindustry, uncontrolled urbanization, wildfires, weak social participation, and low integration of indigenous communities are crucial issues for the performance of biosphere reserves. On the other hand, these territories have the potential as models toward post-extractive economies. Enhancing participatory governance, biosphere reserves shall serve as a) agents for configuring the future as an eco-social pact with the territory; b) pilot test areas for alternative futures; and c) places to promote the social collective as a conscious agent of the future.

Combining features on vertical ground reaction force signal analysis for multiclass diagnosing neurodegenerative diseases

Neurodegenerative diseases (NDDs), which are caused by the degeneration of neurons and their functions, affect a significant part of the world’s population. Although gait disorders are one of the critical and common markers to determine the presence of NDDs, diagnosing which NDD the patients have among a group of NDDs using gait data is still a significant challenge to be addressed. In this study, we addressed the multi-class classification of NDDs and aim to diagnose Parkinson’s disease (PD), Amyotrophic lateral sclerosis disease (AD), and Huntington’s disease (HD) from a group containing NDDs and healthy control subjects. We also examined the impact of disease-specific identified features derived from VGRF signals. Detrended Fluctuation Analysis (DFA), Dynamic Time Warping (DTW) and Autocorrelation (AC) were used for feature extraction on Vertical Ground Reaction Force (VGRF) signals. To compare the performance of the features, we employed Support Vector Machines, K-Nearest Neighbors, and Neural Networks as classifiers. In three-class problem addressing the classification of AD, PD and HD 93.3% accuracy rate was achieved, while in the four classes case, in which NDDs and HC groups were considered together, 93.5% accuracy rate was yielded. Considering the disease-specific impact of features, it is revealed that while DFA based features diagnose patients with AD with the highest accuracy, DTW has been shown to be more successful in diagnosing PD. AC based features provided the highest accuracy in diagnosing HD. Although gait disorder is common for NDDs, each disease may have its own distinctive gait rhythms; therefore, it is important to identify disease-specific patterns and parameters for the diagnosis of each disease. To increase the diagnostic accuracy, it is necessary to use a combination of features, which were effective for each disease diagnosis. Determining a limited number of disease-specific features would provide NDD diagnostic systems suitable to be deployed in edge-computing environments.

Personal Time and Transmigration Time Travel

Lewis argued that although paradoxes such as the famous Grandfather Paradox can be solved, only a limited set of time travel fiction is consistent. In this paper, I discuss how to extend a Lewisian approach to a class of time travel fiction not considered by Lewis: transmigration or mental time travel fiction. To this end, Lewis’s definition of personal time needs refining, and this is the primary focus of my paper. I discuss some alternative refinements of Lewis’s definition: a Solely Mental definition and a Causal definition. I end by also applying these definitions to cases of reverse aging.

Drawing Transitions

Climate change and the associated imperatives for decarbonization and energy transition are changing the stakes of urbanization. There is little doubt that the next quarter century will be one of the most decisive, disruptive, and possibly transformative times for human societies and our coexistence with planetary systems. This is a critical moment for the agency of design and its capacity to cocreate these transformations. This is relevant for the material realities of the constructed environment as well as for its forms of representation—the unbuilt, experimental, and speculative imaging of future worlds. This project is focused on the latter. Climate transitions, and in particular just climate transitions, are less of a problem of technology, but moreover a problem of imagination; the imagination of socioenvironmental coexistences at both the intimate scale of individual relations and at the territorial and temporal scale and complexity of the crises that society faces. This visual essay positions the potential agency of scroll drawings as tools for thinking with, in the visioning and speculative prototyping for the transition toward alternative futures.

Temporal dynamics of depressive symptoms and cognitive decline in the oldest old: dynamic time warp analysis of the Leiden 85-plus study.

The prevalence of depressive symptoms and cognitive decline increases with age. We investigated their temporal dynamics in individuals aged 85 and older across a 5-year follow-up period. Participants were selected from the Leiden 85-plus study and were eligible if at least three follow-up measurements were available (325 of 599 participants). Depressive symptoms were assessed at baseline and at yearly assessments during a follow-up period of up to 5years, using the 15-item Geriatric Depression Scale (GDS-15). Cognitive decline was measured through various tests, including the Mini Mental State Exam, Stroop test, Letter Digit Coding test and immediate and delayed recall. A novel method, dynamic time warping analysis, was employed to model their temporal dynamics within individuals, in undirected and directed time-lag analyses, to ascertain whether depressive symptoms precede cognitive decline in group-level aggregated results or vice versa. The 325 participants were all 85years of age at baseline; 68% were female, and 45% received intermediate to higher education. Depressive symptoms and cognitive functioning significantly covaried in time, and directed analyses showed that depressive symptoms preceded most of the constituents of cognitive impairment in the oldest old. Of the GDS-15 symptoms, those with the strongest outstrength, indicating changes in these symptoms preceded subsequent changes in other symptoms, were worthlessness, hopelessness, low happiness, dropping activities/interests, and low satisfaction with life (all P's < 0.01). Depressive symptoms preceded cognitive impairment in a population based sample of the oldest old.

Fashioning identity and resistance: Shilpa Chavan's HUM (we/us)

ABSTRACT Shilpa Chavan's short film HUM (we/us) blends fashion design, filmmaking, and social critique into a powerful artistic statement. This essay examines Chavan's innovative approach, exploring how she has collaborated to craft a visual and sonic narrative about identity, resistance, and community through a lens of postcolonial India. The analysis employs multiple theoretical frameworks, including gender theory, critical fashion practices, postcolonial studies, psychoanalysis, and sound analysis. Chavan's use of recycled materials and hybrid aesthetics creates a critical space for reimagining fashion's activist role in society, challenging dominant norms while envisioning new possibilities. The essay situates HUM (we/us) within the context of contemporary Indian and international art, drawing comparisons with works by artists such as Tejal Shah, Pushpamala N., Isaac Julien, and Zanele Muholi. It also examines the film's social and cultural impact, its critical reception, and its potential influence on future artistic practices. Through detailed analysis, HUM (we/us) emerges as a compelling example of interdisciplinary art practice sparking meaningful dialogue about complex social issues, demonstrating the power of art to challenge hegemonic narratives and imagine alternative futures.

Temperature Compensation using Constraint based Dynamic Time Warping in Guided Waves

Introduction: Guided waves being highly sensitive to temperature variations, temperature compensation algorithms, such as Optimal Baseline Selection, Baseline Signal Stretch and Scale Transform demonstrate effective performance under limited conditions. Dynamic Time Warping (DTW) has shown excellent compensation performance, however it comes with a substantial computational burden of O(N*N), where N represents the number of samples in each signal. Methodology: DTW works by construction of cost matrix that maps every point in one time series to all the points in the other time series, that results in complexity O(N*N).This problem can be solved by narrowing the search window using global constraints. The two most common constraints in the literature are the Sakoe-Chiba band and the Itakura Parallelogram. This paper uses Sakoe-Chiba band as a global constraint, the Sakoe-Chiba band is defined through a window size parameter which determines the largest temporal shift allowed from the diagonal. Temperature compensation performance of DTW Sakoe-Chiba is tested using the available OGW dataset #2 provided by Jochen Moll et al. The OGW dataset has been generated using 12 number of piezoelectric transducers bounded to CFRP (Carbon Fiber Reinforced Plastic) plate and varying the temperature from 20-60 degree with an increment of 0.5 degree. The signal is recorded for 1300 microseconds, that results in 13000 samples/time stamps(N). Initial results show that the Sakoe-Chiba constraint based DTW performs well and at a significantly lower cost, as indicated below. Result: The largest temporal shift (r) is estimated using Local Peak Coherence (LPC),for signal at 20 and 60 degree r is 135 samples. This results in complexity O(N*2r) which is very less than conventional DTW compensation technique O(N*N). In the final paper, the analysis will be replicated across a range of temperatures, and the performance of damage detection will be thoroughly discussed.

Efficient semi-supervised clustering with pairwise constraint propagation for multivariate time series

Semi-supervised clustering is an effective method, which improves the clustering performance based on pairwise constraints. However, state-of-the-art methods suffer from two issues: 1) due to the high dimensionality and multiple variables of multivariate time series (MTS) data, the competitive similarity approach DTW is time consuming on large-scale MTS data. 2) Traditional semi-supervised clustering methods could not make full use of pairwise constraints information, which affects clustering performance. To deal with these issues, we propose an efficient semi-supervised clustering with pairwise constraint propagation for MTS data. First, two approximate distance measure methods are designed based on dynamic time warping (DTW) from the perspectives of boundary and dimension reduction, which greatly improve the efficiency of clustering without sacrificing its accuracy. Then, a graph-based clustering method with pairwise constraints propagation (GCPCP) is advanced on multivariate time series data. In GCPCP, the pairwise constraint propagation matrix and the affinity matrix are jointly optimized to exploit the dependence between them, which finally improves the clustering performance. Experimental results on 12 multivariate time series datasets show the effectiveness and efficiency of our proposed method.

The Archive as a Battlefield for the Future: Anti-colonial Struggles and Insurgent Temporality

Anti-fascist, anti-colonial, and anti-imperial struggles of the twentieth century provide important intellectual and political resources as we collectively seek to forge alternative futures in the face of our depleted political present. Setting out with this analytical and political concern, this article is interested in the question of what it means to visit the archive of past struggles and past insurgencies. It argues for a critical historical method that foregrounds the agency of the insurgents themselves—the agency of women, slaves, foreigners, the poor and the peasants—as the subjects of an insurgent universality (Tomba 2019). The article explores the international brigades fighting the fascists during the Spanish Civil War, the peasants gathered to affirm the Second Havana Declaration, and the audience that comprised Thomas Sankara's speech in Harlem. The artifacts of insurgency, the authors suggest, are embodied expressions of collective struggles written not by one but by a multiplicity. They are the artifacts of a collective political subjectivity united in their insurgent claim to disrupt the temporal and spatial delimitations of politics in modernity—enacting an insurgent universality. The archive of insurgent universality is our future for it provides compelling ways to think, write, and act politically toward the present.

‘Already doing the work’: social work, abolition and building the future from the present

Social work internationally is currently subject to debate. Some call for the abolition of social work, detailing legacies of harm, inadequate practices and theoretical limitations. Central to abolitionist thought is the tradition of community work to build alternative futures in the present, an area currently receiving less attention. This article adopts an auto-ethnographic method, drawing on the authors’ experiences of social work in the UK – in childhood and as a professional career, respectively – to consider the limitations of social work responses to childhood harm, alongside existing community harm-reduction practices. Four themes are identified that capture the limitations of social work intervention, as well as acts of community care and resistance. These are: the extent of engagement with context and community knowledge; resources for caring; legacies of harm; and the role of social work in relation to community harm-reduction work. Implications for research methods and social work practice are discussed.

The desire to learn: the alienation and reimagining of pedagogy on YouTube, Twitch and TikTok

ABSTRACT This paper aims to understand the perspectives of young people involved in online learning communities on what they consider valuable for their learning. We do this by bringing their experiences with online community pedagogies (OCPs) into conversation with their critiques of school pedagogies (SPs) to see whether we can understand this interaction as generative of ‘alternative’ pedagogies. Whereas previously alternative pedagogies were understood to arise from ‘new’ technologies, we argue that such pedagogies arise from needs created by youth’s experiences with navigating these different pedagogical contexts. We interviewed 37 community members from six learning communities on YouTube, Twitch and TikTok. We show that youth perceive school as controlling, imposing pressure to perform, and disconnected from what they think matters and qualifies expertise. Based on their online experiences, youth emphasised the importance of control, experimentation space, and being enabled to act on societal issues. Using Deleuze and Guattari’s conceptualisation of ‘desire’, and Appadurai’s ‘global imaginaries’, we interpret OPs dialoguing with youth’s alienating perspectives on schooling as ‘reimagined pedagogies’ that act as collective voices for global (re-)imaginaries of school. We conclude with the importance of taking youth’s experiences with alienation and OPs seriously as objects of thought for alternative pedagogic futures.

Clustering plasma concentration-time curves: applications ofunsupervised learning in pharmacogenomics

ABSTRACT Pharmaceutical researchers are continually searching for techniques to improve both drug development processes and patient outcomes. An area of recent interest is the potential for machine learning (ML) applications within pharmacology. One such application not yet given close study is the unsupervised clustering of plasma concentration-time curves, hereafter, pharmacokinetic (PK) curves. In this paper, we present our findings on how to cluster PK curves by their similarity. Specifically, we find clustering to be effective at identifying similar-shaped PK curves and informative for understanding patterns within each cluster of PK curves. Because PK curves are time series data objects, our approach utilizes the extensive body of research related to the clustering of time series data as a starting point. As such, we examine many dissimilarity measures between time series data objects to find those most suitable for PK curves. We identify Euclidean distance as generally most appropriate for clustering PK curves, and we further show that dynamic time warping, Fréchet, and structure-based measures of dissimilarity like correlation may produce unexpected results. As an illustration, we apply these methods in a case study with 250 PK curves used in a previous pharmacogenomic study. Our case study finds that an unsupervised ML clustering with Euclidean distance, without any subject genetic information, is able to independently validate the same conclusions as the reference pharmacogenomic results. To our knowledge, this is the first such demonstration. Further, the case study demonstrates how the clustering of PK curves may generate insights that could be difficult to perceive solely with population level summary statistics of PK metrics.

Adaptive Sliding Window–Dynamic Time Warping-Based Fluctuation Series Prediction for the Capacity of Lithium-Ion Batteries

Accurately predicting the capacity of lithium-ion batteries is crucial for improving battery reliability and preventing potential incidents. Current prediction models for predicting lithium-ion battery capacity fluctuations encounter challenges like inadequate fitting and suboptimal computational efficiency. This study presents a new approach for fluctuation prediction termed ASW-DTW, which integrates Adaptive Sliding Window (ASW) and Dynamic Time Warping (DTW). Initially, this approach leverages Empirical Mode Decomposition (EMD) to preprocess the raw battery capacity data and extract local fluctuation components. Subsequent to this, DTW is employed to forecast the fluctuation sequence through pattern-matching methods. Additionally, to boost model precision and versatility, a feature recognition-based ASW technique is used to determine the optimal window size for the current segment and assist in DTW-based predictions. The study concludes with capacity fluctuation prediction experiments carried out across various lithium-ion battery models. The results demonstrate the efficacy and extensive applicability of the proposed method.