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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]

In this study, an analysis of hourly air temperatures in four groups of 32 stations from the UK highland (5 stations), UK lowland (4 stations), Italian highland (11 stations), and Italian lowland (12 stations) at different altitudes was carried out over the period from 2002 to 2021. The study aimed to examine the trends of each hour of the day during this period, over different averaging time windows (10‐day, 30‐day, and 60‐day). The trends were computed using the Mann–Kendall trend test and Sen's slope estimator. The similarity of trends within and across the groups of stations was assessed using the hierarchical clustering with dynamic time warping technique. An additional analysis was conducted to show the correlation of trends among the group of stations using the correlation distance matrix. Hierarchical clustering and distance correlation analysis show trend similarities and correlations, also indicating dissimilarities among different groups. Using 30‐day averages, significant warming trends in specific months at the Italian stations are evident, especially in February, July, August, and December. The UK highland stations did not show statistically significant trends, but clear pattern similarities were found within the groups, especially in certain months. The ultimate goal of this article is to provide insights into temperature dynamics and climate change characteristics on regional and diurnal scales. [ABSTRACT FROM AUTHOR]

ABSTRACT The Coupled Model Inter‐comparison Project phase 6 (CMIP6) provides a suite of general circulation models (GCMs) and Socioeconomic Shared Pathways (SSPs) primarily for continental‐scale climate assessments. However, adapting these models for sub‐national assessments, particularly in countries with varied geography like Ecuador, and for complex variables such as precipitation, introduces challenges, including uncertainties in selecting appropriate GCMs and SSPs. To address these issues, we adopt a biogeographical approach that integrates regional climatic variations. Our analysis explores 26 GCMs, four SSP scenarios and four 20‐year time frames from WorldClim to evaluate discrepancies between the GCM precipitation projections, historical data and national climate projections across five Ecuadorian bioregions. This approach enabled us to sort the GCMs by annual precipitation medians, classify their monthly precipitation using Dynamic Time Warping (DTW) clustering, and develop ensembles highlighting both the largest and average precipitation anomalies within and beyond the bioregions. Among the 26 models examined, 16 projected an increase in annual precipitation in Ecuador, especially during the wet seasons, with the BCC‐CSM2‐MR model showing peak values, notably in the Choco region and eastern Amazon basin. Conversely, 10 models, with CMCC‐ESM2 showing the largest decreases, projected reduced precipitation across almost all Ecuadorian territories, except the Choco region. The largest reductions were in the Amazon basin, raising concerns about reduced precipitation. Discrepancies, primarily in the Andes and Galapagos bioregions, reveal the challenges posed by their complex topography and insular environments. While the GCMs captured spatial patterns of ENSO, our research was constrained to 20‐year averages, making direct comparison with historical records infeasible, highlighting the need for further research with shorter time frames and finer spatial resolutions. The variability in precipitation was linked to geographical factors, GCM configurations and unexpected SSP outcomes. Therefore, selecting GCMs and climatic indices tailored to specific bioregions is recommended for effective climate change impact assessments. [ABSTRACT FROM AUTHOR]

The degradation of clamping force in the core support barrel, which forms the internal structure of a nuclear power plant, has the potential to significantly impact the plant's safety and reliability. Previous studies have concentrated on the detection of clamping force degradation but have been constrained in their ability to identify the precise size and position. This study proposes a novel methodology for diagnosing the size and position of clamping force degradation in core support barrels, combining deep-learning techniques and dynamic time warping (DTW) algorithms. DTW is applied to the magnitude data of the ex-core neutron noise signal obtained in the frequency domain, thereby enabling the effective learning of changes in sensor data values. Moreover, autoencoder-based (AE-based) representation learning is utilized to extract features of the data, preventing overfitting and thus enhancing the robustness of the model. The experiment results demonstrate that the size and position of clamping force degradation can be accurately predicted. It is expected that this research will contribute to enhancing the precision and efficiency of internal structure monitoring in nuclear power plants. [ABSTRACT FROM AUTHOR]

The ever-growing use of digital systems has led to the accumulation of vast datasets, particularly time series, depicting the temporal evolution of variables and systems. Analysing these time series presents a tremendous challenge due to their inherent complexity and heterogeneity. Addressing an industrial need in the pharmaceutical wholesale sector, this paper introduces a new clustering method for time series: TSCAPE. The TSCAPE method uses a distance matrix calculated using dynamic time warping, followed by multidimensional scaling to project time series into a 2D Euclidean space, thus improving the last clustering stage by K-Means. Unlike conventional techniques, this approach, based on clustering of representation of distances in a Euclidean plane rather than on the curve shape, directly enhances the efficiency of the clustering process. The methodology exhibits significant potential for diverse applications, accommodating varied data types and irregular time series shapes. The research compares multiple variants and proposes metrics to assess their effectiveness on two open-access datasets. The results demonstrate the method's superiority over "only distance comparison clustering techniques", like dynamic time warping and K-Means, with future prospects aimed at predictive applications and refining the clustering process by exploring alternative, more powerful clustering algorithms. [ABSTRACT FROM AUTHOR]

Deployment of renewable energy sources has caused a seismic shift in the world energy arena. Individual and coordinated efforts across countries and regions are shaping the world for the future, including business models which are supported globally to achieve net zero goals by 2050. This has resulted in changing cost structures, prices, and investment in energy uses, and approaching towards most sustainable environments for most of the regions. Our aim in this paper is to identify clusters of countries, where within a particular cluster, the levels of deployment of renewable energy sources are similar while across clusters, they are different. We propose a time series clustering method capturing the time-varying features of the renewable energy time series of 130 countries to enable the assessment of how similar or how different the usage is in relation to the Organisation for Economic Co-operation and Development (OECD) status of countries, their regional location and their income grouping. We use Dynamic Time Warping (DTW) which is a method that calculates an optimal match between two given time series with certain restrictions. Using DTW, we the adopt the Partitioning Around Medoids technique in a fuzzy framework to obtain cluster solutions. Our analysis shows that both 4-cluster and 5-cluster solutions best capture country separation based on OECD status, regional location and income grouping. [ABSTRACT FROM AUTHOR]

In this study, an effective diagnostic method for wiring networks based on reflectometry technique, the K-Nearest Neighbour (KNN) classifier, and Dynamic Time Warping (DTW) was developed. The proposed approach relies on a two-fold process: the offline process and the online process. In the offline process, basic circuit elements-based modelling and the Finite-Difference Time-Domain (FDTD) numerical method are employed to simulate Time Domain Reflectometry (TDR) and generate necessary datasets simultaneously. These datasets are then used to train and obtain classification and regression models. The DTW distance is combined with the KNN classifier to derive these models. In the online process, the models are utilised to identify, locate, and characterise faults in Wiring Networks Under Test based on their TDR response. Numerical and experimental results are presented to illustrate the performance and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

The rise of data from smart city services and the emergence of advanced algorithms have emphasized the need for a deeper understanding of the underlying patterns of urban mobility and the potential opportunities for more efficient urban planning and policymaking. By identifying communities with similar mobility patterns, researchers can gain better insights into how people move within and between different regions. Traditional community detection methodologies mainly focused on identifying geographic communities defined by shared locations. However, this perspective overlooks the broader definition of communities. Communities can also emerge from shared social interests, collective actions, and activity patterns. This implies that geographically disparate areas might exhibit similar patterns, which is particularly relevant in the study of mobility pattern similarity. Rather than focusing on regions with strong spatial interactions, this study aims to identify regions that show more similarity to each other than to other areas. Such similarities may indicate parallel urban functionalities, which are essential for effective urban planning and policymaking. To bridge this gap, our study introduces a customized community detection algorithm that employs Dynamic Time Warping (DTW) to quantitatively assess the similarity in mobility patterns between different communities. This advanced approach not only improves the identification of comparable mobility patterns but also demonstrates remarkable flexibility, broadening its application to various other social phenomena. The results demonstrate the effectiveness of the proposed model in capturing complex mobility patterns across different locations and days of the week. [ABSTRACT FROM AUTHOR]

This study evaluates mathematical tools (principal component analysis, dynamic time warping, and the Kolmogorov–Smirnov hypothesis test) to analyse global and local data from dynamic signatures to reduce subjectivity and increase the reproducibility of handwriting examination using a two-step approach. A dataset composed of 1 800 genuine signature samples, 870 simulated signatures, and 60 disguises (30 formally similar or "autosimulated" and 30 random but different from usual) provided by 30 volunteers was collected. The first step involved global data analysis using principal component analysis and a hypothesis test performed for 62 global characteristics, and associations of these characteristics were analysed through calculations of multivariate distance followed by a hypothesis test. The second step involved the analysis of local characteristics including vertical and horizontal positions, speed, pressure gradient, acceleration, and jerk point-to-point, by using dynamic time warping followed by a hypothesis test. Optimization of sensitivity and specificity metrics of the hypothesis test was explored by varying its stringency and observing accuracy rates for the simulated and genuine groups. A P -value threshold of 1 × 10−10 was found to be optimal, making the test more restrictive and yielding accuracy rates of 96.7% for genuine global data and 88.9% for simulated data. The same cut-off value for local characteristics provided an average accuracy rate of 95.4% for genuine samples and 94.7% for simulated samples, demonstrating high accuracy for both simulated and genuine samples. However, the method did not offer reasonable accuracy rates for disguises, consistent with observations in traditional handwriting examination. Our approach provided satisfactory results for forensic examination use. The visualization of graphs and signatures and analysis of all identifying elements of handwriting by the examining expert are still essential. In future studies, we plan to perform blind tests to validate our approach and propose a rigorous methodology. [ABSTRACT FROM AUTHOR]

Many companies and institutions focus on autonomous vehicles. Accordingly, the commercialization of fully autonomous vehicles is expected to proceed rapidly. Autonomous vehicle companies are already demonstrating or commercializing vehicles on real roads. However, autonomous vehicle data is not disclosed to the public, and related academic research is very rare. In this study, changes in the functional levels of autonomous driving sensors in various environments were analyzed using actual collected autonomous driving data. A hypothesis was established by reviewing previous studies related to the functional levels of sensors, and the hypothesis was verified by comparing actual autonomous driving data using two different statistical analysis methods. The hypothesis was tested using the 2-sample K-S test and the DTW algorithm, with different sensor elements used in each verification stage. As a result of the analysis, we found that sensor performance changed on rainy and cloudy days compared to sunny days. This study confirms that the functional levels of autonomous vehicle sensors change depending on the environment. The results of this study are expected to serve as foundational data for establishing standards and criteria for safety evaluations of autonomous vehicles in the future. [ABSTRACT FROM AUTHOR]

The Fundão dam breach, located in Mariana-Minas Gerais, Brazil, took place in November 2015 and caused social, economic and environmental impacts in the affected region. Here we use synthetic control to assess its economic impact on the municipality of Mariana. We analyzed real GDP per capita as the response variable and used socioeconomic indicators to characterize the municipalities affected by the dam breach and select the control group. We propose a hybrid method that uses cluster analysis as a sample preselection step before synthetic control. The initial donor sample of 818 municipalities was reduced using hierarchical clustering or time series clustering (dynamic time warping). Then, we applied the synthetic control. The results obtained in the synthetic control without the prior sample selection stage did not prove to be a good counterfactual for Mariana. In addition, as the sample size of the control group is large in this first situation, the computational time of the analysis was large, exceeding 5 h. When the approaches using clustering and time series clustering were used, the time spent was reduced by more than 99% in both situations and the counterfactual obtained had a satisfactory fit. [ABSTRACT FROM AUTHOR]

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. [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.

Background: A substantial number of patients treated systemically with synthetic glucocorticoids undergo emotional disturbances during treatment. Patients with cutaneous T-cell lymphoma frequently experience skin inflammation and itching and often require glucocorticoid treatment. Objective: This case-series study aimed to examine how emotional and skin-related symptoms interact throughout glucocorticoid treatment. Methods: Five cutaneous T-cell lymphoma patients undergoing systemic glucocorticoid treatment completed daily ecological momentary assessments for on average 30 assessments. Fluctuations in their emotions and symptoms were analyzed using undirected and directed dynamic time warp analyses, and were visualized in symptom networks. Results: Toward the end of the glucocorticoid treatment, a decline was found in positive psychological symptoms. Idiographic dynamic time warp analyses revealed highly variable symptom networks. Directed time-lag group-level analyses revealed irritability, enthusiastic, and excited as variables with highest outstrength, in which mainly decreasing levels of positive emotions were associated with a higher likelihood of experiencing increases in itchy skin and skin problems the next day. Conclusion: The end of glucocorticoid treatment, potentially via the induction of hypocortisolism, seems to coincide with decreased energy, motivation, and enthusiasm. Itch and skin problems could be a consequence of low-positive emotions the day before.

Understanding spatial variation in ocean waves is critical for erosion planning and infrastructure projects. The study is aimed at (1) performing a cluster analysis to categorize the wave statistics over space and (2) determining the important drivers affecting spatial variations of wave statistics in a low energy, fetch limited environment. In this study, 29 wave gauges were deployed in Back Bay Biloxi, Mississippi. Raw pressure and processed wave height were clustered using two algorithms: Euclidian and Dynamic Time Warping. The Euclidean algorithm was applied to raw and processed data. However, due to the computationally expensive nature of Dynamic Time Warping, this algorithm could not be used on raw pressure data and was only applied to processed wave data. Therefore, three combinations of distance algorithms and data were compared: (1) Euclidean algorithm on raw pressure data, (2) Euclidean algorithm on processed wave height data, and (3) Dynamic Time Warping algorithm on processed wave height data. The results showed similar clustering for Euclidean and Dynamic Time Warping on processed data, with most gauges falling into one cluster. Results from this study reveal that the dendrogram trees of the Euclidean and Dynamic Time Warping algorithms on processed data are similar, where most of the wave gauges fall in one cluster. Conversely, the Euclidian algorithm on the raw pressure data distributed the wave gauges more evenly between the clusters. Additionally, the Euclidean algorithm on the raw pressure data showed that water depth significantly affects wave clustering. [ABSTRACT FROM AUTHOR]

Time series classification (TSC) is a challenging task due to the diversity of types of features that may be relevant for different classification tasks, including trends, variance, frequency, magnitude, and various patterns. To address this challenge, several alternative classes of approach have been developed. While kernel, neural network, and hybrid approaches perform well overall, some specialized approaches are better suited for specific tasks. In this paper, we propose a new similarity-based classifier, Proximity Forest version 2.0 (PF 2.0), which outperforms previous state-of-the-art similarity-based classifiers across the UCR benchmark and outperforms other state-of-the-art methods on specific datasets in the benchmark that are best addressed by similarity-base methods. PF 2.0 incorporates three recent advances in time series similarity measures — (1) computationally efficient early abandoning and pruning to speedup elastic similarity computations; (2) a new elastic similarity measure, Amerced Dynamic Time Warping ( ADTW ); and (3) cost function tuning. It rationalizes the set of similarity measures employed, reducing the eight base measures of the original PF to four and using the first derivative transform with all similarity measures, rather than a limited subset. It also incorporates HYDRA, a dictionary-based transform. We have re-implemented PF 1.0 and implemented PF 2.0 framework in Java, making the PF framework more efficient. [ABSTRACT FROM AUTHOR]

To comprehensively analyze the influence of water regulation on the outflow of the three major reservoirs and the flow rates at key sections in the Dongjiang River Basin, this study employs the Mann-Kendall trend test to analyze the changes in the rainfall-runoff trend across the Dongjiang River Basin. Through the utilization of the continuous sliding window method and the multivariate dynamic time warping (MDTW) method, typical low-flow events are identified. Furthermore, similar events within the Dongjiang River Basin are effectively pinpointed. The investigation centers on the outflow processes of the three major reservoirs and the corresponding flow variations at critical sections. The results indicate that since the establishment of the three major reservoirs, there has been no significant alteration in rainfall-runoff patterns during both flood seasons and dry seasons in the Dongjiang River Basin. After water regulation, the compliance rate of dry year flow at the Boluo and Longchuan sections experienced a substantial increase, rising from 42% and 86% to 59% and 98%, respectively, which is significantly higher than that before water regulation. In examining four typical events under similar conditions, the outflow processes of the three major reservoirs demonstrated heightened stability. Notably, the average flow at the Boluo section escalated from 260 to 381 m3/s. This pivotal enhancement underscores the necessity for water regulation, providing a theoretical foundation for its continuation.

Inflation expectations are a crucial variable for central banks. However, empirically examining their properties is challenging. This paper juxtaposes the properties of consumer and professional expectations. It also assesses the degree of forward- and backward-lookingness and the information content of expectations. We apply entropy-based measures (common information and mutual common information) to capture nonlinear dependencies and dynamic time warping to account for different lags in the relationships. The study covers 12 inflation-targeting economies from the European region. The results suggest that in most countries, professionals are more forward-looking, and consumers follow professionals. Both groups of economic agents present expectations that are aligned in terms of information content. However, cross-country differences occur. These results imply that, from the central bank’s point of view, communication and practices designed to shape expectations, even if understood mostly by specialists, are effective also for consumers. The novelty of this study lies in its use of alternative methods to tackle the formation and dependencies between heterogeneous expectations. This avoids the drawbacks of a standard approach and allows broader conclusions to be drawn.

AbstractIn this study, we focus on visualizing trajectory data, a type of data based on a series of temporal observations. We present an adapted version of t-distributed stochastic neighborhood embedding (t-SNE) tailored for trajectory data. This method is designed to preserve the inherent curved structure of trajectory data by incorporating a robust distance measure. Furthermore, it demonstrates the ability to maintain the data structure even in the presence of missing values at different time points. The performance of the proposed method is rigorously evaluated through a simulation study and demonstrated its effectiveness in visualizing two types of trajectory data, Gait data and NBA data. [ABSTRACT FROM AUTHOR]

Emotional voice conversion(EVC) aims to convert the speaker's voice from one emotion state to another without changing the speaker and the voice content. In the early emotional voice conversion task, it is difficult to deal with simple fundamental frequency(F0) features, which is one of the most important features in emotional voice expression. In general, the linear conversion method is used when processing the discrete F0 features, which leads to the poor effect of the emotional voice conversion method only using the spectral(SP) features. In this study, we propose an emotional voice conversion system using a F0 feature conversion method based on neural network(NN) training of multi-dimensional log F0 features. This method can effectively process the F0 feature to achieve better emotional conversion effects. Meanwhile, the system uses a deep bidirectional long short-term memory(DBiLSTM) network to train the SP features to learn the context of the voice spectrum. The extraction of SP features helps us understand and reconstruct the timbre of speech signals. In the preprocessing, the improved Dynamic Time Warping(DTW) algorithm is used to improve the accuracy of speech frame alignment and further increase the quality of emotional voice conversion. Through these methods, the SP and F0 features of emotional voice can be converted at the same time. The experimental results show that the system has a good effect on emotional voice conversion. [ABSTRACT FROM AUTHOR]