Dynamic Time Research Articles

The green Fintech paradox: understanding the dynamics of green brand positioning and user decision-making in the digital financial landscape

Purpose The ever-evolving landscape of financial technology (Fintech) has revolutionised payment methods and raised questions about what drives user behaviour in adopting these innovative solutions. This study, using narrative transportation theory as an underpinning theory, aims to investigate into the dynamics of green user behaviour in adopting Fintech payments. Design/methodology/approach This study used a deductive approach, and with data obtained from 635 respondents through the purposive sampling technique, partial least squares structural equation modelling was employed to yield significant insights. Findings The study found a positive association between green brand positioning and product differentiation. However, it unexpectedly didn't impact user attitudes towards Fintech payments. Green brand image and perceived performance positively influenced product differentiation. Perceived product differentiation fully mediated the association between green brand positioning and user attitudes. The study introduced fear of missing out's (FOMO) moderating role, enriching eco-conscious marketing insights and user behaviour understanding. Research limitations/implications This study reveals crucial implications for marketers, policymakers and user experience (UX) designers operating within the Fintech industry. It emphasises green brand positioning's impact on product differentiation, user attitudes and its mediating role. It advocates for sustainability integration, innovation, strategic messaging and user-centric improvements to optimise user perceptions and competitiveness in the evolving Fintech landscape. The study's cross-sectional design may limit the ability to establish causal relationships over time and overlook temporal changes in green Fintech adoption dynamics; thus, longitudinal studies are warranted to better understand the evolving nature of user attitudes and behaviours towards green Fintech payments. Originality/value This study adds novelty to the existing body of literature by introducing the dimension of innovation appeal to green brand positioning and employing narrative transportation theory in the Fintech realm. The findings also add novelty by highlighting the moderating impact of fear of missing out in predicting the association between green brand positioning and product differentiation in the realm of green Fintech and green use behaviour.

Synchronization under saturable nonlinearity.

In this theoretical work, we introduce a nonlinear gain saturation law representative of the experimentally observed properties manifested by phenomena ranging from aeroacoustic shear layers in self-sustained cavity oscillations to flame heat release rate in thermoacoustic instabilities. Furthermore, this type of saturable gain may be relevant for a wider class of physical systems, such as active laser media in photonics. The nonlinearity discussed herein governs the fullscale behavior of a self-oscillator exhibiting linear loss under large amplitude perturbations, in contrast to the cubic damping and linear gain of the Van der Pol model. A distinctive characteristic of the proposed equation is the simple, well behaved gain term in the slow timescale dynamics.

AISClean: AIS data-driven vessel trajectory reconstruction under uncertain conditions

In maritime transportation, intelligent vessel surveillance has become increasingly prevalent and widespread by collecting and analyzing high massive spatial data from automatic identification system (AIS). The state-of-the-art AIS devices contain various functionalities, such as position transmission, tracking navigation, etc. Widely equipped shipboard AIS devices provide a large amount of real-time and historical vessel trajectory data for maritime management. However, the original AIS data often suffers from unwanted noise (i.e., poorly tracked timestamped points for vessel trajectories) and missing (i.e., no data is received or transmitted for a long term) data during signal acquisition, transmission, and analog-to-digital conversion. This degradation in data quality poses significant risks, including potential miscalculations in vessel collision avoidance systems, inaccuracies in emission calculations, and challenges in port management. In this work, a data-driven vessel trajectory reconstruction framework considering historical features is proposed to enhance the reliability of vessel trajectory. Specifically, a series of statistical methods are proposed to identify noisy data and missing data. Then, a model combining Geohash and dynamic time warping algorithms is developed to restore the trajectories degraded by random noise and missing data in vessel trajectories. Comparative experiments with baseline methods on multiple datasets verify the effectiveness of the proposed data-driven model.

Cavity Controlled Upconversion in CdSe Nanoplatelet Polaritons.

Exciton-polaritons provide a versatile platform for investigating quantum electrodynamics effects in chemical systems, such as polariton-altered chemical reactivity. However, using polaritons in chemical contexts will require a better understanding of their photophysical properties under ambient conditions, where chemistry is typically performed. Here, we used cavity quality factor to control strong light-matter interactions and in particular the excited state dynamics of colloidal CdSe nanoplatelets (NPLs) coupled to a Fabry-Pérot optical cavity. With increasing cavity quality factor, we observe significant population of the upper polariton (UP) state, exemplified by the rare observation of substantial UP photoluminescence (PL). Excitation of the lower polariton (LP) states results in upconverted PL emission from the UP branch due to efficient exchange of population between the LP, UP and the reservoir of dark states present in collectively coupled polaritonic systems. In addition, we measure time scales for polariton dynamics ∼100 ps, implying great potential for NPL based polariton systems to affect photochemical reaction rates. State-of-the-art quantum dynamical simulations show outstanding quantitative agreement with experiments, and thus provide important insight into polariton photophysical dynamics of collectively coupled nanocrystal-based systems. These findings represent a significant step toward the development of practical polariton photochemistry platforms.

Semantic Web Techniques for Extracting and Analyzing of Cropland Abandonment in Hilly Areas

Due to rising rural labor costs, farmland abandonment is common in China's hilly areas. Timely, accurate extraction of its spatiotemporal changes is crucial for sustainable farmland use. This study presents a novel approach for extracting abandoned cropland using NDVI time series from Sentinel-2 satellite images, exploiting the difference in vegetation phenology between abandoned and cultivated croplands. Dynamic Time Warping (DTW) algorithm was used to determine the similarity between NDVI time-series curves of different cropland types. The similarity metrics were used to find the optimal NDVI threshold for abandoned cropland through F1-score evaluation. The method's overall accuracy is 92.4%, higher than comparison methods at 84.60% and 75.6%. The approach captures year-round vegetation changes, expands time dimension data, and improves accuracy. Spatiotemporal analysis revealed decreased patch size, increased shape complexity, and more dispersed distribution of abandoned cropland over time. Major factors were proximity to settlements, roads, water bodies.

Multiplier and investment in modern Russia in the context of stimulating economic development in the context of sanctions

Aim. To analyze the degree of investment impact on economic growth in modern Russia.Objectives. To study the multiplier effect as a key tool for stimulating economic activity and creating new jobs, developing innovations and reducing social inequality; to identify its significance for the national economy in modern conditions of inherent uncertainty.Methods. The research is based on the empirical study of the multiplier effect and gas pedal effect on the materials of the modern Russian economy. The analysis is based on the use of dynamic time series, definition of dependent and independent variables in the context of identified correlations between them. The data of literary sources on the assessment of the impact of the investment multiplier effect on economic growth were also studied.Results. The multiplier effect can have a significant impact on the formation of the structure of the economy, contributing to an increase in production, reducing unemployment and improving the standard of living of the population. The study of this effect allows us to better understand the mechanisms of economic functioning and develop recommendations for optimizing investment policy to achieve sustainable economic growth, which is important for modern Russia developing under sanctions pressure. The study of paired regression revealed a mathematical relationship between the increase in investment in fixed capital and the growth of gross domestic product (GDP), as well as between GDP growth and the increase in investment in fixed capital. The high correlation coefficient (r = 0.945896) and coefficient of determination (R² = 89.47 %) confirm that there is a strong relationship between fixed capital investment and GDP. This indicates that most of the changes in output can be explained through changes in fixed capital investment.Conclusions. The results of the analysis confirm the importance of investment in fixed capital as a key factor of economic growth and development in modern Russia. Strengthening investment activity allows not only to increase the level of economic development as a whole, but also contributes to the creation of favorable conditions for sustainable growth of income of the population, innovation activity, etc. In this regard, investment stimulation should become one of the priorities in the current Russian economic policy.

Multi-objective optimization design of rail grinding profile in Curve Section of Subway based on wear Evolution and representative worn profile

Curve rail grinding has always been one of the key points of subway maintenance and repair. The appropriate grinding can effectively reduce wear. A multi-objective optimization design method for grinding profiles in curved sections is proposed. Firstly, representative grinding profiles are selected as the initial population using the dynamic time regularization algorithm (DTW). Then, the optimal design range is determined based on wear characterization analysis, and mathematical expressions of wheel profiles are chosen as design variables to establish a parametric model. Next, the prediction model considering the evolution of wheel wear is incorporated into the multi-objective function, and objective function adaptive weight adjustment coefficient factors are introduced to establish the multi-objective optimization model for wheel profiles. The Latin hypercubic sampling method is employed to establish the RBF agent model for simulation calculation, and the optimization design of wheel profiles is carried out using the TS-NSGA-II multi-objective algorithm.

Accelerating time series similarity search under Move-Split-Merge distance via dissimilarity space embedding

The ubiquity sensory devices across scientific and industrial settings have made time-series data mining an important research topic in the IoT domain. Among all time-series mining techniques, the similarity search is considered the most essential and noteworthy because it is frequently employed as a fundamental subroutine for other procedures. The Dynamic Time Warping (DTW) was once considered the best similarity measure in time-series data mining community; however, two recent studies have demonstrated that the Move-Split-Merge (MSM) performs better than DTW in both supervised and unsupervised settings. Computing MSM between two time-series incurs costly computation because MSM has quadratic time complexity. Regrettably, there is little research on accelerating time-series similarity search under MSM. This paper provides an efficient method named DSEMSM for speeding up this procedure. DSEMSM utilizes dissimilarity space embedding to transform time-series into low-dimensional vectors. The filter-and-verification framework is subsequently employed to reduce the large number of actual MSM computations and to identify the final search results from the candidate set. The furthest prototype selection strategy and lower bound based technique are applied in DSEMSM to improve its retrieval accuracy and efficiency, respectively. We validate the effectiveness of the proposed method on a large number of datasets. The experimental results indicate that, in comparison to its relevant competitors, DSEMSM achieves a more stable speedup rate and higher retrieval accuracy.

Assessment of the seismic failure of reinforced concrete structures considering the directional effects of ground motions

Seismic intensity measures at different levels significantly impact the seismic damage of building clusters. Reinforced concrete (RC) structures are a type of building widely used in different regions worldwide. A large amount of field seismic damage observation data indicates that within the same seismic intensity zone, the directional effects of varying ground motions lead to significant differences in damage to RC structures on different floors. However, relatively little research has been conducted on estimating the seismic fragility and vulnerability of RC structures considering the directional effects of seismic action. To further study the directional effect of seismic motion on the damage of different floors, this study conducted dynamic time history and spectral analyses on 1472,379 acceleration records in multiple directions obtained from monitoring at nine actual seismic stations during the 2008 Wenchuan earthquake in China. This paper innovatively reports on the actual damage features of RC structures during the Mw6.2 magnitude Jishishan earthquake in Gansu Province, China, on December 18, 2023 (the most severe destructive earthquake in China in 2023) and analyses the influence of earthquake directionality on the damage to different floors of RC structures. A three-dimensional numerical model of a four-story RC frame structure was established using numerical simulation and the finite element method. Different intensity measures were taken to input seismic excitations of different intensities into the RC structure in the 0°, 30°, 60°, and 90° directions, and damage simulation analysis was conducted. The nonlinear dynamic time history curves of structures under multiple directions of seismic excitation in different intensity zones have been developed. Using the interstory stiffness and interstory displacement angle as engineering demand parameters, damage assessment curves and stress clouds for RC structures with 1–4 floors considering different seismic directional effects were generated. The analysis results and major findings indicate that the seismic sequence in the 0° direction has a relatively heavy impact on the first floor of the RC structure in zone IX, which is consistent with the actual field observation results.

Understanding multiple timescales in quantum dissipative dynamics: Insights from quantum trajectories

Understanding multiple timescales in quantum dissipative dynamics: Insights from quantum trajectories

A temperature compensation approach using dynamic time warping for electro-mechanical admittance-based concrete structural damage identification

A temperature compensation approach using dynamic time warping for electro-mechanical admittance-based concrete structural damage identification

Detection of Maize Crop Phenology Using Planet Fusion

Accurate identification of crop phenology timing is crucial for agriculture. While remote sensing tracks vegetation changes, linking these to ground-measured crop growth stages remains challenging. Existing methods offer broad overviews but fail to capture detailed phenological changes, which can be partially related to the temporal resolution of the remote sensing datasets used. The availability of higher-frequency observations, obtained by combining sensors and gap-filling, offers the possibility to capture more subtle changes in crop development, some of which can be relevant for management decisions. One such dataset is Planet Fusion, daily analysis-ready data obtained by integrating PlanetScope imagery with public satellite sensor sources such as Sentinel-2 and Landsat. This study introduces a novel method utilizing Dynamic Time Warping applied to Planet Fusion imagery for maize phenology detection, to evaluate its effectiveness across 70 micro-stages. Unlike singular template approaches, this method preserves critical data patterns, enhancing prediction accuracy and mitigating labeling issues. During the experiments, eight commonly employed spectral indices were investigated as inputs. The method achieves high prediction accuracy, with 90% of predictions falling within a 10-day error margin, evaluated based on over 3200 observations from 208 fields. To understand the potential advantage of Planet Fusion, a comparative analysis was performed using Harmonized Landsat Sentinel-2 data. Planet Fusion outperforms Harmonized Landsat Sentinel-2, with significant improvements observed in key phenological stages such as V4, R1, and late R5. Finally, this study showcases the method’s transferability across continents and years, although additional field data are required for further validation.

Revitalizing the single batch environment: a ‘Quest’ to achieve fairness and efficiency

In the realm of computer systems, efficient utilization of the CPU (Central Processing Unit) has always been a paramount concern. Researchers and engineers have long sought ways to optimize process execution on the CPU, leading to the emergence of CPU scheduling as a field of study. In this research, we have analyzed the single offline batch processing and investigated other sophisticated paradigms such as time-sharing operating systems and wildly used algorithms, and their shortcomings. Our work is directed toward two fundamental aspects of scheduling: efficiency and fairness. We propose a novel algorithm for batch processing that operates on a preemptive model, dynamically assigning priorities based on a robust ratio, employing a dynamic time slice, and utilizing periodic sorting to achieve fairness. By engineering this responsive and fair model, the proposed algorithm strikes a delicate balance between efficiency and fairness, providing an optimized solution for batch scheduling while ensuring system responsiveness.

Do not forget the electrons: Extending moderately-sized nuclear networks for multidimensional hydrodynamic codes

Nuclear networks are widely used coupled with hydrodynamical simulations of explosive scenarios to account for the change of nuclear species and energy generation rate due to nuclear reactions. In this way, there is a feedback mechanism between the hydrodynamical state and the nuclear processes. Unfortunately, the timescale of nuclear reactions is orders of magnitude smaller than the dynamical timescale that drives hydrodynamical simulations. Therefore, these nuclear networks are usually very small, reduced in most cases to a dozen elements, especially when simulations are carried out in more than one dimension. We present here an extended nuclear network, with 90 species, designed for being coupled with hydrodynamic simulations, which includes neutrons, protons, electrons, positrons, and the corresponding neutrino and anti-neutrino emission. This network is also coupled with temperature, making it extremely robust and, together with its size, unique of its kind. The inclusion of electron captures on free protons makes the network very appropriate for multidimensional studies of Type Ia supernova explosions, especially when the exploding object is a massive white dwarf. We perform several tests that are relevant to simulate explosive scenarios, such as Type Ia supernovae and core-collapse supernovae. We compare the results of the 90 nuclei network with a standard alpha -chain network with 14 elements to evaluate the differences in the energy generation rate. We also evaluate the relevance of including the electrons in the network in terms of generated yields and how it affects the pressure of a degenerate fluid such as that of white dwarfs. The results obtained with the 90-nuclei network have been verified with a much larger 2000-nuclei network built from REACLIB (WinNet), in terms of nuclear energy generation rate, pressure, and produced yields. The results obtained with the proposed medium-sized network compare fairly well, to a few percent, with those computed with in scenarios reproducing the gross physical conditions of current Type Ia supernova explosion models. In those cases where the carbon and oxygen fuel ignites at high density, the high-temperature plateau typical of the nuclear statistical equilibrium regime is well defined and stable, allowing large integration time steps. We show that the inclusion of electron captures on free protons substantially improves the estimation of the electron fraction of the mixture. Therefore, the pressure is better determined than in networks where electron captures are excluded, which will ultimately lead to more reliable hydrodynamic models. Explosive combustion of helium at low density, occurring near the surface layer of a white dwarf, is also better described with the proposed network, which gives nuclear energy generation rates much closer to than typical reduced alpha networks. A nuclear network with N=90 species, including electrons, aimed at multidimensional calculations of supernova explosions is described and verified. The proposed network is suitable for the study of Type Ia supernova explosions because it provides better values of pressure and electron abundance than other existing networks with smaller or even a similar size but without including electron capture processes.

An Early Warning Model for Turbine Intermediate-Stage Flux Failure Based on an Improved HEOA Algorithm Optimizing DMSE-GRU Model

As renewable energy sources such as wind and photovoltaics continue to enter the grid, their intermittency and instability leads to an increasing demand for peaking and frequency regulation. An efficient dynamic monitoring method is necessary to improve the safety level of intelligent operation and maintenance of power stations. To overcome the insufficient detection accuracy and poor adaptability of traditional methods, a novel fault early warning method with careful consideration of dynamic characteristics and model optimization is proposed. A combined loss function is proposed based on the dynamic time warping and the mean square error from the perspective of both shape similarity and time similarity. A prediction model of steam turbine intermediate-stage extraction temperature based on the gate recurrent unit is then proposed, and the change in prediction residuals is utilized as a fault warning criterion. In order to further improve the diagnostic accuracy, a human evolutionary optimization algorithm with lens opposition-based learning is proposed for model parameter adaptive optimization. Experiments on real-world normal and faulty operational data demonstrate that the proposed method can improve the detection accuracy by an average of 1.31% and 1.03% compared to the long short-term memory network, convolutional neural network, back propagation network, extreme learning machines, gradient boosting decision tree, and LightGBM models.

Applications of LIF to Document Natural Variability of Chlorophyll Content and Cu Uptake in Moss

Chlorophyll has long been used as a natural indicator of plant health and photosynthetic efficiency. Laser-induced fluorescence (LIF) is an emerging technique for understanding broad spectrum organic processes and has more recently been used to monitor chlorophyll response in plants. Previous work has focused on developing a LIF technique for imaging moss mats to identify metal contamination with the current focus shifting toward application to moss fronds and aiding sample collection for chemical analysis. Two laser systems (CoCoBi a Nd:YGa pulsed laser system and Chl-SL with two blue continuous semiconductor diodes) were used to collect images of moss fronds exposed to increasing levels of Cu (1, 10, and 100 nmol/cm2) using a CMOS camera. The best methods for the preprocessing of images were conducted before the analysis of fluorescence signatures were compared to a control. The Chl-SL system performed better than the CoCoBi, with dynamic time warping (DTW) proving the most effective for image analysis. Manual thresholding to remove lower decimal code values improved the data distributions and proved whether using one or two fronds in an image was more advantageous. A higher DTW difference from the control correlated to lower chlorophyll a/b ratios and a higher metal content, indicating that LIF, with the aid of image processing, can be an effective technique for identifying Cu contamination shortly after an event.

Smartphone-based markers of social connectivity in schizophrenia and bipolar disorder

Social isolation and social impairment are hallmarks of progression as well as predictors of relapse in psychiatric disorders. We conducted a pilot study to assess the feasibility of sensing the social activity phenotype and loneliness using active and passive markers collected using a smartphone application. The study included 9 schizophrenia and bipolar disorder patients followed in the Bipolar Longitudinal study for at least 1 month and for whom mobile communication data was collected using the Beiwe smartphone application. Subjects completed daily surveys on digital and in-person social activity, and feelings of being outgoing or lonely. We described the level and variability of social activity features. We employed k-means clustering to identify “important contacts”. Further, we investigated whether social network-derived features of mobile communication are independent predictors of weekly counts of outgoing calls and text, weekly average self-reported digital social activity, and loneliness using mixed effect models and clustering with dynamic time warping distance. Subjects were followed between 5 and 208 weeks (number of days of observation = 2538). The k-means cluster analysis approach identified the number of “important contacts” among close friends and family members as reported in clinical interviews. The cluster analysis and longitudinal regression analysis indicate that the number of individuals a person communicates with on their phone is an independent predictor of perceived loneliness, with stronger evidence when “important contacts” only are included. This study provides preliminary evidence that the number of “important contacts” a person communicates with on their phone is a promising marker to capture subjects’ engagement in mobile communication activity and perceived loneliness.

Similarity measurement of symbolic sequence based on complexity estimate and dynamic time warping

Similarity measurement of symbolic sequence based on complexity estimate and dynamic time warping

Confidence Is Influenced by Evidence Accumulation Time in Dynamical Decision Models

AbstractConfidence judgments are closely correlated with response times across a wide range of decision tasks. Sequential sampling models offer two competing explanations for the relationship between confidence and response time: According to some models, decision time directly influences confidence. Other models explain the correlation by linking subjective confidence computation to the decision process dynamics. In previous model comparisons, drift diffusion-based confidence models that do not explicitly consider decision time in the computation of confidence provided superior model fits compared to race models that directly included decision time in the internal computation of confidence. In the present study, we present support for the assumption that confidence explicitly takes decision time and post-decisional accumulation time into account. We propose the dynamical visibility, time, and evidence (dynaViTE) model, an extension of the dynamical weighted evidence and visibility (dynWEV) model. DynaViTE assumes that confidence is not solely based on the final amount of accumulated evidence but explicitly includes time in the computation of confidence. Model comparisons using four previously published data sets with different perceptual decision tasks showed a good model fit of dynaViTE, indicating that the relationship between confidence and response time is not only due to the close link in the accumulation process but also to an explicit inclusion of time in the computation of confidence.

Evaluating temporal variations in access to multi-tier hospitals using personal vehicles and public transit: Implications for healthcare equity

Understanding healthcare accessibility, or the ability to access healthcare services, has significant implications for both individual well-being and community equity. However, existing studies seldom account for temporally varying factors such as traffic conditions and hospital schedules, resulting in miscalculation of accessibility. This study addresses this gap by introducing a framework that evaluates accessibility to multi-tier hospitals, factoring in both spatial and temporal aspects, using public transit (PT) and personal vehicles (PVs), and assesses its impact on horizontal and vertical equity. Implemented in Shanghai, China, we employ the Gaussian two-step floating catchment area method for accessibility quantification and utilize map APIs for dynamic travel time data. Our analysis reveals: (i) notable temporal fluctuations in healthcare accessibility, especially for PT, and their significant impact on both horizontal and vertical equity due to varying travel times and hospital schedules; (ii) larger disparities in higher-tier hospital accessibility compared to lower-tier ones; (iii) greater horizontal equity using PV-based accessibility and higher vertical equity using PT-based accessibility. These findings highlight the need to offer customized transit to healthcare facilities, expand telehealth services, incorporate equity in healthcare resource allocation, incentivize healthcare professionals to work in underserved areas, and develop outreach programs to improve accessibility and equity.