Multiple Time Scales Research Articles

Medium- and long-term optimal operation of a hybrid energy system enhanced by cascade hydropower energy storage system

To address the grid connection challenges derived from the high penetration of intermittent new energy sources, some generation companies are trying to use existing cascade hydropower stations to develop “large-scale cascade hydropower energy storage systems” (LCHES). They intend to combine the LCHES with new energy power to become a new type of hybrid power system (the LCHES-WP hybrid power system). For such an emerging hybrid power system, performance in the medium- and long-term operation is more worthy of attention. Therefore, we propose “monthly LCHES-WP operation strategy” and “short-term dispatch strategy for LCHES” to constrain the operation of the LCHES-WP hybrid power system. On this basis, a multiple time-scales nested optimizing model is presented to estimate the performance in the medium- and long-term operation. A case study in the Yellow River Basin in China was presented based on the baseline scenario (1800 MW of hydroturbine, 1200 MW of pump, 2000 MW of new energy, and 0.24 billion m3 of regulating storage capacity). It reveals that (1) the LCHES-WP hybrid power system can achieve a consistent output process every day during a month with low energy curtailment and low energy supply shortage, which indicates that it can meet satisfactory performance in medium to long-term operation. (2) it will significantly increase energy curtailment (controlling the energy supply shortage is as low as the baseline scenario) without the pumping system or sufficient regulating storage capacity. (3) based on sensitivity analysis, it is indicated that the capacity of new energy can further improve to 5000 ∼ 6000 MW. The simulation framework and the result provided in this paper can be referred to by the same research.

Error analysis of a highly efficient and accurate temporal multiscale method for a fractional differential system

This work is concerned with a nonlinear coupled system of fractional ordinary differential equations (FODEs) with multiple scales in time. The micro system is a nonlinear differential equation with a periodic applied force and the macro system is a fractional differential equation. We are interested in the effective long-term computation of the solution. The main challenge is how to obtain the solution of the coupled fractional problem at a lower computational cost, which calls for us to develop an effective analysis and computation framework. We derive a periodic auxiliary system to approximate the original system and give error estimates. Then we propose a numerical scheme based on the auxiliary problem and analyze the discrete error. An efficient multiscale algorithm that is also applicable to 2-D and 3-D problems is designed. The results of numerical experiments demonstrate the accuracy and computational efficiency of the proposed multiscale method. It is observed that, the computational time is significantly reduced and the multiscale method performs very well in comparison to the fully resolved simulation. Furthermore, as a particular example of applications, we consider a simplified model of the atherosclerotic plaque growth problem, and simply evaluate the effect of fractional parameter and damping coefficient on plaque growth and discuss the physical implication.

Spontaneous emergence of rudimentary music detectors in deep neural networks

Music exists in almost every society, has universal acoustic features, and is processed by distinct neural circuits in humans even with no experience of musical training. However, it remains unclear how these innate characteristics emerge and what functions they serve. Here, using an artificial deep neural network that models the auditory information processing of the brain, we show that units tuned to music can spontaneously emerge by learning natural sound detection, even without learning music. The music-selective units encoded the temporal structure of music in multiple timescales, following the population-level response characteristics observed in the brain. We found that the process of generalization is critical for the emergence of music-selectivity and that music-selectivity can work as a functional basis for the generalization of natural sound, thereby elucidating its origin. These findings suggest that evolutionary adaptation to process natural sounds can provide an initial blueprint for our sense of music.

The strengthened impact of water availability at interannual and decadal time scales on vegetation GPP

AbstractWater availability (WA) is a key factor influencing the carbon cycle of terrestrial ecosystems under climate warming, but its effects on gross primary production (EWA‐GPP) at multiple time scales are poorly understood. We used ensemble empirical mode decomposition (EEMD) and partial correlation analysis to assess the WA‐GPP relationship (RWA‐GPP) at different time scales, and geographically weighted regression (GWR) to analyze their temporal dynamics from 1982 to 2018 with multiple GPP datasets, including near‐infrared radiance of vegetation GPP, FLUXCOM GPP, and eddy covariance–light‐use efficiency GPP. We found that the 3‐ and 7‐year time scales dominated global WA variability (61.18% and 11.95%), followed by the 17‐ and 40‐year time scales (7.28% and 8.23%). The long‐term trend also influenced 10.83% of the regions, mainly in humid areas. We found consistent spatiotemporal patterns of the EWA‐GPP and RWA‐GPP with different source products: In high‐latitude regions, RWA‐GPP changed from negative to positive as the time scale increased, while the opposite occurred in mid‐low latitudes. Forests had weak RWA‐GPP at all time scales, shrublands showed negative RWA‐GPP at long time scales, and grassland (GL) showed a positive RWA‐GPP at short time scales. Globally, the EWA‐GPP, whether positive or negative, enhanced significantly at 3‐, 7‐, and 17‐year time scales. For arid and humid zones, the semi‐arid and sub‐humid zones experienced a faster increase in the positive EWA‐GPP, whereas the humid zones experienced a faster increase in the negative EWA‐GPP. At the ecosystem types, the positive EWA‐GPP at a 3‐year time scale increased faster in GL, deciduous broadleaf forest, and savanna (SA), whereas the negative EWA‐GPP at other time scales increased faster in evergreen needleleaf forest, woody savannas, and SA. Our study reveals the complex and dynamic EWA‐GPP at multiple time scales, which provides a new perspective for understanding the responses of terrestrial ecosystems to climate change.

Concentration and correlation analysis of hydro-meteorological factors in the northern sub-basin of Lake Issyk-Kul in Central Asia

This paper, based on the non-parametric tests and wavelet analysis, analyzed monthly precipitation and runoff data of the small basin in the northern part of the Lake Issyk-Kull basin over the nearly 62-year period, the concentration and correlation of precipitation and runoff in the small bowl of the north Lake Issyk-Kull basin. The results indicated that (1) the Cholpon-Ata River runoff (runoff 1) and the Chong-Ak-Suu River runoff (runoff 2) in the small watershed in the northern part of Lake Issyk-Kull basin are from June to April, January, and from September to November showed a significant increasing trend. The significant abrupt growth change occurred in 1983, while the precipitation at the Cholpon-Ata station showed a significant increasing trend in February, with a sharp point around 1980. (2) The concentration degree (Cd) index of runoff 1 in the small watershed in the northern part of the Lake Issyk-Kull basin has increased but not significantly, and the Cd index of precipitation and runoff 2 showed an insignificant decrease. (3) The Cd index of runoff 1 in the small watershed in the northern part of Lake Issyk-Kull basin existed for 10 years and 18 years, and the Cd index of River 2 existed for 9 years and 13 years. In contrast, the precipitation Cd index was significant at 4- and 15-year cycles. (4) On multiple time scales, they have a significant negative correlation between the runoff and precipitation in the small watershed in the northern part of the Lake Issyk-Kul basin from May to September.

Research on simulation method of multiple time scales dissolution process in tube electrode pulse electrochemical machining

Tube electrode pulsed electrochemical machining (TE-PECM) has received widespread attention as an electrochemical perforation technique that is widely used for machining deep small holes/oblique small holes such as air film holes in blades. However, the TE-PECM process is characterized by complex multi-physics field coupling relationships and multiple time scales for multi-physics interactions, making the simulation of the pulsed electrolytic processing process computationally huge and difficult to perform. To accurately describe the multi-physical field coupling relationship for pulsed electrochemical processing. In this paper, a k-ε gas-liquid two-phase flow model has been introduced to model the interpolar gas-liquid two-phase flow field variation law, and on this basis, a coupled multi-physics field model for pulsed electrochemical machining processing is established to provide an accurate description of the interpolar multi-physics interaction mechanism. A quasi-steady-state iterative simplification algorithm based on heat source averaging is proposed to solve the problem that the time scale span of the dissolution process leads to a huge amount of simulation calculations. The simulation analyzes the dissolution characteristics at the micro time scale and macro time scale and compares them with the full precision method. The results show that the simulation accuracy of the two methods is the same, but the simulation calculation time of the simplified algorithm is reduced by two-thirds. The experimental platform was designed and built to carry out a series of experiments under different processing times, and the results effectively verified the correctness of the simulation method.

Practices and Innovations in Analyzing Digital Enabling High-Quality Development of Vocational Education Based on Time-Series Data Analytics

Abstract With the rapid development of the social economy and the continuous progress of science and technology, vocational education plays a crucial role in cultivating high-quality talents and promoting economic development. In this paper, based on the requirements of practice and innovation of digitization-enabled high-quality development of vocational education, a time-series decomposition model based on Transformer is proposed, which is defined as the DMR former model. The original data are decomposed using the temporal decomposition model, combined with the multi-scale fusion residual attention mechanism to capture and process the temporal feature information of vocational education in multiple time scales, and finally, the obtained results are analyzed. The results show that vocational education performs poorly in the classroom effect, most of the attention of vocational education students in the classroom is not concentrated, and the degree of liking for the classroom is lower than 0.5. After the digital empowerment of classroom effect, the student’s performance can be stabilized at more than 85 compared to the average of about 60 in the previous period, which is a good effect of improvement. After improving the curriculum of the College of Vocational Education, the employment rate of students increased to more than 95%. The high-quality development of vocational education and meeting the social demand for talent can be promoted through time series data analysis and digital empowerment.

Short-Term Power Forecasting and Uncertainty Analysis of Wind Farm at Multiple Time Scales

Short-Term Power Forecasting and Uncertainty Analysis of Wind Farm at Multiple Time Scales

Assessment and prediction of meteorological drought using machine learning algorithms and climate data

Monitoring drought in semi-arid regions due to climate change is of paramount importance. This study, conducted in Morocco’s Upper Drâa Basin (UDB), analyzed data spanning from 1980 to 2019, focusing on the calculation of drought indices, specifically the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at multiple timescales (1, 3, 9, 12 months). Trends were assessed using statistical methods such as the Mann-Kendall test and the Sen’s Slope estimator. Four significant machine learning (ML) algorithms, including Random Forest, Voting Regressor, AdaBoost Regressor, and K-Nearest Neighbors Regressor, were evaluated to predict the SPEI values for both three and 12-month periods. The algorithms’ performance was measured using statistical indices. The study revealed that drought distribution within the UDB is not uniform, with a discernible decreasing trend in SPEI values. Notably, the four ML algorithms effectively predicted SPEI values for the specified periods. Random Forest, Voting Regressor, and AdaBoost demonstrated the highest Nash-Sutcliffe Efficiency (NSE) values, ranging from 0.74 to 0.93. In contrast, the K-Nearest Neighbors algorithm produced values within the range of 0.44 to 0.84. These research findings have the potential to provide valuable insights for water resource management experts and policymakers. However, it is imperative to enhance data collection methodologies and expand the distribution of measurement sites to improve data representativeness and reduce errors associated with local variations.

Timekeepers for Trace Elements in the Global Ocean: The Thorium Stopwatches

The various isotopes of thorium offer oceanographers tools for tracking particulate matter in the ocean across multiple timescales. To learn about the cycling of trace elements in the ocean, in this perspective we focus on the latest applications of thorium’s longest-lived naturally occurring isotopes: 232Th, 230Th, 228Th, and 234Th. From desert dust to marine snow to sediment porewaters, thorium measurements can be used to derive rates of trace element input and removal, using the assumptions of radioactive disequilibrium. Opportunities exist to fine tune and improve the application of these already versatile stopwatches, but GEOTRACES-era data clearly demonstrate their utility.

Droplet tilings in precessive fields: hysteresis, elastic defects, and annealing.

Two-component Marangoni contracted droplets can be arranged into arbitrary two-dimensional tiling patterns where they display rich dynamics due to vapor-mediated long-range interactions. Recent work has characterized the centered hexagonal honeycomb lattice, showing it to be a highly frustrated system with many metastable states and relaxation occurring over multiple timescales [Molina et al., Proc. Natl. Acad. Sci. U. S. A., 2021, 118, e2020014118]. Here, we study this system under the influence of a rotating gravitational field. High amplitudes are able to completely disrupt droplet-droplet interactions, making it possible to identify a transition between field-dominated and interaction-dominated regimes. The system displays complex hysteresis behavior, the details of which are connected to the emergence of linear mesoscale structures. These mesoscale features display an elasticity that is governed by the balance between gravity and long-range vapor-mediated attractions. We find that disorder plays an important role in determining the dynamics of these features. Finally, we demonstrate annealing the system by progressively reducing the field amplitude, a process that reduces configurational energy compared to a rapid quench. The ability to manipulate vapor-mediated interactions in deliberately designed droplet tilings provides a novel platform for table-top explorations of multi-body interactions.

Response of solar-induced chlorophyll fluorescence-based spatial and temporal evolution of vegetation in Xinjiang to multiscale drought.

Climate change and human activities have increased droughts, especially overgrazing and deforestation, which seriously threaten the balance of terrestrial ecosystems. The ecological carrying capacity and vegetation cover in the arid zone of Xinjiang, China, are generally low, necessitating research on vegetation response to drought in such arid regions. In this study, we analyzed the spatial and temporal characteristics of drought in Xinjiang from 2001 to 2020 and revealed the response mechanism of SIF to multi-timescale drought in different vegetation types using standardized precipitation evapotranspiration index (SPEI), solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) data. We employed trend analysis, standardized anomaly index (SAI), Pearson correlation, and trend prediction techniques. Our investigation focused on the correlations between GOSIF (a new SIF product based on the Global Orbital Carbon Observatory-2), NDVI, and EVI with SPEI12 for different vegetation types over the past two decades. Additionally, we examined the sensitivities of vegetation GOSIF to various scales of SPEI in a typical drought year and predicted future drought trends in Xinjiang. The results revealed that the spatial distribution characteristics of GOSIF, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) were consistent, with mean correlations with SPEI at 0.197, 0.156, and 0.128, respectively. GOSIF exhibited the strongest correlation with SPEI, reflecting the impact of drought stress on vegetation photosynthesis. Therefore, GOSIF proves advantageous for drought monitoring purposes. Most vegetation types showed a robust response of GOSIF to SPEI at a 9-month scale during a typical drought year, with grassland GOSIF being particularly sensitive to drought. Our trend predictions indicate a decreasing trend in GOSIF vegetation in Xinjiang, coupled with an increasing trend in drought. This study found that compared with that of the traditional greenness vegetation index, GOSIF has obvious advantages in monitoring drought in the arid zone of Xinjiang. Furthermore, it makes up for the lack of research on the mechanism of vegetation GOSIF response to drought on multiple timescales in the arid zone. These results provide strong theoretical support for investigating the monitoring, assessment, and prediction of vegetation response to drought in Xinjiang, which is vital for comprehending the mechanisms of carbon and water cycles in terrestrial ecosystems.

Brain mechanisms of temporal processing in impulsivity: Relevance to attention-deficit hyperactivity disorder.

In this article, we critique the hypothesis that different varieties of impulsivity, including impulsiveness present in attention-deficit hyperactivity disorder, encompass an accelerated perception of time. This conceptualisation provides insights into how individuals with attention-deficit hyperactivity disorder have the capacity to maximise cognitive capabilities by more closely aligning themselves with appropriate environmental contexts (e.g. fast paced tasks that prevent boredom). We discuss the evidence for altered time perception in attention-deficit hyperactivity disorder alongside putative underlying neurobiological substrates, including a distributed brain network mediating time perception over multiple timescales. In particular, we explore the importance of temporal representations across the brain for time perception and symptom manifestation in attention-deficit hyperactivity disorder, including a prominent role of the hippocampus and other temporal lobe regions. We also reflect on how abnormalities in the perception of time may be relevant for understanding the aetiology of attention-deficit hyperactivity disorder and mechanism of action of existing medications.

Analysis and interpretation of first passage time distributions featuring rare events.

In this contribution we consider theory and associated computational tools to treat the kinetics associated with competing pathways on multifunnel energy landscapes. Multifunnel landscapes are associated with molecular switches and multifunctional materials, and are expected to exhibit multiple relaxation time scales and associated thermodynamic signatures in the heat capacity. Our focus here is on the first passage time distribution, which is encoded in a kinetic transition network containing all the locally stable states and the pathways between them. This network can be renormalised to reduce the dimensionality, while exactly conserving the mean first passage time and approximately conserving the full distribution. The structure of the reduced network can be visualised using disconnectivity graphs. We show how features in the first passage time distribution can be associated with specific kinetic traps, and how the appearance of competing relaxation time scales depends on the starting conditions. The theory is tested for two model landscapes and applied to an atomic cluster and a disordered peptide. Our most important contribution is probably the reconstruction of the full distribution for long time scales, where numerical problems prevent direct calculations. Here we combine accurate treatment of the mean first passage time with the reliable part of the distribution corresponding to faster time scales. Hence we now have a fundamental understanding of both thermodynamic and kinetic signatures of multifunnel landscapes.

Virtual Prosumers’ P2P Transaction Based Distribution Network Expansion Planning

The deployment of prosumers with distributed energy resources (DERs) has facilitated the prosumer-centric transactive market, giving rise to the consequent necessity for the coordination between prosumers and the distribution networks. This paper proposes an incentive-based expansion planning method considering virtual peer-to-peer (P2P) transaction among prosumers, which is characterized as a tri-level planning-operation-transaction framework. Each prosumer optimizes its internal response of DERs and external P2P transaction jointly, formulating virtual P2P transaction. Under such a paradigm, the distribution company (DISCO) identifies the optimal expansion planning in the network assets, while the independent distribution system operator (IDSO) is responsible for the optimal operation of the expanded distribution networks. To derive appropriate incentives for each prosumer, the DISCO and IDSO allocate long-term investment network charges and short-term operation network charges on prosumers, along with the designed financial transmission rights coupling multiple time scales. Through such incentives, self-profit driven virtual transactions can relieve operation problems and postpone expansion planning, acting as a short-term alternative. Moreover, a risk-based approach is proposed for prosumer to flexibly change strategy according to different risk preference. Finally, results from the case study demonstrate the superiority and effectiveness of the proposed method in the collaborative application of virtual transaction in the expansion planning.

Multi-Dimensional Economy-Durability Optimization Method for Integrated Energy and Transportation System of Net-Zero Energy Buildings

With the increasing penetration of renewable energy and zero-emission vehicles (ZEVs), the integrated energy and transportation system (IETS) is considered a promising solution for achieving net-zero energy buildings (NZEBs). However, the tight coupling and the conflict of interests between the transportation and energy systems increase the difficulty of collaborative operation scheduling. Therefore, this paper proposes a multi-dimensional economy-durability (MDED) optimization method for IETS of NZEBs. The multidimensionality includes multiple subjects of energy, transportation, and IETS, and multiple time scales of day-ahead and intraday. In the day-ahead stage, the comprehensive cost-optimal scheduling strategy with joint intelligent parking lot (IPL) is constructed for the collaborative operation of IETS. In the intraday stage, two scheduling strategies are applied to satisfy the operation of the transportation and energy systems, respectively. For the transportation system, a coordinated scheduling strategy based on charging service satisfaction (CSS) is presented to coordinate ZEVs demands with diverse vehicle types and service manners. And for the energy system, a rolling optimization strategy based on a dynamic economy-health matrix (DEHM) is designed to track the day-ahead scheduling plan. The results indicate that the proposed method resolves the conflict between the charging needs and the comprehensive cost of the IETS system. In addition, the economic potential and coordination capability of fast charging services in IETS coordinated scheduling are verified. Finally, the proposed DEHM has significant advantages for the system cost and health compared to the conventional weighting matrix.

Deep Attention Networks With Multi-Temporal Information Fusion for Sleep Apnea Detection.

Sleep Apnea (SA) is a prevalent sleep disorder with multifaceted etiologies that can have severe consequences for patients. Diagnosing SA traditionally relies on the in-laboratory polysomnogram (PSG), which records various human physiological activities overnight. SA diagnosis involves manual scoring by qualified physicians. Traditional machine learning methods for SA detection depend on hand-crafted features, making feature selection pivotal for downstream classification tasks. In recent years, deep learning has gained popularity in SA detection due to its capability for automatic feature extraction and superior classification accuracy. This study introduces a Deep Attention Network with Multi-Temporal Information Fusion (DAN-MTIF) for SA detection using single-lead electrocardiogram (ECG) signals. This framework utilizes three 1D convolutional neural network (CNN) blocks to extract features from R-R intervals and R-peak amplitudes using segments of varying lengths. Recognizing that features derived from different temporal scales vary in their contribution to classification, we integrate a multi-head attention module with a self-attention mechanism to learn the weights for each feature vector. Comprehensive experiments and comparisons between two paradigms of classical machine learning approaches and deep learning approaches are conducted. Our experiment results demonstrate that (1) compared with benchmark methods, the proposed DAN-MTIF exhibits excellent performance with 0.9106 accuracy, 0.9396 precision, 0.8470 sensitivity, 0.9588 specificity, and 0.8909 [Formula: see text] score at per-segment level; (2) DAN-MTIF can effectively extract features with a higher degree of discrimination from ECG segments of multiple timescales than those with a single time scale, ensuring a better SA detection performance; (3) the overall performance of deep learning methods is better than the classical machine learning algorithms, highlighting the superior performance of deep learning approaches for SA detection.

Dynamics of a non-ideally driven oscillating system under frictional slip

The Sommerfeld Effect pertains to the non-linear jump phenomenon near the resonance frequency of an excited structure powered by a non-ideal drive that is incapable to supply the required power to the oscillating sub-system. Often this situation is dealt with by increasing the viscous damping present in the system at the expense of considerable power loss. However, the use of non-viscous damping for the attenuation of the Sommerfeld Effect is considered in very few articles. In this paper, the dynamics of a non-ideal DC motor-driven vibrating system with frictional slip as the dissipation element is presented. Use of the method of multiple time scales is employed to find the response of the system semi-analytically. The non-linear dynamical characteristics of the response as the motor speed crosses the resonance condition is analyzed using the linear stability analysis and subsequently, the obtained response is verified using numerical results. The transition of the operating condition between stable and unstable zones and the post-resonance dynamic behavior of such systems indicated that under proper parametric condition, it is possible to achieve a smooth variation of operating speed via a combination of stick and slip motion. Furthermore, the rate of voltage increment is also found to play a pivotal role in surpassing the critical speed of the system.

An Integrated Computational Framework for the Neurobiology of Memory Based on the ACT-R Declarative Memory System

Memory is a complex process that spans multiple time-scales and stages, and, as expected, involves multiple brain regions. Traditionally, computational models of memory are either too abstract (Shiffrin & Steyvers, 1997) to be meaningfully connected to a biological substrate, or, when explicitly connected, are narrowly focused on one specific region and process (Blum & Abbott, 1996; Weber et al., 2017). By contrast, a comprehensive model of memory with a plausible neural interpretation would be extremely valuable to drive further research in memory function and dysfunction. In this paper, we attempt to fill in this gap by providing a detailed biological analysis of ACT-R’s declarative memory system. This system, developed over four decades, has evolved into a consistent framework that describes how memories are formed, retrieved, forgotten, mistaken, and merged. Building on existing mappings between some components and their biological counterpart, as well as the existing literature, this paper provides a comprehensive view of how the framework’s various computations map onto different brain regions, their network dynamics and functional connectivity, and biological structure. We also show that these mappings provide further insights and explanations for puzzling findings in the memory disorders literature. Finally, we outline the remaining gaps (such as the transition from episodic to semantic memory) and how they could be addressed by future research and modeling efforts.

Sensory neurons couple arousal and foraging decisions in Caenorhabditis elegans

Foraging animals optimize feeding decisions by adjusting both common and rare behavioral patterns. Here, we characterize the relationship between an animal’s arousal state and a rare decision to leave a patch of bacterial food. Using long-term tracking and behavioral state classification, we find that food leaving decisions in Caenorhabditis elegans are coupled to arousal states across multiple timescales. Leaving emerges probabilistically over minutes from the high arousal roaming state, but is suppressed during the low arousal dwelling state. Immediately before leaving, animals have a brief acceleration in speed that appears as a characteristic signature of this behavioral motif. Neuromodulatory mutants and optogenetic manipulations that increase roaming have a coupled increase in leaving rates, and similarly acute manipulations that inhibit feeding induce both roaming and leaving. By contrast, inactivating a set of chemosensory neurons that depend on the cGMP-gated transduction channel TAX-4 uncouples roaming and leaving dynamics. In addition, tax-4-expressing sensory neurons promote lawn-leaving behaviors that are elicited by feeding inhibition. Our results indicate that sensory neurons responsive to both internal and external cues play an integrative role in arousal and foraging decisions.