Long-term Time Scales Research Articles

Detection of seizure clustering using ultralong-term subcutaneous EEG monitoring

AbstractEpileptic seizures usually occur unpredictably yet not necessarily at random times. Cyclical patterns of seizure recurrence have been broadly described. Long-term seizure documentation may reveal individual cyclical seizure patterns; however, seizure reporting by patients or their caregivers is often inaccurate. Modern technologies for at-home monitoring of epileptic seizures enable continuous EEG recording for objective seizure counts over long-term timescales. Here, we present the clinical case of a patient with temporal lobe epilepsy due to hippocampal sclerosis who underwent ultralong-term subcutaneous EEG recording over a 1-year period, which revealed the occurrence of seizure clusters at three- to four-weekly intervals. The case demonstrates the possibility of detection of subject-specific seizure timing using a minimally invasive subcutaneous EEG recorder for ultralong-term at-home seizure monitoring. Information about individual seizure timing may contribute to a more personalized chronotherapeutic treatment approach in which antiseizure medication can be maintained at low doses during the low-risk periods and increased briefly before the anticipated seizures.

Divergent temporal responses of native macroinvertebrate communities to biological invasions.

Biological invasions pose a major threat to biodiversity, ecosystem functioning, and human well-being. Non-native species can have severe ecological impacts that are transformative, affecting ecosystems across both short-term and long-term timescales. However, few studies have determined the temporal dynamics of impact between these scales, impeding future predictions as invasion rates continue to rise. Our study uses a meta-analytical approach to dissect the changing taxonomic and functional impacts of biological invasions on native macroinvertebrate populations and communities in freshwater ecosystems across Europe, using a recently collated European long-term time series spanning several decades. Our findings reveal a complex temporal pattern: while initial stages of invasions (i.e. five years after the first record of non-native species) often exhibited benign impacts on macroinvertebrate abundance, richness, or functional diversity, the long-term (i.e. the period following the early invasion) effects became predominantly negative. This pattern was consistent between taxonomic and functional metrics for impacts at both the population and species level, with taxonomic metrics initially positively affected by invasions and functional metrics being more stable before also declining. These results suggest that even initially benign or positively perceived impacts could be eventually superseded by negative consequences. Therefore, understanding the magnitude of invasion effects increasingly requires long-term studies spanning several years or decades to offer insights into effective conservation strategies prioritising immediate and future biodiversity protection efforts. These findings also highlight the importance of integrating multiple taxonomic, functional and temporal components to inform adaptive management approaches to mitigate the negative effects of current and future biological invasions.

Secular dynamics and the lifetimes of lunar artificial satellites under natural force-driven orbital evolution

In this paper, we study the long-term (time scale of several years) orbital evolution of lunar satellites under the sole action of natural forces. In particular, we focus on secular resonances, caused either by the influence of the multipole moments of the lunar potential and/or by the Earth’s and Sun’s third-body effect on the satellite’s long-term orbital evolution. Our study is based on a simplified secular model obtained in ‘closed form’, i.e., without expansions in the satellite’s orbital eccentricity. Contrary to the case of artificial Earth satellites, in which many secular resonances compete in dynamical impact, we give numerical evidence that for lunar satellites only the 2g−resonance (ω̇=0) affects significantly the orbits at secular timescales. We interpret this as a consequence of the strong effect of lunar mascons. We show that the lifetime of lunar satellites is, in particular, nearly exclusively dictated by the 2g resonance. By deriving a simple analytic model, we propose a theoretical framework which allows for both qualitative and quantitative interpretation of the structures seen in numerically obtained lifetime maps. This involves explaining the main mechanisms driving eccentricity growth in the orbits of lunar satellites. In fact, we argue that the re-entry process for lunar satellites is not necessarily a chaotic process (as is the case for Earth satellites), but rather due to a sequence of bifurcations leading to a dramatic variation in the structure of the separatrices in the 2g resonance’s phase portrait, as we move from the lowest to the highest limit in inclination (at each altitude) where the 2g resonance is manifested.

Selecting the best solar EUV proxy for long-term timescale applications

Solar EUV proxies are a solution to the difficulties in obtaining solar EUV time series covering extended periods or during times without measurements available. Despite being very similar at interannual and decadal timescales, proxies are not identical. The upper atmosphere, highly sensitive to this spectral range, can serve as a “witness” of solar EUV variations to help selecting the best proxy. Its choice can be critical depending on the problem we are trying to solve through the proxy implementation and on the nature of the proxies’ non-identical part variation. We analyze the best proxy selection process whose outcome depends on the method and on the physics knowledge we have of the system. This study aims to contribute and highlight certain critical aspects of selection methodologies. A case where this issue becomes critical is upper atmosphere long-term trends’ estimation in order to detect the increasing greenhouse gas effect along the last decades.

Data extension of high-resolution wind speed database by fusing meteorological observation and local objective analysis data with POD–LSE

Newly generated high-resolution meteorological reanalysis data have been published over the past few years on short records. Producing high-resolution data in long-term time scales is challenging due to high computational costs and complex weather models. In contrast, urban meteorological observations have been continuously recorded for decades, although at a low spatial resolution. This study proposes a data fusion method using Proper Orthogonal Decomposition (POD) and Linear Stochastic Estimation (LSE). It fuses meteorological observations and high spatial resolution local objective analysis (LA) data to reproduce a high-resolution and long-term fusion database for urban areas without observations. First, the accuracy of LA data was verified by comparison with meteorological observations. Subsequently, the accuracy of the generated fusion data was investigated by comparison with Doppler lidar observations. Finally, the performance of single- and multi-time LSE in data fusion is discussed. These results indicate that the proposed method generates high-resolution and long-term fusion wind speed data that are similar to the Doppler lidar observations. In addition, the fusion model built using hourly data can accurately generate a highly sampled database at 10-min intervals. The single-time LSE method is recommended to be applied over the multi-time delay method for data fusion.

Analysis of streamflow and rainfall trends and variability over the Lake Kariba catchment, Upper Zambezi Basin

ABSTRACT Hydro-meteorological trend analysis is critical for assessing climate change and variability at basin and regional levels. This study examined the long- and short-term trends from stream discharge and rainfall data in the Lake Kariba catchment. A trend and change point analysis was carried out on the mean, minimum and maximum monthly average time series for 14 gauging stations that are located within the Kariba catchment. The Mann–Kendall and the Pettitt tests were used to determine the trend and any changes in the long-term average of the time series. The magnitude of the trend was determined by Sen's slope method. The results indicate that generally there has been a decreasing trend in river and rainfall long-term mean values across the catchment. A statistically significant trend (p ≤ 0.05) was observed at Zambezi River at Lukulu, Senanga and Victoria Falls, with a positive correlation in Pearsons's coefficient of water levels and rainfall at Lukulu (0.312) and Senanga (0.365). The decrease in the time series trend and the change point observed have been attributed to anthropogenic activities, climate change and variability impact on the catchment. The findings are critical for climate risk management and reduction decisions for near- and long-term timescales.

Optimization and scheduling scheme of park-integrated energy system based on multi-objective Beluga Whale Algorithm

To improve the consumption rate of renewable energy and ensure the stability of power and heat supply within the industrial park, a park-integrated energy system (PIES), which incorporates electric heating equipment, energy storage devices, and multiple micro sources, is established. Based on the established PIES, a price-based demand response (PDR) mechanism is introduced to establish a multi-objective optimization and scheduling model with two time scales: day-ahead scheduling and intra-day scheduling, aiming to minimize the impact on the main grid and ensure the flexibility of system operation and real-time scheduling. With the objectives of minimizing operating costs and maximizing environmental benefits for PIES, a hybrid algorithm named Non-Dominated Sorting Beluga Whale Optimization (NSBWO), which combines the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Beluga Whale Optimization (BWO) algorithm is proposed to solve the optimization problem of the model. Simultaneously, an adaptive penalty function is introduced in the algorithm to handle constraint problems in optimization and additional penalty term for wind and solar curtailment is included within the constraints to improve the consumption rate of sustainable energy. The proposed NSBWO algorithm exhibits more robust search capabilities and faster convergence speed than the NSGA-II and MOPSO algorithm. Simulation results show that the proposed NSBWO algorithm can achieve low-carbon economic scheduling of the system in both long-term and short-term time scales and improve renewable energy consumption. Due to its flexibility, intra-day scheduling has lower operating costs than day-ahead scheduling.

Seasonal to interannual variations of daily growth rate of a Tridacna shell from Palau Island, western Pacific, and paleoclimatic implications

Seasonal-interannual climate change is an important component of Earth's climate system and has a significant impact on ecosystems and social systems. However, the short-run of modern observational data limits our understanding of the seasonal-interannual climates on long-term timescales. The natural archives can provide information on climate change before the industrial period, but most of their temporal resolution is too low to capture the seasonal-interannual climate signals. Tridacna spp. is the largest marine bivalve, and its shell has the potential to trace climatic/environmental changes on a daily to interannual scale. In this study, we investigated the daily growth pattern of a modern Tridacna gigas shell (PL-1) from Palau based on laser scanning confocal microscopy images. The results showed that the growth pattern of PL-1 was affected by effective solar radiation primarily, followed by nutrients on the seasonal timescale. On the interannual timescale, the growth rate of the shell was modulated by ENSO, which affected the effective solar radiation and nutrients accepted by Tridacna. The growth rate of Tridacna PL-1 was accelerated during the ENSO positive phase, and vice versa. However, due to a lack of situ observations, the effects of nutrients on Tridacna and the symbiotic zooxanthella in detail need to be further investigated. This study indicates that the daily growth rate of Tridacna shells in equatorial regions may have the potential to reconstruct seasonal-interannual climate/environment variations.

Long-Term Infrasound Sensor Calibration and Characterization

Abstract Previous testing has shown that infrasound sensors deployed in the field can exhibit notable deviations from their nominal, lab-based calibrations. These variations may be due to changes in environmental conditions, long-term sensor drift, or other unresolved features. In early 2018, we installed two identical infrasound elements with five infrasound sensors at each element (Chaparral M50A, Chaparral M64LN, CEA/Martec MB2005, CEA/Seismowave MB3a, and Hyperion IFS-5113A). These sensors were accepted or under consideration for use in the International Monitoring System network of the Comprehensive Nuclear-Test-Ban Treaty. Each element had all sensors connected to a single digitizer and port to the atmosphere. We also recorded instrument enclosure air temperature and humidity and external air temperature. Using the MB2005 as the reference, we examine the relative sensor response (both magnitude and phase) as a function of time and frequency and compare it with quarterly laboratory calibrations and environmental conditions. We find that the magnitude response for all sensors exhibits some variability in both the lab and field, with the amplitude variations often >5%. The field-based variations are more severe and occur on both long-term (months) and short-term (hours) timescales. Short-term variability correlates with changes in environmental conditions and is considerable (up to 25%) for the Chaparral M50A and noticeable (∼5%) for the French Alternative Energies and Atomic Energy Commission (CEA) MB3a. Long-term magnitude variability for the Chaparral M50A was up to 50% and does not clearly correlate with environmental conditions. The other sensors show some long-term magnitude offsets, but they have relatively stable responses in the conditions we examined. The MB3a also displays some frequency-dependent magnitude variability and shows a minor dependence on temperature. Phase estimates are stable and near zero for all sensors tested. These results strongly suggest sensor response and variability due to environmental conditions should be considered in future infrasound data interpretation and sensor selection and development.

Timescale effects on the driving mechanisms of post-fire recruitment in Larix gmelinii population

Post-fire recruitment plays a crucial role in enhancing forest resilience. While previous research highlights the importance of early post-fire forest recovery in determining future forest structure, little has breen reported on whether this relationship exists in the boreal coniferous forests of Eastern Siberia. Additionally, there is a lack of research on whether there are differences in the driving mechanisms that affect juvenile recruitment across different timescales. This study compared key biophysical factors affecting recruitment across short-term and long-term timescales. Leveraging field data and dendrochronological techniques, we determined the germination years of saplings and seedlings in 73 plots across eight fire times. We quantified annual recruitment and cumulative recruitment rates post-fire for each plot, examining the correlation between the time to achieve 75% cumulative recruitment post-fire and the number of recruits, thereby assessing the importance of initial recruitment. Utilizing spatial Generalized Linear Mixed Models (GLMMs), we inferred the impact of biophysical factors on recruitment across short- and long-term timescales. Subsequently, we visualized the relationship patterns between predictor variables and recruitment. The findings revealed that plots with a faster post-fire recruitment pace exhibited a greater quantity of recruitment. On long-term timescales, growing-season mean temperature (GSMT) exhibited a positive correlation with Larix gmelinii recruitment. Basal area demonstrated a robust positive effect on Larix gmelinii recruitment, displaying significance at long-term timescales. The stand age between 60 to 150 years, representing the middle-mature stage of Larix gmelinii, is conducive to post-fire recruitment. Downed dead wood (DDW) exhibited a highly significant boost to recruitment at both timescales, albeit with a relatively weaker effect observed at the long-term scale. Our findings provide some confirmation of the critical role of immediate post-fire recruitment in forest resilience, while also unveiling timescale effects that influence the mechanisms of post-fire recruitment. The findings furnish timely new perspectives on integrating temporal scales into model predictions of the recovery process and adaptive forest management strategies.

Leguminous cover orchard improves soil quality, nutrient preservation capacity, and aggregate stoichiometric balance: A 22-year homogeneous experimental site

Soil and water conservation measures, widely promoted to control soil erosion in erosive orchards, can alter soil aggregates, thereby influencing the soil quality index (SQI). However, it's still unclear how these measures affect soil aggregate nutrients, stoichiometry, and their association with SQI at a long-term timescale. This study focused on a 22-year homogeneous orchard in subtropical hilly region. Five soil and water conservation measures, namely clean tillage (CT), engineering measures (EM), tillage measures (TM), non-leguminous biological measures (NLBM), and leguminous biological measures (LBM), were implemented. Sampling depths were 0–20 cm, 20–40 cm, and 40–60 cm. The results revealed that LBM significantly enhanced SQI across the soil profile, increased the proportion of macroaggregates, and improved aggregate stability, soil organic carbon, and total nitrogen. Additionally, LBM demonstrated the highest capacity for carbon and nitrogen preservation. Soil stoichiometry revealed carbon and nitrogen limitations in the orchard soils, and leguminous cover proved effective in alleviating these limitations and maintaining the stoichiometric balance of soil aggregates. Under a long-term timescale, these measures mitigated or even surpassed the impact of soil depth on soil aggregates and SQI. The improvement of orchard SQI through the measures was primarily achieved by enhancing aggregate stability. There was no significant correlation between soil organic carbon and SQI in relation to fruit yield, ascribed to the intense competition for water and nutrients between cover plants and citrus trees. In conclusion, LBM emerges as the most beneficial soil and water conservation measure for enhancing orchard SQI, preserving nutrients, and maintaining aggregate stoichiometric balance. It holds the potential to improve orchard yield and contribute to sustainable development.

A multi-dimensional connectedness and spillover between green bond and Islamic banking equity: Evidence from country level analysis

The paper delves into the multidimensional connections and spillover between green bonds and Islamic banking stocks from eleven countries. Throughout this study, we further explored optimal hedging mechanisms, as well as optimal portfolio weighting mechanisms, with Islamic banking equities and green bonds. Our empirical results show that there is a moderate level of interrelation between green bonds and Islamic banking indices, with low connectedness throughout the long-term and medium-term. The time-varying spillover effects become higher, albeit low, in the early periods of both SOR and COVID-19 at short-, medium-, and long-term scales, which suggests some diversification and hedging benefits from holding portfolios of the two assets. Country-based Islamic bank markets are not largely affected by disturbances that emerged in some other markets in the short-, medium-, or long-term timescales. The Chinese green bond market has a tendency to be the greatest net risk transceiver for both the green bond and Islamic bank markets over the medium and long term, whilst the global green bond index has been the leading net risk transmitter during the short term. Only the UAE and Saudi Islamic Banking indices act as net risk transmitters in the short and medium term. The empirical findings further suggest that global risk variables are dependable drivers of the extent of spillover between Islamic banking indices and green bonds. Our research shows that owning Islamic bank stocks during COVID-19 and SOR minimizes the significant risk linked to holding green bonds, which has profound implications for risk management and portfolio creation. Thus, our study offers important implications for economic agents.

Deep Learning LSTM-based approaches for 10.7 cm solar radio flux forecasting up to 45-days

Accurate forecasting of F10.7 index is important on short, medium and long-term timescales since F10.7 is an excellent proxy of solar activity and it plays an important role within the Space Weather framework. The analysis of the signatures of transient solar radio emission and its prediction are a challenging task as the underpinning physical processes are typically nonlinear, non-stationary and chaotic. In this paper we want to present three Deep Learning approaches for the daily forecasting of the adjusted F10.7 solar radio flux up to 45 days, using a family of Long Short Term Memory (LSTM) based models. We investigated two novel hybrid architectures: the LSTM model used in combination with Fast Iterative Filtering as decomposition algorithm (FIF-LSTM) and a method based on Multi-Head-Attention architecture (FIF-LSTM-MHA). FIF is a robust decomposition signal method very suitable for analyzing non-linear and non-stationary time series and it is used to separate the original time series into different oscillation components according to frequency, derived without leaving the time domain before to be fed into the neural network. The Attention mechanism is able to keep track of long-term dependencies in data sequences and improve the computational efficiency of the prediction model by reducing the effect of irrelevant information, mimicking human attention and selecting the most critical input. Our comparative analysis evaluated the models’ performance for different time lags and solar activity levels. The results indicated that the hybrid models achieve better performance than the LSTM model for mid-range F10.7 predictions while the LSTM achieves better performance within the first few time lags. FIF-LSTM-MHA gives more promising output for longer forecasts since it tends to smooth the prediction curve due to the peculiarity of the Attention module to discard less relevant features of the time series and highlight the global trend.

Reconciling patterns of long-term topographic growth with coseismic uplift by synchronous duplex thrusting

How long-term changes in surface topography relate to coseismic uplift is key to understanding the creation of high elevations along active mountain fronts, and remains hotly debated. Here we investigate this link by modeling the development of growth strata and the folding of river terraces above the Pishan duplex system in the southern Tarim Basin. We show that synchronous duplex thrusting of two neighboring faults with varying slip rates, associated with in-sequence propagation of the Pishan thrust system, is required to explain the presence of opposite-dipping panels of growth strata on the duplex front, and basinward migration of terrace fold crests. Importantly, this process of synchronous thrusting within the duplex reconciles the discrepancy between the deformation of terrace folds at the 10−1–100 million-year timescale and the maximum coseismic uplift of the 2015 Mw 6.4 Pishan earthquake on the frontal thrust. These results suggest that topography mismatch at different time scales can reflect the long-term kinematic evolution of fault systems. Thus, our study highlights the importance of characterizing complex subsurface fault kinematics for studying topographic growth, and motivates rethinking of the mountain building process in worldwide active fold-and-thrust belts, from short-term to long-term timescales.

Regional to global assessments of ocean transparency dynamics from 1997 to 2019

Water transparency, often quantified using the Secchi disk depth (Zsd), is a key parameter for assessing the water quality and ecological health in marine environments. However, a limited understanding of global Zsd dynamics over the past two decades exists. To assess the variations of ocean transparency over multiple timescales, the Ocean Colour Climate Change Initiative (OC-CCI) datasets are used to retrieve monthly global Zsd products during 1997–2019. On regional to global scales, significant Zsd variations on seasonal, interannual, and long-term timescales are identified. Seasonal and interannual variations of Zsd in most open oceans are found to be closely linked to phytoplankton dynamics and climate change. In the coastal zones, the Zsd variability is attributed to various physical and environmental factors, including seasonal monsoons, river outflow, sediment resuspension, eutrophication, etc. Moreover, the climatological seasonal Zsd variations of 22 typical ocean areas and the associated influencing factors are investigated on regional scales. The long-term trends of Zsd suggest the widespread expansion of oligotrophic waters within the North Pacific, North Atlantic, and South Indian Ocean gyres, indicating ongoing ocean desertification. The OC-CCI Zsd datasets can be used to build a merged centennial time series of Zsd in marine optics by linking to historical measurements. By relying on satellite-derived products, global and time-varying images offer a more comprehensive understanding of regional to global Zsd dynamics.

Renewable Electric Energy System Planning Considering Seasonal Electricity Imbalance Risk

The growing renewable integration significantly enhances the seasonal electricity imbalance of the electric energy system. However, traditional power system planning methods usually take into account the hourly power balance within typical days but seldom consider the seasonal imbalance risk in the long-term timescale. Therefore, this paper proposes a generation-transmission-storage co-planning model considering the seasonal imbalance risk brought by the long-term uncertainty of renewable generation in the power system. The Conditional Value at Risk (CVaR) method is introduced to quantify the seasonal imbalance risk. The power system energy balance constraints are decoupled into short-term (i.e., hourly) and long-term (i.e., monthly) energy balance so that the operation of the power system can be considered within different timescales and the seasonal imbalance risk could be calculated. The validity of the proposed model is first investigated via a modified Garver's 6-bus system case study. Furthermore, the research on the HRP-38 system (HRP represents high renewable penetration) illustrates that our proposed model could effectively control the energy imbalance risk from multiple timescales. Finally, the proposed method is applied to the whole China power system at the province level, proving that the proposed planning method could effectively enhance the reliability of China's power system, decreasing both the average annual short-term and long-term energy imbalance by 30%.

Multiband optical variability on diverse time-scales of the TeV blazar TXS 0506 + 056, the first cosmic neutrino source

ABSTRACT We report the first extensive optical flux and spectral variability study of the TeV blazar TXS 0506 + 056 on intranight to long-term time-scales using BVRI data collected over 220 nights between 2017 January 21 to 2022 April 9 using eight optical ground-based telescopes. In our search for intraday variability (IDV), we have employed two statistical analysis techniques, the nested ANOVA test and the power enhanced F-test. We found the source was variable in 8 nights out of 35 in the R-band and in 2 of 14 in the V-band yielding duty cycles (DC) of 22.8 per cent and 14.3 per cent, respectively. Clear colour variation in V − R was seen in only 1 out of 14 observing nights, but no IDV was found in the more limited B, I, and B − I data. During our monitoring period the source showed a 1.18 mag variation in the R-band and similar variations are clearly seen at all optical wavelengths. We extracted the optical (BVRI) SEDs of the blazar for 44 nights when observations were carried out in all four of those wavebands. The mean spectral index (α) was determined to be 0.897 ± 0.171.

A Multi-Factor Combination Model for Medium to Long-Term Runoff Prediction Based on Improved BP Neural Network

Taking a certain coastal area of Jiangsu province as the research background, this study scientifically predicts the runoff on the medium and long-term time scale according to the changes of various climate factors such as atmospheric circulation, sea surface temperature, and solar activity in the first half of the year. A lag correlation is established between various related climate factors and the monthly runoff process in the research area for the previous 1–6 months. Selecting advantageous factors and constructing a significant factor set. Using the improved BP (Back-Propagation) artificial neural network model and combining it with the sensitivity analysis method, a specific number of 8-factor combinations are selected from the set of significant factors for medium and long-term runoff prediction. After that, the prediction results are compared with the forecasting effects of two multi-factor combination runoff simulation schemes formed by stepwise regression and Spearman rank correlation methods. The study concluded that the multi-factor combination simulation effect formed through sensitivity analysis was the best. The 20% standard forecast qualification rate of the three schemes is not significantly different. The Mean Absolute Relative Error of the multi-factor combination training and validation periods simulated through sensitivity analysis is the smallest among the three schemes, which are 36.61% and 38.01%, respectively. The Nash Efficiency Coefficient in the validation period is 0.45, which is far better than other schemes and has better generalization ability. The Standard Deviation of Relative Error in the training and validation periods is much smaller than other schemes, and the dispersion of relative errors is the smallest.

A New Multi-Physics Coupled Method for the Temperature Field of Dry Clutch Assembly

The temperature field of the clutch assembly is critical for the clutch design and operation life. Current modeling methods of the temperature of the clutch assembly suffer from insufficient accuracy or a limited time scale for the complicated multi-physics coupling between the contact force, friction-generated heat, heat transfer, and thermal deformation in the clutch assembly in harsh operation conditions. In order to improve the accuracy of temperature field simulation and achieve long-term time scale, a new approach to modeling the temperature is proposed based on CFD simulation and decoupling technology. Firstly, the flow-thermal bi-directional coupling method is employed to determine the convective boundary conditions between the clutch assembly and the ambient air, improving the model’s accuracy. Secondly, the thermal-solid decoupling method is then used to reduce the computational time. The temperature of the clutch assembly during the continuous engagement and disengagement process is performed using this approach and verified by the rig test. The results demonstrate that the temperature of the outer, middle, and inner diameters of the pressure plate by the model agrees well with that by the rig test. For the first engagement and disengagement processes, the proportion of simulated temperature deviations exceeding 5 °C from the measured data is only 3.03%. For the last engagement and disengagement process, while the maximum temperature of the clutch is above 350 °C, the maximum temperature deviation between simulation and measurement is 4.99%. It proves that the approach proposed for modeling the dry clutch assembly temperature field has high accuracy while achieving long-term time-scale simulation.

Multi-scale analysis of the co-movement between China's new energy vehicle industry and Tesla: Evidence from capital market

China is currently the world's largest new energy vehicle market, and the development of its new energy vehicles is crucial to the sustainable development of mankind. As a leader in the new energy vehicle industry, Tesla's entry into the Chinese market has an important impact on its new energy vehicle industry, and the study of the relationship between the two is of great significance in promoting the development of China's new energy vehicle industry. Therefore, we analyzed the complex relationship between Tesla and China's new energy vehicle industry from 2013 to 2022 based on the stock market perspective using modal decomposition, Maximum mutual information coefficient, and transfer entropy. Mutual information coefficient results show that Tesla has stronger co-movements with China's New Energy Vehicle Manufacturing sector, and its strength is highest in the medium- and long-term time scales, up to 0.196 and 0.529, respectively. whereas the transfer entropy results show that Tesla has a stronger information transfer effect on the Vehicle Manufacturing sector and Charging Pile sector than on the New Energy Vehicles Battery sector and New Energy Vehicles Parts sector. However, in general, Tesla's information overflow to the whole Chinese new energy vehicle industry is on the rise. The Chinese government can appropriately give Tesla certain favorable policies and encourage Chinese enterprises to cooperate with it, giving full play to Tesla's catfish effect and technology demonstration effect, and then promoting the further development of China's new energy vehicle industry.