Original Time Research Articles

Research on Timing Compensation of Shadow Photograph System with Three-sequence Laser Sparking

The accuracy of the laser sparking time interval measurement plays a key role in the measurement results when the projectile velocity is measured by shadow photograph system with three-sequence laser sparking. By comparing with the original single-chip measurement results, it was found that there was a certain deviation in the measurement results of the laser sparking time. Then the principle of the laser sparking time measurement circuit is introduced in detail, and the reasons for this error phenomenon are summarized. And analyze the original laser sparking time measurement data column and the data column accurately measured with an oscilloscope, use the Pearson correlation coefficient to describe the correlation between the two data columns, and obtain the sample correlation coefficient, the first laser sparking interval coefficient r1=0.50, the second laser sparking interval coefficient r2=0.52. According to the three-level division rule, when 0.4≤|r|<0.7, the two variables are significantly correlated. In order to compensate the original data, it is necessary to find a suitable Δ value to minimize the residual sum of squares of the measured column data after compensation. The mathematical model is developed and solved for the nonlinear system of equations, and the optimal value of Δ for the original data is obtained using a global search for the optimal value: Δ1= 0.238μs, Δ2= 0.201μs. Comparing the compensated data with the original data, The standard deviation of the measured columns of the first flash interval decreased from 0.117μs to 0.022μs after compensation, and the second decreased from 0.102μs to 0.014μs. This shows that the two columns error of compensated data are significantly reduced, which is important for the improvement of the velocity measurement accuracy of the target channel.

Defects Detection of Onboard Cable Termination in EMUs Using Partial Discharge Measurement and SDP-DL Framework

Termination is the most important part of on-board cable, which plays a vital role in ensuring the continuous and reliable power supply to EMUs (electrical multiple units). The maintenance time of EMUs is limited, so the time left for PD (partial discharge) measurement of on-board cable termination is very short, leading to the obvious decreasing of detection accuracy. For addressing this issue, this paper proposed an SDP (symmetrized dot pattern)–DL (deep learning) framework to detect the insulation defects using time series PD pulse signal. First, a PD measurement platform and experimental samples were prepared in laboratory to obtain the time series PD pulse signals of four typical insulation defects. Then, the time series PD signals were converted into SDP images using the proposed parameter optimization method. Finally, the SDP-DL framework was proposed, and three specific and typical methods were utilized, i.e., CNN (convolutional neural network), SAE (stacked auto encoder) and DBN (deep belief network). The results show that the performance of SDP-DBN method is the best, and the insulation defects can be detected with accuracy of 96.1%. In addition, the visualization ability of data increases after SDP transformation of the original PD time series signal.

Short-Term Forecasting of Monkeypox Cases Using a Novel Filtering and Combining Technique.

In the modern world, new technologies such as artificial intelligence, machine learning, and big data are essential to support healthcare surveillance systems, especially for monitoring confirmed cases of monkeypox. The statistics of infected and uninfected people worldwide contribute to the growing number of publicly available datasets that can be used to predict early-stage confirmed cases of monkeypox through machine-learning models. Thus, this paper proposes a novel filtering and combination technique for accurate short-term forecasts of infected monkeypox cases. To this end, we first filter the original time series of the cumulative confirmed cases into two new subseries: the long-term trend series and residual series, using the two proposed and one benchmark filter. Then, we predict the filtered subseries using five standard machine learning models and all their possible combination models. Hence, we combine individual forecasting models directly to obtain a final forecast for newly infected cases one day ahead. Four mean errors and a statistical test are performed to verify the proposed methodology's performance. The experimental results show the efficiency and accuracy of the proposed forecasting methodology. To prove the superiority of the proposed approach, four different time series and five different machine learning models were included as benchmarks. The results of this comparison confirmed the dominance of the proposed method. Finally, based on the best combination model, we achieved a forecast of fourteen days (two weeks). This can help to understand the spread and lead to an understanding of the risk, which can be utilized to prevent further spread and enable timely and effective treatment.

Step-over of strike-slip faults and overpressure fluid favor occurrence of foreshocks: Insights from the 1975 Haicheng fore-main-aftershock sequence, China

This study analyzed and summarized in detail the spatial and temporal distributions of earthquakes, tidal responses, focal mechanisms, and stress field characteristics for the M 7.3 Haicheng earthquake sequence in February 1975. The foreshocks are related to the main fault and the conjugate faults surrounding the extension step-over in the middle. The initiation timing of the foreshock clusters and the original time of the mainshock were clearly modulated by the Earth's tidal force and coincided with the peak of dilational volumetric tidal strain. As a plausible and testable hypothesis, we proposed a fluid-driven foreshock model, by which all observed seismicity features can be more reasonably interpreted with respect to the results of existing models. Together with some other known examples, the widely existing step-over along strike-slip faults and associated conjugate faults, especially for extensional ones in the presence of deep fluids, favor the occurrence of short-term foreshocks. Although clustered seismicity with characteristics similar to those of the studied case is not a sufficient and necessary condition for large earthquakes to occur under similar tectonic conditions, it undoubtedly has a warning significance for the criticality of the main fault. Subsequent testing would require quantification of true/false positives/negatives.

SafeDrop: Detecting Memory Deallocation Bugs of Rust Programs via Static Data-flow Analysis

Rust is an emerging programming language that aims to prevent memory-safety bugs. However, the current design of Rust also brings side effects, which may increase the risk of memory-safety issues. In particular, it employs ownership-based resource management and enforces automatic deallocation of unused resources without using the garbage collector. It may therefore falsely deallocate reclaimed memory and lead to use-after-free or double-free issues. In this article, we study the problem of invalid memory deallocation and propose SafeDrop , a static path-sensitive data-flow analysis approach to detect such bugs. Our approach analyzes each function of a Rust crate iteratively in a flow-sensitive and field-sensitive way. It leverages a modified Tarjan algorithm to achieve scalable path-sensitive analysis and a cache-based strategy for efficient inter-procedural analysis. We have implemented our approach and integrated it into the Rust compiler. Experiment results show that the approach can successfully detect all such bugs in our experiments with a limited number of false positives and incurs a very small overhead compared to the original compilation time.

The mortality risk after myocardial infraction in migrants compared with natives: a systematic review and meta-analysis.

The evidence on the risk of mortality after myocardial infarction (MI) among migrants compared with natives is mixed and limited. The aim of this study is to assess the mortality risk after MI in migrants compared to natives. This study protocol is registered with PROSPERO, number CRD42022350876. We searched the Medline and Embase databases, without time and language constraints, for cohort studies that reported the risk of mortality after MI in migrants compared to natives. The migration status is confirmed by country of birth, both migrants and natives are general terms and are not restricted to a particular country or area of destination or origin. Two reviewers separately screened searched studies according to selection criteria, extracted data, and assessed data quality using the Newcastle-Ottawa Scale (NOS) and risk of bias of included studies. Pooled estimates of adjusted and unadjusted mortality after MI were calculated separately using a random-effects model, and subgroup analysis was performed by region of origin and follow-up time. A total of 6 studies were enrolled, including 34,835 migrants and 284,629 natives. The pooled adjusted all-cause mortality of migrants after MI was higher than that of natives (OR, 1.24; 95% CI, 1.10-1.39; I2 = 83.1%), while the the pooled unadjusted mortality of migrants after MI was not significantly different from that of natives (OR, 1.11; 95% CI, 0.69-1.79; I2 = 99.3%). In subgroup analyses, adjusted 5-10 years mortality (3 studies) was higher in the migrant population (OR, 1.27; 95% CI, 1.12-1.45; I2 = 86.8%), while adjusted 30 days (4 studies) and 1-3 years (3 studies) mortality were not significantly different between the two groups. Migrants from Europe (4 studies) (OR, 1.34; 95% CI, 1.16-1.55; I2 = 39%), Africa (3 studies) (OR, 1.50; 95% CI, 01.31-1.72; I2 = 0%), and Latin America (2 studies) (OR, 1.44; 95% CI, 1.30-1.60; I2 = 0%) had significantly higher rates of post-MI mortality than natives, with the exception of migrants of Asian origin (4 studies) (OR, 1.20; 95% CI, 0.99-1.46; I2 = 72.7%). Migrants tend to have lower socioeconomic status, greater psychological stress, less social support, limited access to health care resources, etc., therefore, face a higher risk of mortality after MI in the long term compared to natives. Further research is needed to confirm our conclusions, and more attention should be paid to the cardiovascular health of migrants. https://www.crd.york.ac.uk/prospero/, identifier: r CRD42022350876.

Short-term traffic flow prediction: An ensemble machine learning approach

The inconvenience of travel, air pollution and consequent economic losses caused by traffic congestion have seriously restricted the healthy and sustainable development of cities in China. In this context, as the main component of current and future urban traffic management measures, intelligent transportation system is an important means to improve the traffic efficiency of road network and alleviate urban traffic congestion. Traffic flow prediction plays an important role in this connection. In this paper, an ensemble short-term traffic flow prediction method based on optimized variational mode decomposition (OVMD) and combined long short-term memory network (LSTM) is proposed. The method consists of three main components: 1. Use the improved bat algorithm to optimize the parameters of VMD to achieve better decomposition effect; 2. Use the optimized variational mode decomposition algorithm (OVMD) to decompose the unstable original traffic flow time series data into relatively stable multiple Intrinsic Mode Functions (IMFs); 3. The optimized L-BILSTM model is established by combining the basic long short-term memory network with the bidirectional long short-term memory network. It can better extract information from traffic flow data and improve the accuracy of prediction results.In the empirical study part, the traffic flow data of Changsha City is used to verify the prediction model proposed in this paper. The influence of the application of the variational mode decomposition algorithm to the training set data and the overall data on the final prediction results is also compared and analysed.

Infection and Nonunion Following Operative Treatment of Lateral Humeral Condyle Fractures.

Lateral humeral condyle fractures (LHCF) are the second most frequent type of pediatric elbow fracture. The purpose of this study is to characterize infections and nonunions of LHCF treated at a single institution. This is the largest series on infections and nonunions of LHCF to date. Pediatric patients undergoing surgical treatment for a LHCF from 2012 to 2022 at a single children's hospital were identified for a retrospective review. Patients who were diagnosed with an active infection or nonunion after surgery were included. Data collected included demographics, original treatment course, presentation, effective treatments, outcomes, and timing of events. Out of 863 surgical patients, 12 (1.4%) patients developed 13 infections: 8 were diagnosed with superficial cellulitis, 3 with soft tissue infections, and 2 with osteomyelitis. Eleven fractures were stabilized with percutaneous pins and 1 with buried pins. The average time to infection diagnosis was 29 days and the most common presenting symptom was increased or new onset of pain. All 12 patients received antibiotics for an average 18 days, 6 required hospital admission, 3 required surgical incision and drainage, and 2 required intravenous antibiotics without admission. One patient that developed osteomyelitis developed a nonunion. Ten (1.2%) surgical patients developed nonunion. There were 3 Weiss type II fractures and 7 type III fractures. On average, nonunions were diagnosed 12 weeks after initial treatment. Nine patients underwent nonunion surgery, and all went on to union. Patient's elbows were immobilized for an average 16 weeks and at least 5 patients required an average of 10 physical therapy sessions to regain their range of motion. Infection and nonunion are rare complications of LHCF, but greatly change the timeline and number of healthcare interactions required for healing. Infectious complications typically require admission, additional surgery, or emergency department visits. Nonunions require extensive cast time, additional surgery, and rehabilitation. Level IV - case series.

Forecasting nonstationary time series

AbstractMany variables show a tendency to increase over time in line with their nonstationary nature. It is notable, however, that the original time series can be transformed into a sequence of jumps measured by time distances between the successive maxima and present the resulting series as the compound Poisson process, which has powerful consequences discussed in the paper. Firstly, the jump‐generating process is stationary, unlike the one generating the original data. Secondly, the dynamics of a variable can be determined using solely the properties of the derived stationary counterpart. Thirdly, using this framework for prediction offers substantial advantages. The proposed methodology allows forecasting the number of periods necessary for a process to achieve the desired level and decomposing the path leading to that level into jumps of different size. It also gives a unique insight into the shape of the trajectory over the prediction horizon, which the traditional approach to the forecasting of nonstationary time series is incapable of providing.

Real-time high-precision landslide displacement monitoring based on a GNSS CORS network

GNSS technology is an effective all-weather, continuous and real-time method of detecting three-dimensional landslide displacement. However, current positioning methods such as the network real-time kinematic (RTK) technique based on virtual reference station (VRS) technology have some limitations, particularly a sensitivity to atmospheric modeling error. In this study, taking advantage of existing GNSS continuously operating reference stations (CORS), we propose a network RTK algorithm (virtual atmosphere constraint (VAC)) for high-precision landslide monitoring. Network modeling atmosphere delays were used as virtual observations for the terminal position solution, by which their accuracy information can be considered. Additionally, a noise reduction method that combines complementary ensemble empirical mode decomposition and median filtering was proposed to reduce GNSS signal residual errors in the original RTK coordinate time series. To validate the effectiveness of the algorithm, we processed one week of data from two monitoring receivers on the Heifangtai landslide and four surrounding CORS using the VAC and VRS methods. Results show that the original displacement time series processed with VRS was prone to jitter, whereas the VAC method exhibited better accuracy, stability, and ambiguity fixing rates. Horizontal and vertical RMS errors at a stable monitoring point were 1.7 cm and 3.9 cm for VRS-RTK and 0.9 cm and 2.3 cm for VAC-RTK; thus, VAC reduced the error by 40.1% and 20.5%, respectively. After noise reduction, the RMS values of the VAC method at the stable and the other collapsed monitoring point were both reduced to an mm-level accuracy.

Improving the prediction performance of soluble solids content (SSC) in kiwifruit by means of near-infrared spectroscopy using slope/bias correction and calibration updating

Soluble solids content (SSC) is particularly important for kiwifruit, as it not only determines its flavor, but also helps assess its maturity. Visible/near-infrared (Vis/NIR) spectroscopy has been widely used to evaluate the SSC of kiwifruit. Still, the local calibration models may be ineffective for new batches of samples with biological variability, which limits the commercial application of this technology. Thus, a calibration model was developed using one batch of fruit and the prediction performance was tested with a different batch, which differs in origin and harvest time. Four calibration models were established with Batch 1 kiwifruit to predict SSC, which were based on full spectra (i.e., partial least squares regression (PLSR) model based on full spectra), continuous effective wavelengths (i.e., changeable size moving window-PLSR (CSMW-PLSR) model), and discrete effective wavelengths (i.e., competitive adaptive reweighted sampling-PLSR (CARS-PLSR) model and PLSR-variable importance in projection (PLSR-VIP) model) respectively. The Rv2 values of these four models in the internal validation set were 0.83, 0.92, 0.96, and 0.89, with corresponding RMSEV values of 1.08 %, 0.75 %, 0.56 %, and 0.89 %, and RPDv values of 2.49, 3.61, 4.80, and 3.02, respectively. Clearly, all four PLSR models performed acceptably in the validation set. However, these models performed very poorly in predicting the Batch 2 samples, with their RMSEP values all exceeding 1.5 %. Although the models could not be used to predict exact SSC, they could still interpret the SSC values of Batch 2 kiwifruit to some extent because the predicted SSC values could fit a specific line. To enable the CSMW-PLSR calibration model to predict the SSC of Batch 2 kiwifruit, the robustness of this model was improved by calibration updating and slope/bias correction (SBC). Different numbers of new samples were randomly selected for updating and SBC, and the minimum number of samples for updating and SBC was finally determined to be 30 and 20, respectively. After calibration updating and SBC, the new models had average Rp2, average RMSEP, and average RPDp values of 0.83 and 0.89, 0.69 % and 0.57 %, and 2.45 and 2.97, respectively, in the prediction set. Overall, the methods proposed in this study can effectively address the issue of poor performance of calibration models in predicting new samples with biological variability and make the models more robust, thus providing important guidance for the maintenance of SSC online detection models in practical applications.

A novel nonlinear grey Bernoulli model NGBM(1,1,t^p,α) and its application in forecasting the express delivery volume per capita in China.

The grey prediction is a common method in the prediction. Studies show that general grey models have high modeling precision when the time sequence varies slowly, but some grey models show low modeling precision for the high-growth sequence. The paper researches the grey modeling for the high-growth sequence using the extended nonlinear grey Bernoulli model NGBM(1,1,t⌃p,α). To improve the nonlinear grey Bernoulli model NGBM(1,1,t⌃p,α)'s prediction precision and make data have better adaptability to the model, the paper makes improvements in the following three aspects: (1) the paper improves the accumulated generating sequence of original time sequence, i.e. making a new transformation of traditional accumulated generating sequence; (2) the paper improves the model's structure, extends the grey action and builds an extended nonlinear grey Bernoulli model NGBM(1,1,t⌃p,α); (3) the paper improves the model's background value and uses the value of cubic spline function to approximate the background value. Because the parameters of the new accumulated generating sequence transformed, the nonlinear grey Bernoulli model's time response equation and the background value are optimized simultaneously, the prediction precision increases greatly. The paper builds an extended nonlinear grey Bernoulli model NGBM(1,1,t⌃2,α) using the method proposed and seven comparison models for China's express delivery volume per capita. Comparison results show that the extended nonlinear grey Bernoulli model built with the method proposed has high simulation and prediction precision and shows the precision superior to that of seven comparison models.

Uncertainties surrounding the oldest fossil record of diatoms

Molecular clocks estimate that diatom microalgae, one of Earth’s foremost primary producers, originated near the Triassic–Jurassic boundary (200 Ma), which is close in age to the earliest, generally accepted diatom fossils of the genus Pyxidicula. During an extensive search for Jurassic diatoms from twenty-five sites worldwide, three sites yielded microfossils initially recognized as diatoms. After applying stringent safeguards and evaluation criteria, however, the fossils found at each of the three sites were rejected as new diatom records. This led us to systematically reexamine published evidence in support of Lower- and Middle-Jurassic Pyxidicula fossils. Although Pyxidicula resembles some extant radial centric diatoms and has character states that may have been similar to those of ancestral diatoms, we describe numerous sources of uncertainty regarding the reliability of these records. We conclude that the Lower Jurassic Pyxidicula fossils were most likely calcareous nannofossils, whereas the Middle Jurassic Pyxidicula species has been reassigned to the Lower Cretaceous and is likely a testate amoeba, not a diatom. Excluding the Pyxidicula fossils widens the gap between the estimated time of origin and the oldest abundant fossil diatom record to 75 million years. This study underscores the difficulties in discovering and validating ancient microfossils.

Short-Term Subway Passenger Flow Prediction Based on Time Series Adaptive Decomposition and Multi-Model Combination (IVMD-SE-MSSA)

The accurate forecasting of short-term subway passenger flow is beneficial for promoting operational efficiency and passenger satisfaction. However, the nonlinearity and nonstationarity of passenger flow time series bring challenges to short-term passenger flow prediction. To solve this challenge, a prediction model based on improved variational mode decomposition (IVMD) and multi-model combination is proposed. Firstly, the mixed-strategy improved sparrow search algorithm (MSSA) is used to adaptively determine the parameters of the VMD with envelope entropy as the fitness value. Then, IVMD is applied to decompose the original passenger flow time series into several sub-series adaptively. Meanwhile, the sample entropy is utilized to divide the sub-series into high-frequency and low-frequency components, and different models are established to predict the sub-series with different frequencies. Finally, the MSSA is employed to determine the weight coefficients of each sub-series to combine the prediction results of the sub-series and get the final passenger flow prediction results. To verify the prediction performance of the established model, passenger flow datasets from four different types of Nanning Metro stations were taken as examples for carrying out experiments. The experimental results showed that: (a) The proposed hybrid model for short-term passenger flow prediction is superior to several baseline models in terms of both prediction accuracy and versatility. (b) The proposed hybrid model is excellent in multi-step prediction. Taking station 1 as an example, the MAEs of the proposed model are 3.677, 5.7697, and 8.1881, respectively, which can provide technical support for subway operations management.

Feature extraction based on time-series topological analysis for the partial discharge pattern recognition of high-voltage power cables

In the partial discharge (PD) pattern recognition of power cables, the existing time–frequency features often exert an impact on recognition accuracy because of insufficient discrimination. A novel PD feature extraction and identification method on the basis of time-series topological data analysis (TDA) was proposed in this paper. Firstly, original PD sequence was reconstructed as point cloud in phase space based on optimized symbolic entropy. Then, a PD topological space is constituted with point cloud to extract its persistent homology features. On this basis, persistence diagrams and barcodes were calculated and visually expressed as Betty curves. Finally, Betty curves were input into an optimized 1D convolution neural network (1D-CNN) model to recognize four typical PD patterns and carry out comparison experiments. The visualization produced by t-distributed stochastic neighbor embedding (t-SNE) shows that TDA features possess significant discrimination, experiencing an increase of 11.25% in the overall recognition accuracy and reaching 98.00% compared with original PD sequence and time–frequency features. Meanwhile, the computation cost of the proposed algorithm is optimized within the permissible range for real-time applications.

Serious Mortgage Arrears among Immigrant Descendant and Native Participants in a Low-Income Public Starter Mortgage Program: Evidence from Norway

Although low-income homeownership programs serving vulnerable families at the lower end of the income distribution have been the focus of housing policy in many countries over the past 50 years, little is known about the post-origination experiences of immigrant families participating in these programs. Notably absent from the extant literature are studies examining the sustainability of homeownership among immigrant homebuyers and their susceptibility to falling behind on payments and experiencing mortgage defaults, evictions, or short sales. Utilizing data from 8263 families participating in Norway’s Starter Mortgage Program (Startlån) during the first three calendar years after mortgage origination, we examine the extent to which serious mortgage arrears varies by immigrant background. Two primary questions shape our research: (1) What is the incidence of serious mortgage arrears among Western, Eastern European, and non-Western immigrant homeowners relative to ethnic Norwegians participating in a public low-income homeownership program? and (2) What pre- and post-origination characteristics of applicants and households, mortgage terms at the time of origination, and experiences of household financial vulnerability or economic shocks predict heterogeneity in serious mortgage arrears by immigrant backgrounds? We found that 6.1% of ethnic Norwegian, 6.2% of Western, 4.9% of non-Western, and 3.2% of Eastern European immigrant homeowners participating in the Starter Mortgage Program were in serious mortgage arrears at least once during the first three calendar years after mortgage origination. Results from our negative binomial regression analyses suggest that program participants who were sole owners, with larger families, and higher debt were more likely to experience serious mortgage arrears; these effects were accentuated for ethnic Norwegians. Additionally, mortgage terms at the time of origination produced differential effects by immigrant background. Compared to Western and Eastern European immigrant homeowners, ethnic Norwegians were more likely to have experienced serious mortgage arrears if they purchased a single-family home, had larger LTV and DTI ratios, or if the Startlån share of their mortgages was higher. Non-Western immigrant mortgagors were more likely to make late mortgage payments if they had larger LTV ratios, interest-only mortgage servicing, or if they were more reliant on Startlån funds to finance their mortgages; however, this risk was reduced if they had fixed-rate mortgages. Financial vulnerability in terms of higher debt or fewer assets also increased the risk of serious mortgage arrears for ethnic Norwegians and non-Western immigrant homeowners, while increases in real wealth reduced that risk for all immigrant mortgagor groups.

Seismicity of Ireland, and why it is so low: How the thickness of the lithosphere controls intraplate seismicity

SUMMARY Ireland and neighbouring Britain share much of their tectonic history and are both far from active plate boundaries at present. Their seismicity shows surprising lateral variations, with very few earthquakes in Ireland but many low-to-moderate ones in the adjacent western Britain. Understanding the cause of these variations is important for our understanding of the basic mechanisms of the intraplate seismicity distributions and for regional hazard assessment. The distribution of microseismicity within Ireland and its underlying causes have been uncertain due to the sparsity of the data sampling of the island, until recently. Here, we use the data from numerous recently deployed seismic stations in Ireland and map its seismicity in greater detail than previously. The majority of detectable seismic events are quarry and mine blasts. These can be discriminated from tectonic events using a combination of the waveform data, event origin times, and the epicentres’ proximity to quarries and mines, catalogued or identified from the satellite imagery. Our new map of natural seismicity shows many more events than known previously but confirms that the earthquakes are concentrated primarily in the northernmost part of the island, with fewer events along its southern coast and very few deeper inland. Comparing the seismicity with the recently published surface wave tomography of Ireland and Britain, we observe a strong correspondence between seismicity and the phase velocities at periods sampling the lithospheric thickness. Ireland has relatively thick, cold and, by inference, mechanically strong lithosphere and has very few earthquakes. Most Irish earthquakes are in the north of the island, the one place where its lithosphere is thinner, warmer and, thus, weaker. Western Britain also has relatively thin lithosphere and numerous earthquakes. By contrast, southeastern England and, probably, eastern Scotland have thicker lithosphere and, also, few earthquakes. The distribution of earthquakes in Ireland and Britain is, thus, controlled primarily by the thickness and mechanical strength of the lithosphere. The thicker, colder, stronger lithosphere undergoes less deformation and features fewer earthquakes than thinner, weaker lithosphere that deforms more easily. Ireland and Britain are tectonically stable and the variations in the lithospheric thickness variations across them are estimated to be in a 75–110 km range. Our results thus indicate that moderate variations in the lithospheric thickness within stable continental interiors can exert substantial control on the distributions of seismicity and seismic hazard—in Ireland, Britain and elsewhere around the world.

Deep Temporal Contrastive Clustering

Recently the deep learning has shown its advantage in representation learning and clustering for time series data. Despite the considerable progress, the existing deep time series clustering approaches mostly seek to train the deep neural network by some instance reconstruction based or cluster distribution based objective, which, however, lack the ability to exploit the sample-wise (or augmentation-wise) contrastive information or even the higher-level (e.g., cluster-level) contrastiveness for learning discriminative and clustering-friendly representations. In light of this, this paper presents a deep temporal contrastive clustering (DTCC) approach, which for the first time, to our knowledge, incorporates the contrastive learning paradigm into the deep time series clustering research. Specifically, with two parallel views generated from the original time series and their augmentations, we utilize two identical auto-encoders to learn the corresponding representations, and in the meantime perform the cluster distribution learning by incorporating a k-means objective. Further, two levels of contrastive learning are simultaneously enforced to capture the instance-level and cluster-level contrastive information, respectively. With the reconstruction loss of the auto-encoder, the cluster distribution loss, and the two levels of contrastive losses jointly optimized, the network architecture is trained in a self-supervised manner and the clustering result can thereby be obtained. Experiments on a variety of time series datasets demonstrate the superiority of our DTCC approach over the state-of-the-art. The code is available at https://github.com/07zy/DTCC .

Chaotic time series wind power prediction method based on OVMD-PE and improved multi-objective state transition algorithm

As the global wind power generation capacity is constantly increasing, the problems of safe operation and utilization after grid connection are becoming more and more prominent. Aiming at the problem of low accuracy and stability of wind power time series with chaotic characteristics, a time series prediction method combining chaotic characteristic processing and neural networks is proposed. First, optimal variational mode decomposition with permutation entropy (OVMD-PE) is used to decompose the original wind power time series and overcome the disadvantage of insufficient mode aliasing encountered by empirical and integrated empirical modes. Second, an improved multi-objective state transition algorithm is proposed to determine the weight coefficients among the neural networks and improve the accuracy of the reconstructed predictive neural networks. Finally, the combined prediction method is used to study and analyze the wind power data from a wind farm in Xinjiang, China, from the perspectives of multiple scenarios and multiple time scales. The experimental results show that OVMD-PE can successfully deal with chaotic characteristics and the improved algorithm has improved the prediction accuracy. Compared with other traditional prediction models, the combined prediction model has higher robustness and stability.

Using EEMD mode decomposition in combination with machine learning models to improve the accuracy of monthly sea level predictions in the coastal area of China

In the context of climate change and human activities, the global sea level is facing a rising trend, which poses serious challenges to the ecological environment of coastal areas. In this study, we selected the monthly mean sea level (MSL) time series of 9 stations in the coastal areas of China as the research object. First, we analyzed the spatiotemporal distribution characteristics of the monthly MSL in the coastal areas of China. Secondly, we analyzed the ability of ensemble empirical mode decomposition (EEMD) to decompose the monthly MSL series. Finally, we choose three machine learning models, namely Back Propagation (BP), K-Nearest Neighbor (KNN), and Long Short-Term Memory (LSTM) neural network models to compare model prediction effect between single machine learning models with machine learning models combined with EEMD. The results show that except for the YANTAI (YT) station, which showed an insignificant downward trend, the monthly MSL of other stations showed an upward trend, indicating that the coastal areas of China are facing the risk of sea level rise. EEMD can effectively reduce the complexity of the original monthly MSL time series, and different intrinsic mode functions (IMFs) reflect changes in monthly MSL at different frequencies. Comparing the single machine learning model and the machine learning model combined with EEMD, it is found that the simulation effect of the machine learning model combined with EEMD is better than that of the single model. The model with the best prediction effect on monthly MSL in the coastal areas of China is LSTM-EEMD, followed by KNN-EEMD. This study provides an important reference for systematically understanding sea level changes and selecting an appropriate monthly MSL prediction model in the coastal areas of China.