Nonstationary Changes Research Articles

Will a nonstationary change in extreme precipitation affect dam security in China?

Due to the changing climate and environment, the stationary assumption used to calculate the design precipitation or flood of dams is no longer suitable, threatening dam reliability and safety. To see whether nonstationary changes in extreme precipitation will impact existing dams in China, we quantify the exceedance of extreme precipitation over the design intensity by the impact level (R), where the future extreme precipitation intensity is calculated from the nonstationary generalized Pareto (GP) distribution. We mainly focus on medium- (reservoir capacity greater than 0.1 billion m3), large- (reservoir capacity greater than 1 billion m3), and super-sized dams (reservoir capacity greater than 10 billion m3), which have design return periods longer than 100 years. We find that about 15 %-20 % of regions in China may have significant increases in extreme precipitation in the future, mainly in the southeast and northwest. 15 % of medium- and large-sized dams of China may be significantly affected (R greater than 50 %), and more dams are expected to be affected by 1-DAY (156) and 3-DAY (149) precipitation, compared as opposed to 5-DAY (128) precipitation. For super-sized dams, there is no possible significant impact in any of the basins (all R < 100%) by 2100 under the current nonstationary assumption, but results using CMIP6 data show that dams over the Zhujiang River Basin and Xinanjiang River Basin are expected to be impacted (R greater than 100 %) under the SSP585 scenario. However, under the SSP126 scenario, the impacts of extreme precipitation are consistent with the nonstationary analysis results, which suggest non-significant impacts in all basins.

Variability of the Spectrum of the Young Supernova Remnant G11.2-0.3

The spectrum of G11.2-0.3 has been refined by bringing the published intensity measurements to the “artificial moon” flux scale, and the dynamics of its changes on different time scales from 0.4 to more than ~50 years has been investigated. An increase in the fluxes of radio emission of G11.2-0.3 for ≥30 years at 3 cm leqslant lambda leqslant 375 cm with a frequency dependence was found: the average rate of changes decreases proportionally to log(f), and at frequencies f geqslant 10 GHz, the increase gave way to a decrease. Measurements with the RT-32 radio telescope of the Svetloe observatory (IAA RAS) in 2013–2019 showed a decrease in fluxes of G11.2-0.3 against the background of rapid nonstationary changes with an average rate of ( - 5.4 pm 6.6) %/year at a wavelength lambda = 6.2cm and ( - 1.5 pm 0.9) %/year at lambda = 3.5 cm. The stages of growth and decline of fluxes are separated by an epoch 2016.9 pm 0.6. The spectrum of G11.2-0.3 is the spectra sum of the shell and the plerion, with each of its parameters determined by the method developed for the 1972.5 epoch. The values of the spectral indices α1 of the shell and α2 of PWN are obtained: alpha {{1}_{{1972}}} = 0.77 and alpha {{2}_{{1972}}} = 0.251. The dynamics of radio emission from the remnant reflects the scenario of interaction between the shock wave and CSM. Possible reasons for evolutionary and non-stationary changes are discussed.

Digital Differential Protection for $3\phi$ Solid-State Transformers

This article presents the development, implementation, and testing of a digital differential protection for three-phase (3φ) solid-state transformers (SSTs). The developed digital differential protection is designed to implement the ANSI 87 T protection function. The differential currents used by the developed protection are created by the dq0 components of the currents flowing in the high and low voltage sides of a 3φ SST. Internal faults are detected and identified using the energy contents of high-frequency subbands present in the dq0 differential currents. The desired high-frequency subbands are extracted using the phaselet transform that processes signals without sensitivity to nonstationary changes in their frequency and/or phases. Energy contents in the extracted high-frequency subbands allow accurate, fast, and reliable detection and identification of faults in any part of a 3φ SST. The proposed digital differential protection is implemented for performance testing using a laboratory 25-kVA 3φ SST. Performance results demonstrate accurate, fast, and reliable response to different fault and nonfault events. Furthermore, responses of the developed differential protection are found to be consistent regardless of the fault type, fault location, and/or loading level.

Geonomics: Forward-Time, Spatially Explicit, and Arbitrarily Complex Landscape Genomic Simulations.

Understanding the drivers of spatial patterns of genomic diversity has emerged as a major goal of evolutionary genetics. The flexibility of forward-time simulation makes it especially valuable for these efforts, allowing for the simulation of arbitrarily complex scenarios in a way that mimics how real populations evolve. Here, we present Geonomics, a Python package for performing complex, spatially explicit, landscape genomic simulations with full spatial pedigrees that dramatically reduces user workload yet remains customizable and extensible because it is embedded within a popular, general-purpose language. We show that Geonomics results are consistent with expectations for a variety of validation tests based on classic models in population genetics and then demonstrate its utility and flexibility with a trio of more complex simulation scenarios that feature polygenic selection, selection on multiple traits, simulation on complex landscapes, and nonstationary environmental change. We then discuss runtime, which is primarily sensitive to landscape raster size, memory usage, which is primarily sensitive to maximum population size and recombination rate, and other caveats related to the model’s methods for approximating recombination and movement. Taken together, our tests and demonstrations show that Geonomics provides an efficient and robust platform for population genomic simulations that capture complex spatial and evolutionary dynamics.

An assessment of climate change impact on air masses arriving in Athens, Greece

Climate change is inherently linked to long-term non-stationary changes in the characteristics and frequency of weather patterns. The present study attempts to identify the statistical changes of weather patterns in Athens Greece, from the comparative assessment of 96-h backward trajectories between historic (1980–2009) and future (2020–2049) climatology derived from the IPCC RCP4.5 and RCP8.5 scenarios. Arrival heights at 750 m, 1500 m, and 3000 m above sea level are considered to account for the impact of the planetary boundary layer and the lower free troposphere. The analysis of the historic period yields 7 dominant patterns for all heights determined independently, with similar spatial characteristics but varying frequency of occurrence. The classification of backward trajectories under future climate using the same historic clusters reveals percentage changes from locally short-distance travelling patterns to longer-distance ones with a predominant northbound direction. As a second experiment, backward trajectories are re-clustered independently reaching again the same type of clusters but with observable changes in the cluster origins and trajectory lengths.

Impacts of climate variations on non-stationarity of streamflow over Canada

With climate change, understanding and assessing the impact of climate variations on non-stationary changes of streamflow is of importance in the hydrologic and atmospheric sciences. In this study, tempo-spatial and scaling effects in the impacts of 18 climate variations on nonstationary streamflow for 279 watersheds across Canada are explored. Specifically, the change point and trends of streamflow are examined through Pettitt's test and Mann-Kendall test. Spatial patterns of correlations between the climate variations and flow rates over Canada, especially their non-stationarity, are investigated at seasonal and decadal scales. The patterns are also quantified by seven spatial classification algorithms under method uncertainty. A series of findings regarding the impacts are revealed. For instance, nonstationary changes of streamflow exist for approximately 9% of Canadian watersheds and most of them are located in Prairie Provinces and the eastern coast. The Atlantic Multidecadal Oscillation, Niño 12, Niño 3, Niño 4, and Niño 3.4 pose significant impacts on Canadian streamflow, which vary with watersheds and seasons. The impacts are closely associated with human activities, e.g., significant impacts of climate variations on populated-area streamflow over Canada. Different climatic variations have different time-varying effects on streamflow. All watersheds have obvious clustering characteristics and four spatial patterns are identified, which is insensitive with classification algorithm. These findings are conducive to understanding the hydrological impacts of atmospheric circulation and enhancing the reliability of hydrological prediction.

Evaluation of Statistical Downscaling Methods for Simulating Daily Precipitation Distribution, Frequency, and Temporal Sequence

HighlightsNine statistical downscaling methods from three downscaling categories were evaluated.Weather generator-based methods had advantages in simulating non-stationary precipitation.Differences in downscaling performance were smaller within each category than between categories.The performance of each downscaling method varied with climate conditions.Abstract. Spatial discrepancy between global climate model (GCM) projections and the climate data input required by hydrological models is a major limitation for assessing the impact of climate change on soil erosion and crop production at local scales. Statistical downscaling techniques are widely used to correct biases of GCM projections. The objective of this study was to evaluate the ability of nine statistical downscaling methods from three available statistical downscaling categories to simulate daily precipitation distribution, frequency, and temporal sequence at four Oklahoma weather stations representing arid to humid climate regions. The three downscaling categories included perfect prognosis (PP), model output statistics (MOS), and stochastic weather generator (SWG). To minimize the effect of GCM projection error on downscaling quality, the National Centers for Environmental Prediction (NCEP) Reanalysis 1 data at a 2.5° grid spacing (treated as observed grid data) were downscaled to the four weather stations (representing arid, semi-arid, sub humid, and humid regions) using the nine downscaling methods. The station observations were divided into calibration and validation periods in a way that maximized the differences in annual precipitation means between the two periods for assessing the ability of each method in downscaling non-stationary climate changes. All methods were ranked with three metrics (Euclidean distance, sum of absolute relative error, and absolute error) for their ability in simulating precipitation amounts at daily, monthly, yearly, and annual maximum scales. After eliminating the poorest two performers in simulating precipitation mean, distribution, frequency, and temporal sequence, the top four remaining methods in ascending order were Distribution-based Bias Correction (DBC), Generator for Point Climate Change (GPCC), SYNthetic weather generaTOR (SYNTOR), and LOCal Intensity scaling (LOCI). DBC and LOCI are bias-correction methods, and GPCC and SYNTOR are generator-based methods. The differences in performances among the downscaling methods were smaller within each downscaling category than between the categories. The performance of each method varied with the climate conditions of each station. Overall results indicated that the SWG methods had certain advantages in simulating daily precipitation distribution, frequency, and temporal sequence for non-stationary climate changes. Keywords: Climate change, Climate downscaling, Downscaling method evaluation, Statistical downscaling.

Condition-Driven Soft Transition Modeling and Monitoring Strategy for Complex Nonstationary Process

Abstract Due to the frequent switching of operation condition, the real industrial processes show typical nonstationary characteristics. In some cases, the switching is frequent and may not be instantaneous, revealing typical transition characteristics different from steady operation. In this work, a condition-driven soft transition modeling and monitoring method is proposed to deal with this problem. The condition modes are obtained by an automatic sequential condition-mode division algorithm, then a fine-grained mode recognition strategy is developed to further separate the condition mode into steady and transition submodes. The steady submode model is established by designing a conditional autoencoder network which can more closely describe each steady submode and facilitate evaluation of the relationship between transition submode and each steady submode. Finally, an online monitoring strategy is designed which can capture the nonstationary process changes. A real industrial case illustrates the effectiveness and superiority of the proposed method, which establishes a more accurate model for nonstationary processes by revealing the transition feature.

Complexity in psychological self-ratings: implications for research and practice

BackgroundPsychopathology research is changing focus from group-based “disease models” to a personalized approach inspired by complex systems theories. This approach, which has already produced novel and valuable insights into the complex nature of psychopathology, often relies on repeated self-ratings of individual patients. So far, it has been unknown whether such self-ratings, the presumed observables of the individual patient as a complex system, actually display complex dynamics. We examine this basic assumption of a complex systems approach to psychopathology by testing repeated self-ratings for three markers of complexity: memory, the presence of (time-varying) short- and long-range temporal correlations; regime shifts, transitions between different dynamic regimes; and sensitive dependence on initial conditions, also known as the “butterfly effect,” the divergence of initially similar trajectories.MethodsWe analyzed repeated self-ratings (1476 time points) from a single patient for the three markers of complexity using Bartels rank test, (partial) autocorrelation functions, time-varying autoregression, a non-stationarity test, change point analysis, and the Sugihara-May algorithm.ResultsSelf-ratings concerning psychological states (e.g., the item “I feel down”) exhibited all complexity markers: time-varying short- and long-term memory, multiple regime shifts, and sensitive dependence on initial conditions. Unexpectedly, self-ratings concerning physical sensations (e.g., the item “I am hungry”) exhibited less complex dynamics and their behavior was more similar to random variables.ConclusionsPsychological self-ratings display complex dynamics. The presence of complexity in repeated self-ratings means that we have to acknowledge that (1) repeated self-ratings yield a complex pattern of data and not a set of (nearly) independent data points, (2) humans are “moving targets” whose self-ratings display non-stationary change processes including regime shifts, and (3) long-term prediction of individual trajectories may be fundamentally impossible. These findings point to a limitation of popular statistical time series models whose assumptions are violated by the presence of these complexity markers. We conclude that a complex systems approach to mental health should appreciate complexity as a fundamental aspect of psychopathology research by adopting the models and methods of complexity science. Promising first steps in this direction, such as research on real-time process monitoring, short-term prediction, and just-in-time interventions, are discussed.

Predicting the Design Rainfall for Target Years and Flood Safety Changes by City Type using Non-Stationary Frequency Analysis and Climate Change Scenario

Predicting the Design Rainfall for Target Years and Flood Safety Changes by City Type using Non-Stationary Frequency Analysis and Climate Change Scenario

A probabilistic estimation approach for the failure forecast method using Bayesian inference

Positive-feedback mechanisms such as fatigue induce a self-accelerating behavior, captured by models displaying infinite limit-state asymptotics, collectively known as the failure forecast method (FFM). This paper presents a Bayesian model parameter estimation approach to the fully nonlinear FFM implementation and compares the results to the classic linear regression formulation, including a regression uncertainty model. This process is demonstrated in a cyclic loading fatigue crack propagation application, both on a synthetic data set and on a full fatigue experiment. A novel ”switch point” parameter is included in the Bayesian formulation to account for nonstationary changes in the growth parameter.

Estimating the design flood under the influence of check dams by removing nonstationarity from the flood peak discharge series

Abstract The construction of check dams in northwestern China has resulted in nonstationary changes in flood peak discharge series; the stationary assumption of the conventional hydrological frequency analysis is no longer satisfied. According to the characteristics of the construction and operation of check dams, the nonstationarity of flood peak discharge series are largely induced by changes in the effective runoff generation area (i.e., the basin area minus the area controlled by check dams). Knowing the power function relationship between the flood peak discharge and the basin area, we can remove the influence of the effective runoff generation area and convert the original nonstationary series into a stationary series. This de-nonstationarity method can achieve stationarity in the first and second moments simultaneously. Therefore, we can calculate the design value of the reconstructed series using the conventional frequency analysis method. According to the effective runoff generation area under design conditions, we can then obtain the corresponding design flood of the original series. We applied this method to the Mahuyu River basin to obtain the design flood under nonstationarity. Due to the consideration of the deterministic influence of check dams during the de-nonstationarity process, the uncertainty analyzed by the bootstrap method is obviously small.

Modelling grain active ventilation process in the silos

Changes in grain raw material properties in a complex thermophysical process are presented in the article, and a multifactorial mathematical model for developing engineering solutions for active ventilation is obtained. A numerical study of non-stationary changes in temperature and moisture content in a one-dimensional grain layer along with the height of a metal silo depending on environmental conditions is carried out. The simulation of the temperature and moisture fields in siloses is important for assessing the state and determining the parameters of the active ventilation system. The results show a significant air velocity effect on cooling and the speed of the drying process. The overall duration of drying and cooling of the grain top layer depends slightly on the initial parameters of the air such as the temperature and the moisture content. The obtained model is reliable to predict the drying kinetics for different storage conditions and different grain types. With the help of the presented model, it is possible to substantiate the safe grain storage modes in conditions of heterogeneity and temporal variations of standard parameters (temperature, air humidity).

Cuffless Blood Prediction with Fingertip Pulse Wave

Cuffless method for blood pressure measurement is an important methods for continuous health status monitoring. A pulse wave is a periodic time-series signal that reflects a non-linear, non-stationary change in the pulse signal over time. Traditional ways of pulse wave based blood pressure assessment rely on feature extraction from pulse signals, which are usually signal quality dependent and lack of consistence among studies. In this paper, a method of blood pressure measurement of using continuous pulse waveform and long-term and short-term memory network is proposed, which avoids the process of manually extracting waveform features. Experiments were performed with both pulse wave signals and the arterial blood pressure signals form the MIMIC database. Empirical mode decomposition was applied for signal preprocessing, and the time series of the pulse wave was analyzed to establish a Long Short-Term Memory neural network for blood pressure assessment. An average prediction accuracy of 83.2% was achieved.

Photoplethysmography-derived approximate entropy and sample entropy as measures of analgesia depth during propofol-remifentanil anesthesia.

The ability to monitor the physiological effect of the analgesic agent is of interest in clinical practice. Nonstationary changes would appear in photoplethysmography (PPG) during the analgesics-driven transition to analgesia. The present work studied the properties of nonlinear methods including approximate entropy (ApEn) and sample entropy (SampEn) derived from PPG responding to a nociceptive stimulus under various opioid concentrations. Forty patients with ASA I or II were randomized to receive one of the four possible remifentanil effect-compartment target concentrations (Ceremi) of 0, 1, 3, and 5ng·ml-1 and a propofol effect-compartment target-controlled infusion to maintain the state entropy (SE) at 50 ± 10. Laryngeal mask airway (LMA) insertion was applied as a standard noxious stimulation. To optimize the performance of ApEn and SampEn, different coefficients were carefully evaluated. The monotonicity of ApEn and SampEn changing from low Ceremi to high Ceremi was assessed with prediction probabilities (PK). The result showed that low Ceremi (0 and 1ng·ml-1) could be differentiated from high Ceremi (3 and 5ng·ml-1) by ApEn and SampEn. Depending on the coefficient employed in algorithm: ApEn with k = 0.15 yielded the largest PK value (0.875) whereas SampEn gained its largest PK of 0.867 with k = 0.2. Thus, PPG-based ApEn and SampEn with appropriate k values have the potential to offer good quantification of analgesia depth under general anesthesia.

Statistical Modeling of the Global River Runoff Using GCMs: Comparison with the Observational Data and Reanalysis Results

Specific methods are proposed to assimilate the results of the “historical” experiments on 28 climate models. The results of the analysis confirm the hypothesis regarding a stationary character of changes in the global river runoff during “instrumental” period (approximately 150 years). Part of the models (about one third) reproduces the non-stationary changes in the global runoff with respect to the mean. At the same time, the number of such models indicating increased runoff is exactly equal to the number of models that indicate a decrease in runoff. The models generally reproduce well the coefficient of variation of global river runoff in comparison with the observational data, as well as the small value of the coefficient of asymmetry. The model of the Gaussian white noise is optimal for the description of the majority of the annual time series of global river runoff generated by the GCMs.

Spatiotemporal Changes and Frequency Analysis of Multiday Extreme Precipitation in the Huai River Basin during 1960 to 2014

Floods and droughts are more closely related to the extreme precipitation over longer periods of time. The spatial and temporal changes and frequency analysis of 5-day and 10-day extreme precipitations (PX5D and PX10D) in the Huai River basin (HRB) are investigated by means of correlation analysis, trend and abrupt change analysis, EOF analysis, and hydrological frequency analysis based on the daily precipitation data from 1960 to 2014. The results indicate (1) PX5D and PX10D indices have a weak upward trend in HRB, and the weak upward trend may be due to the significant downward trend in the 21st century, (2) the multiday (5-day and 10-day) extreme precipitation is closely associated with flood/drought disasters in the HRB, and (3) for stations of nonstationary changes with significant upward trend after the abrupt change, if the whole extreme precipitation series are used for frequency analysis, the risk of future floods will be underestimated, and this effect is more pronounced for longer return periods.

Speech reverberation suppression for time-varying environments using weighted prediction error method with time-varying autoregressive model

In this paper, a novel approach for the task of speech reverberation suppression in non-stationary (changing) acoustic environments is proposed. The suggested approach is based on the popular weighted prediction error (WPE) method, yet, instead of considering fixed reverberation prediction weights, our method takes into account the more generic time-varying autoregressive (TV-AR) model which allows dynamic estimation and updating for the prediction weights over time. We use an initial estimate of the prediction weights in order to optimally select the TV-AR model order and also to calculate the TV-AR coefficients. Next, by properly interpolating the calculated coefficients, we obtain the ultimate estimate of reverberation prediction weights. Performance evaluation of the proposed approach is shown not only for fixed acoustic rooms but also for environments where the source and/or sensors are moving. Our experiments reveal further reverberation suppression as well as higher quality in the enhanced speech samples in comparison with recent literature within the context of speech dereverberation.

Non-Stationary Representation for Continuity Aware Head Pose Estimation Via Deep Neural Decision Trees

The problem of image-based head pose estimation attracts intensive attention due to a large number of applications such as face analysis and attention modeling. Existing methods often convert head pose estimation into the pose classification problem, but ignored the non-stationary appearance change brought about by the equally distributed bins. This paper targets the head pose estimation problem via deep neural decision trees, where the non-linear property of the representative appearance is learned together with the bin classification probability. First, we use Convolutional Neural Network (CNN) to get pose related features. Second, we apply the Fully Connected (FC) layer on the learned features to extract branch weight for each Euler angle and the representative values for each bin. Third, we employ neural decision tree on the branch weight to get bin classification probability. To explicitly characterize the relationship between the adjacent pose intervals, we embed continuity of the head angles into the tree architecture by constructing the bridge-tree. The final estimation is obtained via a weighted sum between the estimated bin probability and the representative bin values. We evaluate our methods on different public datasets including Pointing’04, Chinese Academy of Sciences-Pose, Expression, Accessory, and Lighting (CAS-PEAL) and Biwi Kinect Head Pose and find that, the proposed method outperforms as compared to state-of-the-art. Besides, we leverage the template marching based alignment for data preprocessing and demonstrate its superiority over traditional alignment methods on the task of head pose estimation.

A study of thermo-elastic characteristics of the machine tool spindle

In order to avoid the failure of machine tools spindles in the real machining process due to an increase in temperature, it is essential to predict its thermal behavior in the designing phase. The characteristics of machine tools significantly depend on the thermal-elastic behavior of the spindle. These parameters directly affect the productivity and quality of machining operations. This paper presents a thermal - elastic model of the machine tool spindle which was based on the quasi-static model of bearings and the finite element (FE) model of the spindle shaft. Based on quasi-static model of bearings with angular contact, heat generated and thermal contact resistances (TCR) are determined for each position of the balls. The aforementioned constraints have been applied to the 3D FE model of the spindle which allowed for establishing non-stationary change of temperature and thermal deformation. In order to prove the efficacy of the proposed model, experimental measurements of spindle and bearing temperatures were done using thermocouples and thermal imager.