Nonstationary Model Research Articles

Operational optimisation of a microgrid using non-stationary hybrid switched model predictive control with virtual storage-based demand management

Demand-shaping mechanisms are a key component of modern energy management systems, although not unproblematic. The degree of social acceptance of interference with demand or generation and the ease of integration of various types of non-critical loads depends on the method of their implementation. In addition, the critical load pool typically includes devices with different response times. The energy management systems currently in use often cannot meet user expectations. Especially when considering other vital aspects, such as energy market spread, storage wear, or connection to the utility grid and neighbouring microgrids. The authors adopted an approach of unifying demand side management and response in the form of virtual energy storage. Said store allows for the accommodation of loads operating under differing scheduling horizons. Such a new concept allows management not only in terms of quantity but also in terms of time. The storage is the focal point of a comprehensive energy management system based on switched model predictive control. The receding horizon algorithm relies on a non-stationary hybrid microgrid model. The study compares the operating costs of microgrids with virtual storage, allowing only demand postponement, preponement or bidirectional operation. The energy management system is also examined for sensitivity to changes in the weight matrices of the cost function, horizon length and forecast inaccuracy. Introducing virtual energy storage reduces microgrid operating costs by up to 16%. The decrease in control performance is proportional to the prediction accuracy, and the sensitivity allows for customisation.

Fluids-Triggered Swarm Sequence Supported by a Nonstationary Epidemic-Like Description of Seismicity

Abstract The variation in Coulomb failure stress (CFS) plays a crucial role in either increasing or decreasing seismic activity. In cases in which the standard epidemic-type aftershock sequence (ETAS) model does not adequately fit seismicity data, the potential deviations from empirical laws are explored. These deviations may arise from stress changes imparted by aseismic transients that lead swarm-like earthquake sequences to occur. The time-dependent background rate of seismicity serves as an indicator for detecting changes in CFS or the presence of transient aseismic forcing. We investigate seismic anomalies in the slow deforming Mt. Pollino, Italy seismogenic area, where a 4-yr-long swarm-like sequence partially filled a previously hypothesized seismic gap. The primary process of this seismic swarm is still under debate. Employing a nonstationary ETAS model on a new template-matching high-resolution catalog, we suggest a slow-slip event and fluid interplay as the main aseismic forces in triggering and developing this swarm-like sequence.

The effectiveness of selection in a species affects the direction of amino acid frequency evolution.

Nearly neutral theory predicts that species with higher effective population size (N e ) are better able to purge slightly deleterious mutations. We compare evolution in high-N e vs. low-N e vertebrates to reveal which amino acid frequencies are subject to subtle selective preferences. We take three complementary approaches, two measuring flux and one measuring outcomes. First, we fit non-stationary substitution models of amino acid flux using maximum likelihood, comparing the high-N e clade of rodents and lagomorphs to its low-N e sister clade of primates and colugos. Second, we compare evolutionary outcomes across a wider range of vertebrates, via correlations between amino acid frequencies and N e . Third, we dissect the details of flux in human, chimpanzee, mouse, and rat, as scored by parsimony - this also enables comparison to a historical paper. All three methods agree on which amino acids are preferred under more effective selection. Preferred amino acids tend to be smaller, less costly to synthesize, and to promote intrinsic structural disorder. Parsimony-induced bias in the historical study produces an apparent reduction in structural disorder, perhaps driven by slightly deleterious substitutions. Within highly exchangeable pairs of amino acids, arginine is strongly preferred over lysine, and valine over isoleucine, consistent with more effective selection preferring a marginally larger free energy of folding. These two preferences match differences between thermophiles and mesophilic relatives. These results reveal the biophysical consequences of mutation-selection-drift balance, and demonstrate the utility of nearly neutral theory for understanding protein evolution.

STUDY ON KINETICS OF BIOMASS PYROLYSIS IN THE FIXED BED. 2. ANALYSIS OF THE RESULTS OF CALCULATING THE THERMAL DECOMPOSITION OF DIFFERENT TYPES OF SOLID FUEL

Using the constructed non-stationary model of the dynamics of the release of volatile substances in a fixed bed [1], extensive numerical studies on three types of biomass pyrolysis Wood Birch, Wood quebracho and Bagasse were performed in order to determine the light gas composition satisfactorily coinciding with experimental data. As a result of research, the following composition of light gas (in mass fractions) was determined for the first time: CO = 0.464, CO2 = 0.101, H2 = 0.01, H2O = 0.23, C6H6.2O0.2 = 0.058, and C1.16H4 = 0.137, which differs from the light gas composition given in modern sources copies [2−4] (in mass fractions): CO = 0.396, CO2 = 0.209, H2 = 0.019, H2O = 0.249, and C6H6.2O0.2 = 0.127 [3], also CO = 0.46, CO2 = 0.08, H2 = 0.015, H2O = 0.23, C6H6.2O0.2 = 0.058, and CH4 = 0.157 [2]. Obtained calculation results for Wood quebracho with refined composition of light gas in terms of dry mass at a temperature of 800 °С: H2 = 15.56 %, CO2 = 7.134 %, CO = 51.4 %, C1.16H4 = 23.71 % (CH4 = 16.6 %, C2H4 = 7.46 %), and C6H6.2O0.2 = 2.21 % — agree satisfactorily with the experimental data. At the exit from the retort, mixed pyrolysis gas composition changes little over time and differs in the values of the gas components from the pyrolysis gases compositions obtained in the elementary volumes of the retort. This discrepancy is explained by the fact that the initial gas mixture contains components obtained at low temperatures with a high content of H2 and CO2 and a low concentration of CO and C1.16H4. Bibl. 20, Fig. 11, Tab. 4.

A Bayesian hierarchical spatio-temporal model for summer extreme temperatures in Spain

A statistical study was made of the summer extreme temperatures over peninsular Spain in the last forty years. Records from 158 observatories regularly distributed over Iberia with no missing data were available for the common period from 1981 to 2020. For this purpose, a hierarchical spatio-temporal model with a Gaussian copula and a generalized extreme value parametrization of the extreme events was used. The temporal trend in maximum extreme temperatures was studied making use of both a stationary model and a nonstationary one that takes into account the influence of anthropogenic climate change on extreme temperatures using the global mean temperature as a function of time for the study period. The results led to a better fit of the nonstationary model, with there being a 3.5-fold greater increase in the 20-year return level of the extreme temperatures than in that corresponding to the global mean temperature.

EVALUATION OF THE SCATTER OF LIQUID LAUNCH VEHICLE POGO OSCILLATION AMPLITUDES DUE TO THE INFLUENCE OF THE SCATTER OF INTERNAL FACTORS

Almost all liquid launch vehicle developers faced the problem of ensuring stability in relation to POGO oscillations. The level of POGO amplitudes oscillations of the launch vehicle can be significantly affected by the scatter of internal factors. The study aims to create a mathematical model that can determine the range of POGO amplitudes in liquid launch vehicles. This will be demonstrated through the example of the Dnipro launch vehicle, which is affected by a variety of internal factors that cause its POGO amplitudes to vary. We developed the non-linear non-stationary mathematical model of POGO oscillations of the prototype of the Dnipro space launch vehicle. The model is built by taking into account the two lower vibration modes of the LV structure, two lower oscillation modes of the oxidizer feedline, and the first oscillation mode of the fuel feedline of the propulsion system. Modeling of dynamic processes was conducted in a combination of four liquid rocket engines based on the schematic of the staged rocket engine. The simulation takes into account cavitation phenomena in the engine pumps and delay times in the gas generators’ chambers. We have developed a method for determining the scatter of the POGO oscillations caused by the action of internal factors, which is based on the use of the LP uniformly distributed sequences. As internal factors, the frequencies, decrements, and shapes of LV structural oscillation modes, the values of pressurization of the propellant tanks, and the engines’ specific thrust impulses were considered. Based on the results of the calculations, the dependence of the POGO amplitudes in two regions of LV instability was determined, and the lower and upper enveloping curves for the POGO amplitudes were constructed. It is shown that the maximum POGO amplitudes oscillations in the first region of instability lie in the range from 0.23 g to 0.72 g and in the second region of instability — from 0 g to 0.60 g. Variants of combinations of internal factors, which provided the largest and smallest values of POGO amplitudes, were analyzed. This made it possible to determine the internal factors, the scatter of which has the greatest effect on the POGO amplitudes scatter: frequency, decrement, shape coefficients of oscillations of the oxidizer feedlines and the LV 1st mode structural longitudinal oscillations in the payload cross-section.

Suitable habitat evaluation and ecological security pattern optimization for the ecological restoration of Giant Panda habitat based on nonstationary factors and MCR model

Enhancing the regional connectivity, coordination, and integrity of Giant Panda National Park can protect giant pandas. The construction and optimization of the ecological security pattern in giant panda habitat can effectively promote the information exchange among giant panda populations of Giant Panda National Park in Ya'an area. Based on climate change, geographic location, human activities, and other factors, the Maxent model was used to evaluate the non-stability of giant panda habitat suitability, and the minimum cumulative resistance (MCR) model was used to construct the ecological security pattern. Based on the gravity model and relevant image verification analysis, we proposed an optimal solution to the ecological problem of giant panda corridor groups. From our research, good habitat suitability of giant pandas was detected, with a high degree of local fragmentation. The areas of the most suitable habitat, the more suitable habitat, and the generally suitability were 1970.12 km2, 2346.51 km2, and 1902.37 km2, respectively. Suitable habitat was located between 2100 m and 3400 m in elevation, with a less than 30° slope. The average temperature of the coldest season in the suitable area was around - 2 ∼ 2 °C, the precipitation of the wettest month was between 140 ∼ 160 mm, and the mean diurnal range was between 9 ∼ 14 °C. Valuation results for nonstationary factors beyond these ranges will not fall within the Habitat Suitability Zone. In total, 13 ecological sources, 13 ecological corridors, 6 ecological barrier points, and 4 ecological pinch points were identified and divided into 4 ecological corridor groups to locate and repair ecological problems, achieving the purpose of building and optimizing ecological security patterns. The integrated technical frame work proposed in this work, which combined the suitability evaluation, ecological safety pattern construction and ecological restoration, considered the nonstationary variations in factors and the actual species ranges. It also emphasized the importance of the relationship between the species’ suitability and the ecological security pattern. The results of this work have important implications for examining the habitat modifications, gene exchange between giant panda populations, and habitat conservation of giant pandas.

A non-stationary bivariate extreme value model to estimate real-time pedestrian crash risk by severity at signalized intersections using artificial intelligence-based video analytics

Vehicle-pedestrian crashes are generally severe due to the vulnerability of pedestrians compared to the occupants of vehicles. However, the estimation of pedestrian crash risk by severity has not been given adequate attention in the field of proactive safety assessments applying traffic conflict techniques. This study proposes a novel analytical framework to estimate real-time pedestrian crash risk by severity at the signal cycle level while incorporating the effect of time-varying exogenous variables. Specifically, the study applies a non-stationary bivariate extreme value model to jointly model the post encroachment time and Delta-V for estimating real-time pedestrian crash risk by severity at individual signal cycles. The proposed framework is tested on 144 h of video data collected from three signalized intersections in Queensland, Australia. The developed bivariate extreme value model has been found to reliably predict severe and non-severe pedestrian crash frequencies compared to the historical crash records of severe and non-severe pedestrian crashes at those signalized intersections. Results suggest that the frequency of pedestrian conflicts per signal cycle and average pedestrian speed in a signal cycle are associated with real-time pedestrian crash risks. In addition, pedestrian conflicts per signal cycle and average vehicle speed per cycle were associated with the interaction severity component of the non-stationary bivariate extreme value model. The proposed proactive estimation of pedestrian crash risk by severity levels can help design time-sensitive countermeasures for vulnerable road users.

Characteristics and Channel Capacity Studies of a Novel 6G Non-Stationary Massive MIMO Channel Model Considering Mutual Coupling

Characteristics and Channel Capacity Studies of a Novel 6G Non-Stationary Massive MIMO Channel Model Considering Mutual Coupling

On a Partially Non-Stationary Vector AR Model with Vector GARCH Noises: Estimation and Testing

On a Partially Non-Stationary Vector AR Model with Vector GARCH Noises: Estimation and Testing

REPRESENTATION OF A NON-STATIONARY MODEL OF BAROCLINIC OCEAN MOTION USING THE FICTITIOUS DOMAIN METHOD

This paper presents a groundbreaking non-stationary model, intricately crafted using the fictitious domain technique, to delve into the complex dynamics of baroclinic ocean motion. This study marks a significant leap in our understanding of water mass interaction, shedding light on the profound impact of temperature and salt gradients on sea currents.The methodology uses modified Navier-Stokes equations for viscous, incompressible flow, considering advection, diffusion, and Coriolis force.The results of this study underscore the immediate and tangible implications of our research. The solutions unveiled the pivotal role of pressure and temperature differentiation in the genesis of ocean currents. The analysis demonstrated that by integrating nonlinear terms and detailed modeling of initial and boundary conditions, we can markedly improve the precision of water mass movement forecasts. This work underscores the urgent necessity for further research into dynamic ocean modeling to enhance our ability to predict climate change.This article introduces truly innovative approaches to numerical modeling, which hold immense potential for the future of the field. These approaches have the power to transform existing models of sea currents and pave the way for the development of more efficient methods for monitoring and predicting the state of the marine environment.

Research and application of commodity demand forecasting algorithm based on improved ARIMA-LSTM

Through thorough investigation and research, it has been determined that commodities possess a certain degree of timeliness, with long production cycles and short storage times. Accurately predicting customer demand can effectively reduce costs. This paper proposes an improved model based on ARIMA-LSTM, incorporating data cross-correlation analysis for effective data classification. By leveraging the unique characteristics of both ARIMA and LSTM prediction time series models, the data is divided into stationary and non-stationary models for accurate forecasting of future demand for goods. After rigorous testing on the test set, our proposed model achieved impressive results with an accuracy rate of 0.855, precision rate of 0.834, and recall rate of 0.854 respectively. Therefore, the model proposed in this paper has a good performance in forecasting the sales volume of general commodities with time series.

Mathematical Analysis of the Wind Field Characteristics at a Towering Peak Protruding out of a Steep Mountainside

Wind field characteristics in a complex topography are significantly influenced by the nature of the surrounding terrains. This study employs onsite measurements to investigate the wind field characteristics at a towering peak protruding out of a steep mountainside, where butterfly−lookalike landscape platform will be constructed; the impact of the surrounding topography on the wind flow is highlighted. The results showed that the blocking effect of the mountains in the mountainous side of the valley caused a significant drop in the mean wind speed from that direction. The stationary test (reverse arrangement test) indicated that the wind speed had a strong nonstationary characteristic, necessitating the employment of a steady and nonstationary wind speed model to assess the wind turbulence characteristics. The three directions’ wind turbulence integral scales were critically influenced by the occurrence of the wind speedup effect, unexpectedly resulting in the vertical turbulence integral scale being the greatest of the three. Furthermore, the measured wind turbulence properties under both wind speed models showed certain variations from the recommended specifications. Consequently, the impact of the local terrain and the speedup effect on the wind characteristics must be thoroughly evaluated to ensure the structural stability of structures installed at a similar topography.

Modelling non-stationarity in extreme rainfall using large-scale climate drivers

Flood estimates used in engineering design are commonly based on intensity–duration–frequency (IDF) curves derived from historical extreme rainfalls. Under global warming these extreme rainfalls are increasing, threatening the capacity of existing infrastructure to resist failure as IDF curves traditionally assume no change in rainfall magnitude. Hence, there is a need to investigate the implications of non-stationarity in extreme rainfalls used to derive IDFs across storm durations and annual exceedance probabilities (AEPs). One way of doing this is to incorporate covariates into the fitted probability distribution. However, there are few studies which examine non-stationarity in extreme rainfall using large-scale climate drivers as covariates, with little consensus on which covariate is the most appropriate. Here we evaluate non-stationarity in extreme rainfall across different durations, from the 1 in 5 AEP to the 1 in 100 AEP, using potential large-scale climate drivers including global and continental mean temperature, continental diurnal temperature range, continental dewpoint temperature, continental precipitable water, the Indian Ocean Dipole, the El Niño Southern Oscillation, and the Southern Annular Mode. These covariates are linked to the three parameters of the Generalized Extreme Value distribution to identify the most appropriate form of non-stationary probability model. We analyse 16 different storm durations from 6 min to 7 day annual maxima across 46 stations in Australia. Based on the Akaike Information Criteria, precipitable water is the superior covariate at a large proportion of stations irrespective of storm duration. However, when the modelled quantile changes are inspected, only global temperature is able to adequately capture the variability in changes across both storm duration and annual exceedance probability. Further, when regional average values of mean temperature, diurnal temperature range, dewpoint temperature and precipitable water were used as covariates, there was no improvement in the model performance compared to continental-wide covariates, particularly for short durations. The results using a mean global temperature covariate show that stationary historical rainfall quantile estimates are underestimated by 12 % – 9 % for frequent (1 in 5 AEP) and 23 % – 13 % for rare (1 in 100 AEP) short duration (6 min – 30 min) events. Moving forward our results suggests infrastructure design needs to incorporate non-stationarity in IDFs to ensure infrastructure and flood planning levels are not under-designed.

Nonstationary frequency analysis of extreme precipitation: Embracing trends in observations

Knowledge of the recurrence intervals of precipitation extremes is vital for infrastructure design, risk assessment, and insurance planning. However, trends and shifts in rainfall patterns globally pose challenges to the application of extreme value analysis (EVA) which relies critically on the assumption of stationarity. In this paper, we explore: (1) the suitability of nonstationary (NS) models in the presence of statistically significant trends, and (2) their potential in modeling out-of-sample data to improve frequency analysis of extreme precipitation. We analyze the benefits of using a nonstationary Generalized Extreme Value (GEV) model for annual extreme precipitation records from Southern Brazil. The location of the GEV distribution is allowed to change with time. The unknown GEV model parameters are estimated using Bayesian techniques coupled with Markov chain Monte Carlo simulation. Next, we use GAME sampling to compute the evidence (and their ratios, the so-called Bayes factors) for stationary and nonstationary models of annual maximum precipitation. Our results show that the presence of a statistically significant trend in annual maximum precipitation alone does not justify the use of a NS model. The location parameter of the GEV distribution must also be well defined, otherwise, stationary models of annual maximum precipitation receive more support by the data. These findings reiterate the importance of accounting for GEV model parameters and predictive uncertainty in frequency analysis and hypothesis testing of annual maximum precipitation data records. Furthermore, a meaningful EVA demands detailed knowledge about the origin and persistence of observed changes.

Study on Non-Stationary Wind Speed Models and Wind Load Design Parameters Based on Data from the Beijing 325 m Meteorological Tower, 1991–2020

As the capital of China and a densely populated major city, the characteristics of Beijing’s near-surface wind field change significantly with the increase in the density of underlying urban structures. The high randomness of natural wind makes it extremely difficult to develop a universally applicable wind-resistant load design code based on topographic factors and architectural features. This article takes the wind speeds recorded at 15 different height levels within the urban area by the 325 m meteorological tower in Beijing from 1991 to 2020 as the research subject. It quantifies the wind speed trends at different heights and introduces time-varying functions to establish a non-stationary wind speed model based on the optimal model. Additionally, it compares the basic wind speeds and wind pressure height variation coefficients obtained from measurements with the standards. The results show that, during the past 30 years of urbanization, the near-surface wind speed in the Beijing area has shown a decreasing trend. The model incorporating time-varying functions exhibited the best fit and demonstrated good predictive capabilities, with its calculated basic wind speeds being relatively high. The wind pressure height variation coefficient values in Beijing are between Class C and Class D terrains, being closer to Class C at lower altitudes. The conclusion reveals that urbanization has a significant impact on wind speeds, primarily concentrated at lower height levels, and that the basic wind speeds calculated based on standards underestimate the actual conditions when this impact is not considered. Although the average wind speed’s wind profile index across the entire time series is mostly greater than the fixed value of 0.3 given by Class D, this represents an overestimated wind profile index for maximum wind speeds.

A data-driven soft sensor model for coal-fired boiler SO2 concentration prediction with non-stationary characteristic

Environmental issues have received significant global attention, and achieving ultra-low emission has become a crucial goal for thermal power plant. Pollutant prediction is essential to achieving ultra-low emission control. Due to the frequent adjustment of operating conditions, the coal-fired power generation process is a typical non-stationary process with complex changes along the time direction. A single model cannot effectively portray the complex non-stationary characteristics and model mismatch may occur in the prediction, which brings new challenges for modeling coal-fired SO2 emission. Therefore, the condition-driven category boosting (CatBoost) soft sensor model is proposed for real-time prediction of SO2 concentration. First, the thermal process time series is rearranged into different data slices. These data slices vary along the direction of the condition indicator, which can characterize the process characteristics on different slices. Second, the load-based sequential condition division (LSCD) algorithm is designed to realize the division of boiler operation process. The characteristics of process are effectively captured by variational auto-encoder (VAE), and the data slices with similar process characteristics are merged into the same mode. Non-stationary process along the time direction is reorganized into different condition modes along the load direction. Finally, the high-resolution local CatBoost models under different modes are constructed to achieve fine-grained prediction of SO2 concentration. The effectiveness of the proposed condition-driven model is verified by an industrial example of 1000 MW coal-fired boiler. The prediction results show that the proposed model can effectively divide the operation conditions and achieve accurate prediction of SO2 concentration. In the typical models in SO2 predicting, the proposed model has better performance with a determination coefficient of 0.92.

Evaluating precision medicine tools in cystic fibrosis for racial and ethnic fairness.

Patients with cystic fibrosis (CF) experience frequent episodes of acute decline in lung function called pulmonary exacerbations (PEx). An existing clinical and place-based precision medicine algorithm that accurately predicts PEx could include racial and ethnic biases in clinical and geospatial training data, leading to unintentional exacerbation of health inequities. We estimated receiver operating characteristic curves based on predictions from a nonstationary Gaussian stochastic process model for PEx within 3, 6, and 12 months among 26,392 individuals aged 6 years and above (2003-2017) from the US CF Foundation Patient Registry. We screened predictors to identify reasons for discriminatory model performance. The precision medicine algorithm performed worse predicting a PEx among Black patients when compared with White patients or to patients of another race for all three prediction horizons. There was little to no difference in prediction accuracies among Hispanic and non-Hispanic patients for the same prediction horizons. Differences in F508del, smoking households, secondhand smoke exposure, primary and secondary road densities, distance and drive time to the CF center, and average number of clinical evaluations were key factors associated with race. Racial differences in prediction accuracies from our PEx precision medicine algorithm exist. Misclassification of future PEx was attributable to several underlying factors that correspond to race: CF mutation, location where the patient lives, and clinical awareness. Associations of our proxies with race for CF-related health outcomes can lead to systemic racism in data collection and in prediction accuracies from precision medicine algorithms constructed from it.

A Spatially Non-Stationary Fading Channel Model for Simulation and (Semi-) Analytical Study of ELAA-MIMO

A Spatially Non-Stationary Fading Channel Model for Simulation and (Semi-) Analytical Study of ELAA-MIMO

Non-Stationary 3D M2M Channel Modeling and Verification With Aircraft-to-Aircraft, Drone-to-Drone, Vehicle-to-Vehicle, and Ship-to-Ship Measurements

Non-Stationary 3D M2M Channel Modeling and Verification With Aircraft-to-Aircraft, Drone-to-Drone, Vehicle-to-Vehicle, and Ship-to-Ship Measurements