Average Model Research Articles

Effects of surface morphology on the performance of water-lubricated thrust bearings

Purpose As the crucial support component of the propeller power system, the reliability of the operation of submersible pumps is influenced by the lubrication performance of water-lubricated thrust bearings. When the water-lubricated thrust bearings are under start-stop or heavy load conditions, the effect of surface morphology is crucial as the mixed lubrication regime is encountered. This paper aims to develop one mixed lubrication model for the water-lubricated thrust bearings to predict the effects of surface skewness, kurtosis and roughness orientation on the loading carrying capacity and tribological behavior. Design/methodology/approach This paper developed one improved mixed lubrication model specifically for the water-lubricated thrust bearing system. In this model, the hydrodynamic model was improved by using the height of the rough surface and its probability density function, combined with the average flow model. The asperity contact model was improved by using the equation for the Pearson system of frequency curves to characterize the non-Gaussian aspect of surface roughness distribution. Findings According to the results, negative skewness, large kurtosis and lateral surface pattern can improve the tribological performance of water-lubricated thrust bearings. Optimizing the surface morphology is a reasonable design method that can improve the performance of water-lubricated thrust bearings. Originality/value In this paper, one mixed lubrication model specifically for the water-lubricated thrust bearing with the effect of surface roughness into consideration was developed. Based on the developed model, the effect of surface morphology on tribological behavior can be evaluated. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2023-0247/

The Changing and Predicted Trends in Chronic Obstructive Pulmonary Disease Burden in China, the United States, and India from 1990 to 2030.

This study analyzed the burden of chronic obstructive pulmonary disease (COPD) in China, the United States, and India from 1990 to 2019 and projected the trends for the next decade. This study utilized the GBD 2019 to compare the age-standardized incidence rate (ASIR), age-standardized mortality rate (ASMR), age-standardized disability-adjusted life years (DALYs) rate, and the proportion attributed to different risk factors in China, the United States, and India. Joinpoint models and autoregressive integrated moving average (ARIMA) models were employed to capture the changing trends in disease burden and forecast outcomes. From 1990 to 2019, China's age-standardized COPD incidence and mortality rates decreased by 29% and 70%, respectively. In the same period, India's rates decreased by 8% and 33%, while the United States saw an increase of 9% in COPD incidence and a 22% rise in mortality rates. Smoking and ambient particulate matter pollution are the two most significant risk factors for COPD, while household air pollution from solid fuels and low temperatures are the least impactful factors in the United States and India, respectively. The proportion of risk from household air pollution from solid fuels is higher in India than in China and the United States. Predictions for 2030 suggest that the age-standardized DALY rates, ASIR, and ASMR in the United States and India are expected to remain stable or decrease, while China's age-standardized incidence rate is projected to rise. Over the past three decades, the incidence of COPD has been decreasing in China and India, while showing a slight increase in the United States. Smoking and ambient particulate matter pollution are the primary risk factors for men and women, respectively. The risk of household air pollution from solid fuels in India needs attention.

Degradation prognostics of aerial bundled cables based on multi-sensor data fusion

ABSTRACT Development of advanced health monitoring sensors and high-performance computing enabled multi-sensors information to analyse degradation in complex engineering systems/components. These sensors are often aimed to capture different physical aspects of a system. Thus, each sensor only contains partial information about the same degradation process. A significant need is to devise a contemporary data fusion method that effectively integrates independent multi-sensors measurements leading to a better prognosis of the degradation process. Unlike conventional data fusion methodologies that fuse multiple sensors’ information prior implementation of prognosis step, this paper presents a novel fusion framework based on multi-sensor prognosis data. The framework is developed using Particle Filtering (PF) technique coupled with Auto-Regressive Integrated Moving Average (ARIMA) model. The proposed framework is applied on historical Non-Destructive Testing (NDT) database generated through Ultrasonic Probe Listening and Thermal Imaging of in-service aerial bundled cables (ABCs) installed in coastal regions to evaluate cable degradation over time. Promising results of f -steps prediction scheme from fused degradation values, as compared to using individual measurement mode data, indicates the efficacy of the proposed method.

Dynamic wave tunnel lining GPR images multi-disease detection method based on deep learning

Ground-penetrating radar (GPR) is widely used in tunnel lining non-destructive testing. However, diverse diseases need more data, leading to low accuracy in multi-disease detection. This paper proposes a dynamic wave tunnel lining GPR images multi-disease detection method based on deep learning to improve the detection accuracy of multi-diseases. Firstly, the tunnel lining generative adversarial networks (TLGAN) model generates multi-dimensional GPR images. Secondly, we designed the Wave-backbone model according to the different disease shape attribute characteristics. Meanwhile, multi-dimensional space fusion combined with the global dynamic wave detection networks Wave-DetNets detects the captured multi-scale global waveform features. Finally, the disease depth was further estimated by detecting results. The experimental results show that the average model accuracy in this paper for identifying cavities, loost, and steel rebars is more than 91%, the omission rate reduces to 0.04, and the detection speed reaches 37FPS with a 320 × 320 GPR image sizing. The model has excellent stability and is better than the existing detection model.

Dynamic Model of AC–AC Dual Active Bridge Converter Using the Extended Generalized Average Modeling Framework

Dynamic Model of AC–AC Dual Active Bridge Converter Using the Extended Generalized Average Modeling Framework

Parameter identification algorithm for ship manoeuvrability and wave peak model based multi-innovation stochastic gradient algorithm use data filtering technique

This paper addresses the issue of identifying ship motion parameters and wave peak frequency. Utilising the Euler discretisation principle, we establish a discrete-time auto-regressive moving average model with exogenous input (ARMAX) for the ship-wave system. Furthermore, we develop a filtering-based stochastic gradient algorithm for the system by applying filtering techniques and auxiliary model identification idea. A filtering-based multi-innovation stochastic gradient algorithm, utilizing the multi-innovation identification theory, was developed to enhance the convergence rate and accuracy of parameter identification. This approach was found to be more effective than the filtering-based stochastic gradient algorithm. Simulation results validate the efficacy of the proposed algorithm in parameter identification.

East Asia Atmospheric River Forecast With a Deep Learning Method: GAN‐UNet

AbstractAccurate forecasting of atmospheric rivers (ARs) holds significance in preventing losses from extreme precipitation. However, traditional numerical weather prediction (NWP) models are computationally expensive and can be limited in accuracy due to inaccurate physical parameter settings. To overcome these limitations, we propose a deep learning (DL) model, called GAN‐UNet, to forecast the AR occurrence, position, and intensity in East Asia. GAN‐UNet can capture the complex nonlinear relationship between the inputs at the past moment, including the vertically integrated water vapor transport (IVT), zonal wind at 850 hPa (U850), and meridional wind at 850 hPa (V850), and the forecast output (IVT, U850, or V850), whose results are comparable to NWP models. In addition, the average model (AM) by integrating the results generated by GAN‐UNet and European Centre for Medium‐Range Weather Forecasts (ECMWF) outperforms all the NWP models selected in this study, demonstrating its potential to improve the performance of NWP through the DL method. Specifically, the 5‐day average F1 scores of the AM are 0.777 and 0.845, whose values are significantly better than those obtained by ECMWF (0.712 and 0.794) in the two key regions of East Asia; The AM 5‐day average intersection over unions are 0.706 and 0.688 while the values of ECMWF are 0.675 and 0.64; in terms of intensity forecast, GAN‐UNet and AM exhibited lower differences in most of the intensity bins, except for the final bin with IVT more than 825 kg m−1 s−1. With this thorough analysis, GAN‐UNet is shown as an effective model to forecast ARs.

Empirical Evaluation of the Time Series Forecasting Method by Combining ARIMA with RBFNN under the Additive Model

Time series data is a series of values observed through repeated measurements at different times. Time series data is a type of data present in almost all different fields of life. Time series prediction is an significant problem in time series data mining. Accurate forecasting is crucial to support decision making in many areas of life. Therefore, improving the precision of time series predicting is a interesting mission for experts in this field. Many models for predicting time series have been proposed from traditional time series models as Auto Regressive Integrated Moving Average (ARIMA) model to artificial neural network (ANN) models. ARIMA is a linear model therefore it can only take the linear characteristics in time series. In contrast, Radial Basis Function Neural Network (RBFNN) is a non-linear model therefore it can not predict effectively seasonal or trend changes in time series. To combine the strengths of these two models, in this study, we experimentally evaluate the hybrid method between ARIMA and RBFNN on real time series data from different fields. Experimental results demonstrate that the combined method outperforms each model used individually in terms of accuracy.

Robustness Evaluation of LSTM-based Deep Learning Models for Bitcoin Price Prediction in the Presence of Random Disturbances

As Deep Learning (DL) continues to be widely adopted, the growing field of study on the robustness of DL approaches in finance is gaining steam. This paper investigates the robustness of a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) intended for daily closing price predictions of Bitcoin (BTC). The research entails reproducing and adjusting an LSTM design from previous research, with an emphasis on evaluating the robustness of the network. The network is trained using data that has been disturbed by Gaussian noise to assess robustness, and the effect on predictions made outside of the sample is examined. To examine the impact of adding Gaussian noise layers and noisy dense layers on training accuracy and out-of-sample predictions, further robustness tests are conducted. The results show that the LSTM network has remarkable robustness to random disturbances in the data. Nevertheless, the Root Mean Square Error (RMSE) of the prediction increases with the addition of Gaussian noise and noisy dense layers. When random noise is present in the training data, the Autoregressive Integrated Moving Average (ARIMA) model is more vulnerable to it than the LSTM, according to the robustness of the two models. These findings highlight how robustness DL techniques are overall when compared to more conventional linear methods. However, because these models are black-box, the study highlights the significance of comprehensive testing. Although the robustness of the LSTM is impressive, it is important to understand that each network may behave differently depending on the circumstances.

Exploring carbon dioxide generation in combustion of long-flame coal in Huating mining area by using ReaxFF MD

Coal combustion contributes to 44% of global fuel CO2 emissions. Until now, there has less effective means to study the generation mechanism of carbon dioxide. To approach the generation mechanism of CO2 in coal combustion process at atomic level, the structure information of long-flame coal from Huating mining area, Gansu, China was characterized by ultimate analysis, 13C NMR and XPS. According to these information, one new average structure model was established, its parameters are consistent with the experimental results. Then reactive force field (ReaxFF) molecular dynamics (MD) simulations in 2000–3500 K were performed. Four types of intermediates (Rn–O, Rn–CHO, Rn–CO2, and C2O2) that correspond to four generation routes of CO2 were obtained from the ReaxFF MD simulations. The intermediates can generate CO2 through attacks from oxygen-containing radicals or cleavage of the C–C bond. Rn–O and Rn–CHO can generate CO as intermediates. The oxidation route of acetylene, which first oxidised into C2O2 and then further oxidised into CO2, was analysed using density functional theory (DFT) calculations at the M062X/6–311** level. The DFT results demonstrate the precision of ReaxFF MD calculations. The results can improve the understanding of CO2 generation during coal combustion.

Positive Affect and Physical Activity Associations in Women and Their Spouses.

Only 21% of U.S. women meet the recommended physical activity guidelines, placing them at increased risk for long-term conditions such as heart disease and diabetes. Physical activity is influenced by individual and interpersonal factors (e.g., romantic partners). Individual factors, such as positive affect, are associated with lower mortality risk and improved health behaviors. This secondary data analysis, guided by Fredrickson's Broaden and Build Theory, aims to examine the relationship between positive affect of married women (n = 115 couples) and their physical activity behavior on the same- and next- day, while also considering their spouses' positive affect. Two population average models assessed the relationship of calm and happy (positive affect) to physical activity. Physical activity was assessed as the sum of the minutes of moderate-to-vigorous physical activity (MVPA) over the prior 24 hours. Covariates of age, baseline activity frequency, education, marital quality, and race/ethnicity were also included. Women's happiness (β = 0.15, p < .005), not calmness (β = -0.03, p = .60), was found to have a significant association with same-day MVPA. Spouses' happiness (β = 0.11, p = .045) was significantly associated with women's next-day MVPA while their calmness (β = -0.04, p = .44) was not. The results of this study support that incorporating positive affect could be valuable for improving physical activity behaviors. Spouse reports provide additional context to consider in physical activity promotion research.

Improving Power Quality Using D-STATCOM with Capacitor as Energy Storage System

Power electronics are used more frequently to raise awareness of power quality which must be considered when designing modern power systems for effective operation and continuous power supply to various load centers. Otherwise, it will be hazardous for residential and commercial customers, reducing the production of their work due to faults and equipment damage. Electronic technology known as Flexible AC Transmission System (FACTS) may improve power stability to make the electric power system efficient. Oscillation damping in the Power system is still a big concern for operating a safe and dependable power system. The static synchronous compressor (STATCOM) is connected to assist power networks with poor power quality and voltage regulation. Although there are several uses, voltage stability is the most frequently used. An average model of the D-STATCOM control approach is presented in this work to improve the transmission of power quality flow and voltage stability in electric power systems. An increase in system voltage stability, reactive power flow control for different loads, and dynamic response of STATCOM are the primary goals of this research work.

Enhancing Predictive Accuracy through the Analysis of Banking Time Series: A Case Study from the Amman Stock Exchange

This empirical research endeavor seeks to enhance the accuracy of forecasting time series data in the banking sector by utilizing data from the Amman Stock Exchange (ASE). The study relied on daily closed price index data, spanning from October 2014 to December 2022, encompassing a total of 2048 observations. To attain statistically significant results, the research employs various mathematical techniques, including the non-linear spectral model, the maximum overlapping discrete wavelet transform (MODWT) based on the Coiflet function (C6), and the autoregressive integrated moving average (ARIMA) model. Notably, the study’s findings encompass the comprehensive explanation of all past events within the specified time frame, alongside the introduction of a novel forecasting model that amalgamates the most effective MODWT function (C6) with a tailored ARIMA model. Furthermore, this research underscores the effectiveness of MODWT in decomposing stock market data, particularly in identifying significant events characterized by high volatility, which thereby enhances forecasting accuracy. These results hold valuable implications for researchers and scientists across various domains, with a particular relevance to the fields of business and health sciences. The performance evaluation of the forecasting methodology is based on several mathematical criteria, including the mean absolute percentage error (MAPE), the mean absolute scaled error (MASE), and the root mean squared error (RMSE).

Applying k‐nearest neighbors to time series forecasting: Two new approaches

AbstractThe k‐nearest neighbors algorithm is one of the prominent techniques used in classification and regression. Despite its simplicity, the k‐nearest neighbors has been successfully applied in time series forecasting. However, the selection of the number of neighbors and feature selection is a daunting task. In this paper, we introduce two methodologies for forecasting time series that we refer to as Classical Parameters Tuning in Weighted Nearest Neighbors and Fast Parameters Tuning in Weighted Nearest Neighbors. The first approach uses classical parameters tuning that compares the most recent subsequence with every possible subsequence from the past of the same length. The second approach reduces the neighbors' search set, which leads to significantly reduced grid size and hence a lower computational time. To tune the models' parameters, both methods implement an approach inspired by cross‐validation for weighted nearest neighbors. We evaluate the forecasting performance and accuracy of our models. Then, we compare them to other approaches, especially, Seasonal Autoregressive Integrated Moving Average, Holt Winters, and Exponential Smoothing State Space Model. Real data examples on retail and food services sales in the United States and milk production in the United Kingdom are analyzed to demonstrate the application and efficiency of the proposed approaches.

Air quality index prediction using seasonal autoregressive integrated moving average transductive long short‐term memory

AbstractWe obtain the air quality index (AQI) for a descriptive system aimed to communicate pollution risks to the population. The AQI is calculated based on major air pollutants including O3, CO, SO2, NO, NO2, benzene, and particulate matter PM2.5 that should be continuously balanced in clean air. Air pollution is a major limitation for urbanization and population growth in developing countries. Hence, automated AQI prediction by a deep learning method applied to time series may be advantageous. We use a seasonal autoregressive integrated moving average (SARIMA) model for predicting values reflecting past trends considered as seasonal patterns. In addition, a transductive long short‐term memory (TLSTM) model learns dependencies through recurring memory blocks, thus learning long‐term dependencies for AQI prediction. Further, the TLSTM increases the accuracy close to test points, which constitute a validation group. AQI prediction results confirm that the proposed SARIMA–TLSTM model achieves a higher accuracy (93%) than an existing convolutional neural network (87.98%), least absolute shrinkage and selection operator model (78%), and generative adversarial network (89.4%).

Forecasting of tilapia (Oreochromis niloticus) production in Bangladesh using ARIMA model

Tilapia farming has expanded rapidly in Bangladesh over the years thanks to a suitable climate for aquaculture and a consistently increasing demand for the fish rich in vitamins and minerals. A clear picture regarding the future trend of tilapia production in Bangladesh is still not available, however. The purpose of this study was to estimate parameters that fit into the Autoregressive Integrated Moving Average (ARIMA) model for forecasting tilapia production in Bangladesh. This was accomplished by calibrating and validating the ARIMA model taking into account the lowest values of the Akaike Information Criterion (AIC) and Bayesian Information Criteria (BIC), graphical arrangements of autocorrelation function (ACF) and partial autocorrelation function (PACF) plots. The best model derived was ARIMA (1, 1, 1), which showed an upward trend of tilapia production since 2006 to date and predicted a similar trend until the year 2040. If this trend continues, the yearly tilapia outturn in the country may reach 690,000 MT, with an upper limit of 1.15 million MT and lower limit of 0.23 million MT, reflecting a substantial increase of around 118% over that produced in 2021. The results of this study will serve as a valuable resource for researchers, decision-makers, academics, and tilapia entrepreneurs, enabling them to develop effective action plans to optimize tilapia production in Bangladesh and strategize for the future integration of tilapia within the country.

Multi-objective optimization integrating weighted average surrogate model and NSGA-II intelligent algorithm applied to a self-excited oscillation mixer used in mixed flow descaling

This study proposes a multi-objective optimization method based on a hybrid of computational fluid dynamics (CFD) and experimental techniques, which integrates a weighted average surrogate model and the NSGA-II intelligent algorithm. To validate the feasibility of this method, structural optimization for the self-excited oscillation mixer (SEOM) was conducted. The optimization objectives aimed to reduce potential abrasives accumulation in the mixed-flow cavity and minimize erosion on the elbow of the slurry outlet caused by mixed slurry, thereby enhancing the service life and rust removal efficiency of the mixed flow rotary jet descaling (MFD). A comparison between the optimized model and original model revealed a 2.51% increase in negative pressure within the cavity of the optimized model, as well as a 10.36% decrease in velocity at the outlet of mixed slurry based on simulation results. Experimental methods measured both mass flow rate of inhaled abrasive particles and impact force of mixed slurry outlet. The experimental results demonstrated that compared to its original counterpart, the optimized model exhibited superior performance with a 22.81% increase in abrasive particle flow rate and an 11.34% reduction in mixed slurry impact force indirectly, verifying better simulation effectiveness for SEOM with optimized structure. This approach also enhanced efficiency and surface quality of MFD processing strip steel, providing guidance for structural optimization of similar SEOMs used in mixed flow descaling.

Leveraging prescription monitoring program data to evaluate the implementation of buprenorphine telehealth flexibilities: An interrupted time series analysis in Texas

BackgroundIn March 2020, policy changes by the Substance Abuse and Mental Health Services Administration and the Drug Enforcement Administration aimed to maintain access to office-based opioid treatment services by easing telehealth buprenorphine prescribing restrictions. However, the effectiveness of these changes remains largely unmeasured. The objective of this study was to measure the effectiveness of COVID-19-related telehealth flexibilities in an all-payer cohort from the Texas Prescription Monitoring Program. MethodsUsing Texas Prescription Monitoring Program data, we identified oral buprenorphine and buprenorphine/naloxone prescriptions dispensed in Texas between September 1, 2019, and September 26, 2020. Weekly counts of prescriptions, prescribing physicians, and dispensing pharmacies were analyzed. An autoregressive integrated moving average (ARIMA) model estimated changes in prescription volume between pre-implementation (September 1, 2019 - February 15, 2020) and post-implementation (April 12, 2020 - September 26, 2020) periods. ResultsPre-flexibility, an average of 8898 (SD: 342) buprenorphine prescriptions were dispensed to 7829 (SD: 326) patients weekly. This declined to 8360 (SD: 247) prescriptions and 7661 (SD: 229) patients post-flexibility. Adjusted for seasonality, this represented a statistically significant average decline of −257.27 (95% CI: −426.06, −88.49) patients and −647.01 (95% CI: −856.67, −437.36) prescriptions per week. DiscussionOur results suggest a modest decline in buprenorphine dispensing volume early in the COVID-19 pandemic. While difficult to assess its significance, it can be assumed that telehealth flexibilities mitigated a potentially larger decline. Future research should explore system and individual-level barriers to telehealth utilization.

Optimized RMDL with Transfer Learning for Sentiment Classification in the MapReduce Framework

A core task in sentiment analysis is sentiment categorization, and it is crucial to understand user feelings based on their remarks in social media or product evaluations. Due to ambiguous phrases, refusal words, and other factors, categorizing sentiment presents several challenging issues. The objective of this research is to develop a hybrid optimization-based deep learning model and MapReduce framework-based sentiment categorization approach. The review document is taken from a dataset and used in this case with the MapReduce methodology. MapReduce is a software framework and programming model for analyzing massive volumes of data that consists of two phases, mapper and reducer. BERT tokenization and aspect term extraction are executed in the mapper phase, whereas sentiment analysis is performed in the reducer stage utilizing random multimodal deep learning (RMDL) with transfer learning and AlexNet and ResNet 50 as pre-trained models. In addition, the exponential coot political algorithm (ECPA) is offered as an optimization algorithm for weight optimization in RMDL. The ECPA is obtained by combining the exponential weighted moving average model (EWMA) with the coot algorithm, as well as a political optimizer (PO). The proposed ECPA_RMDL model has acquired 90.9% precision, 89.7% recall, and 89.9% f-measure.

Robust relation of streamwise velocity autocorrelation in atmospheric surface layers based on an autoregressive moving average model

We construct an autoregressive moving average (ARMA) model consisting of the history and random effects for the streamwise velocity fluctuation in boundary-layer turbulence. The distance to the wall and the boundary-layer thickness determine the time step and the order of the ARMA model, respectively. Based on the autocorrelation's analytical expression of the ARMA model, we obtain a global analytical expression for the second-order structure function, which asymptotically captures the inertial, dynamic and large-scale ranges. Specifically, the exponential autocorrelation of the ARMA model arises from the autoregressive coefficients and is modified to logarithmic behaviour by the moving-average coefficients. The asymptotic expressions enable us to determine model coefficients by existing parameters, such as the Kolmogorov and the Townsend–Perry constants. A consequent double-log expression for the characteristic length scale is derived and is justified by direct numerical simulation data with $Re_\tau \approx 5200$ and field-measured neutral atmospheric surface layer data with $Re_\tau \sim O(10^6)$ from the Qingtu Lake Observation Array site. This relation is robust because it applies to $Re_\tau$ from $O(10^4)$ to $O(10^6)$ , and even when the statistics of natural ASL deviate from those of canonical boundary-layer turbulence, e.g. in the case of imbalance in energy production and dissipation, and when the Townsend–Perry constant deviates from traditional values.