Time-varying Behavior Research Articles

Numerical Analysis of Friction Reduction and ATSLB Capacity of Lubricated MTS with Textured Micro-Elements

The simulation analysis numerically investigates the thermoelastic lubricated interfacial Textured Micro-Element (TME) load-bearing contact, a theoretical model is proposed, and the effective friction reduction and Anti-Thermoelastic Scuffing Load bearing (ATSLB) capacity between random rough Meshing Teeth Surfaces (MTS) are presented, the mechanism linking interfacial thermoelastic lubrication, TME meshing friction reduction and ATSLB is revealed. The real contact domain area between MTS with multi-scale Micro-Element Textures (MET) is obtained for the numerical calculation of the three-dimensional equivalent TME contact volume, which is the correlation bridge between friction reduction and ATSLB of the thermoelastic lubrication interface. The proposed theoretical model predicts the time-varying behaviour of the textured meshing interface friction reduction with TME contact load under thermoelastic lubrication conditions. Numerical simulations show that the textured interface meshing volume is the key to solving the load-bearing problem of line contact between randomly rough teeth surfaces. The friction coefficients of the MTS are reduced by 13–24%. The lubricated load-bearing and friction reduction behaviour between the textured MTS is quantified by the thermoelastic voids of TME interface and actual meshing volume ratio, which provides a new perspective for further insight into the lubrication and friction reduction behaviour between the MTS with multi-scale MET-ATSLB coupling mechanism.

Energy security: the role of shale technology

Sustainable energy systems are sensitive to the countries’ energy portfolio decisions, shaping geopolitics and contributing to the global energy security (ES). Accordingly, this paper applies the “Markov regime-switching” method to explore the impact of “the North American shale technology” (NAST) on behavioral regimes of the US energy security measurements (ESM), e.g., diversity of primary energy demand ({mathrm{ESI}}_{mathrm{I}}), net energy import dependence ({mathrm{ESI}}_{mathrm{II}}), non-fossil fuel resource portfolio ({mathrm{ESI}}_{mathrm{III}}), and crude oil import dependency ({mathrm{ESI}}_{mathrm{IV}}). The findings confirm time-varying and asymmetric behavior of the US ESM before and after the NAST. Specifically, the overall interaction of substitution effect and scale effect of NAST strengthens the US energy systems through {mathrm{ESI}}_{mathrm{I}}, {mathrm{ESI}}_{mathrm{III}}, and {mathrm{ESI}}_{mathrm{IV}}, while {mathrm{ESI}}_{mathrm{II}} leads to higher risks of the US energy supply security. Consequently, the shale reserves development, diversification of primary energy demand and import supply, and advanced energy transport and trading policies, are suggested to overcome the barriers in achieving (i) availability, (ii) accessibility, (iii) affordability, and (iv) acceptability aspects of ES and vulnerability reduction of the US energy systems in respect of risk and resilience.

DeepAPP: A Deep Reinforcement Learning Framework for Mobile Application Usage Prediction

This paper aims to predict a set of apps a user will open on her mobile device in the next time slot. Such an information is essential for many smartphone operations, e.g., app pre-loading and content pre-caching, to improve user experience. However, it is hard to build an explicit model that accurately captures the complex environment context and predicts a set of apps at one time. This paper presents a deep reinforcement learning framework, named as DeepAPP, which learns a model-free predictive neural network from historical app usage data. Meanwhile, an online updating strategy is designed to adapt the predictive network to the time-varying app usage behavior. To transform DeepAPP into a practical deep reinforcement learning system, several challenges are addressed by developing a context representation method for complex contextual environment, a general agent for overcoming data sparsity and a lightweight personalized agent for minimizing the prediction time. Extensive experiments on a large-scale anonymized app usage dataset reveal that DeepAPP provides high accuracy (precision 70.6 percent and recall of 62.4 percent) and reduces the prediction time of the state-of-the-art by 6.58×. A field experiment of 29 participants demonstrates DeepAPP can effectively reduce launch time of apps.

Online-Dynamic-Clustering-Based Soft Sensor for Industrial Semi-Supervised Data Streams.

In the era of big data, industrial process data are often generated rapidly in the form of streams. Thus, how to process such sequential and high-speed stream data in real time and provide critical quality variable predictions has become a critical issue for facilitating efficient process control and monitoring in the process industry. Traditionally, soft sensor models are usually built through offline batch learning, which remain unchanged during the online implementation phase. Once the process state changes, soft sensors built from historical data cannot provide accurate predictions. In practice, industrial process data streams often exhibit characteristics such as nonlinearity, time-varying behavior, and label scarcity, which pose great challenges for building high-performance soft sensor models. To address this issue, an online-dynamic-clustering-based soft sensor (ODCSS) is proposed for industrial semi-supervised data streams. The method achieves automatic generation and update of clusters and samples deletion through online dynamic clustering, thus enabling online dynamic identification of process states. Meanwhile, selective ensemble learning and just-in-time learning (JITL) are employed through an adaptive switching prediction strategy, which enables dealing with gradual and abrupt changes in process characteristics and thus alleviates model performance degradation caused by concept drift. In addition, semi-supervised learning is introduced to exploit the information of unlabeled samples and obtain high-confidence pseudo-labeled samples to expand the labeled training set. The proposed method can effectively deal with nonlinearity, time-variability, and label scarcity issues in the process data stream environment and thus enable reliable target variable predictions. The application results from two case studies show that the proposed ODCSS soft sensor approach is superior to conventional soft sensors in a semi-supervised data stream environment.

Levy flight-improved grey wolf optimizer algorithm-based support vector regression model for dam deformation prediction

Considering the strong non-linear time-varying behavior of dam deformation, a novel prediction model, called Levy flight-based grey wolf optimizer optimized support vector regression (LGWO-SVR), is proposed to forecast the displacements of hydropower dams. In the proposed model, the support vector regression is used to create the prediction model, whereas the Levy flight-based grey wolf optimizer algorithm is employed to search the penalty and kernel parameters for SVR. In this work, a multiple-arch dam was selected as a case study. To validate the proposed model, the predicted results of the model are compared with those derived from Grid Search algorithm, Particle Swarm Optimization, Grey Wolf Optimizer algorithm, and Genetic algorithm. The results indicate that the LGWO-SVR model performs well in the accuracy, stability, and rate of prediction. Therefore, LGWO-SVR model is suitable for dam engineering application.

The Adaptive Dynamics of the Halloween Effect: Evidence from a 120-Year Sample from a Small European Market

The Halloween effect predicts that stock markets in the winter months (November through April) generate significantly higher returns than in the summer months (May through October). This paper examines the time-varying behavior of the Halloween effect within a new historical dataset that covers about 120 years of Portuguese stock market history. We combine subsample analysis with rolling window analysis to show that the performance of the anomaly has varied in an adaptive fashion over time. The anomaly existed during the first four decades of the 20th century. Afterward, it vanished for 60 years, reappearing only at the beginning of the 21st century. However, in the first two decades of the new century, the effect seems to be a mere reflection of the excess return generated in January. Overall, the time-varying performance of the Halloween effect supports the adaptive market hypothesis for the Portuguese stock market.

An AI-Inspired Spatio-Temporal Neural Network for EEG-Based Emotional Status.

The accurate identification of the human emotional status is crucial for an efficient human-robot interaction (HRI). As such, we have witnessed extensive research efforts made in developing robust and accurate brain-computer interfacing models based on diverse biosignals. In particular, previous research has shown that an Electroencephalogram (EEG) can provide deep insight into the state of emotion. Recently, various handcrafted and deep neural network (DNN) models were proposed by researchers for extracting emotion-relevant features, which offer limited robustness to noise that leads to reduced precision and increased computational complexity. The DNN models developed to date were shown to be efficient in extracting robust features relevant to emotion classification; however, their massive feature dimensionality problem leads to a high computational load. In this paper, we propose a bag-of-hybrid-deep-features (BoHDF) extraction model for classifying EEG signals into their respective emotion class. The invariance and robustness of the BoHDF is further enhanced by transforming EEG signals into 2D spectrograms before the feature extraction stage. Such a time-frequency representation fits well with the time-varying behavior of EEG patterns. Here, we propose to combine the deep features from the GoogLeNet fully connected layer (one of the simplest DNN models) together with the OMTLBP_SMC texture-based features, which we recently developed, followed by a K-nearest neighbor (KNN) clustering algorithm. The proposed model, when evaluated on the DEAP and SEED databases, achieves a 93.83 and 96.95% recognition accuracy, respectively. The experimental results using the proposed BoHDF-based algorithm show an improved performance in comparison to previously reported works with similar setups.

Modeling World Natural Gas and Crude Oil Trade Networks

World energy security is sensitive to stable energy environment and geopolitics that depend on inter-regional energy trade strategies. However, this study uses the network connectedness measures of Diebold-Yilmaz to find the trade cooperation/competition preference for non-renewables' security development. To this end, the dynamic asymmetries of weighted spillover effects of natural gas and crude oil trade flow components are analyzed throughout world trade networks before- and after the sharp fall in crude oil prices. The results exhibit that natural gas and crude oil trade networks experience asymmetric- and time-varying behavior in response to transitory- vs. permanent components as well as positive- vs. negative shocks pre-and post- sharp fall in crude oil prices. Specifically, the natural gas and crude oil trade networks show a lesser degree of integration after 2014. Moreover, the natural gas trade network represents more (less) weighted sensitivity to short-term fluctuations (long-term trend) than the crude oil trade network pre-and post- 2014. As the other comparative result, a higher (lower) degree of integration is detected in the crude oil trade network in response to the positive (negative) shocks compared to the natural gas trade network pre-and post- 2014. Therefore, a lower level of cooperation preference among regions is proposed in the natural gas and crude oil trade networks in response to a sharp fall in crude oil prices. Also, decisions about energy conservation, environmental protection, and protection of strategic resources can affect a region's natural gas and crude oil supply and demand security.

A comprehensive strategy for the distribution network resilience enhancement considering the time-varying behaviors of typhoon path

• A comprehensive planning-operation framework including resilience-constrained unit commitment is tailored for the resilient distribution network. • A spatially and temporally extended N-k uncertainty set of the vulnerable lines is constructed. • A resilience-oriented two-stage robust optimization model for the distribution network is presented. Typhoon, as a high-impact and low-probability extreme event, can damage the components in power networks, thereby resulting in power outages or blackouts. To enhance the distribution network resilience against typhoon attacks, this paper proposes a resilience-oriented two-stage robust optimization model, in which resilience-constrained unit commitment schemes and planning-operational restoration measures are incorporated into a prevention and emergency response framework. In the prevention response stage, line hardening, flexible devices deployment, and unit commitment are performed before the typhoon attacks. During the typhoons, the emergency response is conducted to mitigate power outages by regulating soft open points, battery storage systems, and generation units. Moreover, considering the time-varying behaviors of typhoon path, the simulation technique for a sequential typhoon attack is developed to construct the spatially and temporally extended N - k uncertainty set for overhead line status. Thereafter, a tight approximation method is introduced to allow the proposed model to be a tractable mixed-integer linear programming problem, which can be easily solved by a nested column-and-constraint generation algorithm. Numerical results show that the comprehensive resilience-oriented strategies can respond rapidly to the worst-case scenario of typhoon attacks with cost-effective performance.

A meta-heuristic optimization-based method for parameter estimation of an electric arc furnace model

Throughout distribution systems, it is usual to find non-linear time-varying loads, such as electric arc furnaces (EAFs), which are widely used in the steel-making industrial sector. Due to the process of melting and refining metals, the EAFs consume large blocks of power (active and reactive power) causing significant power quality disturbances, such as harmonics and voltage fluctuations on distribution networks. Different EAF parametric models have been proposed with the purpose to predict the voltage and current waveforms and then evaluate the performance of the reactive power compensation devices. This paper proposes a novel methodology for the optimal estimation of parameters of an electric arc furnace model, which can achieve lower execution times and error rates compared to some state-of-the-art methods. The methodology was evaluated using three meta-heuristic optimization algorithms such as the particle swarm optimization (PSO) algorithm, the vortex search algorithm (VSA) and the crow search algorithm (CSA); using real and simulated data. From the results, the proposed methodology based on meta-heuristic optimization approaches worked efficiently, allowed estimating the parameters of the electric arc furnace model using a single optimization step, capture the non-sinusoidal, non-linearity and time-varying random behavior that exhibit the real electric arc furnace samples and obtained relative errors of the total harmonic distortion between the measured and estimated voltage and arc current signals around 1.23% and 0.62%, respectively.

Unsteady radiative–convective flow of a compressible fluid: a numerical approach

This article is designed to inspect the thermal effects of an unsteady compressible flow of a viscous fluid through a symmetric channel. Combined effects of convective heat transfer, magnetic field, and radiation are also given special attention in this article. Basic laws of mass, momentum, and energy for compressible flow are employed in the modeling of the current problem. In addition, slip boundary conditions are also implemented in the analysis of the above thermal flow problem. Coupled nonlinear differential equations are solved numerically using explicit finite difference technique. Finally, the influence of different sundry parameters on the axial velocity, flow rate, and heat transfer are visualized through graphs. Time variant behavior of flow rate is calculated. Outcomes of the results reveal that the increment of the flow rate is related to the increase of compressibility parameters. Enhancement in the temperature profiles in the presence of radiation number is also reported. This model is the most general version of peristalsis of compressible flow in view of natural convection and radiation impact with extensive applications in aircraft industry, geophysics, and other industrial situations (cooling of electronic equipment, heat exchangers, and so forth).

Explosivity and Time-Varying Granger Causality: Evidence from the Bubble Contagion Effect of COVID-19-Induced Uncertainty on Manufacturing Job Postings in the United States

This study evaluates the explosive behavior and Granger causality episodes in manufacturing job postings in the United States (JOBPUS) and COVID-19-induced uncertainty (COVIDEMV). This study applied the novel unit root tests with explosive behavior, and the novel time-varying Granger causality test for a sample period ranging from 1 January 2020 to 29 July 2022. Further, this study used date stamping to identify the subperiods of the explosive behavior and causality. The findings revealed that JOBPUS exhibits explosive behavior, with several episodes of exuberance (bubbles) across the sample period while COVIDEMV does not exhibit explosivity during the period. However, the results of the causality provide evidence of bidirectional causality, with several episodes between the variables. Moreover, the episodes of the explosivity and causality coincide with significant episodes in the history of the COVID-19 pandemic worldwide and in the United States particularly, such as the date when United States recorded a COVID-19-related death toll of over 100,000 people for the first time, after the presidential election, after Halloween celebrations, after the discovery and administration of COVID-19 vaccines as well as the discovery of the Delta and the Omicron variants of COVID-19. Therefore, the time-series characteristics of JOBPUS and its causal nexus with COVIDEMV largely depend on the intensity of the instability caused by the pandemics. Hence, explosivity and time-varying causal behavior should necessarily be accounted for when modelling the job market conditions in the United States, particularly during pandemic-related crises.

The impact of COVID-19 on GCC equity and debt markets: Evidence from TVP-VAR estimation

COVID-19 has rapidly evolved into a global pandemic and has strongly impacted financial markets of the world, including the Gulf Cooperation Council (GCC) region. Since the outbreak is unprecedented, there is a need to analyze the effects of the disease on volatility spillovers between equity and bond markets. We empirically investigated the impact of the COVID-19 pandemic on the financial equity and debt markets in the GCC region. We used the TVP-VAR dynamic connectedness approach to measure risk transmission in the GCC market. This study investigated the time-varying behavior of GCC equity and conventional and Islamic debt markets using data from 1 January 2019 to 30 August 2021. The results were also validated by performing a DCC-GARCH analysis to check the shock and spillovers among the GCC markets. We found the persistent shock transmitter roles of equity markets to bond and Sukuk markets in the GCC region, and the total dynamic connectedness increased during the first wave of the COVID-19 pandemic. Overall, the significant level of interconnectedness exists within the GCC markets.

Integrated decision-making about China's energy poverty alleviation based on system dynamics

An energy poverty policy simulation model is developed to make integrated decision and optimize China's energy poverty alleviation. In the model, system dynamic modeling is used to investigate the time-varying behavior of energy poverty system over time, and scenario analysis is carried to test and determine the most efficient scenario. The results show that energy poverty will move toward a sustainable alleviation from 2021 to 2030. Compared with 2011, the energy poverty index in scenario Ⅰ - Ⅵ will decrease by 47.98%, 62.08%, 43.19%, 53.31%, 62.22% and 88.42% in 2030. Both employment and financial expenditure have a significant positive effect on energy poverty alleviation, while energy structure adjustment plays a double-sided role in alleviating energy poverty. Combination strategy is the most efficient scenario, where China could achieve United Nations Sustainable Development Goal (SDG) 7 of providing clean and affordable energy access to all by 2031.

Reactive–diffusion epidemic model on human mobility networks: Analysis and applications to COVID-19 in China

The complex dynamics of human mobility, combined with sporadic cases of local outbreaks, make assessing the impact of large-scale social distancing on COVID-19 propagation in China a challenge. In this paper, with the travel big dataset supported by Baidu migration platform, we develop a reactive–diffusion epidemic model on human mobility networks to characterize the spatio-temporal propagation of COVID-19, and a novel time-dependent function is incorporated into the model to describe the effects of human intervention. By applying the system control theory, we discuss both constant and time-varying threshold behavior of proposed model. In the context of population mobility-mediated epidemics in China, we explore the transmission patterns of COVID-19 in city clusters. The results suggest that human intervention significantly inhibits the high correlation between population mobility and infection cases. Furthermore, by simulating different population flow scenarios, we reveal spatial diffusion phenomenon of cases from cities with high infection density to cities with low infection density. Finally, our model exhibits acceptable prediction performance using actual case data. The localized analytical results verify the ability of the PDE model to correctly describe the epidemic propagation and provide new insights for controlling the spread of COVID-19.

Natural course of co-use of alcohol and cannabis in adolescents and young adults in Montréal, Canada.

Little is known about changes in alcohol and cannabis co-use over time in adolescents and young adults. We sought to describe the natural course of alcohol and cannabis co-use from age 12 to 17 and 20 to 31 and to describe frequent (i.e., ≥ once/month) binge drinking (i.e., ≥ 5 drinks/occasion) and cannabis use from age 20 to 31. Data were drawn from two longitudinal studies conducted in Montréal, Canada: AdoQuest (n = 1,852) and the Nicotine Dependence in Teens (NDIT) study (n = 1,293). Complete data on alcohol and cannabis were available for 620 12-17-year-olds (58% female) followed from 2005 to 2011 in AdoQuest, and 673 20-31-year-olds (56% female) followed from 2007 to 2020 in NDIT. We reported proportions of co-use, exclusive drinking ("drinking"), exclusive cannabis use ("cannabis use"), frequent co-use, frequent binge drinking, and frequent cannabis use by age and sex. Co-use rose from 2% at age 12-13 to 23% at age 17, was stable at 44% at age 20 and 24, and then decreased to 37% at age 31. Drinking rose from 20% to 51%, and cannabis use was consistently rare (< 2%). During young adulthood, frequent co-use declined from 21% at age 20 to 12% at age 31, and frequent binge drinking declined from 29% at age 20 to 22% at age 31. Frequent cannabis use increased from 6% at age 20 to 11% at age 31. Alcohol and cannabis use should be studied as time-varying behaviors. Co-use patterns should be monitored over time in this population. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A reinforcement learning-based hybrid modeling framework for bioprocess kinetics identification.

Constructing predictive models to simulate complex bioprocess dynamics, particularly time-varying (i.e., parameters varying over time) and history-dependent (i.e., current kinetics dependent on historical culture conditions) behavior, has been a longstanding research challenge. Current advances in hybrid modeling offer a solution to this by integrating kinetic models with data-driven techniques. This article proposes a novel two-step framework: first (i) speculate and combine several possible kinetic model structures sourced from process and phenomenological knowledge, then (ii) identify the most likely kinetic model structure and its parameter values using model-free Reinforcement Learning (RL). Specifically, Step 1 collates feasible history-dependent model structures, then Step 2 uses RL to simultaneously identify the correct model structure and the time-varying parameter trajectories. To demonstrate the performance of this framework, a range of in-silico case studies were carried out. The results show that the proposed framework can efficiently construct high-fidelity models to quantify both time-varying and history-dependent kinetic behaviors while minimizing the risks of over-parametrization and over-fitting. Finally, the primary advantages of the proposed framework and its limitation were thoroughly discussed in comparison to other existing hybrid modeling and model structure identification techniques, highlighting the potential of this framework for general bioprocess modeling.

Detection of the instantaneous frequency degradation due to damages of a fixed offshore jacket platform using the iEEMD‐based Hilbert Huang transform under a wave excitation

In online structural health monitoring of the fixed offshore jacket platform, the determination of the time-dependent degradation of structures due to damages is the most important step in a maintenance strategy. This paper presents the application of the Hilbert Huang transform method based on the iEEMD (improved ensemble empirical mode decomposition) algorithm to identify the instantaneous natural frequency degradation due to damages of the fixed offshore structure under the random wave excitation. The iEEMD algorithm is proposed to restrain the mode mixing and used to adaptively process the big response data of the offshore structure during the wave loading with a wide frequency range. The vibration response measurement experiment for the offshore structure was conducted in a wave tank in this study. The identified fundamental natural frequencies by the iEEMD-based Hilbert Huang method were evaluated and compared with results of the numerical model of the eigenvalue analysis. The main result demonstrates that the instantaneous frequency degradation and nonlinear time-varying behavior due to the brace damage of the fixed offshore jacket platform can be successfully identified by the iEEMD-based Hilbert Huang transform technique under a wave load.

Layerwise formulation of poroelastic composite plate under pre-buckling and thermal shock loading

The present article scrutinizes the coupled time-variant poro-thermoelasticity behavior of functionally-graded graphene reinforced (FG-GPLR) composite plates embedded in an elastic foundation, intending to provide innovative solutions to preserve operating mechanisms toward harmful impacts of thermal shock load. The nanocomposite plate is initially stressed in three different orientations and subjected to thermal shock loading. The temperature distribution is acquired by employing the celebrated energy balance equation. The analytical solving approach is utilized for differential motion equations of the system based upon substitution of the equivalent Fourier series expansion for each state variable. Eventually, mapping the structure's response from Laplace to the time domain is carried out by a modified version of the celebrated Dubner and Abates' approach. Convergence of the outcomes regarding time is monitored and certified. The acquired results record the impact of varied parameters like length to thickness ratio of the structure, distribution pattern, and weight-content of GPL on the coupled time-variant poro-thermoelastic response of the FG-GPLR composite plate. One of the main conclusions of this survey is that increasing the weight-content of GPL in the way of locating more GPL near the upper and bottom surface of the plate applies enhancing impacts on the system's resistance against detrimental consequences of thermal shocks.

Dynamic testing and analysis of the world's first metal 3D printed bridge

The MX3D Bridge is the world’s first additively manufactured metal bridge. It is a 10.5 m-span footbridge, and its dynamic response is a key serviceability consideration. The bridge has a flowing, sculptural form and its response to footfall was initially studied using a 3D finite element (FE) model featuring the designed geometry and material properties obtained from coupon tests. The bridge was tested using experimental modal analysis (EMA) and operational modal analysis (OMA) during commissioning prior to installation. The results have shown that the measured vibration response of the bridge under footfall excitation is 200% greater than predictions based on the FE model and contemporary design guidance. The difference between predicted and measured behaviour is attributed to the complexity of the structure, underestimation of the modal mass in the FE model, and the time-variant modal behaviour of the structure under pedestrian footfall. Both OMA and EMA give a dominant natural frequency for the bridge of between 5.19 Hz and 5.32 Hz, higher than the FE model prediction of 4.31 Hz, and average damping estimates across all modes of vibration below 15 Hz of 0.61% and 0.74% respectively, higher than the 0.5% assumed within the design guidance, slightly reducing the peak response factor predicted for the bridge.