Time-varying Data Research Articles

Dynamic Data Transaction in Crowdsensing Based on Multi-Armed Bandits and Shapley Value

Crowdsensing gradually forms a big data market where workers are willing to trade reusable data with different data collectors. It is challenging for the data collector to choose the transaction party due to the changeable value of the data, while determining the transaction price is also a tough issue. In this paper, we research the dynamic data transaction in crowdsensing. The contribution of the new data to the collector is modeled as the Shapley value, with each worker as a player in the cooperative game. The data collector then judges the contribution of the worker and determines the transaction object. To maximum the profit in the transaction, the collector will dynamically adjust the offering price to workers. The contextual bandit model is utilized in the price decision, with each candidate price as an arm and the time-variant data value as the context. Based on the classic LinUCB learning policy, we learn the mapping of the observed data value and the reward, and estimate the optimal reward in current transaction. The simulation on the data demonstrates that the actual reward got by the collector is close to the maximum reward he can get, which verifies the effectiveness of our scheme.

Dynamic Nonlinear Matrix Completion for Time-Varying Data Imputation

Classical matrix completion methods focus on data with stationary latent structure and hence are not effective in missing value imputation when the latent structure changes with time. This paper proposes a dynamic nonlinear matrix completion (D-NLMC) method, which is able to recover the missing values of streaming data when the low-dimensional nonlinear latent structure of the data changes with time. The paper provides an efficient approach to updating the nonlinear model dynamically. D-NLMC incorporates the information of new data and remove the information of earlier data recursively. The paper shows that the missing data can be estimated if the change of latent structure is slow enough. Different from existing online or adaptive low-rank matrix completion methods, D-NLMC does not require the local low-rank assumption and is able to adaptively recover high-rank matrices with low-dimensional latent structures. Note that existing high-rank matrix completion methods have high-computational costs and are not applicable to streaming data with varying latent structures, which fortunately can be handled by D-NLMC efficiently and accurately. Numerical results show that D-NLMC outperforms the baselines in real applications.

Do the carry trades respond to geopolitical risks? Evidence from BRICS countries

This paper aims to analyze the implications of geopolitical risks on the return and volatility of carry trade transactions in the context of BRICS countries for the period 2006–2020. Fixed effects regressions considering the sample countries as a single portfolio document that geopolitical risks are correlated with volatility, while the results are inconclusive for returns. The non-parametric time-varying coefficients panel data estimations further indicate that the effect of geopolitical risks on carry trade volatility is amplified during the Global Financial Crisis and the post-2016 episode. Moving to the disaggregated data, the time-varying robust Granger causality test of Rossi and Wang (2019) show that geopolitical risks have a significant in-sample predictive power for both carry trade return and volatility during a myriad of sub-periods, which can not be captured by standard constant parameter techniques in the presence of instabilities. Overall, our empirical results suggest that the exposure to geopolitical risks should be taken into account by global investors for risk diversification purposes when entering carry trade positions in BRICS countries.

Data communications network for real-time industrial control systems

The advancements in network technologies and the evolution of the Internet of Things (IoT) have made supporting industrial control systems over probabilistic data networks promising. However, control systems’ communication over the traditional data networks is faced with problems of instability in feedback control and poor quality of performance due to time-varying data propagation delay. This paper presents two approaches that can enable real-time industrial control over non-deterministic computer networks allowing control system designers to take advantage of the existing communication infrastructures. The first approach is based on system-level interaction over two wires network called the collaboration network. The second approach is based on the implementation of the virtual local area network (VLAN). This method allows real-time control of industrial equipment or systems over IP-based networks while other computers are connected. Nodes providing real-time control services have the same PortID on the VLAN switch. This approach minimizes data traffic and reduces time-varying delay in system control over IP networks. The first approach was modeled and simulated using Proteus ISIS software. Two PIC16F877A microcontrollers were used to represent two nodes. CISCO packet tracer was used in the second approach to model and simulate IP-based control system communications over the traditional data network. Results indicate that the use of a two-wire collaborative network approach to a real-time control system is effective but requires an additional network alongside the main data traffic channel. VLAN, therefore, presents a more flexible approach that relies on the same infrastructures.

Data-driven multi-output prediction for TBM performance during tunnel excavation: An attention-based graph convolutional network approach

A deep learning-based multi-output prediction model is developed to better understand and more accurately estimate tunnel boring machine (TBM) performance in each segment ring during the deep excavation under complex underground environments. The novelty lies in the development of a new deep learning approach named att-GCN, which feasibly integrates the graph convolutional networks (GCN) and scaled dot-product attention mechanism to improve model performance and interpretability. It is proved that our proposed att-GCN model is outstanding in significantly enhancing the prediction performance and effectively capturing the influence between monitoring points. As a case study, the proposed method is validated in a Singapore Mass Rail Transit (MRT) construction project, where seven features associated with the TBM machine are input for att-GCN training and testing. Experimental results reveal that the att-GCN model can exhibit a powerful capability in simultaneously predicting two targets named penetration rate (y1) and energy consumption (y2), reaching the mean absolute percentage error (MAPE) value at 15.475% and 15.173%, respectively. In terms of prediction accuracy, att-GCN is superior to some state-of-the-art algorithms, including deep neural network (DNN), random forest (RF), and support vector regression (SVR). Moreover, an online-learning version of att-GCN is designed. When the objective value is gradually known and fed into att-GCN during the tunneling procedure, the model can yield more impressive performance under the MAPE of 8.504% (y1) and 7.934% (y2). Accordingly, the real-time estimation of TBM performance based on the time-varying monitoring data provides valuable evidence to realize the intelligent control of TBM tunneling, which can ultimately improve construction efficiency and reliability.

How did welfare attitudes change during the COVID-19 pandemic in Germany? An empirical analysis using panel data

Abstract The COVID-19 pandemic has had severe social and economic consequences. Governments have implemented or expanded a number of policy measures to cope with these consequences. In the paper, we ask whether there is more support for general social policy measures to compensate for the new uncertainties arising from the pandemic. Using survey data collected in two panel waves in March and June/July 2020, we analyse how public welfare attitudes have changed during the first phase of the pandemic in Germany. In addition to the individual-level survey data, we use time-varying regional data on infection and unemployment rates. We provide descriptive results and employ fixed-effects regressions. Our results show small changes in welfare attitudes, but we do not find evidence for increased public support for general social policy measures.

Time-series analysis of multidimensional clinical-laboratory data by dynamic Bayesian networks reveals trajectories of COVID-19 outcomes

Background and ObjectiveCOVID-19 severity spans an entire clinical spectrum from asymptomatic to fatal. Most patients who require in-hospital care are admitted to non-intensive wards, but their clinical conditions can deteriorate suddenly and some eventually die. Clinical data from patients’ case series have identified pre-hospital and in-hospital risk factors for adverse COVID-19 outcomes. However, most prior studies used static variables or dynamic changes of a few selected variables of interest. In this study, we aimed at integrating the analysis of time-varying multidimensional clinical-laboratory data to describe the pathways leading to COVID-19 outcomes among patients initially hospitalised in a non-intensive care setting. MethodsWe collected the longitudinal retrospective data of 394 patients admitted to non-intensive care units at the University Hospital of Padova (Padova, Italy) due to COVID-19. We trained a dynamic Bayesian network (DBN) to encode the conditional probability relationships over time between death and all available demographics, pre-existing conditions, and clinical laboratory variables. We applied resampling, dynamic time warping, and prototyping to describe the typical trajectories of patients who died vs. those who survived. ResultsThe DBN revealed that the trajectory linking demographics and pre-existing clinical conditions to death passed directly through kidney dysfunction or, more indirectly, through cardiac damage. As expected, admittance to the intensive care unit was linked to markers of respiratory function. Notably, death was linked to elevation in procalcitonin and D-dimer levels. Death was associated with persistently high levels of procalcitonin from admission and throughout the hospital stay, likely reflecting bacterial superinfection. A sudden raise in D-dimer levels 3–6 days after admission was also associated with subsequent death, possibly reflecting a worsening thrombotic microangiopathy. ConclusionsThis innovative application of DBNs and prototyping to integrated data analysis enables visualising the patient's trajectories to COVID-19 outcomes and may instruct timely and appropriate clinical decisions.

Sleep Disturbance and Quality of Life in Rheumatoid Arthritis: Prospective mHealth Study.

BackgroundSleep disturbances and poor health-related quality of life (HRQoL) are common in people with rheumatoid arthritis (RA). Sleep disturbances, such as less total sleep time, more waking periods after sleep onset, and higher levels of nonrestorative sleep, may be a driver of HRQoL. However, understanding whether these sleep disturbances reduce HRQoL has, to date, been challenging because of the need to collect complex time-varying data at high resolution. Such data collection is now made possible by the widespread availability and use of mobile health (mHealth) technologies.ObjectiveThis mHealth study aimed to test whether sleep disturbance (both absolute values and variability) causes poor HRQoL.MethodsThe quality of life, sleep, and RA study was a prospective mHealth study of adults with RA. Participants completed a baseline questionnaire, wore a triaxial accelerometer for 30 days to objectively assess sleep, and provided daily reports via a smartphone app that assessed sleep (Consensus Sleep Diary), pain, fatigue, mood, and other symptoms. Participants completed the World Health Organization Quality of Life-Brief (WHOQoL-BREF) questionnaire every 10 days. Multilevel modeling tested the relationship between sleep variables and the WHOQoL-BREF domains (physical, psychological, environmental, and social).ResultsOf the 268 recruited participants, 254 were included in the analysis. Across all WHOQoL-BREF domains, participants’ scores were lower than the population average. Consensus Sleep Diary sleep parameters predicted the WHOQoL-BREF domain scores. For example, for each hour increase in the total time asleep physical domain scores increased by 1.11 points (β=1.11, 95% CI 0.07-2.15) and social domain scores increased by 1.65 points. These associations were not explained by sociodemographic and lifestyle factors, disease activity, medication use, anxiety levels, sleep quality, or clinical sleep disorders. However, these changes were attenuated and no longer significant when pain, fatigue, and mood were included in the model. Increased variability in total time asleep was associated with poorer physical and psychological domain scores, independent of all covariates. There was no association between actigraphy-measured sleep and WHOQoL-BREF.ConclusionsOptimizing total sleep time, increasing sleep efficiency, decreasing sleep onset latency, and reducing variability in total sleep time could improve HRQoL in people with RA.

Явное представление сокращенных в размерности уравнений Эйлера и Навье – Стокса несжимаемой жидкости в интегральной форме

In this paper, we have obtained “stationary” systems of integraldifferential equations, which are consequences of the non-stationary Euler and Navier - Stokes equations of an incompressible fluid, and for which there are no time derivatives. If we set a correct problem for them, then we can determine the entire non-stationary flow in the volume without solving the nonstationary problem. It is enough to specify time-varying data only on some surface of this stream. The method of reduction of overdetermined systems of differential equations, proposed earlier by the authors, is used. In this method, in the case of a successful choice of the additional constraint equation, the overdetermined systems of differential equations are reduced to PDE systems of dimension less than that of the original PDE systems. The Euler and Navier-Stokes equations themselves act as the constraint equations, and the dimension is reduced for the integral equations, which are obtained using the Helmholtz theorem on the expansion of an arbitrary vector field into vortex and potential components. The peculiarity of this work lies in the fact that all the equations reduced in dimension are obtained in an explicit form, in contrast to the previous works of the authors, where up to 200–300 equations with reduced dimensions were proposed. In reduced systems of integral-differential equations, there is integration over space, therefore, reduction over spatial variables by given method is impossible. If this method is generalized a little, then we can obtain a reduced system of integral equations, where the unknowns have no derivatives with respect to space, but integration over space remains. Non-stationary new integral equations are also obtained, which determine the evolution of the flow. In Appendices A and B, sufficient conditions for the correctness of the reduced system of integral-differential equations are derived. Conditions are found under which the Euler equations follow directly from these reduced equations. It is shown how the time-varying data on some surface of this stream should be related. The change of variables, which is used in the reduction of the Navier - Stokes equations, is also investigated he resulting integral equations can be used to study complex vortices in the atmosphere. For example, if devices measuring data on its surface are installed on a flying object, then, knowing the vorticity profile ω0(r) at a certain moment in time, it is possible, by solving these equations, to track vortex activity at a distance from this object in real time.

STEM Education of Kinematics and Dynamics Using Arduino

Ever increasing technological progress opens novel opportunities concerning educational activities, and the ability to use the technology effectively is one of the 21st century’s most demanding skills. The Partnership Forum for 21st-Century Skills (P21) states that no organization can achieve satisfying results without using technology and therefore the use of technology particularly in schools should be at the highest level. Some novel teaching methods have recently emerged based on technological developments, such as STEM education, which is based on the integration of science, technology, engineering, and mathematics. In this study, the motion of a body connected to a pulley system is analyzed by using an Arduino microprocessor and also related mathematical equations. Arduino is a programming platform on which various electronic elements can be connected. There are various Arduino compatible sensors (sound, light, distance, temperature, etc.) that can be coded for their intended use. Due to its cost effectiveness, this technological instrument can be used as a measurement tool in many physics experiments. In this work, the time-varying data of the position of the moving object is determined using the HC-SR04 ultrasonic distance sensor (see Fig. 1). The HC-SR04 distance sensor can determine the distance of the obstacle from the sensor when the transmitted signal is reflected from an obstacle and returned to the sensor, and the Arduino IDE computer program is used to load the corresponding codes into the Arduino and to retrieve the incoming data.

MTV: Visual Analytics for Detecting, Investigating, and Annotating Anomalies in Multivariate Time Series

Detecting anomalies in time-varying multivariate data is crucial in various industries for the predictive maintenance of equipment. Numerous machine learning (ML) algorithms have been proposed to support automated anomaly identification. However, a significant amount of human knowledge is still required to interpret, analyze, and calibrate the results of automated analysis. This paper investigates current practices used to detect and investigate anomalies in time series data in industrial contexts and identifies corresponding needs. Through iterative design and working with nine experts from two industry domains (aerospace and energy), we characterize six design elements required for a successful visualization system that supports effective detection, investigation, and annotation of time series anomalies. We summarize an ideal human-AI collaboration workflow that streamlines the process and supports efficient and collaborative analysis. We introduce MTV (MultivariateTime SeriesVisualization), a visual analytics system to support such workflow. The system incorporates a set of novel visualization and interaction designs to support multi-faceted time series exploration, efficient in-situ anomaly annotation, and insight communication. Two user studies, one with 6 spacecraft experts (with routine anomaly analysis tasks) and one with 25 general end-users (without such tasks), are conducted to demonstrate the effectiveness and usefulness of MTV.

Using optimized gaussian mixture model rules and global tracking graph for feature extraction and tracking in time-varying data

Using optimized gaussian mixture model rules and global tracking graph for feature extraction and tracking in time-varying data

Automated assessment of wind damage to windows of buildings at a city scale based on oblique photography, deep learning and CFD

Windows are one of the non-structural components of buildings that are most vulnerable to wind damage. It is important to obtain rapid and accurate predictions of wind-induced window damage in urban areas. A novel automated method for simulating the wind damage to building windows at the city scale is proposed in this study based on oblique photography, deep learning, and computational fluid dynamics technologies. First, a method for extracting building façade pixels from oblique aerial images is proposed to provide the data basis for subsequent window recognition. Second, the Pix2Pix deep learning network is utilized to recognize windows on building façades. Finally, the window damage in building clusters is evaluated automatically based on the window failure model and time-varying wind pressure data obtained through computational fluid dynamics simulations. A real-world community in Shenzhen, China, is used as a case study to showcase the workflow and demonstrate the reliability and feasibility of the proposed method. The proposed automated method overcomes the limitation that existing methods are difficult to apply to real building clusters. The novel method and corresponding case study presented here can provide a reference for urban areas to mitigate the impacts of wind disasters.

Operational Emissions in Prosuming Dwellings: A Study Comparing Different Sources of Grid CO2 Intensity Values in South Wales, UK

This paper analysed operational CO2 emissions from electricity grid interaction in photovoltaic prosumer dwellings in South Wales, UK. Operational CO2 emissions were quantified in four prosumer dwellings aiming to analyse (1) the differences in the result when time-varying data and static emission factors are used, and (2) the association of load-matching indicators to the results. Electricity balance data were obtained through monitoring (April 2020 to March 2021), and three sources for the grid’s CO2 intensity were considered: (1) UK nationwide average time-varying values (UK), (2) South Wales (SW) average time-varying values and (3) the UK Government’s official CO2 emissions factor (EF) for the study period. UK and SW grid CO2 intensity were obtained as dynamic data flows in a 30 min resolution, whereas EF was a year constant. Gross CO2 emissions calculated using SW data reached the highest emissions results: between 67.5% and 69.3% higher than the results obtained using the UK time-varying data, and between 41.1% and 45.1% higher than using the EF. The differences between the obtained yearly net emissions using dynamic data and the EF in each studied dwelling ranged between 6.2% and 294%. Results also show that the definition of geographic boundaries for location-based approach calculations can significantly affect the obtained emissions values.

A soft sensor model based on long&short-term memory dual pathways convolutional gated recurrent unit network for predicting cement specific surface area

The specific surface area of cement is an important index for the quality of cement products. But the time-varying delay, non-linearity and data redundancy in the process industry data make it difficult to establish an accurate online monitoring model. To solve the problems, a soft sensor model based on long&short-term memory dual pathways convolutional gated recurrent unit network (L/S-ConvGRU) is proposed for predicting the cement specific surface area. In this paper, first, as the linear coupling constraint inside the gated recurrent unit network (GRU) hinders the flow of information, parameters L and S are introduced into convolutional gated recurrent unit network (ConvGRU). L and S are decimals in the range (0, 1) which changed its internal linear constraint relationship and enhanced the feature extraction capability of the model. Then, two spatio-temporal feature extraction pathways are designed: long-term memory enhancement pathway and short-term dependence pathway, which capture long-term and short-term time-varying delay information from the sample data. Finally, the two feature extraction pathways mentioned above are applied to the L/S-ConvGRU model and the extracted spatio-temporal features are fused to achieve accurate prediction of the specific surface area of cement. The model was trained using raw data from the cement plant and the experimental results show that L/S-ConvGRU has higher precision and better generalization capability.

Association of Non-Pharmaceutical Interventions to Reduce the Spread of SARS-CoV-2 With Anxiety and Depressive Symptoms: A Multi-National Study of 43 Countries.

Objectives: To examine the association of non-pharmaceutical interventions (NPIs) with anxiety and depressive symptoms among adults and determine if these associations varied by gender and age. Methods: We combined survey data from 16,177,184 adults from 43 countries who participated in the daily COVID-19 Trends and Impact Survey via Facebook with time-varying NPI data from the Oxford COVID-19 Government Response Tracker between 24 April 2020 and 20 December 2020. Using logistic regression models, we examined the association of [1] overall NPI stringency and [2] seven individual NPIs (school closures, workplace closures, cancellation of public events, restrictions on the size of gatherings, stay-at-home requirements, restrictions on internal movement, and international travel controls) with anxiety and depressive symptoms. Results: More stringent implementation of NPIs was associated with a higher odds of anxiety and depressive symptoms, albeit with very small effect sizes. Individual NPIs had heterogeneous associations with anxiety and depressive symptoms by gender and age. Conclusion: Governments worldwide should be prepared to address the possible mental health consequences of stringent NPI implementation with both universal and targeted interventions for vulnerable groups.

Female combatants and rebel group behaviour: Evidence from Nepal

Recent research examines when and why women join rebel groups as combatants. However, we are only beginning to understand how their presence affects rebel group behaviour and conflict dynamics more generally. I address this gap by analysing how women's participation influences two dimensions of rebel behaviour: their relationship to civilians and their fighting performance. I argue that a greater number of female rebels decreases civilian victimization, but also reduces rebel combat performance. I test these propositions using time-varying district-level data from the Nepalese civil war. The results support both expectations. These findings increase our understanding of the effects of women's participation in civil war.

Visual analytics of combustion on time-varying turbulent-flow

Visualization is crucial for analyzing the turbulent combustion simulation. Time-varying data allows us to investigate the evolution process of the turbulent flow field. To study the combustion effects, we calculated the enstrophy of the flow field since high enstrophy region can display valuable features, and extract components based on these features. We isolated large components to track their behaviors and characterized them using volume and spatial locations, which helps scientists to explore the dynamics and temporal changes of intense events individually. We analyzed the components’ structures and visualized them in contouring and statistical charts.

Dynamic concentration factor: A novel parameter for the rigorous evaluation of solar compound parabolic collectors

Solar-driven photoactivated processes have proven to be effective technologies for removing pathogens and chemical contaminants from water. Reflector systems are commonly used to concentrate the sunlight and improve their efficiency. Among them, compound parabolic collectors (CPC) are the most widely used, being the concentration factor (CF), their main design parameter, calculated directly from the geometry. This work presents the development of a novel parameter, called dynamic concentration factor (DCF), to assess the efficiency of a CPC as a function of the solar vector (the beam angle depends on location, season of the year and time of the day) and the reflective properties of the material. The DCF is defined as the ratio between the incident radiation reaching the receiver with and without the collector. DCF of CPC with different acceptance angles have been calculated with a ray-tracing tool and validated with experimental data from actinometry reactions. Analysis of the results has been done by plotting the dependence of the DCF versus the beam angle for increasing reflectivity indexes. This facilitates the estimation of the total radiation available to conduct a photoactivated process, whose reaction rate is proportional to the incident photons in the receiver. Global worldwide results using time-varying local solar irradiation data showed that, although the CPC with an acceptance angle of 90° (CF = 1) is commonly assumed as an optimal configuration due to its ability to concentrate diffuse radiation, the use of CPC with different acceptance angles could be more suitable in some regions of the planet.

Iterative learning control for intermittently sampled data: Monotonic convergence, design, and applications

The standard assumption that a measurement signal is available at each sample in iterative learning control (ILC) is not always justified, e.g., when exploiting time-stamped data from incremental encoders or in systems with data dropouts. The aim of this paper is to develop a computationally tractable ILC framework that is capable of exploiting intermittent data while maintaining favourable properties, including monotonic convergence. A controllability and observability analysis of the intermittent ILC framework leads to appropriate monotonic convergence conditions which allow for missing data. These conditions lead to a new explicit ILC controller design independent of the sampling instances, which is reminiscent of gradient-descent ILC. The approach is demonstrated on both an intuitive example and a practically relevant example which exploits time-varying timestamped data from an incremental encoder.