Time-varying Data Research Articles

Can cumulative loading estimates be used to assess the collective occupational risk of MSD? Evaluation of calculation methods for spinal cumulative loading

For the prevention of musculoskeletal disorders (MSD), the evaluation of manual materials handling (MMH) is important. Cumulative loading can be used to quantify exposure, but there is little applied research on its effectiveness in risk assessment. Therefore, this publication presents an analysis of whether cumulative loading estimates can be used to assess the occupational risk of MSD. Five methods for estimating cumulative spine loads from time-varying data are compared based on real collective exposure data of MMH associated with both high and low incidences of MSD. Results show that cumulative loading estimates can be used to assess the occupational risk of MSD. Still, it should be noted that assessment results are substantially influenced by the recorded level of detail of the exposure data and do not allow for reliable conclusions regarding physiological demands. Further research is needed regarding the inclusion of physiological responses and task sequences. Relevance to industryThe findings can be applied to ergonomic risk assessment in occupational practice. By becoming more aware of the differences between calculation methods and the influence of the level of detail of the exposure data, the planning and implementation of the ergonomic risk assessment can be facilitated.

Efficient and stable circular cartograms for time-varying data by using improved elastic beam algorithm and hierarchical optimization

Efficient and stable circular cartograms for time-varying data by using improved elastic beam algorithm and hierarchical optimization

Assessing Height Variations in Qinghai-Tibet Plateau from Time-Varying Gravity Data and Hydrological Model

Height variations caused by mass change make an important contribution to the tectonic uplift of the Qinghai-Tibet Plateau (QTP). To study the deformation attributable to hydrological loading and real potential tectonic vertical motion, satellite gravity data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) with data from the Global Land Data Assimilation System (GLDAS) and Global Positioning System (GPS) are adopted to estimate height variations in QTP. Based on spherical harmonic function (SHF) and Green’s function (GF), the results show the trend of height variations is unevenly distributed in the spatial domain. The SHF indicated that the rate in the southwest of the QTP is ~1 mm/year, while the northern and eastern show a subtle decreasing trend, which indicates hydrological loading is not the main cause of the uplift observed with GRACE. The maximum annual amplitude of height variations is ~12 mm, reaching the annual maximum around February to March. The average correlation coefficients of SHF, and GF height variations with GPS heights are 0.70 and 0.82, respectively. Based on cross wavelet transform, it is concluded that there are annual signals between the height variations derived from GPS with GRACE (-FO) and GLDAS. Finally, the tectonic vertical motion in the QTP is given by removing the effect of hydrological loading, which shows most GPS stations are uplifted at a rate of 0.06 mm/year–1.97 mm/year.

Environmental Exposures in a Cohort of Swedish Women – Are there Inequalities by Socioeconomic Status?

BACKGROUND/AIM: Recent reviews provide evidence for inequalities in environmental exposures between and within European populations. The aim of this study is to explore differences in exposure to air pollution, road-traffic noise, and greenness by multiple socio-economic determinants in a female cohort in Sweden. METHODS: 20,407 women born in 1914-48 residing in Uppsala County, Sweden, were followed from 1997 until migration, death, or end of follow-up in 2017. We combined survey data, time-varying register data, and spatial-temporal modelled air pollutants (PM₂.₅, PM₁₀, NO₂), road-traffic noise (Lden), and greenness (NDVI) at the residences. We descriptively assessed exposures over time and by categories of socio-economic variables, including age, civil status, employment status, education, individual, household, and area-based income, and municipality type. We applied linear regressions estimated with generalized estimating equations to assess differences in environmental exposures by socio-economic levels. RESULTS: At baseline (1997), the average age of 19,953 women with complete data was 61.9 years, 50% were still employed and 56% lived in urban areas. Compared with the baseline population, 67.8% (n=12,682) remained at the end of follow-up (2017). Over the 20-y follow-up, the average exposure to PM₂.₅, PM₁₀, and NO₂ declined significantly, however, levels of road-traffic noise and greenness were stable. Small but statistically significant differences in exposures were found across socio-economic levels. Suggested predictors of high exposure to air pollution, noise, and low greenness (only PM₁₀ changes compared to references and 95%-CI in μg/m³ shown) include high age (0.09 [0.05; 0.13]), being unmarried (0.09 [0.01; 0.17]), divorced/widowed (0.09 [0.05; 0.12]), or retired (0.03 [0.01; 0.05]), having high education (0.07 [0.004; 0.14]), high individual income (0.03 [0.003; 0.05]), but low area-based income (0.26 [0.20; 0.31]). CONCLUSION: This study provides evidence of a complex interplay between environmental exposure and levels of socio-economic determinants. KEYWORDS: Air pollution, road-traffic noise, greenness, socio-economy, inequality

Containment control for second-order multi-agent systems with intermittent sampled position data under directed topologies

This paper investigates the intermittent containment control problem for second-order multi-agent systems under directed communication topologies. In order to reduce energy consumption and overcome the difficulty of velocity measurement in engineering applications, an intermittent containment control protocol utilizing the present time-varying position data and sampled position data is proposed. Under the assumption on the existence of the directed spanning forest, a necessary and sufficient condition for the stability of the multi-agent system is obtained, which is derived by the coupling coefficients, the eigenvalues of the Laplacian matrix, the period length and the communication width. Finally, the correctness of the theoretical results is validated by experimental simulations.

Operando electrochemical impedance spectroscopy and its application to commercial Li-ion batteries

Electrochemical impedance spectroscopy (EIS) is a non-invasive technique for examining kinetics of electrochemical systems. Applied to energy storage devices, the impedance contains information about the state-of-charge and state-of-health of a battery. Classical EIS measurements, as implemented in many cyclers, are restricted by two important assumptions: linearity and stationarity. However, Li-ion batteries are inherently nonlinear, and are nonstationary while under operation. Classical EIS can thus only be performed on batteries in steady-state, and hence, no information about batteries under operation can be extracted. In this article, operando EIS is introduced as a promising tool for measuring time-varying impedance data of Li-ion batteries. The mathematics behind the impedance extraction are detailed, starting from nonstationary and mildly nonlinear current and voltage data. The technique is then applied to reproducible experiments, while charging and discharging commercial Li-ion batteries, using a commercial potentiostat. The operando impedance is shown to be different from the classical impedance, and the nonlinear behaviour of the battery is studied. Applications of operando EIS are discussed, with the focus on the modelling of the operando impedance data through equivalent circuit models, revealing the evolution of the ECM parameters over time.

FAIFO: UAV-assisted IoT programmable packet scheduling considering freshness

UAVs have the qualities of large coverage, flexible deployment, scalability, low cost and easy programming. Myriad UAVs are flexibly deployed in the air, on the ground and even on submarines, enabling ubiquitous connectivity. Thus, UAV-assisted IoT is considered a potential solution to support time-sensitive applications in sixth-generation (6G) networks. Such as the Industrial Internet of Things (IIoT), smart homes, smart cities and self-driving vehicles. A large amount of sensor data will be collected and carried by drones for transmission to destination nodes for further processing and analysis. Unlike traditional broadband services, most IoT applications transmit short and time-varying data packets carrying sensor updates. Many studies on time-sensitive applications at this stage still focus on optimization of latency, reliability and other metrics. Less attention has been paid to the freshness of the updates and the timeliness of the data. Age of Information (AoI) is a new metric introduced for time-sensitive applications in recent years. Unlike latency, which represents real-time, AoI utilizes the concept of freshness to depict the timeliness of data. The larger the AoI, the less fresh and timely the information is. To better serve time-sensitive applications, we cannot reduce the stringent requirements for latency while ensuring data timeliness. Therefore, we propose Freshness-based Admission in First Out (FAIFO), a novel fine-grained programmable packet scheduling scheme. FAIFO can guarantee the freshness of information for time-sensitive applications while meeting their low-latency requirements. FAIFO first uses the concept of admission control to rapidly filter high-priority (low-latency) packets into queue A to meet the real-time requirements of time-sensitive applications. Subsequently, depending on the freshness level of the packets, FAIFO will decide whether the discarded packets need to be cached in queue B to meet the timeliness requirements of time-sensitive applications. Finally, we improve the credit-based shaper (CBS) algorithm to perform traffic shaping on packets in queue A and queue B to ensure that high-priority queue A is forwarded first and freshness queue B is forwarded in a timely manner. Simulation experiments compare FAIFO with the ideal benchmark Push-In-First-Out (PIFO), state-of-the-art SP-PIFO, and AIFO. The results indicate that our FAIFO can also obtain the approximate PIFO effect, and the delay guarantee is comparable to SP-PIFO and AIFO. More importantly, FAIFO’s guarantee of information freshness is significantly better than other compared algorithms.

Ubiquitous Transmission Service: Hierarchical Wireless Data Rate Provisioning in Space-Air-Ocean Integrated Networks

Space-air-ocean integrated networks (SAOINs), composed of low earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and unmanned surface vehicles (USVs), have been advocated to provide seamless, high-rate, and reliable wireless transmission services for USVs. However, due to the restrictions of limited resources (e.g., spectrum bandwidth, transmission power, etc.), diverse demands of USVs, and selfishness of both UAVs and LEOs, there comes a significant challenge to provision high-quality wireless data rate for USVs to achieve their satisfied quality of experience (QoE). To this end, in this paper, we propose a hierarchical on-demand wireless data rate provisioning scheme to provide ubiquitous transmission services for USVs. Specifically, we first devise a hierarchical wireless data rate provisioning framework. The LEO satellite with an extensive wireless coverage is utilized to provide LEO satellite-to-UAV (L2U) data rate for UAVs with a certain L2U data rate price. Each UAV is employed to provide UAV-to-USV (U2U) data rate for covered multiple USVs with a certain U2U data rate price. We then propose a modified three-stage Stackelberg game to model the wireless data rate assignments among LEO satellites, UAVs, and USVs, where the time-varying data rate demands of USVs are considered to formulate the utility maximization problem. Afterwards, the backward induction approach is leveraged to attain the Stackelberg equilibrium as the solution of the formulated problem, where the closed-form expressions on the optimal strategies of both USVs and UAVs under different data rate budgets are obtained by the nonlinear programming method. Besides, an accelerated conjugate gradient descent (ACGD) based iteration algorithm is also designed to obtain the optimal strategies of the LEO satellites on the L2U data rate prices. At last, extensive simulations are carried out to demonstrate that the proposed scheme can significantly increase the utilities of USVs, as compared to other benchmark schemes.

Classification of cardiac electrical signals between patients with myocardial infarction and normal subjects by using nonlinear dynamics features and different classification models

Myocardial infarction (MI) is one of the leading causes of human mortality and morbidity around the world. Despite that much progress has been made for MI detection based on medical image analysis in recent years, most of them suffer from their expensiveness and invasive nature. In this paper, we demonstrate abnormalities of electrocardiogram (ECG) in a new quantifiable manner using nonlinear dynamics features and different classification models. Time-varying ECG data are represented as three-dimensional vectorcardiogram (VCG) and the underlying cardiac dynamics. Nonlinear dynamics measures, including entropy variability measures, complexity measures and chaotic measures, are calculated and fed into different machine learning methods for the classification task. The extracted nonlinear dynamics measures reflect in-depth cardiac dynamics characteristic, which is shown to be more sensitive to subtle ECG modifications. Therefore, it is expected to provide an early detection tool for latent ECG modifications before obvious diagnostic changes are observed. Experiments on the PTB database are conducted to demonstrate the efficiency of the proposed method.

Analysis of an empirical seismic fragility prediction model of wooden roof truss buildings

To explore the vulnerability of wood roof truss buildings (WRTBs) under earthquake action, the empirical damage typical characteristics of 12,343.4 × 104 m2 and 21,490 buildings of this type of building that suffered different degrees of loss in 22 typical destructive earthquakes in China from 1993 to 2013 were observed and analysed. Based on the analysis methods of the nonlinear vulnerability model, probability statistics, and prediction model, a novel fragility probability matrix, plane, surface, nonlinear regression curve, and function model of different intensity zones under the latest Chinese macrointensity standard (GB/T 17742–2020, CMS-20) assessment were established, and vulnerability curve models were employed based on the damage ratio (DR) and actual cumulative damage probability (ACDP) parameters. According to 1993–1999, 2000–2010, and after 2011, the time-varying fragility specimen data were classified and censusly analysed. The fragility prediction curve models of DR and ACDP were performed using the probabilistic demand modal analysis method. Combined with the matrix probability and confidence interval calculation model, a novel regional mean fragility index domain model for typical structural (WRTB) vulnerability assessment was developed.

Hydro-Geophysical Evaluation of the Regional Variability of Senegal’s Terrestrial Water Storage Using Time-Variable Gravity Data

The Gravity Recovery and Climate Experiment (GRACE) satellite data retrieval experiment has been instrumental in characterizing the global fluctuations in terrestrial water storage (ΔTWS) over the past 20 years. Given the limited availability of hydrological data, GRACE measurements are frequently combined with other climatic models, standardized precipitation index (SPI), and standardized temperature index (STI) data to examine the likelihood of such impacts on hydrology and calculate the groundwater storage changes (ΔGWS). The characterization of the intensity and variability of drought events has been identified based on the Terrestrial Water Storage Deficit Index (TWSI), derived from GRACE mass concentration blocks (mascons) over Senegal during the studied period (April 2002–December 2021). The results are: (1) The average annual precipitation (AAP) rate for the entire period was calculated at 692.5 mm/yr. (2) The GRACE-derived ΔTWS variations were calculated at +0.89 ± 0.34, +0.07 ± 0.36, +1.66 ± 1.20, and +0.63 ± 0.08 cm/yr for Periods I (April 2002–December 2009), II (January 2010–December 2017), III (January 2018–December 2021), and the entire period (April 2002–December 2021), respectively. (3) The ΔGWS changes were estimated to be +0.89 ± 0.31, +0.085 ± 0.33, +1.64 ± 1.11, and +0.63 ± 0.08 cm/yr for Periods I, II, II, and the entire period, respectively. (4) There is good agreement in some years and seasons according to the investigation of the link between the GRACE dataset, STI, and SPI. (5) Senegal’s groundwater storage is increasing at a rate of 0.63 ± 0.08 cm/yr (1.24 ± 0.16 km3/yr) between April 2002 and December 2021. (6) Considering the yearly extraction rates of 1.13 ± 0.11 cm/yr (2.22 ± 0.22 km3/yr), an average recharge rate of +1.76 ± 0.14 cm/yr (+3.46 ± 0.28 km3/yr) was calculated for the studied area. The integrated strategy is instructive and economical.

Modelling Time-Varying Rankings with Autoregressive and Score-Driven Dynamics

AbstractWe develop a new statistical model to analyse time-varying ranking data. The model can be used with a large number of ranked items, accommodates exogenous time-varying covariates and partial rankings, and is estimated via the maximum likelihood in a straightforward manner. Rankings are modelled using the Plackett–Luce distribution with time-varying worth parameters that follow a mean-reverting time series process. To capture the dependence of the worth parameters on past rankings, we utilise the conditional score in the fashion of the generalised autoregressive score models. Simulation experiments show that the small-sample properties of the maximum-likelihood estimator improve rapidly with the length of the time series and suggest that statistical inference relying on conventional Hessian-based standard errors is usable even for medium-sized samples. In an empirical study, we apply the model to the results of the Ice Hockey World Championships. We also discuss applications to rankings based on underlying indices, repeated surveys and non-parametric efficiency analysis.

Distributed Group Coordination of Multiagent Systems in Cloud Computing Systems Using a Model-Free Adaptive Predictive Control Strategy.

This article studies the group coordinated control problem for distributed nonlinear multiagent systems (MASs) with unknown dynamics. Cloud computing systems are employed to divide agents into groups and establish networked distributed multigroup-agent systems (ND-MGASs). To achieve the coordination of all agents and actively compensate for communication network delays, a novel networked model-free adaptive predictive control (NMFAPC) strategy combining networked predictive control theory with model-free adaptive control method is proposed. In the NMFAPC strategy, each nonlinear agent is described as a time-varying data model, which only relies on the system measurement data for adaptive learning. To analyze the system performance, a simultaneous analysis method for stability and consensus of ND-MGASs is presented. Finally, the effectiveness and practicability of the proposed NMFAPC strategy are verified by numerical simulations and experimental examples. The achievement also provides a solution for the coordination of large-scale nonlinear MASs.

A Novel Approach to Select High-Reward Data Items in Big Data Stream Based on Multiarmed Bandit

Mining a big data stream with continuous, unbounded, and time-varying data items is a great challenge. In the situation where computational resources for real-time processing are limited, it is especially hard to select high-value data items from a big data stream. This article studies on selection policies based on the multiarmed bandit. We cache the online arriving data items in different buffers according to the characteristics of data items. These buffers are regarded as the arms of a multiarmed bandit. We pay attention to several key factors in selecting data items including the data item value, processing time, resource consumption, and loss value caused by some discarded items. Thus, a comprehensive reward mechanism for each data item is given as the foundation for the selection of data items, that is, gambling decision-making. We design three selection policies: the improved <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -greedy, the improved upper confidence bound (UCB), and a data item selection policy named dynamic high-reward incentive (DHRI) with active, dynamic, and incentive reward. They are all trying to balance “exploitation and exploration” in a multiarmed bandit. Experimental results show that our proposed approach is effective and outperforms the traditional methods.

A novel method for measuring latent heat of low-temperature PCM phase transition by oxygen bomb calorimeter

The method for the determination on the latent heat of phase change by oxygen bomb calorimetry of low-temperature phase change materials (PCMs) was feasible and validated. Experiments on the solidification of the variable boundary temperature of OP24 (the ratio was 57.5% caprylic acid + 42.5% myristic acid) low-temperature organic PCM, an energy storage medium, were carried out in a modified oxygen bomb calorimeter. In addition, a one-dimensional model of the solid–liquid two-phase cylindrical PCM solidification process was constructed by numerical simulation. Using the measured time-varying data of the PCM axial temperature, the oxygen bomb surface temperature, and the water temperature of the inner cylinder, the model was applied to approximate the latent heat of phase change of OP24 PCM using a cubic polynomial heat balance integration method. The calculated results were accurate for engineering applications when compared to the values obtained by differential scanning calorimetry. Moreover, the invisible phase interface moving with time could be obtained. The calculation process was relatively simple and convenient for engineering applications. Improving the accuracy of water temperature measurements in the inner cylinder could be the way forward to reduce experimental errors in the determination of the latent heat of phase change in low-temperature PCMs using oxygen bomb calorimetry.

What does export diversification do for energy demand? Evidence from the Global North.

This paper contributes to the trade-energy literature by examining the effect of export product diversification on the energy demand of 30 countries located in the Global North over the period 1980-2014. A nonparametric time-varying coefficient panel data model with fixed effects is employed for its ability to produce robust outcomes in the presence of parameter instabilities, nonstationarity, regime shifts, and time variations. As a second step, the paper also utilizes the nonparametric fixed effects extension of Driscoll and Kraay, which is robust to spatial/cross-sectional dependence, as well as to autocorrelation and heteroscedasticity. The results indicate that export diversification lowers overall energy demand in the Global North, and the size of the impact has been on a gradual increase over the years. The paper concludes that if a conscious effort is made to ensure that product diversification is towards energy-efficient goods, export product diversification can serve as a useful strategy for managing energy consumption and mitigating its negative environmental effects.

Transition between individually different and common features in skilled drumming movements.

Why do professional athletes and musicians exhibit individually different motion patterns? For example, baseball pitchers generate various pitching forms, e.g., variable wind-up, cocking, and follow-through forms. However, they commonly rotate their wrists and fingers at increasingly high speeds via shoulder and trunk motions. Despite the universality of common and individually different motion patterns in skilled movements, the abovementioned question remains unanswered. Here, we focus on a motion required to hit a snare drum, including the indirect phase of task achievement (i.e., the early movement and mid-flight phases) and the direct phase of task achievement (i.e., the hit phase). We apply tensor decomposition to collected kinematic data for the drum-hitting motion, enabling us to decompose high-dimensional and time-varying motion data into individually different and common movement patterns. As a result, individually different motion patterns emerge during the indirect phase of task achievement, and common motion patterns are evident in the direct phase of task achievement. Athletes and musicians are thus possibly allowed to perform individually different motion patterns during the indirect phase of task achievement. Additionally, they are required to exhibit common patterns during the direct phase of task achievement.

Titration of Ventilator Settings to Target Driving Pressure and Mechanical Power.

Driving pressure (ΔP) and mechanical power (MP) may be important mediators of lung injury in acute respiratory distress syndrome (ARDS) however there is little evidence for strategies directed at lowering these parameters. We applied predictive modeling to estimate the effects of modifying ventilator parameters on ΔP and MP. 2,622 ARDS patients (Berlin criteria) from the Medical Information Mart for Intensive Care IV database (MIMIC-IV version1.0) admitted to the intensive care unit (ICU) at Beth Israel Deaconess Medical Center between 2008 and 2019 were included. Flexible confounding-adjusted regression models for time varying data were fit to estimate the effects of adjusting PEEP and tidal volume (VT) on ΔP, and adjusting VT and respiratory rate (f) on MP. Reduction in VT reduced ΔP and MP, with more pronounced effect on MP with lower compliance. Strategies reducing f, consistently increased MP (when VT was adjusted to maintain consistent minute ventilation). Adjustment of PEEP yielded a U-shaped effect on ΔP. This novel conditional modeling confirmed expected response patterns for ΔP, with the response to adjustments depending on patients' lung mechanics. Furthermore a VT -driven approach should be favored over a f -driven approach when aiming to reduce MP.

Monitoring Mass Variations in Iraq Using Time-Variable Gravity Data

Iraq is facing a water shortage due to water scarcity and anthropogenic activities. The recent advance in technologies in geophysical methods has made groundwater monitoring possible. Time-variable gravity data and outputs of the climatic model, as well as rainfall data, are integrated to investigate the spatio-temporal mass variations caused by groundwater changes over Iraq. The findings are: (1) For the entire study period (04/2002–12/2020), Period I (04/2002–12/2006), Period II (01/2007–12/2017), and Period III (01/2018–12/2020), the study region had an average annual precipitation rate of 223.4, 252.5, 194.2, and 311.6 mm/y, respectively. (2) The average Terrestrial Water Storage variations (ΔTWSs) varied from −5.79 ± 0.70 to −5.11 ± 0.70 mm/y based on the three different gravity solutions with a mean of −5.51 ± 0.68 mm/y for the entire investigated period. (3) For Periods I, II, and III, the average ΔTWS fluctuation was calculated to be +6.82 ± 1.92, −6.20 ± 1.17, and +28.58 ± 12.78 mm/y, respectively. (4) During the entire period, Periods I, II, and II, the groundwater fluctuation was averaged at −4.86 ± 0.68, +2.47 ± 2.20, −3.79 ± 1.20, and −4.63 ± 12.99 mm/y, respectively, after subtracting the non-groundwater components. (5) At the beginning of the 2007 drought during Period II, a decline in rainfall rate, and significant groundwater withdrawal during Period III all appear to have contributed to groundwater depletion. The Euphrates and Tigris Rivers, as well as the Mesopotamian plain, receive water from the running streams created by the ground relief. The area of the Mesopotamian plain, which has a thicker sedimentary sequence that can reach 9000 m, is found to have a positive TWS signal, indicating that its groundwater potential is higher. The integrated approach is informative and cost-effective.

One Aggregated Approach in Multidisciplinary Based Modeling to Predict Further Students’ Education

In this paper, one multidisciplinary-applicable aggregated model has been proposed and verified. This model uses traditional techniques, on the one hand, and algorithms of machine learning as modern techniques, on the other hand, throughout the determination process of the relevance of model attributes for solving any problems of multicriteria decision. The main goal of this model is to take advantage of both approaches and lead to better results than when the techniques are used alone. In addition, the proposed model uses feature selection methodology to reduce the number of attributes, thus increasing the accuracy of the model. We have used the traditional method of regression analysis combined with the well-known mathematical method Analytic Hierarchy Process (AHP). This approach has been combined with the application of the ReliefF classificatory modern ranking method of machine learning. Last but not least, the decision tree classifier J48 has been used for aggregation purposes. Information on grades of the first-year graduate students at the Criminalistics and Police University, Belgrade, after they chose and finished one of the three possible study modules, was used for the evaluation of the proposed model. To the best knowledge of the authors, this work is the first work when considering mining closed frequent trees in case of the streaming of time-varying data.