Lag Parameter Research Articles

A computational musculoskeletal arm model for assessing muscle dysfunction in chronic obstructive pulmonary disease.

Computational models have been used extensively to assess diseases and disabilities effects on musculoskeletal system dysfunction. In the current study, we developed a two degree-of-freedom subject-specific second-order task-specific arm model for characterizing upper-extremity function (UEF) to assess muscle dysfunction due to chronic obstructive pulmonary disease (COPD). Older adults (65years or older) with and without COPD and healthy young control participants (18 to 30years) were recruited. First, we evaluated the musculoskeletal arm model using electromyography (EMG) data. Second, we compared the computational musculoskeletal arm model parameters along with EMG-based time lag and kinematics parameters (such as elbow angular velocity) between participants. The developed model showed strong cross-correlation with EMG data for biceps (0.905, 0.915) and moderate cross-correlation for triceps (0.717, 0.672) within both fast and normal pace tasks among older adults with COPD. We also showed that parameters obtained from the musculoskeletal model were significantly different between COPD and healthy participants. On average, higher effect sizes were achieved for parameters obtained from the musculoskeletal model, especially for co-contraction measures (effect size = 1.650 ± 0.606, p < 0.001), which was the only parameter that showed significant differences between all pairwise comparisons across the three groups. These findings suggest that studying the muscle performance and co-contraction, may provide better information regarding neuromuscular deficiencies compared to kinematics data. The presented model has potential for assessing functional capacity and studying longitudinal outcomes in COPD.

Dynamics of a predator-prey model with fear effects and gestation delays

&lt;abstract&gt;&lt;p&gt;Recent studies have shown that, in addition to direct predation, fear of predators alters the physiological behavior of prey. Based on this fact, this paper investigates a three-species food chain based on ratio-dependent and Beddington-DeAngelis type functional responses, which incorporates fear effects and two gestation delays. The positivity, boundedness and existence of equilibrium points of the system are investigated, and the local stability behavior of the equilibrium points and the occurrence of Hopf-bifurcation when the time lag parameters exceed the critical values are studied by analyzing the corresponding characteristic equations. The main results show that Hopf-bifurcation occurs when the time delay parameters attain the thresholds. Finally, numerical simulations are performed to verify our main results.&lt;/p&gt;&lt;/abstract&gt;

Outliers in the Ep,z – Eγ relation of Fermi-GBM long-duration gamma-ray bursts

ABSTRACT Long gamma-ray bursts (GRBs) are typically associated with massive star core collapse, while the short GRBs are associated with compact binary mergers. However, recent evidence indicates that some peculiar long-duration bursts may correspond to compact binary mergers origins. In this paper, we use the data of the Fermi Gamma-ray Burst Monitor to search for peculiar long-duration bursts which may be from compact binary mergers based on outlier events in the $E_{p,z}\!-\!E_{\gamma ,\rm iso}$ relation. We obtained 10 outlier events by systematically analysing bursts with $T_{90}\gt 4.2 \rm \ s$ from 2008 August to 2021 July. In order to determine whether these outlier events were from compact binary mergers, we analysed their properties, including spectral lag, hardness ratio, and energy-hardness parameter. Based on the distributions of T90 − HR and T90 − Ep, we calculated the probability of outlier events belonging to the short GRBs. Our analysis indicates that GRB 120304B is likely to arise from the merger of a neutron star and a massive white dwarf. GRB 150210A is likely to arise from massive star core collapse. The other eight GRBs are fuzzy bursts that have both long and short GRBs properties. Additionally, we find that outlier samples have relatively high Ep and low fluences.

Adaptive Internal Model Control Based on Parameter Adaptation

In actual industrial control, many objects have the characteristics of large lag and time-varying parameters, which makes it difficult for traditional PID control to obtain satisfactory control effects. Therefore, engineers need a control algorithm with a better control effect and a simple structure, and thus the internal model control is introduced into industrial process control. Internal model control has received a lot of attention in the control field because of its excellent control effect, and in ideal conditions, it can perfectly suppress external disturbances, and the output strictly tracks the input. On the basis of the internal model control, a two-degrees-of-freedom internal model control was developed. Compared with the conventional feedback control, the internal model control structure mainly embeds an internal model consistent with the control object in the control object, so the deviation between the internal model and the control object determines the quality of the control effect. In this paper, the internal model is changed to an adjustable parameter model, and the model parameters are adjusted in real time using the parameter adaptive algorithm, so that the model output error is as small as possible, or even zero. In order to solve the influence of interference on the system, compensation based on MRAC theory is used. In order to verify the feasibility of the algorithm, it was applied to the landing process control of fixed-wing Unmanned aerial vehicle) UAV and achieved satisfactory results.

An improved grey multivariable time-delay prediction model with application to the value of high-tech industry

To analyse the time lag effects between independent variables and dependent variables, we propose a discrete time-delay grey multivariable modelDMTGM1,Nτ. There are three improvements in this new model compared to the existing models. First, the time lag parameters are assigned different values for each independent variable. A linear correction term expands the new model. Second, with the given time lag, the least square method can be used to calculate the parameter vector. The time response function of DMTGM1,Nτ is generated, which has the advantage of eliminating the jumping errors between discrete and continuous functions over the existing grey forecasting models. Third, all of the feasible combinations of the time lag parameters are compared by using a traversal algorithm to identify the best values with the minimized mean absolute percentage error (MAPE). In three different case studies, the performance of the new model is evaluated and compared to that of a number of mainstream grey models as well as non-grey models. According to the findings, the newly designed model performs significantly better than the compared models.

Forecasting Parameters in the SABR Model

&lt;p&gt;We present two approaches to forecasting parameters in the SABR model. The first approach is the vector autoregressive moving-average model (VARMA) for the time series of the in-sample calibrated parameters, and the second is based on machine learning techniques called epsilon-support vector regression (&amp;epsilon;-SVR). Using daily data of S&amp;amp;P 500 ETF option prices from January 1, 2014, to December 31, 2018, we first calibrate the daily values of the model parameters from the training sample, then conduct out-of-sample forecasting of parameters and pricing of options. Both approaches produce good fits between the forecasted and calibrated parameters for out-of-sample dates. A comparison study shows that using forecasted parameters as inputs, the SABR model generates better pricing results than assuming constant parameters or using lag parameters. We also discuss the market conditions under which one approach outperforms the other.&lt;/p&gt;

Spillover effects and other determinants of medical device uptake in the presence of a medical guideline: An analysis of drug-eluting stents in Germany and Italy.

We investigated the role of spillover effects among hospitals in the diffusion of drug-eluting stents (DES) in Germany and Italy during a period in which the relevant medical guideline clearly recommended their use over bare-metal stents. We used administrative data of hospitalized patients treated with ST-elevation myocardial infarction from 2012 to 2016 to estimate spatial panel models allowing for global spillover effects. We used an inverse-distance weights matrix to capture the geographical proximity between neighboring hospitals and assigned a lower weight to more distant neighbors. For both countries, we found significant positive spatial autocorrelation in most years based on the global Moran's I test, and a significant, positive spatial lag parameter across model specifications, indicating positive spillover effects among neighboring hospitals. We found that private for-profit hospital ownership and hospital competition in Germany and the number of inpatient cases with circulatory system diseases in Italy were other significant determinants of DES adoption. Our results underline the importance of spillover effects among peers for the diffusion of medical devices even in the presence of a positive guideline recommendation. Policymakers might therefore consider promoting various forms of exchange and collaboration among medical staff and hospitals to ensure the appropriate use of medical technologies.

Forecast of carbon emissions in China based on time lag MGM(1,m,N|τ) grey model

ABSTRACT Global climate issues have been gaining international attention in recent years. As the largest developing country and the prime carbon emitter, the Chinese government has proposed a strategic ‘double carbon’ target for carbon emissions. To predict carbon emissions more accurately, clarify the future supply situation and optimise resource allocation, based on the grey M G M ( 1 , m , N | τ ) model, we introduced and applied the particle swarm algorithm to determine the time lag parameter τ and proposed a new M G M ( 1 , m , N | τ ) grey model. We give a detailed modelling procedure, including calculation steps and intelligent optimisation algorithms, by fully considering the effect of time lag. In this study, this new model is used to simulate and forecast China's carbon emissions from 2010 to 2019 and compare it with other traditional grey models and their improved time-lagged forms. The results show that the new model has significant advantages in prediction accuracy and validity, plus good prediction performance for carbon emissions, which can be extended to more macro and micro energy consumption prediction problems.

Time Lag Modeling in Modification of Nakayasu Synthetic Unit Hydrograph

In the development of the synthetic unit hydrograph method, the empirical formula expresses the relation between the watershed morphometry characteristic and the constituent elements of hydrographs that are peak discharge (Qp), time to peak (tp), time lag (TL), and time base (Tb). This research intends to build a model of time lag in order to modify the Nakayasu SUH modeling. The Nakayasu synthetic unit hydrograph (SUH) model, seen as very popular and widely used by practitioners, needs to be adjusted for the main parameters that form the hydrograph to be able to be directly used based on the watershed morphometry characteristic. Based on the specific characteristic of the Nakayasu SUH formulation, this research will focus on developing the Nakayasu SUH model by the modification with adding the watershed characteristic parameter to the time lag parameter (tL). This research was conducted in 23 watersheds in Indonesia. The previous research in the same watersheds indicates five morphometry parameters with a positive effect on the time lag with the ranking due to the strongest effect as follows: watershed area (A), the river length from outlet to the weight point of the watershed (Lc), H = L.Lc, and the main river length (L). However, the two other parameters negatively affect the time lag with the ranking due to the strongest effect as follows: river network density (D) and river slope (K). Then, the modeling of time lag is carried out based on the research result, and the result of the time lag model is as follows: tL=Lc0.1809H-0.00159A0.003459

A Novel Approach to Forecasting High Dimensional S&amp;P500 Portfolio Using VARX Model with Information Complexity

This study considers vector autoregressive models that allow for endogenous and exogeneous regressors VARX using multivariate OLS regression. For the model selection, we follow bozdogan’s entropic or information-theoretic measure of complexity ICOMP criterion of the estimated inverse Fisher information matrix IFIM in choosing the best VARX lag parameter and we established that ICOMP outperform the conventional information criteria. As an empirical illustration, we reduced the dimension of the S&amp;P500 multivariate time series using Sparse Principal Component Analysis (SPCA) and chose the best subset of 37 stocks belonging to six sectors. We then performed a portfolio of stocks based on the highest SPC loading weight matrix, plus the S&amp;P500 index. Furthermore, we applied the proposed VARX model to predict the price movements in the constructed portfolio, where the S&amp;P500 index was treated as an exogeneous regressor of the VARX model. It has been deduced too that the buy-sell decision making in response to VARX (4,0) for a stock outperforms investing and holding the stock over the out-of-sample period.

Strain data correction of distributed optical fiber sensors using strain transfer model with variable shear lag parameters

Distributed optical fiber sensing techniques have been widely utilized in the field of structural health monitoring (SHM) of civil structures. However, strain measurements from distributed optical fiber sensors cannot always accurately reflect the strain of the host material of structures due to the coupled effects of the composite protective layer of the cables and the quality of the surface bonding construction. To address this issue, a method is proposed for correcting the strain data for civil structures measured by surface bonded distributed optical fiber sensors. The layered analysis method is used to establish a strain transfer model from the fiber core, through the inner protective layer, rigid protective layer, outer protective layer, and adhesive layer to the host material of the structure. With the new method, the strain transfer coefficient from the fiber core to the host material of the structures is calculated by updating the shear lag parameters based on a finite element model with a specific bonding length. The results of an experimental model and an actual bridge show that, comparing with the current methods, the proposed method effectively improves the accuracy of strain data from the distributed optical fiber sensors. The proposed method is benefit for the application of distributed optical fiber sensing techniques in monitoring the condition of actual civil structures.

Forecasting of dengue hemorrhages fever cases with autoregression distributed lag model using Poisson regression approach

Dengue hemorrhages fever (DHF) is an infectious disease epidemic in the community. DHF is caused by a virus that is transmitted through the bite of mosquitoes Aedes sp. The aim of this research was to develop and validate a forecasting model that could predict dengue fever (DHF) cases in Bandung, West Java, Indonesia. Model of dengue fever is developed using time series Poisson multivariate regression. Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes the response variable has a Poisson distribution and the logarithm of its expected value can be modeled by a linear combination of unknown parameters. Here, a time series Poisson multivariate regression model was developed using monthly mean temperature and cumulative rainfall over the period 2001–2016. Cross-correlation function between response and predictor is used to decide the lag parameter optimum in autoregression distributed lag model. Model selection and validation were based on the Akaike’s information criterion, mean square error, mean absolute percentage error, and residuals diagnostic test. The R-square obtained is 0.78, means that the temperature, rainfall, past dengue fever cases, season, and trend could explain 78% of the variance of monthly dengue distribution.

Higher-order interactions promote chimera states.

Since the discovery of chimera states, the presence of a nonzero phase lag parameter turns out to be an essential attribute for the emergence of chimeras in a nonlocally coupled identical Kuramoto phase oscillators' network with pairwise interactions. In this Letter, we report the emergence of chimeras without phase lag in a nonlocally coupled identical Kuramoto network owing to the introduction of nonpairwise interactions. The influence of added nonlinearity in the coupled system dynamics in the form of simplicial complexes mitigates the requisite of a nonzero phase lag parameter for the emergence of chimera states. Chimera states stimulated by the reciprocity of the pairwise and nonpairwise interaction strengths and their multistable nature are characterized with appropriate measures and are demonstrated in the parameter spaces.

Fault diagnosis based on online dynamic integration model and transfer entropy

In this paper, the monitoring method based on slow feature analysis and the root cause diagnosis based on transfer entropy are proposed. Different from the traditional methods, this paper performing online modelling to improve the adaptability to different data features and the dynamics of the model. A Dynamic Dissimilarity Index (DDI) is proposed to construct a dynamic online sub-block partitioning model. For process monitoring of multidimensional quality variables, a weighted fusion method is proposed to integrate multiple models so that the monitoring conclusions include the quality changes of the process. The fault variables are identified for quality-related faults, and the traditional method is improved by proposing a transfer entropy method with time lag parameters added to optimize the fault causality diagram. Finally, the study of real chemical processes shows that the proposed method gives more accurate and richer monitoring conclusions and can find the root cause more quickly and efficiently.

Measuring dynamic efficiency with variable time lag effects

This study is focused on the effect of time lag on a two-stage dynamic network data envelopment analysis (DEA) where intermediate products produced in the first stage both contribute to concurrent production in the second stage and affect operations in subsequent periods. We concluded that existing treatments for estimating a uniform time lag parameter do not reflect various operational strategies deployed by individual decision making units (DMUs) over a period of time. As a remedy, we proposed a two-stage dynamic network DEA model that can consider variable time lag effects, namely multiple carryover schemes, optimized for each DMU in efficiency measurements. Empirical validation of the model is provided based on Korean research and development (R&D) institutes and a focused discussion on the technology licensing process. The comparative analysis shows that the dynamics of intellectual capital stock can be represented well by the proposed model, while the existing approaches carry a risk of understating efficiency measures owing to their restrictive nature. The proposed model is particularly robust to the concentration of inputs and/or outputs by recognizing virtual reallocations of resources over time, which can be used as a solution to the discrepancy between production lead time and interval of evaluation in dynamic systems.

Nonlocal thermoelasticity: Transient heat conduction effects on the linear and nonlinear vibration of single-walled carbon nanotubes

This novel study presents a framework to investigate the effects of transient heat conduction on the nonlinear deflection and vibration of single-walled carbon nanotubes (SWCNTs) based on Eringen’s nonlocal elasticity and nonlocal heat conduction theories. The first phase of this research involves modifying the hyperbolic heat conduction theory by including a nonlocality term, in which the scale of energy transport is taken into account. The next phase includes a thermoelasticity solution for carbon nanotubes (CNTs), which is developed by Eringen’s nonlocal theory and implementing the obtained temperature. The differential quadrature method (DQM) is used to solve the transversely induced nonlocal heat conduction. We have shown the effect of thermal waves on the mode shapes and nonlinear vibration of a SWCNT by considering phase lag parameters. All boundaries are considered under a temperature-jump at the nanometer scale to consider the boundary phonon scattering. The effects of several parameters, including time-dependent boundary conditions, time lag, nonlocal parameter, length and radius, and end supports on the frequency response of SWCNTs are investigated.

A General Solution to Determine Strain Profile in the Core of Distributed Fiber Optic Sensors under Any Arbitrary Strain Fields.

Despite recent publications, the strain transfer in distributed optical fiber sensors is still often overlooked and poorly understood. In the first part of this paper, strain transfer is shown to be driven by a second-order differential equation, whether the optical fiber is embedded into the host material or surface-mounted. In this governing equation, only the value of a key parameter, called strain lag parameter, varies according to the attachment configuration and the type of optical fiber used as a sensor. Then, a general solution of the governing equation is proposed. It is an analytical expression established from new boundary conditions that are more adequate than those used previously in the literature and allows the determination of the strain profile in the core of a distributed optical fiber sensor under any arbitrary strain fields. This general solution has been validated by two experiments presented in the third part of the paper. A very good agreement between the analytical solutions and measured strain profiles using a high spatial resolution optical interrogator for both uniform and non-uniform strain fields has been obtained. These results highlight the importance of the strain lag parameter which must be taken into account for a correct interpretation of measurements, especially in the case of important strain gradients.

Thermal Analysis of Lightweight Clay Bricks with Typha-Fiber Additives

This paper assesses the thermal performance of reinforced Typha-fibers unfired clay bricks. Various Typha additive proportions (0%, 1%, 3%, 7%, 15%, and 20%), by weight in a fibrous form, have been prepared for experimental testing. X-ray diffraction and fluorescence analysis showed that exploited clay is suitable for construction purposes with a predominant quartz (SiO2) content. Collected findings showed that higher Typha-fiber proportions produced brick samples with higher porosity levels. The highest recorded porosity, 14.95%, was obtained with the incorporation of a 20% additive, compared to a 1.14% porosity percentage for reference samples. This resulted in the production of more porous bricks with improved thermal properties. In fact, at a 20% Typha-fibers additive content, 0.29 W/m·K and 0.76 kJ/kg·K respective thermal conductivity and specific heat capacity values were recorded, compared to 0.51 W/m·K and 0.59 kJ/kg·K at the reference samples. This represents 43% and 23% gains in thermal conductivity and specific heat capacity, respectively. A dynamic thermal inertia simulation was also carried out in the TRNSYS Type 56 software to evaluate the time lag and decrement factor parameters of prepared specimens as walling structures of a reference house. A total of 38% and 45% gains in the time lag and decrement factor were obtained with the incorporation of the highest additive proportion, 20%, compared to reference samples. Finally, four mathematical models are used to evaluate the thermal performance, in terms of thermal conductivity and specific heat capacity, of the studied specimens as a function of the measured porosity. The comparison drawn between the models’ theoretically predicted thermal properties and experimentally measured ones reflected a positive association between the two as the obtained correlation coefficient R was found to be very close to 1.

A Thermoelastic Piezoelectric Fixed Rod Exposed to an Axial Moving Heat Source via a Dual-Phase-Lag Model

Piezoelectric ceramics are used more in the active control of noise and vibration with sensors and actuators. As a result, a one-dimensional homogeneous thermoelastic fixed piezoelectric rod subjected to a moving heat flow has been considered. The heating rod is affected by three fields, namely, thermal, mechanical, and electric potential. In order to design a reliable smart structure, this study is necessary and the thermopiezoelectric behavior of piezoelectric ceramics must be understood clearly. The Laplace transform method is applied to acquire the distributions of stress, deformation, and temperature fields. The effect of heat source velocity and phase lag parameters on the considered physical fields is displayed graphically, and the results are compared with other models of thermoelasticity. The present findings could find possible applications for designing receiving portions of transformers of Rosen type for voltage magnification.

Photothermoelastic Investigation of Semiconductor Material Due to Distributed Loads

A dynamic mathematical model of photothermoelastic (semiconductor) medium is developed to analyze the deformation due to inclined loads. The governing equations for photothermoelastic with dual phase lag model are framed for two dimensional case and are further simplified by using potential function. Appropriate transforms w.r.t time (Laplace) and w.r.t space variables (Fourier) are employed on the resulting equations which convert the system of equations into differential equation. The problem is examined by deploying suitable mechanical boundary conditions. Specific types of distributed loads as uniformly distributed force and Linearly distributed force are taken to examine the utility of the model. The analytic expressions like displacements, stresses, temperature distribution and carrier density are obtained in the new domain (transformed).To recover the quantities in the physical domain, numerical inversion technique is employed. Numerical computed results with different angle of inclination vs distance are analyzed with and without dual phase lag theories of thermoelasticity in the form of visual representations. It is seen that physical field quantities are sensitive towards photothermoelastic and phase lag parameters.