Model Transformations to Evaluate Transient Thermal Responses at a Tissue Surface

2001 ◽  
Vol 123 (4) ◽  
pp. 370-372
Author(s):  
Gerald M. Saidel and ◽  
Erin H. Liu
Keyword(s):  
Numerical Solutions ◽  
Temperature Response ◽  
Model Transformations ◽  
Distributed Model ◽  
Transient Temperature ◽  
Model Parameters ◽  
Time Interval ◽  
Short Time Interval ◽  
Tissue Surface ◽  
Transient Thermal

For a spatially distributed model describing the transient temperature response of a thermistor-tissue system, Wei et al., [J. Biomech. Eng., 117:74–85, 1995] obtained an approximate transformation for fast analysis of the temperature response at the tissue surface. This approximate transformation reduces the model to a single ordinary differential equation. Here, we present an exact transformation that yields a single differential-integral equation. Numerical solutions from the approximate and exact transformations were compared to evaluate the differences with several sets of parameter values. The maximum difference between the exact and approximate solutions did not exceed 15 percent and occurred for only a short time interval. The root-mean-square error of the approximate solution was no more than 5 percent and within the level of experimental noise. Under the experimental conditions used by Wei et al., the approximate transformation is justified for estimating model parameters from transient thermal responses.

scholarly journals Coupling Disease-Progress-Curve and Time-of-Infection Functions for Predicting Yield Loss of Crops

2000 ◽  
Vol 90 (8) ◽  
pp. 788-800 ◽  
Author(s):  
L. V. Madden ◽  
G. Hughes ◽  
M. E. Irwin
Keyword(s):  
Yield Loss ◽  
Numerical Solutions ◽  
Disease Incidence ◽  
Time Interval ◽  
Short Time Interval ◽  
Time Duration ◽  
Plant Yield ◽  
Disease Progress ◽  
Time Of Infection ◽  
Over Time

A general approach was developed to predict the yield loss of crops in relation to infection by systemic diseases. The approach was based on two premises: (i) disease incidence in a population of plants over time can be described by a nonlinear disease progress model, such as the logistic or monomolecular; and (ii) yield of a plant is a function of time of infection (t) that can be represented by the (negative) exponential or similar model (ζ(t)). Yield loss of a population of plants on a proportional scale (L) can be written as the product of the proportion of the plant population newly infected during a very short time interval (X′(t)dt) and ζ(t), integrated over the time duration of the epidemic. L in the model can be expressed in relation to directly interpretable parameters: maximum per-plant yield loss (α, typically occurring at t = 0); the decline in per-plant loss as time of infection is delayed (γ; units of time-1); and the parameters that characterize disease progress over time, namely, initial disease incidence (X0), rate of disease increase (r; units of time-1), and maximum (or asymptotic) value of disease incidence (K). Based on the model formulation, L ranges from αX0 to αK and increases with increasing X0, r, K, α, and γ-1. The exact effects of these parameters on L were determined with numerical solutions of the model. The model was expanded to predict L when there was spatial heterogeneity in disease incidence among sites within a field and when maximum per-plant yield loss occurred at a time other than the beginning of the epidemic (t > 0). However, the latter two situations had a major impact on L only at high values of r. The modeling approach was demonstrated by analyzing data on soybean yield loss in relation to infection by Soybean mosaic virus, a member of the genus Potyvirus. Based on model solutions, strategies to reduce or minimize yield losses from a given disease can be evaluated.

scholarly journals Short Data-Based Output-Only Identification for Time-Varying Systems with Fast Dynamic Evolution

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Zhi-Sai Ma ◽  
Qian Ding
Keyword(s):  
Ordinary Least Squares ◽  
Operating Conditions ◽  
Dynamic Evolution ◽  
Model Parameters ◽  
Time Interval ◽  
Short Time Interval ◽  
Time Varying ◽  
Tracking Accuracy ◽  
Output Only ◽  
Fast Dynamic

Many engineering systems change appreciably over a relatively short time interval due to their fast evolution in the dynamics. Time-varying (TV) system’s ambient excitation is usually difficult to measure under operating conditions, and its dynamics have to be determined without measuring the excitation. Therefore, short data-based output-only identification for TV systems with fast dynamic evolution is considered in this paper. Deterministic parameter evolution methods are known to track fast dynamic evolution by postulating TV model parameters as deterministic functions of time and selecting proper functional subspaces. However, these methods require a significant number of parameters to represent complicated time-dependencies and dynamics characterized by larger numbers of degrees-of-freedom. In such cases, the ordinary least squares estimation may lead to less accurate or even unreliable estimates. A ridge regression-based deterministic parameter evolution method is proposed to overcome ill-posed problems via regularization and subsequently assessed through numerical and experimental validation. Comparative results confirm the advantages of the proposed method in terms of achievable natural frequency and power spectral density tracking, accuracy, and resolution of TV systems with fast dynamic evolution, when the response data length is relatively short.

Probabilistic Estimates of Transient Climate Sensitivity Subject to Uncertainty in Forcing and Natural Variability

2011 ◽  
Vol 24 (21) ◽  
pp. 5521-5537 ◽  
Author(s):  
Lauren E. Padilla ◽  
Geoffrey K. Vallis ◽  
Clarence W. Rowley
Keyword(s):  
Surface Temperature ◽  
Climate Sensitivity ◽  
General Circulation ◽  
Temperature Response ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Balance Model ◽  
Transient Temperature ◽  
Model Parameters ◽  
The Impact

Abstract In this paper, the authors address the impact of uncertainty on estimates of transient climate sensitivity (TCS) of the globally averaged surface temperature, including both uncertainty in past forcing and internal variability in the climate record. This study provides a range of probabilistic estimates of the TCS that combine these two sources of uncertainty for various underlying assumptions about the nature of the uncertainty. The authors also provide estimates of how quickly the uncertainty in the TCS may be expected to diminish in the future as additional observations become available. These estimates are made using a nonlinear Kalman filter coupled to a stochastic, global energy balance model, using the filter and observations to constrain the model parameters. This study verifies that model and filter are able to emulate the evolution of a comprehensive, state-of-the-art atmosphere–ocean general circulation model and to accurately predict the TCS of the model, and then apply the methodology to observed temperature and forcing records of the twentieth century. For uncertainty assumptions best supported by global surface temperature data up to the present time, this paper finds a most likely present-day estimate of the transient climate sensitivity to be 1.6 K, with 90% confidence the response will fall between 1.3 and 2.6 K, and it is estimated that this interval may be 45% smaller by the year 2030. The authors calculate that emissions levels equivalent to forcing of less than 475 ppmv CO2 concentration are needed to ensure that the transient temperature response will not exceed 2 K with 95% confidence. This is an assessment for the short-to-medium term and not a recommendation for long-term stabilization forcing; the equilibrium temperature response to this level of CO2 may be much greater. The flat temperature trend of the last decade has a detectable but small influence on TCS. This study describes how the results vary if different uncertainty assumptions are made and shows they are robust to variations in the initial prior probability assumptions.

On Multiple Access of a Large Number of Machine-Type Devices in Cellular Networks

2018 ◽  
pp. 105-114
Author(s):  
O. S. Galinina ◽  
S. D. Andreev ◽  
A. M. Tyurlikov
Keyword(s):  
Success Probability ◽  
Random Access ◽  
Time Interval ◽  
Short Time Interval ◽  
Control Mechanisms ◽  
Network Access ◽  
Smart Meters ◽  
Machine To Machine ◽  
Machine To Machine Communication ◽  
Machine Communication

Introduction: Machine-to-machine communication assumes data transmission from various wireless devices and attracts attention of cellular operators. In this regard, it is crucial to recognize and control overload situations when a large number of such devices access the network over a short time interval.Purpose:Analysis of the radio network overload at the initial network entry stage in a machine-to-machine communication system.Results: A system is considered that features multiple smart meters, which may report alarms and autonomously collect energy consumption information. An analytical approach is proposed to study the operation of a large number of devices in such a system as well as model the settings of the random-access protocol in a cellular network and overload control mechanisms with respect to the access success probability, network access latency, and device power consumption. A comparison between the obtained analytical results and simulation data is also offered. 

scholarly journals Improving the Stochastic Model of Ionospheric Delays for BDS Long-Range Real-Time Kinematic Positioning

2021 ◽  
Vol 13 (14) ◽  
pp. 2739
Author(s):  
Huizhong Zhu ◽  
Jun Li ◽  
Longjiang Tang ◽  
Maorong Ge ◽  
Aigong Xu
Keyword(s):  
Real Time ◽  
Long Range ◽  
Ionospheric Delay ◽  
Time Interval ◽  
Short Time Interval ◽  
Dual Frequency ◽  
Triple Frequency ◽  
Ambiguity Fixing ◽  
Power Spectral ◽  
Observation Noise

Although ionosphere-free (IF) combination is usually employed in long-range precise positioning, in order to employ the knowledge of the spatiotemporal ionospheric delays variations and avoid the difficulty in choosing the IF combinations in case of triple-frequency data processing, using uncombined observations with proper ionospheric constraints is more beneficial. Yet, determining the appropriate power spectral density (PSD) of ionospheric delays is one of the most important issues in the uncombined processing, as the empirical methods cannot consider the actual ionosphere activities. The ionospheric delays derived from actual dual-frequency phase observations contain not only the real-time ionospheric delays variations, but also the observation noise which could be much larger than ionospheric delays changes over a very short time interval, so that the statistics of the ionospheric delays cannot be retrieved properly. Fortunately, the ionospheric delays variations and the observation noise behave in different ways, i.e., can be represented by random-walk and white noise process, respectively, so that they can be separated statistically. In this paper, we proposed an approach to determine the PSD of ionospheric delays for each satellite in real-time by denoising the ionospheric delay observations. Based on the relationship between the PSD, observation noise and the ionospheric observations, several aspects impacting the PSD calculation are investigated numerically and the optimal values are suggested. The proposed approach with the suggested optimal parameters is applied to the processing of three long-range baselines of 103 km, 175 km and 200 km with triple-frequency BDS data in both static and kinematic mode. The improvement in the first ambiguity fixing time (FAFT), the positioning accuracy and the estimated ionospheric delays are analysed and compared with that using empirical PSD. The results show that the FAFT can be shortened by at least 8% compared with using a unique empirical PSD for all satellites although it is even fine-tuned according to the actual observations and improved by 34% compared with that using PSD derived from ionospheric delay observations without denoising. Finally, the positioning performance of BDS three-frequency observations shows that the averaged FAFT is 226 s and 270 s, and the positioning accuracies after ambiguity fixing are 1 cm, 1 cm and 3 cm in the East, North and Up directions for static and 3 cm, 3 cm and 6 cm for kinematic mode, respectively.

scholarly journals Evaluation and Validation of a Joint Stress Test to Induce Activity-Related Knee Joint Discomfort — a Prospective Case-Control Study

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Christiane Schön ◽  
Claudia Reule ◽  
Katharina Knaub ◽  
Antje Micka ◽  
Manfred Wilhelm ◽  
...  
Keyword(s):  
Knee Joint ◽  
Healthy Subjects ◽  
Stress Test ◽  
Osteoarthritis Of The Knee ◽  
Time Interval ◽  
Short Time Interval ◽  
Joint Stress ◽  
Joint Health ◽  
The Impact ◽  
Health Function

Abstract Background The assessment of improvement or maintenance of joint health in healthy subjects is a great challenge. The aim of the study was the evaluation of a joint stress test to assess joint discomfort in subjects with activity-related knee joint discomfort (ArJD). Results Forty-five subjects were recruited to perform the single-leg-step-down (SLSD) test (15 subjects per group). Subjects with ArJD of the knee (age 22–62 years) were compared to healthy subjects (age 24–59 years) with no knee joint discomfort during daily life sporting activity and to subjects with mild-to-moderate osteoarthritis of the knee joint (OA, Kellgren score 2–3, age 42–64 years). The subjects performed the SLSD test with two different protocols: (I) standardization for knee joint discomfort; (II) standardization for load on the knee joint. In addition, range of motion (ROM), reach test, acute pain at rest and after a single-leg squat and knee injury, and osteoarthritis outcome score (KOOS) were assessed. In OA and ArJD subjects, knee joint discomfort could be reproducibly induced in a short time interval of less than 10 min (200 steps). In healthy subjects, no pain was recorded. A clear differentiation between study groups was observed with the SLSD test (maximal step number) as well as KOOS questionnaire, ROM, and reach test. In addition, a moderate to good intra-class correlation was shown for the investigated outcomes. Conclusions These results suggest the SLSD test is a reliable tool for the assessment of knee joint health function in ArJD and OA subjects to study the improvements in their activities. Further, this model can be used as a stress model in intervention studies to study the impact of stress on knee joint health function.

Experimental and Theoretical Analysis of Transient Response of Plate Heat Exchangers in Presence of Nonuniform Flow Distribution

2008 ◽  
Vol 130 (5) ◽  
Author(s):  
N. Srihari ◽  
Sarit K. Das
Keyword(s):  
Steady State ◽  
Heat Exchangers ◽  
Sudden Change ◽  
Temperature Response ◽  
Flow Distribution ◽  
Transient Temperature ◽  
Nonuniform Flow ◽  
Flow Rates ◽  
Plate Heat ◽  
Flow Maldistribution

Transient analysis helps us to predict the behavior of heat exchangers subjected to various operational disturbances due to sudden change in temperature or flow rates of the working fluids. The present experimental analysis deals with the effect of flow distribution on the transient temperature response for U-type and Z-type plate heat exchangers. The experiments have been carried out with uniform and nonuniform flow distributions for various flow rates. The temperature responses are analyzed for various transient characteristics, such as initial delay and time constant. It is also possible to observe the steady state characteristics after the responses reach asymptotic values. The experimental observations indicate that the Z-type flow configuration is more strongly affected by flow maldistribution compared to the U-type in both transient and steady state regimes. The comparison of the experimental results with numerical solution indicates that it is necessary to treat the flow maldistribution separately from axial thermal dispersion during modeling of plate heat exchanger dynamics.

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