Time-varying Profiles Research Articles

Performance Analysis of TBR Tire Curing Process

Numerical algorithms and computer programs have been developed to determine optimal cure steps in a tire curing process. A dynamic constrained optimization problem was formulated with the following ingredients: (1) an objective function that measures product quality in terms of final state of cure and temperature history at selected points in a tire; (2) constraints that consist of a process model and temperature limits imposed on cure media; (3) B-spine representation of a time-varying profile of cure media temperature. The optimization problem was solved using the complex algorithm along with a finite element model solver. Numerical simulations were carried out to demonstrate the procedure of determining optimal cure steps for a truck/bus radial tire. © 1999 John Wiley & Sons, Inc. J Apple Polymer Sic 74: 2063–2071, 1999.

Experiment Analysis of TBR Tire Curing Process

The optimization problem was solved using the complex algorithm along with a finit element model solver. Numerical simulations were carried out to demonstrate the procedure of determining optimal cure steps for a truck/bursa dial tire. © 1999 John Wiley & Sons, Inc. J App Poly m Sci 74: 2063–2071, 1999 dynamic constrained optimization problem was formulated with the following ingredients: (1) an objective function that measures product quality in terms of Final state of cure and temperature history a at selected points in a tire; (2) constraints that consist of a process model and temperature limits imposed on cure media; (3) B-splines representation of a time-varying Profile of cure Media temperature

Initial Beliefs Uncertainty

Abstract This paper evaluates how initial beliefs uncertainty can affect data weighting and the estimation of models with adaptive learning. One key finding is that misspecification of initial beliefs uncertainty, particularly with the common approach of artificially inflating initials uncertainty to accelerate convergence of estimates, generates time-varying profiles of weights given to past observations in what should otherwise follow a fixed profile of decaying weights. The effect of this misspecification, denoted as diffuse initials, is shown to distort the estimation and interpretation of learning in finite samples. Simulations of a forward-looking Phillips curve model indicate that (i) diffuse initials lead to downward biased estimates of expectations relevance in the determination of actual inflation, and (ii) these biases spill over to estimates of inflation responsiveness to output gaps. An empirical application with U.S. data shows the relevance of these effects for the determination of expectational stability over decadal subsamples of data. The use of diffuse initials is also found to lead to downward biased estimates of learning gains, both estimated from an aggregate representative model and estimated to match individual expectations from survey expectations data.

Consistent control information driven musculoskeletal model for multiday myoelectric control

Objective. Musculoskeletal model (MM)-based myoelectric interface has aroused great interest in human-machine interaction. However, the performance of electromyography (EMG)-driven MM in long-term use would be degraded owing to the inherent non-stationary characteristics of EMG signals. Here, to improve the estimation performance without retraining, we proposed a consistent muscle excitation extraction approach based on an improved non-negative matrix factorization (NMF) algorithm for MM when applied to simultaneous hand and wrist movement prediction. Approach. We added constraints and L 2-norm regularization terms to the objective function of classic NMF regarding muscle weighting matrix and time-varying profiles, through which stable muscle synergies across days were identified. The resultant profiles of these synergies were then used to drive the MM. Both offline and online experiments were conducted to evaluate the performance of the proposed method in inter-day scenarios. Main results. The results demonstrated significantly better and more robust performance over several competitive methods in inter-day experiments, including machine learning methods, EMG envelope-driven MM, and classic NMF-based MM. Furthermore, the analysis of control information on different days revealed the effectiveness of the proposed method in obtaining consistent muscle excitations. Significance. The outcomes potentially provide a novel and promising pathway for the robust and zero-retraining control of myoelectric interfaces.

Day-ahead optimal operation of active distribution networks with distributed generation and energy storage

Abstract In this work we address the optimal operation in active distribution networks (ADNs) with high penetration of renewable energies and energy storage. The optimal performance of ADNs can include two different optimization problems: Unit Commitment (UC) and Economic Dispatch (ED). The UC problem determines the start-up and shutdown planning of all the dispatchable generation units to supply the electricity demand, minimizing the total cost of operation, while the ED problem determines the active output power of each of the committed units for each hour of the planning horizon. Both problems have the objectives of minimizing the total cost, supplying the demand and complying with the restrictions of the main network. Here the two problems are solved together to achieve the day-ahead optimal operation of active distribution networks with distributed generation and energy storage. A test system based on the IEEE 33-bus distribution network was proposed. The optimal operation problem presented here is analyzed using four scenarios with different renewable generation and load conditions and a time-varying profile for the purchase price of energy from the network. The results reveal that the proposed network together with the optimization methodology can face diverse and highly demanding load situations, with the full use of renewable energies and complying with all the restrictions imposed. The proposed methodology is suitable for use in other optimization problems such as determining the sizing of storage units and distributed generation.

Abstract 9843: Dynamic Importance of Genomic and Nongenomic Risk Factors for Coronary Artery Disease Over the Life Course

Introduction: Current cardiovascular risk models are limited by fixed window estimates of risk rather than lifetime trajectories. We create a novel framework to understand the time-varying profiles of genomic and non-genomic risk factors over the life span. Methods: We examine 325,948 individuals from the UK Biobank (UKB) (57% female, enrollment age 40-70 years, mean follow-up 11.9 years) with baseline clinical risk factors, composite Pooled Cohort Equations (PCE), and coronary artery disease (CAD) polygenic risk score (PRS). We compare Cox hazard ratios (HR) and the proportion of variation explained (PVE) at each age of enrollment. We extend analyses to a younger cohort of 3,821 individuals from the Framingham Health Study (FHS) Offspring Cohort, (51% female, enrollment ages 18-57, mean follow-up 35.1 years). Results: Results: HR and PVE of CAD PRS are greater early in life with HR 2.47, [95% CI 2.45-2.50] at age 40 to HR 1.67 [1.64,1.70] at age 70 in UKB; PVE of 7.8% to 2.3% in UKB. Parallel results were observed in FHS (Fig 1A-C) earlier in the life span. While the variation in lifetime risk increases with time, the HR for single risk factors estimated at each age are dynamic. The PCE, via inclusion of age, absorb much of this variation. Reclassification of intermediate 10-year PCE risk (7.5-19.9%) using PRS is most informative in younger individuals. In the UKB, the top-quintile of PRS, when compared to PCE alone, accurately predicts more individuals under age 55 (1108 versus 844) who ultimately develop CAD, but loses superiority over age 55 (Fig. 1D). Conclusions: Depending on when the question is asked, the HR of CAD of both genomic and non-genomic risk factors changes dramatically, with PRS demonstrating superiority early on. Our novel framework incorporates time-varying effects in portraying risk trajectory rather than fixed-window estimates. We show that polygenic scores also predict lifetime risk of disease early on rather than predicting only early-onset disease.

Unsteady natural convection of nano-encapsulated phase change materials (NEPCMs) inside a random porous medium considering local thermal non-equilibrium condition

A local thermal non-equilibrium analysis is undertaken to simulate unsteady free convective heat transfer inside a square enclosure filled with a random porous medium. There is a time-varying temperature profile at the hot wall. The fluid is a suspension comprising nano-encapsulated phase change materials (NEPCM) particles dispersed in water while aluminum foam, copper foam, or glass balls are considered for the solid matrix. It is found that when the hot wall temperature reaches its minimum value during one oscillation period, most of the cavity is occupied by the NEPCM particles in the solidious phase. For the metallic foams, the heat transfer of the medium is substantially higher than the NEPCM particles. Meanwhile, the Nusselt number of the glass balls is much lower than the suspension. A rise in the dimensionless frequency or amplitude of the temperature profile as well as approaching the dimensionless fusion temperature to 0.5 elevates Nusselt number of the suspension. However, no general conclusion can be drawn for their consequences on the heat transfer in the solid matrix. Among the current alterable parameters, only the contributions of the non-dimensional interface heat transfer coefficient and the non-dimensional amplitude of the temperature profile are found significant.

Improving the melting performance of phase change materials using novel fins and nanoparticles in tubular energy storage systems

The present work proposes an integration of a novel fin structure and Al2O3 nanoparticles as an enhancement technology to improve the melting performance of phase change materials (PCMs) for latent heat thermal energy storage systems. A mathematical model of the melting process of PCMs with nanoparticles in a triple-tube heat exchanger is formulated and validated against the experimental data. The effect of different fin layouts and different volume fractions of nanoparticles on the melting process is discussed and reported, including the evolution and deformation of solid–liquid interfaces, the distribution of isotherms, and the time-varying profile of liquid fraction and average temperature over the entire melting process. The results indicate that the melting characteristic is improved by applying the enhanced strategies of novel fins and nanoparticles. Compared to the original structure, the melting time of four different novel fins is reduced by 80.35%, 77.62%, 77.33%, and 80.65%, respectively, which are attributed to the heat transfer enhancement by adding fin configurations to the system. Al2O3 nanoparticles (at 3%, 6%, and 9%) are integrated into the PCMs, and the results show that the melting time is decreased by 13.1%, 15.6%, and 18.8%, respectively. It can be concluded that the combination of fins and nanoparticles is an efficient way to enhance the meting process of phase change materials for thermal energy storage systems.

Novel Record Replacement Algorithm and Architecture for QoS Management over Local Area Networks.

An effective System-on-Chip (SoC) for smart Quality-of-Service (QoS) management over a virtual local area network (LAN) is presented in this study. The SoC is implemented by field programmable gate array (FPGA) for accelerating the delivery quality prediction for a service. The quality prediction is carried out by the general regression neural network (GRNN) algorithm based on a time-varying profile consisting of the past delivery records of the service. A novel record replacement algorithm is presented to update the profile, so that the bandwidth usage of the service can be effectively tracked by GRNN. Experimental results show that the SoC provides self-aware QoS management with low computation costs for applications over virtual LAN.

Efficient simulation of clinical target response surfaces.

Simulation of combination therapies is challenging due to computational complexity. Either a simple model is used to simulate the response for many combinations of concentration to generate a response surface but parameter variability and uncertainty are neglected and the concentrations are constant—the link to the doses to be administered is difficult to make—or a population pharmacokinetic/pharmacodynamic model is used to predict the response to combination therapy in a clinical trial taking into account the time‐varying concentration profile, interindividual variability (IIV), and parameter uncertainty but simulations are limited to only a few selected doses. We devised new algorithms to efficiently search for the combination doses that achieve a predefined efficacy target while taking into account the IIV and parameter uncertainty. The result of this method is a response surface of confidence levels, indicating for all dose combinations the likelihood of reaching the specified efficacy target. We highlight the importance to simulate across a population rather than focus on an individual. Finally, we provide examples of potential applications, such as informing experimental design.

Light transmission from a strongly coupled QD-Cavity system driven by a laser pulse

Abstract Cavity transmission and second-order coherence function of a quantum dot-cavity system strongly coupled and driven by a laser pulse were studied theoretically. The calculations were carried out by solving the master equation in the Lindblad form. A quantum dot (QD) modeled as a two-level system is assumed to interact with a single-mode quantized electromagnetic field following the Jaynes-Cummings model. Furthermore, a classic pulse with a time-varying profile is considered to interact with the cavity mode. Lindbladian includes spontaneous emission losses, cavity decay and, pure QD dephasing. Cavity transmission and second-order coherence were studied as functions of time considering various pulse shapes and different values of full width at half maximum (FWHM), pulse intensity, and pure QD dephasing. We found that the cavity transmission shows an oscillatory behavior, which is a sign of strong coupling in the system. The pulses considered have similar effects on the cavity transmission. According to the values obtained for the second-order coherence function, the QD-cavity system strongly coupled and excited with classical light works as a quantum light source that exhibits sub-Poissonian statistics and photon bunching phenomenon for all pulses.

Efference copy in kinesthetic perception: a copy of what is it?

A number of notions in the fields of motor control and kinesthetic perception have been used without clear definitions. In this review, we consider definitions for efference copy, percept, and sense of effort based on recent studies within the physical approach, which assumes that the neural control of movement is based on principles of parametric control and involves defining time-varying profiles of spatial referent coordinates for the effectors. The apparent redundancy in both motor and perceptual processes is reconsidered based on the principle of abundance. Abundance of efferent and afferent signals is viewed as the means of stabilizing both salient action characteristics and salient percepts formalized as stable manifolds in high-dimensional spaces of relevant elemental variables. This theoretical scheme has led recently to a number of novel predictions and findings. These include, in particular, lower accuracy in perception of variables produced by elements involved in a multielement task compared with the same elements in single-element tasks, dissociation between motor and perceptual effects of muscle coactivation, force illusions induced by muscle vibration, and errors in perception of unintentional drifts in performance. Taken together, these results suggest that participation of efferent signals in perception frequently involves distorted copies of actual neural commands, particularly those to antagonist muscles. Sense of effort is associated with such distorted efferent signals. Distortions in efference copy happen spontaneously and can also be caused by changes in sensory signals, e.g., those produced by muscle vibration.

Experimental cheap talk games: strategic complementarity and coordination

This paper investigates experimentally the effects of communication in distinct games with complete information. We design four games resulting from the interaction between two incentive elements: strategic complementarity and coordination. These incentive elements allow to analyse the use of cheap talk as an efficiency-enhancing and coordinating device. We implement a restricted communication protocol (one-sided, optional, and closed-form) in repeated settings with fixed partners. Our findings provide robust evidence about how cheap talk interacts with incentives to explain strategic behaviour in a dynamic way. As expected, cheap talk increases efficiency under complementarity incentives, and the coordination level under coordination incentives. As novelty, the use of limited communication in repeated interactions has led to identify specific time-varying message profiles as the most effective messages in the coordination games. While the content of messages is explained by the complementarity incentives, faithfulness to credible messages is determined by the coordination incentives.

Initial beliefs uncertainty

This paper evaluates how initial beliefs uncertainty can affect data weighting and the estimation of models with adaptive learning. One key finding is that misspecification of initial beliefs uncertainty, particularly with the common approach of artificially inflating initials uncertainty to accelerate convergence of estimates, generates time-varying profiles of weights given to past observations in what should otherwise follow a fixed profile of decaying weights. The effect of this misspecification, denoted as diffuse initials, is shown to distort the estimation and interpretation of learning in finite samples. Simulations of a forward-looking Phillips curve model indicate that (i) diffuse initials lead to downward biased estimates of expectations relevance in the determination of actual inflation, and (ii) these biases spill over to estimates of inflation responsiveness to output gaps. An empirical application with U.S. data shows the relevance of these effects for the determination of expectational stability over decadal subsamples of data. The use of diffuse initials is also found to lead to downward biased estimates of learning gains, both estimated from an aggregate representative model and estimated to match individual expectations from survey expectations data.

On the Walén Relation for Alfvénic Fluctuations in Interplanetary Space

Abstract In interplanetary space, the majority of the Alfvénic fluctuations cannot satisfy the Walén relation even though plasma and magnetic field fluctuations are correlated very well. Finding a proper de Hoffman–Teller (HT) wave frame velocity is thus important for characterizing the Alfvénic fluctuations. Recognizing that the Alfvénic fluctuations emanating from different sizes of solar wind streams have their own HT frames, we propose a new scheme called the wave frame with varying velocity (WFVV) method by considering the local averaged HT frame to find a time-varying profile of HT frame velocity. We apply this WFVV method to two Alfvénic events on 2002 October 14 and 17. Unlike the three other schemes discussed in this study, the WFVV method is preferable because it can provide the HT frame variation in more detail, particularly for the large solar wind changes associated with directional discontinuities observed in the October 14 event. For such Alfvénic structures, a time-dependent HT frame derived from the WFVV method, rather than a constant HT frame, produces a better result of the Walén test together with a smaller convection electric field. The difference of estimated HT frame velocities among different schemes was as large as 30 km s−1 for these two events. In addition, we noted that the degree of Alfvénicity tends to decrease with an increasing variance of HT frame velocity, suggesting that a constant HT frame will also give good results for cases of a small variance of HT frame velocity.

Time-resolved temperature profile measurements in the exhaust of a single sector gas turbine combustor at realistic operating conditions

Records of the time-varying temperature profile at flight relevant operating conditions are acquired at the exit of a combustion chamber fitted with a staged, lean-burn fuel injector using high-speed laser induced fluorescence (LIF) at a sample rate of 10 kHz. Temperatures are estimated from the concentration dependent fluorescence of the hydroxyl (OH) radical under the assumption of local equilibrium. Beyond the time-series analysis, the acquired data is correlated with simultaneously acquired OH chemiluminescence sampled in the primary zone near the fuel injector. These analyses reveal a strong influence from the precessing vortex core, originating in the primary zone, on oscillations in the temperature profiles measured at the exit of the combustor.Graphic abstract

P0005TIME-VARYING DIALYSATE BICARBONATE CONCENTRATIONS CAN REDUCE PEAK INTRADIALTIC SERUM BICARBONATE CONCENTRATIONS

Abstract Background and Aims Hemodialysis (HD) treatments using bicarbonate-containing dialysis solutions can result in large intradialytic increases in serum bicarbonate concentration, potentially inducing intradialytic alkalemia. It has been suggested that a time-varying, compared with a constant, dialysate bicarbonate concentration may limit the intradialytic increase in serum bicarbonate concentration (Tobvin & Sherman, Semin Dial 2016). We tested this hypothesis using a mathematical model of bicarbonate transport during HD. Method We used the H+ mobilization model describing bicarbonate transport during HD (Sargent et al, Semin Dial 2018) to compare intradialytic serum bicarbonate concentrations when using constant or time-varying dialysate bicarbonate concentrations that deliver the same total amount of buffer base to the patient during the HD treatment. We employed this model to evaluate different time-varying dialysate bicarbonate concentration profiles that started at a high value and then decreased as a step function with a 10-minute timing resolution. Dialysis time was 210 minutes, dialysis solutions were assumed to contain acetate at 3 mEq/L, and all kinetic parameters were assumed to be identical to those reported by Sargent et al (Semin Dial 2018). All results with time-varying dialysate bicarbonate concentrations were compared to a constant dialysate concentration of 32 mEq/L. Results Example results comparing time-varying (36.0 mEq/L for the initial 40 min, 31.2 mEq/L thereafter) and constant (32 mEq/L) dialysate bicarbonate concentrations are shown in the figure. The time-varying dialysate bicarbonate concentration lowered the peak intradialytic serum bicarbonate by 0.4 mEq/L for approximately one-half of the treatment. Similar reductions in the peak intradialytic serum bicarbonate concentration could be achieved if the initial high dialysate bicarbonate concentration was 37.6 mEq/L for 30 min or 40.8 for 20 min. The optimal initial high dialysate bicarbonate concentrations and the reduction in the peak intradialytic serum bicarbonate concentrations were somewhat dependent on the assumed patient-dependent H+ mobilization coefficient. Conclusion We conclude that a time-varying dialysate bicarbonate concentration can lower the peak intradialytic serum bicarbonate concentrations while delivering the same total amount of buffer base to the patient. Whether this approach will yield improved patient outcomes requires further evaluation.

Unsupervised machine learning via Hidden Markov Models for accurate clustering of plant stress levels based on imaged chlorophyll fluorescence profiles & their rate of change in time

As far back as 1931, studies have shown that Chlorophyll a fluorescence (ChlF) is a useful tool in plant stress detection. Early and accurate detection of plant stress is invaluable in enabling appropriate and timely intervention. One of the major limitations of past work on ChIF-based plant stress identification is that, often, only a small number of commonly used inflection points, locally oriented within the ChlF transient curve, are utilized to calculate a single index value. These singular values offer limited insight into stressor level or stressor type. In this work, we present an unsupervised method for plant stress classification (identification) that utilizes global (versus local) time-varying ChlF signal data obtained via plant video imaging. The contributions of this work are multi-fold: (a) We classify via clustering these time-varying-intensity signals using unsupervised learning via Hidden Markov Models (HMMs). We show how: (b) the proposed globally based feature selection of a plant’s entire ChlF signal profile, via low-pass filtering, can, in some scenarios, improve classification accuracy of plant stress; (c) the rate-of-change-in-time of the plant’s ChlF intensity time-varying profile, as an additional global feature selection, can further improve the plant stress classification accuracy in some scenarios; (d) quantification can improve the proposed HMM classification method in certain scenarios; (e) HMMs allow more variability in categorizing data via clustering than other raw data distance based metrics. (f) We explore the ergodic and Bakis models for HMM state transition matrix initialization. (g) In addition we propose a new method for initialization of the HMM state transition matrix: state information based initial probability assignment (SIPA) and compare it to the often used heuristic initial probability assignment (HIPA) method. (h) We show how using the Bayesian Information Criterion (BIC) as a performance metric allows a good state number selection (using the quantified data with the proposed state transition matrix initialization methods). (i) We present a method to automatically determine the number of existing classes in the data set. Note this is in contrast to existing unsupervised learning methods, where the number of existing classes in the data set is, in general, guessed a priori, and often incorrectly. (j) In conclusion we show experimental results to illustrate the potential and value of the proposed methods to enable more specific and accurate classification of plant stressor levels in some scenarios, and (k) in some instances, stressor types.

Information Weighting Under Least Squares Learning

This paper evaluates how adaptive learning agents weight different pieces of information when forming expectations with a recursive least squares algorithm. The analysis is based on a renewed and more general non-recursive representation of the learning algorithm, namely, a penalized weighted least squares estimator, where a penalty term accounts for the effects of the learning initials. The paper then draws behavioral implications of alternative specifications of the learning mechanism, such as the cases with decreasing, constant, regime-switching, adaptive, and age-dependent gains, as well as practical recommendations on their computation. One key new finding is that without a proper account for the uncertainty about the learning initial, a constant-gain can generate a time-varying profile of weights given to past observations, particularly distorting the estimation and behavioral interpretation of this mechanism in small samples of data. In fact, simulations and empirical estimation of a Phillips curve model with learning indicate that this particular misspecification of the initials can lead to estimates where inflation rates are less responsive to expectations and output gaps than in reality, or “flatter” Phillips curves.

Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective.

The potential offered by the abundance of sensors, actuators, and communications in the Internet of Things (IoT) era is hindered by the limited computational capacity of local nodes. Several key challenges should be addressed to optimally and jointly exploit the network, computing, and storage resources, guaranteeing at the same time feasibility for time-critical and mission-critical tasks. We propose the DRUID-NET framework to take upon these challenges by dynamically distributing resources when the demand is rapidly varying. It includes analytic dynamical modeling of the resources, offered workload, and networking environment, incorporating phenomena typically met in wireless communications and mobile edge computing, together with new estimators of time-varying profiles. Building on this framework, we aim to develop novel resource allocation mechanisms that explicitly include service differentiation and context-awareness, being capable of guaranteeing well-defined Quality of Service (QoS) metrics. DRUID-NET goes beyond the state of the art in the design of control algorithms by incorporating resource allocation mechanisms to the decision strategy itself. To achieve these breakthroughs, we combine tools from Automata and Graph theory, Machine Learning, Modern Control Theory, and Network Theory. DRUID-NET constitutes the first truly holistic, multidisciplinary approach that extends recent, albeit fragmented results from all aforementioned fields, thus bridging the gap between efforts of different communities.