Smooth Function Of Time Research Articles

Assessing heterogeneity in treatment initiation guidelines in longitudinal randomized controlled trials

Treatment initiation guidelines are essential in healthcare, dictating when patients begin therapy. These guidelines are typically assessed through randomized controlled trials (RCTs) to measure their average effect on a population. However, this method may not fully account for patient heterogeneity. We introduce a refined analysis methodology that accounts for diverse times to treatment initiation (TTI) arising from these guidelines. We offer a more detailed perspective on the guidelines’ impact by analyzing homogeneous subpopulations based on their TTI. We develop a longitudinal regression model with smooth time functions to capture dynamic changes in average guideline effects on subpopulations (AGES). A unique weighting mechanism creates pseudo-subpopulations from RCT data, enabling consistent and precise estimation of smooth functions. The efficacy of our approach is validated through theoretical and numerical studies, underscoring its capacity to provide insightful statistical inferences. We exemplify the utility of our methodology by applying it to an RCT of the World Health Organization (WHO) guideline for adults with HIV. This analysis promises to enhance the evaluation of treatment initiation guidelines, leading to more personalized and efficient patient care.

The effect of an annual temporary abstinence campaign on population-level alcohol consumption in Thailand: a time-series analysis of 23 years

RationaleA small number of earlier studies have suggested an effect of temporary abstinence campaigns on alcohol consumption. However, all were based on self-reported consumption estimates.ObjectivesUsing a time series of 23-year...

17th International Conference on Malignant Lymphoma, Palazzo dei Congressi, Lugano, Switzerland, 13 - 17 June, 2023.

Background: In mantle cell lymphoma (MCL), time to relapse is a critical factor, and patients experiencing disease progression within 24 months from treatment start (POD24) have inferior survival. However, the cut-off at 24 months is arbitrary and potentially overly simplified. We aimed to quantify the impact of the timing of disease progression on overall survival, stratified by the most common first-line treatment concepts (Nordic-MCL, R-Bendamustin, and R-CHOP). Methods: Using the population-based Swedish lymphoma register, we identified all systemically treated MCL patients diagnosed between 2006 and 2018. We supplemented this with information on progression and relapse from medical chart reviews through 2022. Patients were categorized based on first-line treatment (Nordic MCL regimen, R-Bendamustin, R-CHOP, and others). POD was defined as a lack of response to primary therapy (progressive disease [PD]) or an initial response followed by a relapse. To assess the impact of POD timing on survival, we used an illness-death model with transition rates estimated using flexible parametric survival models to predict the five-year overall survival (OS) conditional on either being progression-free or experiencing POD as a smooth function of time since primary treatment. Results: A total of 1193 patients receiving systemic treatment were included. The median age at diagnosis was 70 years, and patients were followed for a median of 4 years (range 0–16 years, alive patients, six years). 33% of patients received R-Bendamustin (n = 387, median age 75 years), 30% received the Nordic MCL regimen (n = 351, median age 62 years), and 23% received R-CHOP (n = 276, median age 72 years). Almost half of the patients (48%, n = 571) experienced POD during follow-up. The five-year OS from treatment start was 47%, and progression-free survival was 32%. Among patients treated in the first line with either R-Bendamustin or the Nordic MCL protocol, those with a POD had substantially lower 5-year OS compared to patients who remained progression-free, also for POD occurring up to six years after primary treatment. For patients treated with R-CHOP, the impact of POD was largest if it occurred within 2–3 years after first-line therapy. Keyword: Aggressive B-cell non-Hodgkin lymphoma No conflicts of interests pertinent to the abstract.

A theoretical framework for nonlinear dynamics in finance systems: Implications for stability, control and synchronization

It has been widely observed that most deterministic dynamical systems go into chaos for some values of their parameters. One of the most popular and widely used criteria is the conditional Lyapunov exponents, which constitute average measurements of expansion or shrinkage of small displacements along the synchronized trajectory. The Lyapunov characteristic exponents play a crucial role in the description of the behaviour of dynamical systems as they can be used to analyse the stability limit sets and to check sensitive dependence on initial conditions, that is, the presence of chaotic attractors. In this paper, Lyapunov stability theory and linear matrix inequalities (LMI) are employed to design control functions for the respective, control, and synchronization of the chaotic and hyperchaotic finance systems. The designed linear matrix inequalities (LMI) nonlinear controllers are capable of stabilizing the chaotic and hyperchaotic finance systems at any position as well as controlling it to track any trajectory that is a smooth function of time. The respective chaotic attractors were found to have a moderate value of the largest Lyapunov exponents (0.874959 s^(-1) and 0.650847 s^(-1)) with associated (Lyapunov) dimensions of 1.00 and 2.00 for the chaotic and hyperchaotic finance systems respectively. Based on Lyapunov stability theory and linear matrix inequalities (LMI), some necessary and sufficient criteria for stable synchronous behaviour are obtained and an exact analytic estimate of the threshold coupling, k_th, for complete chaos synchronization is derived. Finally, numerical simulation results are presented to validate the feasibility of the theoretical analysis.

A Flexible Statistical Framework for Estimating Excess Mortality.

Quantifying the impact of natural disasters or epidemics is critical for guiding policy decisions and interventions. When the effects of an event are long-lasting and difficult to detect in the short term, the accumulated effects can be devastating. Mortality is one of the most reliably measured health outcomes, partly due to its unambiguous definition. As a result, excess mortality estimates are an increasingly effective approach for quantifying the effect of an event. However, the fact that indirect effects are often characterized by small, but enduring, increases in mortality rates present a statistical challenge. This is compounded by sources of variability introduced by demographic changes, secular trends, seasonal and day of the week effects, and natural variation. Here, we present a model that accounts for these sources of variability and characterizes concerning increases in mortality rates with smooth functions of time that provide statistical power. The model permits discontinuities in the smooth functions to model sudden increases due to direct effects. We implement a flexible estimation approach that permits both surveillance of concerning increases in mortality rates and careful characterization of the effect of a past event. We demonstrate our tools' utility by estimating excess mortality after hurricanes in the United States and Puerto Rico. We use Hurricane Maria as a case study to show appealing properties that are unique to our method compared with current approaches. Finally, we show the flexibility of our approach by detecting and quantifying the 2014 Chikungunya outbreak in Puerto Rico and the COVID-19 pandemic in the United States. We make our tools available through the excessmort R package available from https://cran.r-project.org/web/packages/excessmort/.

Anomalous structural recovery in the near glass transition range in a polymer glass: Data revisited in light of temperature variability in vacuum oven‐based experiments*

AbstractThere is considerable interest in data reported by Cangialosi et al. [Cangialosi, D., Boucher, V. M., Alegría, A., & Colmenero, J. (2013). Physical Review Letters, 111(9), 095701] that purportedly showed unusual irregular responses in the isothermal structural recovery behavior of glasses because the observation challenges the general view of the dynamics being smooth functions of time as observed in extensive dilatometric experiments by Kovacs [Kovacs, A. J. (1964). In Fortschritte der Hochpolymeren‐Forschung, 3, 394–507. Springer, Berlin, Heidelberg.] in the 1960s. The reported data show an apparent two mechanism structural (enthalpy) recovery at aging temperatures ranging from 6 to 17 K below which is also controversial as it could not be reproduced in work from Koh and Simon [Koh, Y. P., & Simon, S. L. (2013). Macromolecules, 46(14), 5815–5821.] where a second plateau observed in the enthalpy loss curve was not reproduced in nearly 1‐year‐long experiments at 15 K below . Therefore, it is important to determine what might be possible reasons for the apparent non‐smooth relaxations. Here, we examine the possibility that the poor temperature control of a typical vacuum oven could explain the anomalous results. We used the Tool–Narayanaswamy–Moynihan (TNM) model of structural recovery to calculate the effect of typical vacuum oven temperature variation on the structural recovery of polystyrene and find that we can reproduce the reported experimental results. The issue of temperature control using a vacuum oven is discussed, and data from thermocouple measurements of temperature at various locations inside a vacuum oven are shown.

Optimal work associated with off-centered harmonic Brownian motion at any friction damping.

There is extensive literature on how to determine the work involving a Brownian particle interacting with an external field and submerged in a thermal reservoir. However, the information supplied is essentially theoretical without specific calculations to show how this property changes with the system parameters and initial conditions. In this article, we provide explicit calculations of the optimal work considering the particle is under the influence of a time-dependent off-centered moving harmonic potential. It is done for all physical values of the friction coefficient. The system is modeled through a more general version of the Langevin equation which encompasses its classical and quasiclassical version. From the equation that defines the work, the external protocol is found through a fairly current extended version of the Euler-Lagrange equation that unifies the local and nonlocal contributions in a simple expression. The protocol is linear and, unlike previous work, not only changes the initial velocity of the particle but also its acceleration. Calculations were done for friction constants γ spanning all possible values. The periodic γ=1 shows discontinuities in the optimal work of the interplay of concentration and diffusion processes acting periodically in the dynamics. For higher values work appears to be as a smooth function of time, while the truly overdamped, where the inertial effect can be discarded, agrees with the analytical result up to a time where the numerical overdamped algorithm provides a different solution due to its inability to discard entirely the inertial effect.

Capillary fluctuations and energy dynamics for flow in porous media

Capillary energy barriers have important consequences for immiscible fluid flow in porous media. We derive a time-and-space averaging theory to account for the non-equilibrium behavior and understand the role of athermal capillary fluctuations in the context of their relationship to larger scale phenomenological equations. The formulation resolves several key challenges associated with two-fluid flow in porous media: (1) geometric and thermodynamic quantities are constructed as smooth functions of time based on time-and-space averages; (2) averaged thermodynamics are developed for films; (3) multi-scale fluctuation terms are identified, which account for transient behaviors of interfaces and films that occur due to pore-scale events; (4) geometric constraints are derived and imposed on the averaged thermodynamics; (5) a new constitutive model is proposed for capillary pressure dynamics that includes contributions from films; and (6) a time-and-space criterion for representative elementary volume is established based on capillary fluctuations. Capillary fluctuations are assessed quantitatively based on pore-scale simulations and experimental core-flooding data.

CMC foliations of open spacetimes asymptotic to open Robertson–Walker spacetimes

We consider open globally hyperbolic spacetimes $N$ of dimension $n+1$, $n\ge 3$, which are spatially asymptotic to a Robertson-Walker spacetime or an open Friedmann universe with spatial curvature $\tilde\kappa = 0,-1$ and prove, under reasonable assumptions, that there exists a unique foliation by hypersurfaces of constant mean curvature and that the mean curvature function $\tau$ is a smooth time function if $N$ is smooth. Moreover, among the Friedmann universes which satisfy the necessary conditions are those that reflect the present assumptions of the development of the universe.

Local-Linear Estimation of Time-Varying-Parameter GARCH Models and Associated Risk Measures

Abstract In this article, we propose a nonparametric approach to estimating generalized autoregressive conditional heteroskedasticity (1,1) models with time-varying parameters. We model the time-varying parameters as a smooth function of time and estimate them using a local linear estimator. We show that our estimator is consistent and is asymptotically normal and that the proposed estimator outperforms a rolling window estimator in Monte Carlo simulation experiments. We present strong evidence of parameter instabilities using daily returns of stock indices and explore implications to risk management measures, such as value-at-risk and expected shortfall, through backtesting.

A mixed-frequency smooth measure for business conditions

We propose measuring business conditions via estimating a smooth function of time that serves as a common factor for explaining the comovement of economic indicators across the occurred business cycles. This smooth measure is useful for reducing the noises in assessing the state of business conditions, and can be easily established using mixed-frequency indicators and updated in real time. We also conduct an empirical study to show its usefulness in real data.

Air pollution epidemiology: A simplified Generalized Linear Model approach optimized by bio-inspired metaheuristics

Studies in air pollution epidemiology are of paramount importance in diagnosing and improve life quality. To explore new methods or modify existing ones is critical to obtain better results. Most air pollution epidemiology studies use the Generalized Linear Model, especially the default version of R, Splus, SAS, and Stata softwares, which use maximum likelihood estimators in parameter optimization. Also, a smooth time function (usually spline) is generally used as a pre-processing step to consider seasonal and long-term tendencies. This investigation introduces a new approach to GLM, proposing the estimation of the free coefficients through bio-inspired metaheuristics - Particle Swarm Optimization (PSO), Genetic Algorithms, and Differential Evolution, as well as the replacement of the spline function by a simple normalization procedure. The considered case studies comprise three important cities of São Paulo state, Brazil with distinct characteristics: São Paulo, Campinas, and Cubatão. We considered the impact of particles with an aerodynamic diameter less than 10 μm (PM10), ambient temperature, and relative humidity in the number of hospital admissions for respiratory diseases (ICD-10, J00 to J99). The results showed that the new approach (especially PSO) brings performance gains compared to the default version of statistical software like R.

Quantum quench, large N, and symmetry restoration

We globally quench the theory of two dimensional massless fermions (many flavours) with quartic interactions by making the quartic coupling a smooth function of time. Working in a derivative expansion we show that the discrete ℤ2 symmetry in case of the Gross-Neveu model, and the U (1) symmetry in case of the Nambu-Jona-Lasinio2 model, are restored during the zero-temperature quench. For the Gross-Neveu model we show that this can be understood as an effective thermalization. The time of symmetry restoration shows scaling with the quench rate. We identify this with the Kibble-Zurek scaling in the problem. In a suitable double scaling limit, the symmetry restoration may be understood in terms of Liouville quantum mechanics.

The MUSEHubbleUltra Deep Field Survey

Context.The Lyαemitter (LAE) fraction,XLAE, is a potentially powerful probe of the evolution of the intergalactic neutral hydrogen gas fraction. However, uncertainties in the measurement ofXLAEare still under debate.Aims.Thanks to deep data obtained with the integral field spectrograph Multi Unit Spectroscopic Explorer (MUSE), we can measure the evolution of the LAE fraction homogeneously over a wide redshift range ofz ≈ 3–6 for UV-faint galaxies (down to UV magnitudes ofM1500 ≈ −17.75). This is a significantly fainter range than in former studies (M1500 ≤ −18.75) and it allows us to probe the bulk of the population of high-redshift star-forming galaxies.Methods.We constructed a UV-complete photometric-redshift sample following UV luminosity functions and measured the Lyαemission with MUSE using the latest (second) data release from the MUSEHubbleUltra Deep Field Survey.Results.We derived the redshift evolution ofXLAEforM1500 ∈ [ − 21.75; −17.75] for the first time with a equivalent width rangeEW(Lyα) ≥ 65 Å and found low values ofXLAE ≲ 30% atz ≲ 6. The best-fit linear relation isXLAE= 0.07+0.06−0.03z− 0.22+0.12−0.24. ForM1500 ∈ [ − 20.25; −18.75] andEW(Lyα) ≥ 25 Å, ourXLAEvalues are consistent with those in the literature within 1σatz ≲ 5, but our median values are systematically lower than reported values over the whole redshift range. In addition, we do not find a significant dependence ofXLAEonM1500forEW(Lyα) ≥ 50 Å atz ≈ 3–4, in contrast with previous work. The differences inXLAEmainly arise from selection biases for Lyman Break Galaxies (LBGs) in the literature: UV-faint LBGs are more easily selected if they have strong Lyαemission, henceXLAEis biased towards higher values when those samples are used.Conclusions.Our results suggest either a lower increase ofXLAEtowardsz ≈ 6 than previously suggested, or even a turnover ofXLAEatz ≈ 5.5, which may be the signature of a late or patchy reionization process. We compared our results with predictions from a cosmological galaxy evolution model. We find that a model with a bursty star formation (SF) can reproduce our observed LAE fractions much better than models where SF is a smooth function of time.

Modeling the temporal network dynamics of neuronal cultures.

Neurons form complex networks that evolve over multiple time scales. In order to thoroughly characterize these networks, time dependencies must be explicitly modeled. Here, we present a statistical model that captures both the underlying structural and temporal dynamics of neuronal networks. Our model combines the class of Stochastic Block Models for community formation with Gaussian processes to model changes in the community structure as a smooth function of time. We validate our model on synthetic data and demonstrate its utility on three different studies using in vitro cultures of dissociated neurons.

PROBLEMS OF DIFFERENTIAL AND TOPOLOGICAL DIAGNOSTICS. PART 5. THE CASE OF TRAJECTORIAL MEASUREMENTS WITH ERROR

Proposed work is the fifth work of the cycle on differential and topological diagnostics. The article gives an estimate of the errors method of direction fields in the case of not accurate trajectory measurements, but trajectory measurements with an error are limited by the modulus of a given a smooth function of time, and in case this error is a random variable distributed according to the normal law with fixed parameters. We show that in these more complex cases, you can specify the best number of required trajectory measurements, inwhich the proposed algorithms of diagnostics will work constructively, and the malfunction will be determined unambiguously.

GMPP tracking based on model reference LPV control for a PV system with buck converter modelled on bond graph

AbstractA PhotoVoltaic (PV) system with a Linear Parametric Varying (LPV) average model of a buck converter modelled by bond graph methodology, is considered. It is assumed that the voltage generated by the PV panel is a smooth function of time under changes in atmospheric conditions. A model reference is proposed representing the PV system with buck converter operating at the Global Maximum Power Point (GMPP). An LPV control is designed, and an algorithm is proposed in a model reference tracking control configuration assuring that the PV system with buck converter tracks the proposed model reference and hence guaranteeing that this system operates at the GMPP. The proposed algorithm changes the voltage reference based on the power measurement of the PV system assuring that this reference represents the GMPP under shading conditions. The LPV control is designed for an average model of the buck converter solving a feasibility problem based on linear matrix inequalities that assure quadratic stability and minimize a quadratic criterion. The feedback system is tested under shading conditions, and its performance is compared with a standard Perturb and Observe method and with an ideal algorithm based on the irradiance measurement in each section of the PV system.

Chaotic financial system with uncertain parameters - its control and synchronisation

This paper examines the control and synchronisation of the chaotic financial system with uncertain parameters as well as known parameters using adaptive control technique. The designed adaptive controller control and globally synchronises two identical chaotic financial systems evolving from different initial conditions. The designed controller is capable of stabilising the financial system at any position as well as controlling it to track any trajectory that is a smooth function of time. Numerical simulations are presented to demonstrate the feasibility of the proposed schemes.

Modelling Palaeoecological Time Series Using Generalised Additive Models

In the absence of annual laminations, time series generated from lake sediments or other similar stratigraphic sequences are irregularly spaced in time, which complicates formal analysis using classical statistical time series models. In lieu, statistical analyses of trends in palaeoenvironmental time series, if done at all, have typically used simpler linear regressions or (non-) parametric correlations with little regard for the violation of assumptions that almost surely occurs due to temporal dependencies in the data or that correlations do not provide estimates of the magnitude of change, just whether or not there is a linear or monotonic trend. Alternative approaches have used LOESS-estimated trends to justify data interpretations or test hypotheses as to the causal factors without considering the inherent subjectivity of the choice of parameters used to achieve the LOESS fit (e.g. span width, degree of polynomial). Generalized additive models (GAMs) are statistical models that can be used to estimate trends as smooth functions of time. Unlike LOESS, GAMs use automatic smoothness selection methods to objectively determine the complexity of the fitted trend, and as formal statistical models, GAMs, allow for potentially complex, non-linear trends, a proper accounting of model uncertainty, and the identification of periods of significant temporal change. Here, I present a consistent and modern approach to the estimation of trends in palaeoenvironmental time series using GAMs, illustrating features of the methodology with two example time series of contrasting complexity; a 150-year bulk organic matter δ15N time series from Small Water, UK, and a 3000-year alkenone record from Braya-So, Greenland. I discuss the underlying mechanics of GAMs that allow them to learn the shape of the trend from the data themselves and how simultaneous confidence intervals and the first derivatives of the trend are used to properly account for model uncertainty and identify periods of change. It is hoped that by using GAMs greater attention is paid to the statistical estimation of trends in palaeoenvironmental time series leading to more a robust and reproducible palaeoscience.

Chaos control in biological system using recursive backstepping sliding mode control

This paper puts forward the control of chaos in the biological system. A new controller based on recursive backstepping sliding mode control is proposed such that it can control the chaotic dynamics in the biological system to stabilize at any position or to track any trajectory that is a smooth function of time. A proportional integral switching surface is proposed to achieve the stability condition of the error dynamics. Unlike the open loop and open plus closed loop control techniques, the design of proposed controller does not require the parameter perturbation. The required stability condition is derived based on Lyapunov stability theory. Simulation is achieved in MATLAB environment. Numerical simulation results are presented in order to show the effective verification of the proposed controller design. Simulation results correspond that the objective of chaos control is achieved successfully.