Initial Conditions Research Articles

Elderly predialysis care pathways and early morbidity-mortality upon start of dialysis.

The ageing of the population with advanced chronic kidney disease (CKD) increases the complexity of care pathways. Our aim was to identify subgroups of older people according to predialysis care pathways and describe their association with early morbidity-mortality after transition to dialysis. This study included 22,128 incident dialysis patients aged ≥ 75 years during 2009-2017 from the French nationwide registry linked to the National Health Data System. Predialysis care pathways were identified by ascending hierarchical classification based on preselected healthcare use indicators in the previous year. Their association with a composite outcome of death or hospitalization ≥ 50% of the time off dialysis within the first year of dialysis was studied by multivariable logistic regression accounting for demographics, comorbidities, functional status, conditions of dialysis initiation, socioeconomic deprivation index, and home-to-dialysis center travel time. Five care pathway profiles were identified, characterized by limited healthcare use (cluster 1, 28%), non-nephrology ambulatory care (cluster 2, 17%), nephrology ambulatory care (cluster 3, 37%), and a high level of non-nephrology or nephrology hospitalizations (clusters 4 and 5, both 9%). Profile subgroups did not differ according to patient age and comorbidities, but clusters 1, 2 and 4 displayed higher levels of social deprivation. Compared to cluster 3, the odds ratios of primary composite outcome were significantly increased for clusters 1, 4, and 5 (OR (95% CI) of 1.16 (1.08-1.25), 1.17 (1.05-1.32), and 1.12 (1.01-1.25) respectively). Moreover, prolonged hospitalizations were also more common in all groups, compared to cluster 3. Despite similar comorbidity profile, older people with advanced CKD experience very heterogeneous predialysis care pathways, some of which associated with higher burden of hospitalization after the transition to dialysis.

Theoretical Formula of the Critical Hydraulic Condition of Suffusion Initiation

Theoretical Formula of the Critical Hydraulic Condition of Suffusion Initiation

Preassigned Time Adaptive Neural Tracking Control for Stochastic Nonlinear Multiagent Systems With Deferred Constraints.

This article studies a preassigned time adaptive tracking control problem for stochastic multiagent systems (MASs) with deferred full state constraints and deferred prescribed performance. A modified nonlinear mapping is designed, which incorporates a class of shift functions, to eliminate the constraints on the initial value conditions. By virtue of this nonlinear mapping, the feasibility conditions of the full state constraints for stochastic MASs can also be circumvented. In addition, the Lyapunov function codesigned by the shift function and the fixed-time prescribed performance function is constructed. The unknown nonlinear terms of the converted systems are handled based on the approximation property of the neural networks. Furthermore, a preassigned time adaptive tracking controller is established, which can achieve deferred prescribed performance for stochastic MASs that provide only local information. Finally, a numerical example is given to demonstrate the effectiveness of the proposed scheme.

Adaptive Fuzzy Tracking Control With Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear Systems.

For strict-feedback systems with mismatched uncertainties, adaptive fuzzy control techniques are developed to provide global prescribed performance with prescribed-time convergence. First, a class of prescribed-time prescribed performance functions are designed to quantify the performance constraints of the tracking error. Additionally, a novel error transformation function is provided to eliminate the initial value limitations and resolve the singularity issue in previous research. To ensure the convergence of the tracking error into a prescribed bounded region within a prescribed time and satisfactory transient performance, controllers with or without approximating structures are established. Notably, the settling time and initial condition of the prescribed performance function are completely independent of the initial tracking error and system parameters, thereby improving upon existing results. Furthermore, the disadvantage of the semi-global boundedness of tracking error induced by dynamic surface control can be eliminated through the use of a novel Lyapunov-like energy function. Finally, the effectiveness of the proposed strategies is validated through numerical simulations performed on practical examples.

Sliding Mode Control Based on Reinforcement Learning for T-S Fuzzy Fractional-Order Multiagent System With Time-Varying Delays.

This article researches the sliding mode control (SMC) for fuzzy fractional-order multiagent system (FOMAS) subject to time-varying delays over directed networks based on reinforcement learning (RL), α ∈ (0,1) . First, since there is information communication between an agent and another agent, a new distributed control policy ξi(t) is introduced so that the sharing of signals is implemented through RL, whose propose is to minimize the error variables with learning. Then, different from the existed papers studying normal fuzzy MASs, a new stability basis of fuzzy FOMASs with time-varying delay terms is presented to guarantee that the states of each agent eventually converge to the smallest possible domain of 0 using Lyapunov-Krasovskii functionals, free weight matrix, and linear matrix inequality (LMI). Furthermore, in order to provide appropriate parameters for SMC, the RL algorithm is combined with SMC strategy, and the constraints on the initial conditions of the control input ui(t) are eliminated, so that the sliding motion satisfy the reachable condition within a finite time. Finally, to illustrate that the proposed protocol is valid, the results of the simulation and numerical examples are presented.

End-to-End Statistical Model Checking for Parameterization and Stability Analysis of ODE Models

We propose a simulation-based technique for the parameterization and the stability analysis of parametric Ordinary Differential Equations. This technique is an adaptation of Statistical Model Checking, often used to verify the validity of biological models, to the setting of Ordinary Differential Equations systems. The aim of our technique is to estimate the probability of satisfying a given property under the variability of the parameter or initial condition of the ODE, with any metrics of choice. To do so, we discretize the values space and use statistical model checking to evaluate each individual value w.r.t. provided data. Contrary to other existing methods, we provide statistical guarantees regarding our results that take into account the unavoidable approximation errors introduced through the numerical integration of the ODE system performed while simulating. In order to show the potential of our technique, we present its application to two case studies taken from the literature, one relative to the growth of a jellyfish population, and the other concerning a well-known oscillator model.

Computational Modeling of Bone Fracture Healing Under Different Initial Conditions and Mechanical Load.

Computational model of bone healing can replace animal experiments to study the parameters affecting the bone healing process, thus reducing the damage to experimental animals and saving a lot of time. We propose a computational model for continuous simulation of four phases of bone healing to study the effects of mechanical environmental and biological factors, including initial conditions at the fracture site, mechanical stimulus loading, and vascular growth rate. A finite element model of mechanobiological fracture healing containing several pre-determined variables was developed for bone healing after fracture in sheep, which included many relevant parameters and biological effects during fracture healing, such as the effects of mechanical environment, blood supply level in the local fracture area, cell migration and diffusion, and resorption effects of fracture healing. The effects of several parameters on indices such as Young's modulus of the callus during bone healing were obtained by simulation. The initial geometry of the healing tissue and mechanical loading had the greatest effect on fracture healing, and different preset values were likely to cause delayed or non-healing fractures. Changed initial tissue properties of the healing tissue showed a nonlinear effect on fracture healing rather than a linear delay or advancement. Parameters related to angiogenesis had a greater effect on fracture healing compared to those related to cell migration. This paper quantified the effect of fracture healing pre-determined variables on fracture healing to better understand the application of mechanobiology in fracture healing simulation models and optimization of treatment strategies. The importance of initial conditions and loads on fracture healing has been shown to help physicians treat bone nonunion or delayed bone healing after a fracture.

ДОСЛІДЖЕННЯ АНТИОКСИДАНТНИХ ВЛАСТИВОСТЕЙ ЕКСТРАКТІВ ВІДКАСНИКА БЕЗСТЕБЛОВОГО (Carlina acaulis L.), АРНІКИ ГІРСЬКОЇ (Arnica montana L.) ТА КАЛЕНДУЛИ ЛІКАРСЬКОЇ (Calendula officinalis L.)

The intensity of lipid peroxidation (LPO) and oxidative modification of proteins (OMP) was investigated under the influence of 40 % and 70 % aqueous-ethanol plant extracts (PE) of the root of Carlina acaulis L., flowers of Arnica montana L. and Calendula officinalis L. on rat liver hepatocytes under conditions of free radical oxidation initiation in vitro. Investigated plant extracts reduce the formation of free radicals in proteins and lipids, which is evidently associated with the presence of phenolic compounds, flavonoids, and polyphenols in the extracts. The best results were demonstrated by the plant extracts of arnica and calendula. Arnica extract at a concentration of 40 % exhibited better antioxidant properties than its 70 % extract.

Neural Network Control of Underactuated Surface Vehicles With Prescribed Trajectory Tracking Performance.

This article is concerned with the fast and accurate trajectory tracking control problem for a sort of underactuated surface vehicle under model uncertainties and environmental disturbances. A novel neural networks (NNs)-based prescribed performance control strategy is proposed to solve the problem. In the control design, a new type of performance function is constructed which provides a way to predefine the settling time and accuracy, straightforward. Then, a pair of barrier functions are employed to combat not only the position error but also the virtual control input. This evades the possible singularity or discontinuity of the control solution. Next, an initialization technique is exploited, removing the requirement for the initial condition of the control system. Finally, two NNs are employed to deal with the unknown ship nonlinearities. The performance analysis not only demonstrates the effectiveness of the proposed approach but also reveals its robustness against disturbances and unknown reference trajectory derivatives. There is, thus, no need to acquire such knowledge or employ specialized tools to handle disturbances. The theoretical findings are illustrated by a simulation study.

Thermal decomposition and combustion of interior design materials

Research findings on the patterns of thermal decomposition and combustion are reported for widely used interior design materials. The experiments were conducted using a hardware and software system including a thermogravimetric analyzer (to record the characteristics of thermal decomposition), a gas analyzer (with H2, CH4, H2S, SO2, CO and CO2 sensors) and a high-speed camera (to record the characteristics of ignition and combustion). The temperature of the oxidizing medium ranged from 500 to 900°C to investigate the conditions of thermal decomposition initiation and sustained combustion. It was established that the highest concentrations of toxic emissions were typical of the combustion of polypropylene at a maximum temperature of the oxidizing medium (900°C). Wood showed the shortest ignition delay time and the longest duration of combustion. The experimental data were used for a physical problem statement and mathematical model of heat and mass transfer to explore the thermal decomposition and combustion of interior design materials in different rooms. A comparison of the experimental findings with the mathematical modeling results validates the developed model. The growth rates of carbon monoxide and carbon dioxide concentrations were determined for construction (wooden) and interior design (polymer) materials. The maximum concentrations of CO and CO2, and the minimum times taken to reach their threshold values corresponded to wood and polyvinyl chloride panels. This research provides a deeper insight into the thermal decomposition of a wide range of fuels and the formation of gaseous pyrolysis products of these fuels. The results obtained can be used to evaluate the toxicity of construction and interior design materials during compartment fires as well as to estimate the safe egress time and risks involved in the process. They can also serve as a database for the development and testing of mathematical models describing fire outbreaks and propagation in confined spaces.

On a biharmonic coupled system with non-standard nonlinearity: Existence, blow up and numerics

In this paper, we consider a coupled system of two biharmonic equations with damping and source terms of variable-exponent nonlinearities, supplemented with initial and mixed boundary conditions. We establish an existence and uniqueness result of a weak solution, under suitable assumptions on the variable exponents. Then, we show that solutions with negative-initial energy blow up in finite time. To illustrate our theoritical findings, we present two numerical examples.

Characterizing structure formation through instance segmentation

Dark matter haloes form from small perturbations to the almost homogeneous density field of the early universe. Although it is known how large these initial perturbations must be to form haloes, it is rather poorly understood how to predict which particles will end up belonging to which halo. However, it is this process that determines the Lagrangian shape of proto-haloes and it is therefore essential to understand their mass, spin, and formation history. We present a machine learning framework to learn how the proto-halo regions of different haloes emerge from the initial density field. We developed one neural network to distinguish semantically which particles become part of any halo and a second neural network that groups these particles by halo membership into different instances. This instance segmentation is done through the Weinberger method, in which the network maps particles into a pseudo-space representation where different instances can easily be distinguished through a simple clustering algorithm. Our model reliably predicts the masses and Lagrangian shapes of haloes object by object, as well as other properties such as the halo-mass function. We find that our model extracts information close to optimally by comparing it to the degree of agreement between two N-body simulations with slight differences in their initial conditions. We publish our model open source and suggest that it can be used to inform analytical methods of structure formation by studying the effect of systematic manipulations of the initial conditions.

Dynamical Modeling and Qualitative Analysis of a Delayed Model for CD8 T Cells in Response to Viral Antigens.

Although the immune effector CD8 T cells play a crucial role in clearance of viruses, the mechanisms underlying the dynamics of how CD8 T cells respond to viral infection remain largely unexplored. Here, we develop a delayed model that incorporates CD8 T cells and infected cells to investigate the functional role of CD8 T cells in persistent virus infection. Bifurcation analysis reveals that the model has four steady states that can finely divide the progressions of viral infection into four states, and endows the model with bistability that has ability to achieve the switch from one state to another. Furthermore, analytical and numerical methods find that the time delay resulting from incubation period of virus can induce a stable low-infection steady state to be oscillatory, coexisting with a stable high-infection steady state in phase space. In particular, a novel mechanism to achieve the switch between two stable steady states, time-delay-based switch, is proposed, where the initial conditions and other parameters of the model remain unchanged. Moreover, our model predicts that, for a certain range of initial antigen load: 1) under a longer incubation period, the lower the initial antigen load, the easier the virus infection will evolve into severe state; while the higher the initial antigen load, the easier it is for the virus infection to be effectively controlled and 2) only when the incubation period is small, the lower the initial antigen load, the easier it is to effectively control the infection progression. Our results are consistent with multiple experimental observations, which may facilitate the understanding of the dynamical and physiological mechanisms of CD8 T cells in response to viral infections.

A Precision Benchmark Suite for Nuclear Reactor Point Kinetics Equations via Converged Accelerated Taylor Series (CATS)

Extreme benchmarks of 10 or more places for the point kinetics equations for time-dependent nuclear reactor power transients are rare. Therefore, to establish an extreme benchmark, we employ a Taylor series (TS) with continuous analytical continuation to solve the ordinary differential equations of point kinetics including feedback. Nonlinear Wynn-epsilon convergence acceleration confirms the highly precise solutions for neutron and precursor densities. Through adaptive partitioning of time intervals, the proposed Converged Accelerated Taylor Series, or CATS algorithm in double precision, automatically performs successive mesh refinement to obtain high-precision initial conditions for each subinterval, with the intent to reduce propagation error. Confirmation of 10 to 12 places comes from comparison to the BEFD (Backward Euler Finite Difference) algorithm in quadruple precision also developed by the author. We report benchmark results for common cases found in the literature including step, ramp, zigzag, and sinusoidal prescribed reactivity insertions and insertions with nonlinear adiabatic Doppler feedback. We also establish a suite of new prescribed reactivity insertions and insertions with feedback, based on reactivities with Taylor series representations as suggested by the CATS algorithm.

Initiation and development of tectonic stylolite – Vein system in micritic limestone (Les Matelles, France)

In this study, we describe the characteristics of tectonic stylolites and related veins affecting a low-porosity micritic limestone (Jurassic carbonates, Les Matelles, South of France) in order to unravel the conditions of initiation and interaction between pressure-solution and fracturing in such rock. Field description, various petrographic and microstructural investigations (cathodoluminescence, SEM imaging, EBSD analysis), and petrophysical/geochemical analyses (Hg porosimetry, XRD, EPMA) are used. We document that pressure-solution initiates at micropores and propagates along calcite grain contacts, connecting surrounding stylolite micro-segments, and progressively concentrates insoluble material such as clays and siliceous particles. The dissolved material is evacuated to the veins where the newly-formed porous space is progressively filled by calcite cement. These deformation processes are strictly restricted to the stylolitic interface and veins, as no modification of porosity or grain deformation is detected in the neighboring host rock. This is due to the low-permeability of the surrounding host rock impeding the evacuation of dissolved material and fluids through interstitial porosity around the pressure-solution zone, leading to overpressure and veins formation. The water release and microporosity caused by diagenesis of the clay fraction (smectite-illite transformation) are discussed as key diagenetic processes instigating the conditions of pressure-solution initiation, then tectonic stylolite formation in low-porosity limestones.

2D unified atmosphere and wind simulations of O-type stars

Context. Massive and luminous O-type star (O star) atmospheres with winds have been studied primarily using one-dimensional (1D), spherically symmetric, and stationary models. However, observations and theory have suggested that O star atmospheres are highly structured, turbulent, and time-dependent. As such, when making comparisons to observations, present-day 1D modeling tools require the introduction of ad hoc quantities such as photospheric macro- and microturbulence, wind clumping, and other relevant properties. Aims. We present a series of multi-dimensional, time-dependent, radiation-hydrodynamical (RHD) simulations for O stars that encapsulate the deeper sub-surface envelope (down to T ~ 450 kK), as well as the supersonic line-driven wind outflow in one unified approach. Our overarching aim is to develop a framework that is free from the ad-hoc prescriptions that plague present-day 1D models. Here, we start with an analysis of a small set of such multi-dimensional simulations and then compare them to atmospheric structures predicted by their 1D counterparts. Methods. We performed time-dependent, two-dimensional (2D) simulations of O star atmospheres with winds using a flux-limiting RHD finite volume modelling technique. Opacities are computed using a hybrid approach combining tabulated Rosseland means with calculations (based on the Sobolev approximation) of the enhanced line opacities expected for supersonic flows. The initial conditions and comparison models were derived using similar procedures as those applied in standard 1D stationary model atmosphere with wind codes. Results. Structure starts appearing in our simulations just below the iron-opacity peak at ~200 kK. Local pockets of gas with radiative accelerations that exceed gravity then shoot up from these deep layers into the upper atmosphere, where they interact with the line-driven wind outflow initiated around or beyond the variable photosphere. This complex interplay creates large turbulent velocities in the photospheric layers of our simulations, on the order of ~30–100km s−1, with higher values for models with higher luminosity-to-mass ratios. This offers a generally good agreement with observations of large photospheric ‘macroturbulence’ in O stars. When compared to 1D models, the average structures in the 2D simulations display less envelope expansion and no sharp density-inversions, along with density and temperature profiles that are significantly less steep around the photosphere, and a strong anti-correlation between velocity and density in the supersonic wind. Although the wind initiation region is complex and highly variable in our simulations, our average mass-loss rates agree well with stationary wind models computed by means of full co-moving frame radiative transfer solutions. Conclusions. The different atmospheric structures found in 2D and 1D simulations are likely to affect the spectroscopic determination of fundamental stellar and wind parameters for O stars as well as the empirical derivation of their chemical abundance patterns. To qualitatively match the different density and temperature profiles seen in our multi-dimensional and 1D models, we need to add a modest amount of convective energy transport in the deep sub-surface layers and a large turbulent pressure around the photosphere to the 1D models.

Model-Free Output Feedback Path Following Control for Autonomous Vehicle With Prescribed Performance Independent of Initial Conditions

Model-Free Output Feedback Path Following Control for Autonomous Vehicle With Prescribed Performance Independent of Initial Conditions

Assimilation of Water Vapor Retrievals from ZDR Columns Using the 3DVar Method for Improving the Short-Term Prediction of Convective Storms

Abstract The differential reflectivity (ZDR) column is a notable polarimetric signature related to updrafts in deep moist convection. In this study, pseudo–water vapor (qυ) observations are retrieved from observed ZDR columns under the assumption that humidity is saturated within the convection where ZDR columns are detected, and are then assimilated within the 3DVar framework. The impacts of assimilating pseudo-qυ observations from ZDR columns on short-term severe weather prediction are first evaluated for a squall-line case. Radar data analysis indicates that the ZDR columns are mainly located on the inflow side of the high-reflectivity region. Assimilation of the pseudo-qυ observations leads to an enhancement of qυ within the convection, while concurrently reducing humidity in no-rain areas. Sensitivity experiments indicate that a tuned smaller observation error and a shorter horizontal decorrelation scale are optimal for a better assimilation of pseudo-qυ from ZDR columns, resulting in more stable rain rates during short-term forecasts. Additionally, a 15-min cycling assimilation frequency yields the best performance, providing the most accurate reflectivity forecast in terms of both location and intensity. Analysis of thermodynamic fields reveal that assimilating ZDR columns provides more favorable initial conditions for sustaining convection, including sustainable moisture condition, a strong cold pool, and divergent winds near the surface, consequently enhancing reflectivity and precipitation. With the optimal configuration determined from the sensitivity tests, a quantitative evaluation further demonstrates that assimilating the pseudo-qυ observations from ZDR columns using the 3DVar method can improve the 0–3-h reflectivity and accumulated precipitation predictions of convective storms.

Emulating the interstellar medium chemistry with neural operators

Context. The study of galaxy formation and evolution critically depends on our understanding of the complex photo-chemical processes that govern the evolution and thermodynamics of the interstellar medium (ISM). In a computational sense, resolving the chemistry is among the weightiest tasks in cosmological and astrophysical simulations. Aims. Astrophysical simulations can include photo-chemical models that allow for a wide range of densities (n), abundances of different species (ni/n) and temperature (T), and plausible evolution scenarios of the ISM under the action of a radiation field (F) with different spectral shapes and intensities. The evolution of such a non-equilibrium photo-chemical network relies on implicit, precise, computationally costly, ordinary differential equations (ODE) solvers. Here, we aim to substitute such procedural solvers with fast, pre-trained emulators based on neural operators. Methods. We emulated a non-equilibrium chemical network up to H2 formation (9 species, 52 reactions) by adopting the DeepONet formalism, namely: by splitting the ODE solver operator that maps the initial conditions and time evolution into a tensor product of two neural networks (named branch and trunk). We used KROME to generate a training set, spanning −2 < log(n/cm−3) ≤ 3.5, log(20) ≤ log(T/K) ≤ 5.5, −6 ≤ log(ni/n) < 0, and adopting an incident radiation field, F, sampled in 10 energy bins with a continuity prior. We separately trained the solver for T and each ni for ≃4.34 GPUhrs. Results. Compared with the reference solutions obtained by KROME for single-zone models, the typical precision obtained is of the order of 10−2, that is, it is 10 times better when using a training that is 40 times less costly, with respect to previous emulators that only considered a fixed F. DeepONet also performs well for T and ni outside the range of the training sample. Furthermore, the emulator aptly reproduces the ion and temperature profiles of photo dissociation regions as well; namely, by giving errors that are comparable to the typical difference between various photo-ionization codes. The present model achieves a speed-up of a factor of 128× with respect to stiff ODE solvers. Conclusions. Our neural emulator represents a significant leap forward in the modelling of ISM chemistry, offering a good balance of precision, versatility, and computational efficiency. Nevertheless, further work is required to address the challenges represented by the extrapolation beyond the training time domain and the removal of potential outliers.

The Allen–Cahn model with a time-dependent parameter for motion by mean curvature up to the singularity

In this article, we investigate the temporal evolution of arbitrary, simple, closed two-dimensional (2D) and three-dimensional (3D) interfaces under motion driven by mean curvature up to a singularity. To facilitate this investigation, we propose a novel Allen–Cahn (AC) model with a time-dependent interfacial thickness parameter. The original AC equation was developed to model the phase separation of a binary mixture. It is well known that a level set or interface of the solution of the AC equation obeys the dynamics of motion by curvature as the interfacial thickness parameter approaches zero. Generally, it is difficult to find a closed-form analytic solution of the AC equation with any initial condition. Therefore, we need to estimate the solution of the AC equation through computational approaches such as finite difference method (FDM), finite element method (FEM), Fourier-spectral method (FSM), and finite volume method (FVM). Any simple, closed curves and convex surfaces eventually shrink to a point due to motion by mean curvature. Therefore, it becomes necessary to use adaptive mesh techniques to resolve this small size problem. However, even though we use adaptive mesh techniques, we still confront the relatively thick interfacial transition layer when the curves or interfaces become very small. To avoid this problem, we can start with a very small mesh size for a small value of the interfacial parameter, which results in an extremely high computational cost even when using adaptive mesh techniques. To resolve these issues, we present the AC equation with a time-dependent interfacial parameter and develop an adaptive mesh refinement system. To show the superior performance of the proposed mathematical equation and its computational algorithm, we present various numerical experiments and investigate the motion by mean curvature up to the singularity of curves in 2D space and interfaces in 3D space.