Time-dependent Parameters Research Articles

An improved epidemiological-unscented Kalman filter (hybrid SEIHCRDV-UKF) model for the prediction of COVID-19. Application on real-time data

The prevalence of COVID-19 has been the most serious health challenge of the 21th century to date, concerning national health systems on a daily basis, since December 2019 when it appeared in Wuhan City. Nevertheless, most of the proposed mathematical methodologies aiming to describe the dynamics of an epidemic, rely on deterministic models that are not able to reflect the true nature of its spread. In this paper, we propose a SEIHCRDV model – an extension/improvement of the classic SIR compartmental model – which also takes into consideration the populations of exposed, hospitalized, admitted in intensive care units (ICU), deceased and vaccinated cases, in combination with an unscented Kalman filter (UKF), providing a dynamic estimation of the time dependent system's parameters. The stochastic approach is considered necessary, as both observations and system equations are characterized by uncertainties. Apparently, this new consideration is useful for examining various pandemics more effectively. The reliability of the model is examined on the daily recordings of COVID-19 in France, over a long period of 265 days. Two major waves of infection are observed, starting in January 2021, which signified the start of vaccinations in Europe providing quite encouraging predictive performance, based on the produced NRMSE values. Special emphasis is placed on proving the non-negativity of SEIHCRDV model, achieving a representative basic reproductive number R0 and demonstrating the existence and stability of disease equilibria according to the formula produced to estimateR0. The model outperforms in predictive ability not only deterministic approaches but also state-of-the-art stochastic models that employ Kalman filters. Furthermore, the relevant analysis supports the importance of vaccination, as even a small increase in the dialy vaccination rate could lead to a notable reduction in mortality and hospitalizations.

Modeling of Creep Behavior of Particulate Composites with Focus on Interfacial Adhesion Effect.

Evaluation of creep compliance of particulate composites using empirical models always provides parameters depending on initial stress and material composition. The effort spent to connect model parameters with physical properties has not resulted in success yet. Further, during the creep, delamination between matrix and filler may occur depending on time and initial stress, reducing an interface adhesion and load transfer to filler particles. In this paper, the creep compliance curves of glass beads reinforced poly(butylene terephthalate) composites were fitted with Burgers and Findley models providing different sets of time-dependent model parameters for each initial stress. Despite the finding that the Findley model performs well in a primary creep, the Burgers model is more suitable if secondary creep comes into play; they allow only for a qualitative prediction of creep behavior because the interface adhesion and its time dependency is an implicit, hidden parameter. As Young's modulus is a parameter of these models (and the majority of other creep models), it was selected to be introduced as a filler content-dependent parameter with the help of the cube in cube elementary volume approach of Paul. The analysis led to the time-dependent creep compliance that depends only on the time-dependent creep of the matrix and the normalized particle distance (or the filler volume content), and it allowed accounting for the adhesion effect. Comparison with the experimental data confirmed that the elementary volume-based creep compliance function can be used to predict the realistic creep behavior of particulate composites.

Numerical Coefficient Reconstruction of Time-Depending Integer- and Fractional-Order SIR Models for Economic Analysis of COVID-19

In the present work, a fractional temporal SIR model is considered. The total population is divided into three compartments—susceptible, infected and removed individuals. It generalizes the classical SIR model and consists of three coupled time-fractional ordinary differential equations (ODEs). The fractional derivative is introduced to account for the subdiffusion process of confirmed, cured and deceased people dynamics. Although relatively basic, the model is robust and captures the real dynamics, helped by the memory property of the fractional system. In the paper, the issue of an adequate model reconstruction is addressed, and a coefficient identification inverse problem is solved; in particular, the transition and recovering rates, varying in time, are recovered. A least-squares cost functional is minimized for solving the problem. The time-dependent parameters are reconstructed with an iterative predictor–corrector algorithm. Its application is demonstrated via tests with synthetic and real data. What is more, an approach for economic impact assessment is proposed.

Analysis method for the design of long submarine cables

The recent push for renewable electrical energy sources has resulted in a significant increase in the installation of the offshore wind farms. These farms become much bigger than their earlier counterparts and the generated power is much larger. This puts additional performance requirements on the export cables. Long submarine export cables are not only subjected to changing laying conditions along the route, including depth and seabed material variations, but also the loading patterns vary significantly over time. Additionally, applied reactive power compensation measures and the time-dependent grid situation change the distribution of the current along the route. These unique features of long submarine power cables necessitate a new type of approach to their design and construction optimization. This paper introduces a new approach for ampacity calculations of long submarine power cables. As the ampacity calculations form a basis for the design of a cable route, it is of paramount importance that they are performed as accurately as possible. The approach can be applied to general-purpose circuit solvers, and this paper describes how to implement it using the ATP-EMTP software, which is extended to the consideration of concurrent electrical and thermal effects. The approach permits considering all the important local and time-dependent parameters of the analysis simultaneously. In particular, the approach allows simultaneous analysis of the varying laying conditions along the route including changes in the depth of burial and thermal resistivity of the soil together with the temporal variations of the cable loading. Additionally, current characteristics in long AC cable conductors are strongly affected by the reactive power flow, which in turn depends on the location of the reactive power compensation, the reactive power demand for grid operation, and the wind farm voltage reactive power control strategy. Such analysis cannot be performed with any existing analytical tool. Also, numerical tools cannot handle such problems because a 3-dimensional time-dependent analysis of a cable route which is 100 km long would encounter convergence problems as the size of such model would be prohibitive. Hence, at present, the proposed approach is the only one available to solve this complex electro-thermal problem. Several numerical examples illustrate an application of the proposed methodology.

The Cosmology of a Non-Minimally Coupled f(R,T) Gravitation

A non-minimally coupled cosmological scenario is considered in the context of f(R,T)=f1(R)+f2(R)f3(T) gravity (with R being the Ricci scalar and T the trace of the energy-momentum tensor) in the background of the flat Friedmann–Robertson–Walker (FRW) model. The field equations of this modified theory are solved using a time-dependent deceleration parameter for a dust. The behavior of the model is analyzed taking into account constraints from recent observed values the deceleration parameter. It is shown that the analyzed models can explain the transition from the decelerating phase to the accelerating one in the expansion of the universe, by staying true to the results of the observable universe. It is shown that the models are dominated by a quintessence-like cosmological dark fluid at the late universe.

Evaluation of psychological stress, cortisol awakening response, and heart rate variability in patients with chronic prostatitis/chronic pelvic pain syndrome complicated by lower urinary tract symptoms and erectile dysfunction.

Mental stress and imbalance of its two neural stress systems, the autonomic nervous system (ANS) and the hypothalamic-pituitary-adrenal (HPA) axis, are associated with chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) and erectile dysfunction (ED). However, the comprehensive analyses of psychological stress and stress systems are under-investigated, particularly in CP/CPPS patients complicated by lower urinary tract symptoms (LUTS) and ED. Participants were 95 patients in CP/CPPS+ED group, 290 patients in CP/CPPS group, 124 patients in ED group and 52 healthy men in control group. The National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI), the International Index of Erectile Function-5 (IIEF-5) and the International Prostate Symptom Score (IPSS) were used for assessing the disease severity of CP/CPPS, LUTS and ED. Psychometric self-report questionnaires including the Beck Anxiety Inventory (BAI), Perceived Stress Scale (PSS), Type A Personality Test (TAPT) and Symptom Checklist 90 (SCL-90) were completed for distress from physical symptoms. Twenty-five subjects per group were randomly selected for further investigating the changes of the HPA axis and ANS. Saliva samples were taken on 3 consecutive days at 8 specific times with strict reference to time of morning awakening for evaluation of free cortisol. Heart rate variability (HRV) as marker of the ANS was measured using 24 h electrocardiography, and time-and frequency-domain variables were analyzed. The BAI and SCL-90 scores were significantly higher in the CP/CPPS+ED, CP/CPPS and ED groups compared with the control group (p < 0.01). The PSS scores of both groups with ED were significantly higher than the control group (p < 0.01). Compared with the CP/CPPS group, the differences of PSS, SCL-90 and TAPT scores were statistically significant in CP/CPPS+ED patients (p < 0.01). The IPSS scores were shown to have significantly positive correlations with BAI (r = 0.32, p < 0.0001), PSS (r = 0.18, p < 0.01) and SCL-90 (r = 0.19, p < 0.01) in the CP/CPPS patients. However, in all subjects, the IIEF-5 scores were shown to have significantly negative correlations with BAI (r = -0.17，p < 0.001), PSS (r = -0.25，p < 0.0001), SCL-90 (r = -0.20，p < 0.001) and quality of life score in NIH-CPSI (r = -0.14，p = 0.0075). Cortisol awakening response (CAR) parameters and diurnal cortisol levels did not significantly vary between the four groups. Time-dependent parameters of HRV also did not differ significantly across groups. In the frequency domain analysis, low frequency (LF) was significantly lower in ED patients when compared with CP/CPPS+ED patients (p = 0.044) and healthy controls (p = 0.005), high frequency (HF) power was significantly higher in healthy controls compared to patients with ED (p < 0.001), CP/CPPS (p < 0.001) and CP/CPPS+ED (p < 0.001), and the CP/CPPS+ED group had significantly higher LF/HF ratio than the control group (p = 0.001). CP/CPPS and ED patients score exceedingly high on most psychosocial variables. The symptom scores of LUTS and ED positively correlate with the severity of psychological stress. Our findings also suggest that the ANS sympathovagal imbalance is associated with ED and LUTS in CP/CPPS, whereas HPA axis activity is not.

EMHD Nanofluid Flow with Radiation and Variable Heat Flux Effects along a Slandering Stretching Sheet.

Nanofluids have gained prominence due to their superior thermo-physical properties. The current paper deals with MHD nanofluid flow over a non-linear stretchable surface of varying thickness in the presence of an electric field. We investigated the effects of nanometer-sized copper (Cu) particles in water (base fluid) as a nanofluid, as well as non-linear thermal radiation, variable fluid viscosity, Joule heating, viscous dissipation, and non-uniform heat flux. The current study's aim is influenced by the immense applications in industry and machine building. It has been observed that linear stretching sheets have been extensively used in heat transfer research. Moreover, no effort has been made yet to model a non-linear stretching sheet with variable thickness. Furthermore, the effects of electromagnetohydrodynamics (EMHD) boundary-layer flow of a nanofluid with the cumulative impact of thermal radiation, variable viscosity, viscous dissipation, Joule heating, and variable heat flux have been investigated. Sheets with variable thicknesses are practically significant in real-life applications and are being used in metallurgical engineering, appliance structures and patterns, atomic reactor mechanization and paper production. To investigate the physical features of the problem, we first examined the model and identified all the physical properties of the problem. This problem has been formulated using basic laws and governing equations. The partial differential equations (PDEs) that govern the flow are converted into a system of non-dimensional ordinary differential equations (ODE's), using appropriate transformations. The Adam-Bashforth predictor-corrector technique and Mathematica software are utilized to numerically solve the resulting non-dimensionalized system. The interaction of various developing parameters with the flow is described graphically for temperature and velocity profiles. It is concluded that the velocity of nanoparticles declines as the intensity of the magnetic field increases. However, the temperature of the nanomaterials rises, as increasing the values of the electric field also increases the velocity distribution. The radiation parameter enhances the temperature field. The temperature of the fluid increases the occurrence of space- and time-dependent parameters for heat generation and absorption and radiation parameters.

Dynamic Event Tree Analysis as a Tool for Risk Assessment in Nuclear Fusion Plants Using RAVEN and MELCOR

In the broad framework of the nuclear power plants (NPPs) industry, the dynamic probabilistic risk assessment could answer the time dependence deficiency of the event tree and fault tree analysis. The basic event tree approach relies on experts’ pre-constructed accident sequences without exploring the time-dependent nature of an accident scenario, which could strongly affect the accident sequence. Conversely, the effects of events timing can be studied by adopting a dynamic event tree (DET) approach. Developing a DET methodology requires integrating a system code capable of replicating an accident scenario and a logic-driver code able to generate the event tree sequence, trigger plant safety systems, and manage other relevant events throughout the simulation. For this purpose, Methods for Estimation of Leakages and Consequences of Release (MELCOR) and reactor analysis and virtual control environment (RAVEN) have been coupled through a Python script developed by the Sapienza University of Rome, Rome, Italy, to perform DET studies during accident transients in fusion and fission reactors. RAVEN is a software tool developed at the Idaho National Laboratory (INL), Idaho Falls, ID, USA, to act as a control logic driver and post-processing tool for different applications. MELCOR for fusion is a fully integrated design basis and severe accident code that simulates thermal-hydraulic behavior and self-consistently accounting for aerosol transport in nuclear facilities and reactor cooling systems for the evaluation of the source term in fusion reactors. The coupling between these codes will provide a wide range of NPP risk assessment analyses, establishing new best practices. In this work, a preliminary DET study has been performed, selecting as initiating event an ex-vessel loss of coolant accident (LOCA) in the water-cooled lithium lead test blanket system (WCLL TBS) to be tested in International Thermonuclear Experimental Reactor (ITER). Time-dependent parameters such as the intervention of the plasma shutdown system and the closure of the main system isolation valves have been sampled to study evolving system scenarios.

Mathematical Study of unsteady micropolar fluid flow due to non-linear stretched sheet in the presence of magnetic field

The goal of this research is to check irregularity in heat generation, chemical reaction and thermal radiation effect an unsteady micropolar fluid flow. With the help of suitable similarity variables, the group of governing system of PDE and their associative boundary conditions are converted into the family of ODE and then solved using the HAM. The effect of various and important dimensionless parameters on the momentum, micro-rotation, energy and concentration equations have been discussed with graphical representation as well as tabular forms. It is observed that buoyancy force parameter, mixed convection parameter and permeability parameter enhance the velocity distribution while micropolar reduces the velocity profile. The temperature profiles are boosted for time and space dependent heat generation and absorption parameter respectively but reverse trend is observed for Prandtl number and unsteadiness parameter. Skin-friction factor, mass transfer rate and heat transfer rate have been listed and displayed in tables.

Derivation and analytical solutions of a non-linear diffusion equation applied to non-constant heat conductivity and ionic diffusion in glasses.

This paper derives a non-linear diffusion equation discussing two possible applications: the ionic diffusion in glasses and temperature-dependent conductivity in semiconductors. The first equation is a logarithmic diffusion derived formally from the continuity of ion concentration, but the latter is a more phenomenological example. A power-law ansatz with time-dependent parameters maximizes a non-standard entropy and gives a set of coupled motion equations we can solve analytically. These results build the general solution to the non-linear diffusion equation.

Long-Term Swelling Characteristics of Montmorillonite Clay with and without Fly Ash: Wetting–Drying Cycle Influence in 1D Oedometer Condition

Abstract The utilization of fly ash (FA) produced from various thermal power plants and other industrial sources is a big challenge and an opportunity for material and civil engineers. This study aims to utilize FA for improving the engineering characteristics of Montmorillonitic Indian clay (BC) soil. A rigorous testing plan is designed and performed using different proportions of FA (5, 15, 25, and 35 %) to investigate the time-dependent deformation behavior of expansive BC soil and also to examine the influence of the wetting–drying cycle on the time-dependent swelling behavior of reconstituted soils in Oedometric free swell condition. It is proposed to evaluate the time-dependent parameters of the FA-BC matrix under wetting–drying cycles using the elastoviscoplastic considering swelling (EVPS) model. The mineralogical characteristics of both natural and reconstituted samples were investigated using the scanning electron microscope and X-ray diffraction. From the experimental result, it is observed that the time-dependent swelling coefficient decreases with the percentage of FA content increase. The FA used in the study results in decreasing not only the swelling potential of BC soil samples but also the swelling–shrinkage behavior of the soil. It is observed that the reconstituted soils are influenced by both the degree of FA content and the wetting–drying cycles. The swelling strain limit decreases exponentially with the increase of the FA content. The EVPS model works very well in predicting the long-term consolidation and swelling behavior of the FA-BC matrix under different stress states.

Nonextensive hydrodynamics of boost-invariant plasmas

We use quasiparticle anisotropic hydrodynamics to study the non-conformal and non-extensive dynamics of a system undergoing boost-invariant Bjorken expansion. To introduce nonextensivity, we use an underlying Tsallis distribution with a time-dependent nonextensivity parameter q. By taking moments of the quasiparticle Boltzmann equation in the relaxation-time approximation, we obtain dynamical equations which allow us to determine the time evolution of all microscopic parameters including q. We compare numerical solutions for bulk observables obtained using the nonextensive evolution with results obtained using quasiparticle anisotropic hydrodynamics with a Boltzmann distribution function (q rightarrow 1). We show that the evolution of the temperature, pressure ratio, and scaled energy density, are quite insensitive to which distribution function is assumed. However, we find significant differences in the early-time evolution of the bulk pressure which are observed for even small deviations from the Boltzmann distribution function. Finally, we discuss the existence of non-conformal hydrodynamic attractors for the longitudinal and transverse pressures, the bulk and shear viscous corrections, and the nonextensivity parameter q.

Analytical Solution for Controlled Drug Release with Time-Dependent Diffusion Parameter

Drugs seem to diffuse in different manners in a delivery device due to the increment of the device pore size during swelling. However, the diffusion parameter, D, is often assumed constant. In this work, a new developed controlled drug release model with a time-dependent diffusion parameter is compared to one- and two-phase models. The new model was obtained as an improvement of the previous constant and piece-wise constants models. The models are developed by solving an advection–diffusion equation using the Landau transformation method and the separation of variables method. To test these models, we fit experimental data by the developed models using the least squares fitting technique. The curve-fitting result shows that the least squares error of the two-phase and the time-dependent models are 10 times smaller than the single-phase model. The CPU time for the time-dependent model is the lowest, showing that a time-dependent model is the best option among all three tested models considering both factors of the determined least squares error and the time consumption.

Understanding the effects of subsidence on unconfined aquifer parameters by integration of Lattice Boltzmann Method (LBM) and Genetic Algorithm (GA)

Excessive exploitation of groundwater has hitherto led to a significant land subsidence in a considerable number of plains in Iran. The compaction of aquifer layers ends up with changes in aquifer parameters, including hydraulic conductivity (Kx), specific yield (Sy), and compressibility (α). Accordingly, a precise estimation of aquifer parameters, Kx, Sy, and α seems essential for future water resources planning and management. In this study, an innovative inversion solution based on the combination of lattice Boltzmann method (LBM) and genetic algorithm (GA) was developed to determine the aquifer parameters, Kx, Sy, and α in Darab plain (in Fars province, Iran), which is highly subject to land subsidence. Herein, a newly developed LBM for unconfined groundwater flow was employed by incorporating the amount of subsidence measured by synthetic aperture radar interferometry (InSAR) spanning from 2010 to 2016. In order to optimize the aquifer parameters, the whole process of inverse modeling is replicated on the annual basis from 2010 to 2016 which leads to the temporal estimation of the aquifer parameters. Due to the compaction occurring in the aquifer system, a declining temporal trend is observed in the aquifer parameters in most parts of the plain. By fitting a function to time-dependent aquifer parameters, Kx, Sy, and α, their corresponding values and consequently the amount of subsidence in the near future, i.e., 2017, are predicted. The small average relative error (~ 3.5%) between the predicted land subsidence and the InSAR measurements demonstrates the high performance of the proposed inverse modeling approach.

The Interplay between COVID-19 and the Economy in Canada

We propose a generalized susceptible-exposed-infected-removed (SEIR) model to track COVID-19 in Canadian provinces, taking into account the impact of the pandemics on unemployment. The model is based on a network representing provinces, where the contact between individuals from different locations is defined by a data-driven mixing matrix. Moreover, we use time-dependent parameters to account for the dynamical evolution of the disease incidence, as well as changes in the rates of hospitalization, intensive care unit (ICU) admission, and death. Unemployment is accounted for as a reduction in the social interaction, which translates into smaller transmission parameters. Conversely, the model assumes that higher proportions of infected individuals reduce overall economic activity and therefore increase unemployment. We tested the model using publicly available sources and found that it is able to reproduce the reported data with remarkable in-sample accuracy. We also tested the model’s ability to make short-term out-of-sample forecasts and found it very satisfactory, except in periods of rapid changes in behavior. Finally, we present long-term predictions for both epidemiological and economic variables under several future vaccination scenarios.

A Process-based Model with Temperature, Water, and Lab-derived Data Improves Predictions of Daily Culex pipiens/restuans Mosquito Density.

While the number of human cases of mosquito-borne diseases has increased in North America in the last decade, accurate modeling of mosquito population density has remained a challenge. Longitudinal mosquito trap data over the many years needed for model calibration, and validation is relatively rare. In particular, capturing the relative changes in mosquito abundance across seasons is necessary for predicting the risk of disease spread as it varies from year to year. We developed a discrete, semi-stochastic, mechanistic process-based mosquito population model that captures life-cycle egg, larva, pupa, adult stages, and diapause for Culex pipiens (Diptera, Culicidae) and Culex restuans (Diptera, Culicidae) mosquito populations. This model combines known models for development and survival into a fully connected age-structured model that can reproduce mosquito population dynamics. Mosquito development through these stages is a function of time, temperature, daylight hours, and aquatic habitat availability. The time-dependent parameters are informed by both laboratory studies and mosquito trap data from the Greater Toronto Area. The model incorporates city-wide water-body gauge and precipitation data as a proxy for aquatic habitat. This approach accounts for the nonlinear interaction of temperature and aquatic habitat variability on the mosquito life stages. We demonstrate that the full model predicts the yearly variations in mosquito populations better than a statistical model using the same data sources. This improvement in modeling mosquito abundance can help guide interventions for reducing mosquito abundance in mitigating mosquito-borne diseases like West Nile virus.

Feasibility of a longitudinal statistical atlas model to study aortic growth in congenital heart disease

Abstract Background Studying anatomical shape progression over time is of utmost importance to refine our understanding of clinically relevant processes. These include vascular remodelling, such as aortic dilation, which is particularly important in some congenital heart defects (CHD). Purpose A novel methodological framework for analysing three-dimensional (3D) shape changes over time (“growth”) has been applied for the first time in a CHD scenario, i.e., bicuspid aortic valve (BAV) disease, the most common CHD. Methods Three-dimensional aortic shapes (n=94) reconstructed as surface meshes from cardiovascular magnetic resonance imaging (MRI) data represented the input for a longitudinal shape atlas model, using multiple scans over time (n=2–4 scans per patient). This model relies on diffeomorphic transformations in the absence of point-to-point correspondence, and on the correct combination of initialization, estimation, and registration parameters. Results We computed the 3D shape trajectory of an average disease progression over time in our cohort (Picture 1, grey to blue shapes), as well as time-dependent parameters, geometric variations and the average shape of the population (Picture 1, red shape). Results cover a spatiotemporal spectrum of visual and numerical information that can be further used to investigate clinical associations and stratify patients, as such capturing aortic remodelling in the presence of BAV aortopathy. Conclusion This proof-of-concept study demonstrates the feasibility of applying advanced statistical shape models to track disease progression and stratify patients with CHD. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): British Heart Foundation and NIHR BRC

Speckle tracking-derived left atrial strain rate – undervalued and useful predictor of adverse events

Abstract Background Strain rate reflects the rate of change of the distance between two points of the myocardium occurring in the cardiac cycle. It is a time-dependent parameter requiring higher temporal resolution than standard strain assessment. Because of this limitation, speckle tracking-derived left atrial (LA) strain rate analysis is of less interest in the literature. Purpose To analyze the prognostic value of LA strain rate during 2-years follow-up in patients with atrial fibrillation (AF). Methods The prospective study involved 80 patients with nonvalvular AF scheduled for elective electrical cardioversion (mean age 63.5±13 years, 60% male). All patients underwent echo before cardioversion and 24 hours after conversion to sinus rhythm. We analyzed standard echocardiographic parameters. Using the speckle-tracking method, we assessed LA strain rate during ventricular systole LA-SSR, in the early stage of ventricular diastole LA-ESR, and during atrial active pump phase LA-ASR. The last one is possible to measure only in the sinus rhythm. The analysis included 4-chamber and 2-chamber views. The clinical endpoint was predefined as AF recurrence. Results Median time of the current AF episode was 2.1 (IQR 0.5–5) months. Standard echo measurements revealed a median left ventricular ejection fraction of 55% (IQR 46–58) and the median of LA volume indexed to body surface area 42 ml/m2 (IQR 33–51). The successful cardioversion rate was 90%. During follow-up, we noticed AF recurrence in 49 (68%) patients. The median time-to-event was 2 (IQR 1 to 6.9) months. Receiver operating characteristic curve analysis revealed that LA-SSR &amp;lt;−0.73 s–1 (AUC=0.745; p=0.0004) and LA-ESR &amp;gt;−1 s–1 (AUC=0.703; p=0.0015) assessed during AF as well as LA-ESR &amp;gt;−0.88 s–1 (AUC=0.644; p=0.04) and LA-ASR &amp;gt;−1 s–1 (AUC=0.837; p&amp;lt;0.0001) measured in sinus rhythm were the optimal cut-off values for predicting AF recurrence. The figure presents Kaplan-Meier survival analysis for AF recurrence. Conclusions Speckle tracking-derived LA strain rate predicts the recurrence of AF after successful electrical cardioversion. Funding Acknowledgement Type of funding sources: None.

Noise-Suppressing Neural Dynamics for Time-Dependent Constrained Nonlinear Optimization With Applications

Up to date, the existing methods for nonlinear optimization with time-dependent parameters can be classified into two types: 1) static methods are capable of handling inequality constraints but may generate large lagging errors in the solution of the intrinsically time-dependent constrained nonlinear optimization (TDCNO) problem due to the hypothesis of short-time invariance and 2) time-variant methods, e.g., zeroing neural networks, are able to remedy the lagging error but fail to solve the TDCNO problem under inequality constraints. To resolve this contradiction, a noise-suppressing neural dynamics (NSND) model is proposed to solve the TDCNO problem subject to both equality and inequality constraints via the nonlinear complementary problem (NCP) function. The proposed method allows inequality constraints for unknown variables, removes the short-time invariance hypothesis, and further eliminates lagging errors during the solving process in the presence of noises. Besides, the rapid convergence, global stability, and noise processing of the NSND model are verified by the theoretical analyses. Simulation results of illustrative examples, including dimensionality reduction on principal component analyses (PCA) and a robot motion control, show that the NSND model outperforms the existing models for the TDCNO problem.

A Machine Learning Strategy for the Quantitative Analysis of the Global Warming Impact on Marine Ecosystems

It is generally observed that aquatic organisms have symmetric abilities to produce oxygen (O2) and fix carbon dioxide (CO2). A simulation model with time-dependent parameters was recently proposed to better understand the symmetric effects of accelerated climate change on coastal ecosystems. Changes in environmental elements and marine life are two examples of variables that are expected to change over time symmetrically. The sustainability of each equilibrium point is examined in addition to proving the existence and accuracy of the proposed model. To support the conclusions of this research compared to other studies, numerical simulations of the proposed model and a case study are investigated. This paper proposes an integrated bibliographical analysis of artificial neural networks (ANNs) using the Reverse-Propagation with Levenberg–Marquaradt Scheme (RP-LMS) to evaluate the main properties and applications of ANNs. The results obtained by RP-LMS show how to prevent global warming by improving the management of marine fish resources. The reference dataset for greenhouse gas emissions, environmental temperature, aquatic population, and fisheries population (GAPF) is obtained by varying parameters in the numerical Adam approach for different scenarios. The accuracy of the proposed RP-LMS neural network is demonstrated using mean square error (MSE), regression plots, and best-fit output. According to RP-LMS, the current scenario of rapid global warming will continue unabated over the next 50 years, damaging marine ecosystems, particularly fish stocks.