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A useful strategy to examine serial correlation in panel regression models is to adopt serially correlated error terms. The random effect is also present to address the individual-specific heterogeneity. Familiar transformations can restructure the AR(1) process into a serially uncorrelated dynamic model. It may result in unfitting estimation results once the statistical inference is carried on the conditional likelihood. We discuss in the article the consequence of this action. In particular, in the case of large cross-section units and short time-sequences, we provide analytical expressions for the asymptotic bias of maximum likelihood estimates when the analyst naïvely neglects the initial conditions problem. Under several conditions, we compute the size of biases and measure the mean squared errors to illustrate this violation. We also construct a joint regression model of the initial and subsequent responses to handle the initial conditions. Then, we conduct simulation studies to determine the asymptotic behavior. [ABSTRACT FROM AUTHOR]

This study evaluated the simulation performance of mesoscale convective system (MCS)-induced precipitation, focusing on three selected cases that originated from the Yellow Sea and propagated toward the Korean Peninsula. The evaluation was conducted for the European Centre for Medium-Range Weather Forecasts (ECMWF) and National Centers for Environmental Prediction (NCEP) analysis data, as well as the simulation result using them as initial and lateral boundary conditions for the Weather Research and Forecasting model. Particularly, temperature and humidity profiles from 3D dropsonde observations from the National Center for Meteorological Science of the Korea Meteorological Administration served as validation data. Results showed that the ECMWF analysis consistently had smaller errors compared to the NCEP analysis, which exhibited a cold and dry bias in the lower levels below 850 hPa. The model, in terms of the precipitation simulations, particularly for high-intensity precipitation over the Yellow Sea, demonstrated higher accuracy when applying ECMWF analysis data as the initial condition. This advantage also positively influenced the simulation of rainfall events on the Korean Peninsula by reasonably inducing convective-favorable thermodynamic features (i.e., warm and humid lower-level atmosphere) over the Yellow Sea. In conclusion, this study provides specific information about two global analysis datasets and their impacts on MCS-induced heavy rainfall simulation by employing dropsonde observation data. Furthermore, it suggests the need to enhance the initial field for MCS-induced heavy rainfall simulation and the applicability of assimilating dropsonde data for this purpose in the future. [ABSTRACT FROM AUTHOR]

Abstract The role of meteorological droughts and initial conditions (land and atmosphere) in soil moisture drought (SMD) propagation are not yet fully understood. This work uses a drought event‐based causal framework to investigate the relative importance of meteorological drought (MD) duration and intensity and initial conditions that result in surface and rootzone SMD, considering their event‐level propagation time (PT) over South Asia. Initially, spatial variability of drought propagation is assessed by the Propagation Ratio (PR) computed based on MD counts that trigger SMD at various lags. PR depicts 2–3 months slower rootzone propagation than at surface. The gradual decrease in PR with increasing regional aridity indicates faster propagation over humid regions. The causal impact of initial conditions and MD parameters on propagating SMD are evaluated using normalized mutual information and a newly proposed normalized conditional mutual information. We found greater importance of triggering MD parameters followed by initial soil moisture condition on propagating SMD. This behavior is more evident for the surface layer propagation at shorter PT. There is a confounding effect of initial atmospheric conditions on drought propagation through initial soil moisture, depicting the significance of land‐atmosphere interactions prior to propagation. In the rootzone propagation, initial soil moisture has a greater influence on propagation, especially at longer PT, indicating the significance of soil moisture persistence. Stronger causal links obtained through the joint influence of MD parameters on SMD suggest the importance of accounting for MD duration and intensity simultaneously, which are not considered in drought index‐based propagation studies.

On 24 June 1994 at Fairchild Air Force Base, during practice for an air show, a low-flying B-52H aircraft banked its wings vertically and crashed. Emphasizing the activity of modeling drag and gravity, these notes examine the possibility of recovery with several models. First, with algebra, historical data lead to a model where in a free fall near Earth's surface, the distance fallen is proportional to time squared. Calculus then gives an ordinary differential equation to model free fall near Earth's surface. Second, with calculus, historical data lead to various models of drag on objects moving through air. Third, combining models of drag and gravity with Newton's Laws of Motion leads to ordinary differential equations that model free fall in air. Fourth, predictions from such models with ordinary differential equations are consistent with the aircraft crash. Further models examine the possibility of increasing engine thrust to regain the vertical component of lift. All models fit in a first course in differential equations, without requiring any computational machinery. However, numerical experiments show how uninformed use of professional software can produce rounding errors to cause the modelled aircraft to plunge to the bottom of the deepest oceans and shoot up into space. [ABSTRACT FROM AUTHOR]

Ecosystems around the globe are experiencing changes in both the magnitude and fluctuations of environmental conditions due to land use and climate change. In response, ecologists are increasingly using near‐term, iterative ecological forecasts to predict how ecosystems will change in the future. To date, many near‐term, iterative forecasting systems have been developed using high temporal frequency (minute to hourly resolution) data streams for assimilation. However, this approach may be cost‐prohibitive or impossible for forecasting ecological variables that lack high‐frequency sensors or have high data latency (i.e., a delay before data are available for modeling after collection). To explore the effects of data assimilation frequency on forecast skill, we developed water temperature forecasts for a eutrophic drinking water reservoir and conducted data assimilation experiments by selectively withholding observations to examine the effect of data availability on forecast accuracy. We used in situ sensors, manually collected data, and a calibrated water quality ecosystem model driven by forecasted weather data to generate future water temperature forecasts using Forecasting Lake and Reservoir Ecosystems (FLARE), an open source water quality forecasting system. We tested the effect of daily, weekly, fortnightly, and monthly data assimilation on the skill of 1‐ to 35‐day‐ahead water temperature forecasts. We found that forecast skill varied depending on the season, forecast horizon, depth, and data assimilation frequency, but overall forecast performance was high, with a mean 1‐day‐ahead forecast root mean square error (RMSE) of 0.81°C, mean 7‐day RMSE of 1.15°C, and mean 35‐day RMSE of 1.94°C. Aggregated across the year, daily data assimilation yielded the most skillful forecasts at 1‐ to 7‐day‐ahead horizons, but weekly data assimilation resulted in the most skillful forecasts at 8‐ to 35‐day‐ahead horizons. Within a year, forecasts with weekly data assimilation consistently outperformed forecasts with daily data assimilation after the 8‐day forecast horizon during mixed spring/autumn periods and 5‐ to 14‐day‐ahead horizons during the summer‐stratified period, depending on depth. Our results suggest that lower frequency data (i.e., weekly) may be adequate for developing accurate forecasts in some applications, further enabling the development of forecasts broadly across ecosystems and ecological variables without high‐frequency sensor data. [ABSTRACT FROM AUTHOR]

The vapor cloud explosion in open space are greatly affected by the initial conditions of gas cloud and the surrounding environment, while in the gas compressor station, these factors are ubiquitous. While the researches of external influencing factors like the obstacle and initial turbulence effect on natural gas explosion in open space are rare. In this paper, a series of natural gas explosion experiments were conducted and a long-distance natural gas pipeline compressor station was taking as an example to simulate the natural gas leakage and explosion in the typical location. Results showed that the greater the initial turbulence, the greater the peak overpressure of gas explosion. When the initial wind speed is 5 m/s, the peak overpressure can promote 73.4 % higher than that without initial turbulence. However, the overpressure doesn't linearly increase with the increasing of initial wind speed. What's more, the flame speed showed the same law with the overpressure, and the effect of concentration on overpressure is lower than that of initial turbulence and obstacle density. The explosion damaging range in the condenser area is larger than that in the separator area, and the maximum overpressure can reach about 21 kPa and the temperature can reach above 2280K, which provides an important basis for the safety prevention and personnel protection in natural gas compressor station.

The Smoothed Particle Hydrodynamics (SPH) method is among the numerical methods that have attracted the attention of many researchers in recent years. This method as a Lagrangian method based on the movement of particles is also one of the meshless methods. In this method, the initial location or distribution of particles can play an important role in reducing numerical errors and its computational efficiency. In this research, based on wave modeling, which is one of the most conventional hydraulic phenomena, and using previous modeling experiences, six optimal and common distributions of particles including square distribution (SC), Triangular distribution, distribution based on WVT algorithm, distribution based on Greedy algorithm, Hexagonal distribution and Fibonacci distribution based on Fibonacci al-gorithm have been investigated. Based on the results of the pressure, velocity and the free surface level of the fluid in the location of the reference gauges of the laboratory model, it was determined that the square, triangular, WVT, Greedy, Hexagonal and Fibonacci distributions have a modeling error equal to 13.85%, 13.75%, 12.63%, 13.24%, 9.07% and 9.37%, respectively, and the two primary hexagonal and Fibonacci distributions have a modeling error of less than 10%, and in other words, it has the best performance in using the SPH method in order to improve the efficiency of the computational model.

In this paper, fixed-time convergent RNNs with logarithmic settling time are proposed for solving time-variant quadratic programming (QP), where the domain of attraction of the RNN models can be reasonably estimated a priori. Two novel activation functions are designed, and the exact expressions of the settling time functions for each given initial condition are given; and upper bounds of the settling times for any given initial condition can be obtained. In addition, the generic activation functions are constructed and analyzed to achieve logarithmic fixed-time settling. The RNNs based on the generic activation function are designed, which have a faster convergence rate and a fixed-time convergence property. By estimating the bound of the settling time function under given initial conditions in the bounded region, the semi-global fixed-time convergence of the RNN model is in turn established. It is shown that the predefined-time convergence of modified RNN models can be assured by adopting the inverse of the bound. Simulation results verify effectiveness of the proposed computing schemes for the time-variant QP problem solving and the repetitive motion planning of a redundant manipulator. [ABSTRACT FROM AUTHOR]

Fragility curves of retaining walls constitute an efficient tool for the estimation of seismic risk and can be utilized for prevention from potential damage or for immediate decision-making. In this work, fragility curves for cantilever retaining walls of three different heights are proposed, considering cohesionless soil materials. The seismic response of the soil-wall system, in terms of permanent vertical ground displacement of the backfill soil and permanent horizontal displacement of the wall's base, is estimated by conducting non-linear time history analyses, through the 2D finite element simulation method. Five initial conditions are investigated regarding the value of the global factor of safety (FS) under static conditions. An initial value of FS equal to 1.5 is considered for dry conditions. If the presence of the water table is taken into account, the corresponding FS drops to values ranging from 1.4 to 1.1. Parameters that characterize seismic intensity are evaluated based on criteria, in order to identify the intensity measures that best correlate with the system's response. Three damage states are adopted, corresponding to minor, moderate, and extensive damage. The approach of combined damage criteria is also investigated. Finally, fragility curves are derived demonstrating the degree of dependency on initial conditions. [ABSTRACT FROM AUTHOR]

A popular way to estimate the parameters of a hidden Markov model (HMM) is direct numerical maximization (DNM) of the (log-)likelihood function. The advantages of employing the TMB [Kristensen K, Nielsen A, Berg C, et al. TMB: automatic differentiation and Laplace approximation. J Stat Softw Articles. 2016;70(5):1–21] framework in R for this purpose were illustrated recently [Bacri T, Berentsen GD, Bulla J, et al. A gentle tutorial on accelerated parameter and confidence interval estimation for hidden Markov models using template model builder. Biom J. 2022 Oct;64(7):1260–1288]. In this paper, we present extensions of these results in two directions. First, we present a practical way to obtain uncertainty estimates in form of confidence intervals (CIs) for the so-called smoothing probabilities at moderate computational and programming effort via TMB. Our approach thus permits to avoid computer-intensive bootstrap methods. By means of several examples, we illustrate patterns present for the derived CIs. Secondly, we investigate the performance of popular optimizers available in R when estimating HMMs via DNM. Hereby, our focus lies on the potential benefits of employing TMB. Investigated criteria via a number of simulation studies are convergence speed, accuracy, and the impact of (poor) initial values. Our findings suggest that all optimizers considered benefit in terms of speed from using the gradient supplied by TMB. When supplying both gradient and Hessian from TMB, the number of iterations reduces, suggesting a more efficient convergence to the maximum of the log-likelihood. Last, we briefly point out potential advantages of a hybrid approach. [ABSTRACT FROM AUTHOR]

The Smoothed Particle Hydrodynamics (SPH) method is among the numerical methods that have attracted the attention of many researchers in recent years. This method as a Lagrangian method based on the movement of particles is also one of the meshless methods. In this method, the initial location or distribution of particles can play an important role in reducing numerical errors and its computational efficiency. In this research, based on wave modeling, which is one of the most conventional hydraulic phenomena, and using previous modeling experiences, six optimal and common distributions of particles including square distribution (SC), Triangular distribution, distribution based on WVT algorithm, distribution based on Greedy algorithm, Hexagonal distribution and Fibonacci distribution based on Fibonacci al-gorithm have been investigated. Based on the results of the pressure, velocity and the free surface level of the fluid in the location of the reference gauges of the laboratory model, it was determined that the square, triangular, WVT, Greedy, Hexagonal and Fibonacci distributions have a modeling error equal to 13.85%, 13.75%, 12.63%, 13.24%, 9.07% and 9.37%, respectively, and the two primary hexagonal and Fibonacci distributions have a modeling error of less than 10%, and in other words, it has the best performance in using the SPH method in order to improve the efficiency of the computational model. [ABSTRACT FROM AUTHOR]

This investigation delineates an innovative approach to fortify the secure key exchange process by integrating the robustness of the RSA algorithm with the unpredictability of a chaotic system, thereby advancing the security framework for color image encryption. Within this scheme, encryption keys are derived from a chaotic system, the initial conditions of which are dynamically modulated by the delta feature extracted from the source image. Such a design ensures that the system's behavior inherently adapts to the input image. The initial values and parameters governing the five-dimensional chaotic system are securely transmitted from sender to recipient via the RSA algorithm. Subsequently, diffusion and confusion processes are orchestrated through the application of two uniquely computed key matrices, which operate on the image at the column and row levels, respectively. This mechanism is instrumental in altering pixel values throughout the image. Performance evaluation of the proposed algorithm is quantified by several metrics: a high Number of Pixels Change Rate (NPCR) value of 99.621% illustrates its efficacy in pixel value modification, while a Peak Signal-to-Noise Ratio (PSNR) value of 8.898 implies the retention of image quality post-encryption. Furthermore, an Unified Average Changing Intensity (UACI) value of 33.823% signifies the algorithm's proficiency in introducing substantial variations in pixel intensities. The results corroborate the algorithm's competency in encrypting color images, underpinning its utility in diverse applications that necessitate stringent data and image protection measures against unauthorized access. [ABSTRACT FROM AUTHOR]

This study presents new empirical evidence on the role of time trends in low pay persistence. We utilize population-wide tax records to track monthly labour market trajectories of initially low-paid workers. By performing age- and qualification-specific regressions, we find that low pay persistence reduces with time. However, the magnitude is highly heterogeneous across workforce characteristics. For a qualified worker in their early 20s, the risk of staying on low-pay declines by, on average, 5–10% points after one year. For a worker in their 50s, persistence remains almost unchanged regardless of their qualification level. We conclude that policy initiatives need to be more nuanced than a simple one-size-fits-all approach by accounting for time trends in low-pay persistence. [ABSTRACT FROM AUTHOR]

In this article, we solve a class of coupled systems of nonlinear differential equations with appropriate initial, boundary, and four-point boundary conditions. We use quasilinearization to linearise these systems of equations and then use the Haar wavelets collocation approach to get the numerical solutions. We propose three quasilinearization schemes and observe that among them two schemes converge faster than the third one. We have also compared our results with other existing results that demonstrate the accuracy and effectiveness of our approach. The convergence of the schemes is also presented. The theory can be used to design and analyse the algorithm for the solution of SBVPs with the aid of Haar Wavelets. [ABSTRACT FROM AUTHOR]

Floods are among natural disasters which cause the largest damages worldwide each year, inducing fatalities of human lives, destruction of infrastructure and economical losses. Consequently, forecasting this type of events through hydro‐meteorological models is still of great importance from a civil protection point of view since it allows to reduce hydrological risk by means of early warning systems. Nevertheless, hydrological model initialization in ungauged basins, where there is lack of direct measurements of meteorological information, is a known issue affecting the entire prediction chain. The present study evaluates the possibility of using forecasts provided by the meteorological model MOLOCH developed by CNR‐ISAC forcing the FEST‐WB hydrological model developed by Politecnico di Milano to perform discharge simulations assuming that the forecasting errors are negligible when using the first 24 h of time horizon. The study is carried out in the urban catchments of Milan city, the Seveso‐Olona‐Lambro (SOL) river basins, located in northern Italy. The main hydro‐meteorological variables are analysed by comparing the spatialized and observed meteorological data, provided by an official regional network of weather stations plus a citizen scientists' contribution with the meteorological model forecasts. Moreover, a sensitivity analysis following the well‐known one‐factor‐at‐a‐time methodology is accomplished with the aim of defining which atmospheric forcing, beyond rainfall, mostly affects flowrate forecasts. Results generally show satisfactory correspondences between forecasts and observed data for the discharge variable at daily scale, although an underestimation of precipitation, particularly for severe events in summer, is present. Therefore, using meteorological forecasts to create daily initial conditions for hydrological model, instead of ground observations, might be a reliable and valuable approach, even if some considerations should be borne in mind when coupling the two models. [ABSTRACT FROM AUTHOR]

Despite that multistable isolators have been demonstrated to achieve the superior isolation performance compared with the linear ones, their performances are still inferior at ultra-low frequencies with the multi-solution phenomenon. Furthermore, their performances are dependent on the variation of initial conditions. Therefore, this paper proposes a fast fixed-time sliding mode controller to solve the ineffectiveness of the bistable dual-stage vibration isolator with external disturbances facing the above issues. Firstly, the bistable dual-stage isolator is modelled in the analytical form, and simulated compared with its linear counterpart to show its problems at some conditions. Secondly, the fast fixed-time sliding mode controller is presented with the modeling of sliding manifold and control inputs. Thirdly, the numerical simulation of the bistable dual-stage isolator with the proposed control is conducted, the results of which are compared with the finite-time sliding mode control. Finally, the outstanding feature of the proposed controller is concluded that the short convergence time becomes bounded with increasing initial conditions at both low and high excitation amplitudes. This work provides a valuable method to improve the isolation performance and robustness of the multistable vibration isolators. [ABSTRACT FROM AUTHOR]

Abstract Ecosystems around the globe are experiencing changes in both the magnitude and fluctuations of environmental conditions due to land use and climate change. In response, ecologists are increasingly using near‐term, iterative ecological forecasts to predict how ecosystems will change in the future. To date, many near‐term, iterative forecasting systems have been developed using high temporal frequency (minute to hourly resolution) data streams for assimilation. However, this approach may be cost‐prohibitive or impossible for forecasting ecological variables that lack high‐frequency sensors or have high data latency (i.e., a delay before data are available for modeling after collection). To explore the effects of data assimilation frequency on forecast skill, we developed water temperature forecasts for a eutrophic drinking water reservoir and conducted data assimilation experiments by selectively withholding observations to examine the effect of data availability on forecast accuracy. We used in situ sensors, manually collected data, and a calibrated water quality ecosystem model driven by forecasted weather data to generate future water temperature forecasts using Forecasting Lake and Reservoir Ecosystems (FLARE), an open source water quality forecasting system. We tested the effect of daily, weekly, fortnightly, and monthly data assimilation on the skill of 1‐ to 35‐day‐ahead water temperature forecasts. We found that forecast skill varied depending on the season, forecast horizon, depth, and data assimilation frequency, but overall forecast performance was high, with a mean 1‐day‐ahead forecast root mean square error (RMSE) of 0.81°C, mean 7‐day RMSE of 1.15°C, and mean 35‐day RMSE of 1.94°C. Aggregated across the year, daily data assimilation yielded the most skillful forecasts at 1‐ to 7‐day‐ahead horizons, but weekly data assimilation resulted in the most skillful forecasts at 8‐ to 35‐day‐ahead horizons. Within a year, forecasts with weekly data assimilation consistently outperformed forecasts with daily data assimilation after the 8‐day forecast horizon during mixed spring/autumn periods and 5‐ to 14‐day‐ahead horizons during the summer‐stratified period, depending on depth. Our results suggest that lower frequency data (i.e., weekly) may be adequate for developing accurate forecasts in some applications, further enabling the development of forecasts broadly across ecosystems and ecological variables without high‐frequency sensor data.

Event-based hydrological models are extensively adopted for the estimation of design floods and in operational flood forecasting frameworks. However, an accurate estimation of the initial hydrologic condition (IHC) is essential in enhancing the predictive capability of an event-based hydrological model. Hence, in this study, IHCs of an event-based conceptual model are estimated using two different methods: (1) assimilation of observed variables such as streamflow and soil moisture using an ensemble Kalman filter and (2) states obtained from the continuous model calibrated using four different calibration metrics. The observed flood events at the Jagdalpur catchment are simulated using a conceptual hydrologic model setup at two spatial resolutions (lumped and semi-distributed). The results of the study demonstrate that IHCs estimated by the continuous models perform better than those obtained through data assimilation. The performance of semi-distributed event-based models was found to be outperforming their lumped counterparts demonstrating the advantage of increased model resolution. The states obtained from the continuous models calibrated using Nash–Sutcliffe Efficiency (NSE) are performing well in initialising the event-based models. The median efficiency of the semi-distributed event-based model (based on states from the NSE calibrated continuous model) is 0.91 and 0.77 during calibration and validation periods, respectively. HIGHLIGHTS Methods to estimate the initial hydrologic condition (IHC) to initialise event-based models were evaluated.; Streamflow assimilation in both lumped and semi-distributed models led to improved simulations.; Soil moisture assimilation yielded slightly better predictions in the semi-distributed model.; Semi-distributed event-based model, initialised by IHCs extracted from the corresponding continuous model, is outperforming other models.;

Offshore fixed metal platforms have unique characteristics in specific location such as shallow waters, and oil platform may be physically connected to seabed. Offshore platforms in the sea are affected by complex and destructive forces caused by wind, waves, marine currents, earthquakes and even large displacement vortex forces. The purpose of refurbishing metal fixed platforms is to somehow equalize the capacity of the structure with its seismic requirements. In some cases, they increase the capacity of the structure to meet seismic requirements. In this research, dynamic vibration absorbers or dampers, such as passive dampers, fluid system adjusted for damping vibrations and reduced structural response against wave and earthquake hydrodynamic loads will be used as an efficient control method. The purpose of this research is to improve the performance of the liquid platform vibration damping of the metal platform. The results show that equipping offshore platforms with liquid damping systems has a good performance on the safety and dynamic behaviour of the platforms and has a significant effect on the displacement response and acceleration of the platform under study at its highest deck level. Also, as the water depth in the tank increases, the damping value for the rectangular and cylindrical tanks decreases and the rectangular tank creates more control and damping force than the cylindrical tank.

This paper investigates the effects of the employment rate on economic growth during the transition process. We start with the mainstream growth econometrics approach that controls for convergence and, in order to control for heterogeneity of countries in our sample, we control our estimates for transition-specific indicators such as initial conditions (pre-transition history), governance quality, privatisation methods as well as various indicators of institutional development. We use a wide range of model specifications using fixed effects as well as Bayesian averaging to address the problem of model uncertainty in 24 countries during the 1995-2019 period. Contrary to the neoclassical growth model assumptions, we find that the employment rate is one of the most important growth factors even after three decades. Results also indicate that convergence (initial level of development) robustly explains a part of cross-country growth rate differentials, while the effects of the initial conditions (pre-transition history) are robust, but fade out after the first decade. We do not find evidence that physical capital and population growth explain the growth in our sample.

Due to large northward heat transport, the Atlantic meridional overturning circulation (AMOC) strongly affects the climate of various regions. Its internal variability has been shown to be predictable decades ahead within climate models, providing the hope that synchronizing ocean circulation with observations can improve decadal predictions, notably of the North Atlantic subpolar gyre (SPG). Climate predictions require a starting point which is a reconstruction of the past climate. This is usually performed with data assimilation methods that blend available observations and climate model states together. There is no unique method to derive the initial conditions. Moreover, this can be performed using full-field observations or their anomalies superimposed on the model's climatology to avoid strong drifts in predictions. How critical ocean circulation drifts are for prediction skill has not been assessed yet. We analyze this possible connection using the dataset of 12 decadal prediction systems from the World Meteorological Organization Lead Centre for Annual-to-Decadal Climate Prediction. We find a variety of initial AMOC errors within the predictions related to a dynamically imbalanced ocean states leading to strongly displaced or multiple maxima in the overturning structures. This likely results in a blend of what is known as model drift and initial shock. We identify that the AMOC initialization influences the quality of the SPG predictions. When predictions show a large initial error in their AMOC, they usually have low skill for predicting internal variability of the SPG for a time horizon of 6-10 years. Full-field initialized predictions with low AMOC drift show better SPG skill than those with a large AMOC drift. Nevertheless, while the anomaly-initialized predictions do not experience large drifts, they show low SPG skill when skill also present in historical runs is removed using a residual correlation metric. Thus, reducing initial shock and model biases for the ocean circulation in prediction systems might help to improve their prediction for the SPG beyond 5 years. Climate predictions could also benefit from quality-check procedure for assimilation/initialization because currently the research groups only reveal the problems in initialization once the set of predictions has been completed, which is an expensive effort.

Abstract Floods are among natural disasters which cause the largest damages worldwide each year, inducing fatalities of human lives, destruction of infrastructure and economical losses. Consequently, forecasting this type of events through hydro‐meteorological models is still of great importance from a civil protection point of view since it allows to reduce hydrological risk by means of early warning systems. Nevertheless, hydrological model initialization in ungauged basins, where there is lack of direct measurements of meteorological information, is a known issue affecting the entire prediction chain. The present study evaluates the possibility of using forecasts provided by the meteorological model MOLOCH developed by CNR‐ISAC forcing the FEST‐WB hydrological model developed by Politecnico di Milano to perform discharge simulations assuming that the forecasting errors are negligible when using the first 24 h of time horizon. The study is carried out in the urban catchments of Milan city, the Seveso‐Olona‐Lambro (SOL) river basins, located in northern Italy. The main hydro‐meteorological variables are analysed by comparing the spatialized and observed meteorological data, provided by an official regional network of weather stations plus a citizen scientists' contribution with the meteorological model forecasts. Moreover, a sensitivity analysis following the well‐known one‐factor‐at‐a‐time methodology is accomplished with the aim of defining which atmospheric forcing, beyond rainfall, mostly affects flowrate forecasts. Results generally show satisfactory correspondences between forecasts and observed data for the discharge variable at daily scale, although an underestimation of precipitation, particularly for severe events in summer, is present. Therefore, using meteorological forecasts to create daily initial conditions for hydrological model, instead of ground observations, might be a reliable and valuable approach, even if some considerations should be borne in mind when coupling the two models.

Converting arable land to semi-natural grassland has the potential to restore soil structural stability (SSS) and increase soil organic carbon (SOC) storage. However, the effect of the initial soil C level and conditions on the changes following introduction of grassland is poorly understood. We quantified changes in SSS and SOC in topsoil from the Askov straw disposal and cover cropping experiment initiated in 1981 in Denmark before and after its conversion to semi-natural grassland in 2020. Due to different straw input and cover crop use in the arable phase, the grass was established on soils with different initial conditions (11.9–17.9 g C kg−1 oven-dry soil). Soils sampled in 2019 and 2021 from plots subjected to different treatments in the arable phase were analyzed for clay dispersibility measured on two macro-aggregate sizes (ClayDis 1–2 mm and ClayDis 8–16 mm), wet stability of aggregates (WSA) and SOC. Irrespective of initial soil conditions, introduction of semi-natural grassland resulted in a rapid increase in SOC, a decline in ClayDis 1–2 mm and no change in WSA. The relative change in ClayDis 1–2 mm was larger than the change in SOC, and the slope for the relationship between SOC and ClayDis 1–2 mm decreased after conversion to grassland. Based on these observations, we suggest that the additional driver in play was aggregate-binding and -bonding agents being persistent due to the absence of tillage following the conversion to grassland. Notably, changes in SOC and SSS were similar regardless of initial soil conditions, which may be related to a similar productivity in the semi-natural grassland and soil C saturation.

For a class of impulsive fractional differential equation with fractional order r∈(1,2)by means of solution operator Sr(t), we develop the mild solution and deal with the existence results of mild solution for the considered equation with state-dependent delay through three fixed point theorems, namely Banach, Nonlinear Leray–Schauder Alternative and Krasnoselskii’s fixed point theorem. Next, we define the mild solution via an operator function Wr,β(τ)for abstract version of the considered equation. Further, we study the trajectory controllability (which is a stronger notion than other controllability) of the equation. In the last, we present some examples to illustrate the established results through partial fractional differential equations.

Fuel droplet evaporation is essential to the generation of flammable mixtures in thermal engines. Generally, liquid fuel is injected directly into the hot, high-pressure atmosphere to form scattered droplets. Many investigations on droplet evaporation have been conducted with techniques involving the influence of boundaries, such as suspended wires. Ultrasonic levitation is a non-contact and non-destructive technology that can avoid the impact of hanging wire on droplet shape and heat transfer. Besides, it can simultaneously levitate multiple droplets and allow them to associate with each other or be used to study droplet instability behaviors. This paper reviews the influences of the acoustic field on levitated droplets, the evaporation characteristics of acoustically levitated droplets, and the prospects and limitations of ultrasonic suspension methods for droplet evaporation, which can serve as references for relevant studies.

Offshore wind farms equipped with Direct Drive Permanent Magnet Synchronous Generators (DD-PMSG) are drawing increased attention due to their advantage over other variable speed technologies. VSC-HVDC links are considered the most suitable option for transferring power to the onshore system. The integration of VSC-HVDC connected DD-PMSG based offshore wind farms into multi-machine systems is explored in this paper. A novel approach for power flow and initial condition calculations is proposed to facilitate dynamic analysis of the system. For three cases of the most commonly specified quantities of the wind farm, efficient methods have been described. The cases comprise combinations of data like the total output of the wind farm, the number of wind turbines, the wind speed, or the output of individual wind turbine, which are frequently given in literature. This approach enables the user to build the dynamic model of the system in any basic graphical dynamic modeler and numerical computational software without requiring power system toolboxes or electromagnetic transient packages. The proposed methods are highly effective for studies focusing primarily on the dynamic aspects and controls of the system. Case studies and simulations are conducted to verify the proposed technique. [ABSTRACT FROM AUTHOR]

We present a new class of methods for identification and inference in dynamic models with serially correlated unobservables, which typically imply that state variables are econometrically endogenous. In the context of Industrial Organization, these state variables often reflect econometrically endogenous market structure. We propose the use of Generalized Instrument Variables methods to identify those dynamic policy functions that are consistent with instrumental variable (IV) restrictions. Extending popular "two-step" methods, these policy functions then identify a set of structural parameters that are consistent with the dynamic model, the IV restrictions and the data. We provide computed illustrations to both single-agent and oligopoly examples. We also present a simple empirical analysis that, among other things, supports the counterfactual study of an environmental policy entailing an increase in sunk costs. [ABSTRACT FROM AUTHOR]

When the universe is treated as a quantum system, it is described by a wave function. This wave function is a function not only of the matter fields, but also of spacetime. The no-boundary proposal is the idea that the wave function should be calculated by summing over geometries that have no boundary to the past, and over regular matter configurations on these geometries. Accordingly, the universe is finite, self-contained and the big bang singularity is avoided. Moreover, given a dynamical theory, the no-boundary proposal provides probabilities for various solutions of the theory. In this sense it provides a quantum theory of initial conditions. This review starts with a general overview of the framework of quantum cosmology, describing both the canonical and path integral approaches, and their interpretations. After recalling several heuristic motivations for the no-boundary proposal, its consequences are illustrated with simple examples, mainly in the context of cosmic inflation. We review how to include perturbations, assess the classicality of spacetime and how probabilities may be derived. A special emphasis is given to explicit implementations in minisuperspace, to observational consequences, and to the relationship of the no-boundary wave function with string theory. At each stage, the required analytic and numerical techniques are explained in detail, including the Picard–Lefschetz approach to oscillating integrals. [ABSTRACT FROM AUTHOR]

In this article, we describe the xttestms command, which implements the Lagrange multiplier test proposed by Magazzini and Calzolari (2020, Econometric Reviews 39: 115–134). The test verifies the validity of the initial conditions in dynamic panel-data models, which is required for consistency of the system generalized method of moments estimator. [ABSTRACT FROM AUTHOR]

Offshore fixed metal platforms have unique characteristics in specific location such as shallow waters, and oil platform may be physically connected to seabed. Offshore platforms in the sea are affected by complex and destructive forces caused by wind, waves, marine currents, earthquakes and even large displacement vortex forces. The purpose of refurbishing metal fixed platforms is to somehow equalize the capacity of the structure with its seismic requirements. In some cases, they increase the capacity of the structure to meet seismic requirements. In this research, dynamic vibration absorbers or dampers, such as passive dampers, fluid system adjusted for damping vibrations and reduced structural response against wave and earthquake hydrodynamic loads will be used as an efficient control method. The purpose of this research is to improve the performance of the liquid platform vibration damping of the metal platform. The results show that equipping offshore platforms with liquid damping systems has a good performance on the safety and dynamic behaviour of the platforms and has a significant effect on the displacement response and acceleration of the platform under study at its highest deck level. Also, as the water depth in the tank increases, the damping value for the rectangular and cylindrical tanks decreases and the rectangular tank creates more control and damping force than the cylindrical tank. [ABSTRACT FROM AUTHOR]

In this paper we study the dependence of solutions of uncertain initial value problems (UIVP) on the initial values. Introducing a contraction mapping and using Banach Fixed Point Theorem (BFPT), the existence and uniqueness (EaU) of solutions of the UIVP will be proven. We show that under appropriate assumptions, the solutions of UIVP are continues and differentiable with respect to initial conditions (ICs). The paper will be ended by proving a theorem about the existence of solutions of an autonomous UIVP under weaker conditions. This theorem is a generalization of PeanoTheorem to UDEs.

Despite a significant number of theories of friction and wear, the issue of wave formation on contacting surfaces and in the zone ahead of the moving part has not yet received a sufficiently deep justification. Based on accumulated theoretical and experimental data, it is generally accepted to consider the groups of theories: 1) geometric, 2) molecular (adhesive), 3) deformation, 4) combined [1]. The most widespread opinion is that friction is caused by plastic extrusion of the material and the destruction of welding bridges (F. R. Bowden and D. Tabor), which is also confirmed by the hypothesis of Ernst and Margent, where friction is caused by adhesion (at the molecular level and surface roughness). At the same time, the experimental results of F. R. Bowden and D. Tabor indicate that the effect of friction depends on both the adhesion forces and the deformation of the material in the thin surface layer. As a result of the application of significant compressive forces and tangential frictional forces, a large amount of energy is released at the contact of two bodies, and with weak heat exchange with the environment, high gradients of temperature changes occur, and in the case of metal parts, under certain conditions, the welding effect of both individual micro-uniformities and contact surfaces in general, which is confirmed by research results [2]. [ABSTRACT FROM AUTHOR]

When trend and seasonality are detected, the Holt-Winters multiplicative approach is one of the most commonly used methods for forecasting time series data. Choosing the proper initial values for level, trend, and seasonality plays a vital role in this method. In this paper, a new and efficient procedure to choose the initial values for the Holt-Winters multiplicative method is developed. A total of 12 types of agricultural satellite backscatter values are used for analysis, estimated, and compared with the existing Hansun and Holt-Winters methods and the proposed initial setting method with the best smoothing constants. According to the analysis of the mean absolute percentage error, symmetric mean absolute percentage error, Theil-U statistics, and root mean squared error, the proposed approaches outperformed the existing methods in this experiment. [ABSTRACT FROM AUTHOR]