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Inferior vena cava filter (IVCF) implantation is a common method of thrombus capture. By implanting a filter in the inferior vena cava (IVC), microemboli can be effectively blocked from entering the pulmonary circulation, thereby avoiding acute pulmonary embolism (PE). Inspired by the helical flow effect in the human arterial system, we propose a helical retrievable IVCF, which, due to the presence of a helical structure inducing a helical flow pattern of blood in the region near the IVCF, can effectively avoid the deposition of microemboli in the vicinity of the IVCF while promoting the cleavage of the captured thrombus clot. It also reduces the risk of IVCF dislodging and slipping in the vessel because its shape expands in the radial direction, allowing its distal end to fit closely to the IVC wall, and because its contact structure with the inner IVC wall is curved, increasing the contact area and reducing the risk of the vessel wall being punctured by the IVCF support structure. We used ANSYS 2023 software to conduct unidirectional fluid–structure coupling simulation of four different forms of IVCF, combined with microthrombus capture experiments in vitro, to explore the impact of these four forms of IVCF on blood flow patterns and to evaluate the risk of IVCF perforation and IVCF dislocation. It can be seen from the numerical simulation results that the helical structure does have the function of inducing blood flow to undergo helical flow dynamics, and the increase in wall shear stress (WSS) brought about by this function can improve the situation of thrombosis accumulation to a certain extent. Meanwhile, the placement of IVCF will change the flow state of blood flow and lead to the deformation of blood vessels. In in vitro experiments, we found that the density of the helical support rod is a key factor affecting the thrombus trapping efficiency, and in addition, the contact area between the IVCF and the vessel wall has a major influence on the risk of IVCF displacement. [ABSTRACT FROM AUTHOR]

Data quality, especially label quality, may have a significant impact on the prediction accuracy in supervised learning. Training on datasets with label noise causes a degradation in performance and a reduction in prediction accuracy. To overcome the numerical label noise problem in regression, we estimate the posterior distribution of the true label through the Gaussian mixture model (GMM). Then, label noise estimation is proposed by integrating the idea of maximum a posteriori (MAP) estimation with the posterior distribution. Besides, a noise filtering algorithm with MAP estimation (MAPNF) is designed by combining the optimal sample selection framework with the estimator. Extensive experiments are carried out on benchmark datasets and an age estimation dataset to verify the effectiveness of MAPNF. The results on benchmark datasets show that MAPNF outperforms other latest filtering algorithms in improving the generalization performance of different regression models, including noise-sensitive models and noise-robust models. The model error can be reduced by 29.7% to 69.6%. Our proposed approach can also identify erroneous labels in an age estimation dataset (total of 18424). The model trained on the filtered dataset (19% of the data removed) achieves a reduced test error on the dataset by at least 2.68%. The results demonstrate a less-is-better effect by achieving lower prediction errors with fewer high-quality samples. It can be concluded that MAPNF can effectively identify label noise and optimize the data quality. [ABSTRACT FROM AUTHOR]

The orographic effect on the spatial structure of precipitation is a fundamental problem in hydrometeorology that still requires a better understanding of the physical processes involved in the emergence of rainfall patterns and their complex statistical structure. In tropical regions, where meteorological measurements are notoriously sparse and data quality control is often poor or missing, the study of precipitation modeling and prediction is challenging. This research aims to show an innovative approach based on a random cascade downscaling method to generate high‐resolution precipitation products from coarse‐scale precipitation products. This approach also includes a topographic enhancement function for describing the altitudinal variability of precipitation and a numerical diffusion filter to lessen the blockiness problem of random cascades. The suggested approach was applied to analyze some long‐term precipitation statistics in the metropolitan area of Mexico City. The model result agrees closely with the temporal statistics of the selected precipitation products and reflects complex orographic constraints. The proposed downscaling approach becomes an alternative to expensive computational methods and allows urban hydrology applications and analysis of small watersheds to incorporate the effects of complex orography. Plain Language Summary: The Topographic Random Cascade approach is a data‐driven model that may describe the main elements of the space‐time structure of precipitation. This approach also includes a first subcomponent that explains how precipitation changes with the topographic altitude of the terrain and another that smooths the original simulated field and prevents some known technical problems derived from the application of random cascade techniques. In this study, the Topographic Random Cascade approach was applied to describe precipitation in the metropolitan area of Mexico City and surrounding areas; it was also used to improve the spatial resolution of 2‐dimensional precipitation products from IMERG and CHIRPS. In the studied region, where complex orography is present, the influence of terrain on precipitation was considered and an adaptation of a fractal‐based precipitation model was needed to generate a realistic version of precipitation fields at smaller scales than those found in large‐scale precipitation data. The results obtained from this approach highlight the ability to preserve long‐term statistics and represent the overall spatial composition of the orographic precipitation. Based on the achievements of the Topographic Random Cascade approach, its usage can be extended to hydrological and meteorological applications by scientists and practitioners. Key Points: An innovative random cascade approach was used to downscale coarse‐scale precipitation products in a highly complex topographic areaSimulated precipitation fields preserve the overall structure of spatial geometry and the long‐term statistics of the precipitation productsA generalized topographic precipitation enhancement function allows for a better description of the altitudinal variability of precipitation [ABSTRACT FROM AUTHOR]

Numerical simulations have been an increasingly important tool in space physics. Here, we introduce an open-source three-dimensional compressible Hall-Magnetohydrodynamic (MHD) simulation code LAPS (UCLA-Pseudo-Spectral, https://github.com/chenshihelio/LAPS). The code adopts a pseudo-spectral method based on Fourier Transform to evaluate spatial derivatives, and third-order explicit Runge-Kutta method for time advancement. It is parallelized using Message-Passing-Interface (MPI) with a "pencil" parallelization strategy and has very high scalability. The Expanding-Box-Model is implemented to incorporate spherical expansion effects of the solar wind. We carry out test simulations based on four classic (Hall)-MHD processes, namely, 1) incompressible Hall-MHD waves, 2) incompressible tearing mode instability, 3) Orszag-Tang vortex, and 4) parametric decay instability. The test results agree perfectly with theory predictions and results of previous studies. Given all its features, LAPS is a powerful tool for large-scale simulations of solar wind turbulence as well as other MHD and Hall-MHD processes happening in space. [ABSTRACT FROM AUTHOR]

This study explores the use of distortion product otoacoustic emission (DPOAE) as a hearing screening modality for newborns and adults with hearing impairment. The goal is to improve cochlear response by developing digital filter characteristics to make it consistent for specialists to make accurate diagnoses. To accomplish this, the proposed system consists of a DPOAE ER-10C as stimulation and cochlear response probe, a digital signal processor, an oscilloscope, PC, and audio cables. Real-time distortion product frequency components were extracted using a signal processor of TMS320C6713. To validate the system, a senior medical physicist conducted a study for five hearing-normal participants of men and women aged 38 - 55 years old, at Baghdad Medical City / center for hearing and communication in Iraq. The results showed an ability to extract distortion product components in real-time implementation, with the superiority of shape parameters greater than 0.5. In addition, the quantization of filter coefficients was compared for both floating-point arithmetic and fixed-point arithmetic. Noisy environment-based noise reduction techniques have to be investigated by considering the implementation of robust digital signal processing techniques. Finally, the proposed system would contribute to advancements in hearing screening and treatment for those with hearing impairment. [ABSTRACT FROM AUTHOR]

For large eddy simulations (LES) of stratified turbulence in the strongly stratified regime, an artificial neural network (ANN) with five hidden layers is used to construct a sub-grid scale (SGS) model. The ANN is assessed by comparing it to the Smagorinsky model, the dynamic Smagorinsky model, the gradient model, and filtered direct numerical simulation data. In the a priori test, the SGS model using ANN performed better than the Smagorinsky model and the gradient model in terms of the correlation coefficient and relative error of the energy transfer rate. However, the ANN does not provide sufficient energy dissipation when it is applied to LES with a larger filter width because it overpredicts backscatter. To address this problem, we also trained a constrained ANN using a custom loss function that penalizes excessive backscatter. It is shown that the constrained ANN successfully predicts less backscatter, maintaining the high correlation coefficient without ad hoc clipping. These results show that ANN is a promising tool for realizing a highly accurate and stable SGS model for stratified turbulence. [ABSTRACT FROM AUTHOR]

We present relative astrometric and photometric measurements of visual double stars made in 2016–2017, with PISCO2 installed at the 76‐cm refractor of Côte d'Azur Observatory in Nice (France). Our observing list contains orbital couples as well as double stars whose motion is still uncertain. Three different techniques were used for obtaining measurements: Lucky Imaging, Speckle Interferometry and the direct vector autocorrelation (DVA) method. From our observations of 1,510 multiple stars, we obtained 2,918 new measurements with angular separations in the range 0″.1–20″ and an average accuracy of 0.019$$ 0.019 $$. The mean error on the position angles is 1°.1. Most of the position angles were determined without the usual 180° ambiguity with the application of the DVA technique and/or by inspection of the Lucky images or the long integration files. We managed to routinely monitor faint systems (mV≈9−12$$ {m}_V\approx 9-12 $$) with large magnitude differences (up to ΔmV≈4.5$$ \Delta {m}_V\approx 4.5 $$). We have thus been able to measure six systems containing red dwarf stars that had been poorly monitored since their discovery, from which we estimated the stellar masses thanks to Gaia measurements. We also measured the magnitude difference of the two components of 593 double stars with a mean error of 0.1 mag. Thanks to good seeing images and with the use of high‐contrast numerical filters, we have also been able to obtain 46 measurements with an angular separation smaller than the diffraction limit of our instrumentation. Except for a few objects that have been discussed, our measurements are in good agreement with the ephemerides computed with published orbital elements, even for the double stars whose separation is smaller than the diffraction limit. We also report the measurements of 205 new double stars that we found in the files obtained during the observations. [ABSTRACT FROM AUTHOR]

This paper is on the use of a hybrid boundary element method/finite element method (BEM/FEM) to determine the induced electric field in spherical human head models exposed to high-frequency plane electromagnetic (EM) wave. The geometrically simplified models include the homogeneous one and the non-homogeneous one, featuring compartments such as skin, skull, CSF, and brain. Both models are illuminated by plane EM wave at frequencies including 900 and 1800 MHz, 3500 MHz pertaining to 5G communication systems and also 6000 MHz representing the transition frequency related to the EMF safety standards. The numerical results for the electric field induced in both human head models are presented, while the emphasis is on the electric field along the propagation axis. The novelty of this work is related to the subsequent post-processing of the sampled induced field along the model axis by using two different numerical filtering techniques. It is shown that using the peak detection algorithm, the spline interpolation could be used to estimate the signal envelope. The exponentially decaying nature of the envelope allows to assess the penetration depth of the EM radiation within the biological tissue. Moreover, it is shown that the analytically calculated penetration depth, derived for the unbounded medium, is well reproduced by the numerical computation of EM field. [ABSTRACT FROM AUTHOR]

In the field of open-pit geological risk management, landslide failure time prediction is one of the important topics. Based on the analysis of displacement monitoring data, the inverse velocity method (INV) has become an effective method to solve this issue. To improve the reliability of landslide prediction, four filters were used to test the velocity time series, and the effect of landslide failure time prediction was compared and analyzed. The results show that the sliding process of landslide can be divided into three stages based on the INV: the initial attenuation stage (regressive stage), the second attenuation stage (progressive stage), and the linear reduction stage (autoregressive stage). The accuracy of the INV is closely related to the measured noise of the monitoring equipment and the natural noise of the environment, which will affect the identification of different deformation stages. Compared with the raw data and the exponential smoothing filter (ESF) models, the fitting effect of the short-term smoothing filter (SSF) and long-term smoothing filter (LSF) in the linear autoregressive stage is better. A stratified prediction method combining SSF and LSF is proposed. The prediction method is divided into two levels, and the application of this method is given. [ABSTRACT FROM AUTHOR]

This paper first presents six (6) types of benchmark amplitude design specifications, including variable low-pass specification, variable high-pass specification, variable bandpass specification, variable bandstop specification, variable notch-frequency specification, and variable notch-width specification. The design specifications can be taken as typical benchmark specifications targeted at testing as well as validating the effectiveness of various newly developed algorithms for designing various typical variable filters (VFs). Then, the paper provides a novel unity-bounded (UB) function developed for ensuring the stability of those VFs with recursive structures. Finally, a wide variety of simulation results are included for validating the design performance as well as the assured stability by using both the benchmark design specifications and the novel UB function. [ABSTRACT FROM AUTHOR]

This paper studies the dissipative filtering of uncertain singular time-varying delay Markovian jump systems (MJSs) with nonlinear perturbations and generally uncertain transition rates (GUTRs). We focus on the design of a mixed filter, which can include mode-dependent and mode-independent filters in a unified and simple structure. First, a new Lyapunov–Krasovskii functional is proposed through the delay-decomposition approach, and improved Wirtinger-based integral inequalities are used to obtain a sufficient condition to ensure the stochastic admissibility of the considered uncertain nonlinear singular time-varying delay MJSs with dissipative performance. Based on these ingredients, the explicit expression of the desired mixed filter is obtained. Finally, some numerical examples and the filter design of a two-loop circuit network reflect the less conservatism and effectiveness of the method in this article. [ABSTRACT FROM AUTHOR]

Digital filtration is widely used today in many application fields, and with the increased use of low-cost embedded processors, it can be applied to vast areas. A drawback of digital filtration algorithms is the introduction of phase angle shifts in the filtered signals, thereby creating undesirable characteristics in many application fields. In this work, low-pass filters of finite impulse response and infinite impulse response types are designed with an innovative buffering scheme to delay a digitally low-passed signal by an angle ranging from 0° to 180° for real-time signals. The application of the filtration and buffering scheme on a cost-effective embedded processor with limited signal processing capabilities opens the horizons for its applicability in many signal processing fields. In assessing its practicality, the generated filtered output signal is correlated with the original signal (a low-passed version), revealing correlation values reaching 0.99 in certain instances. The novelty of the proposed approach enables its application to a broad-spectrum area of digital signal filtration. [ABSTRACT FROM AUTHOR]

Getting highly accurate output in biomedical data processing concerning biomedical signals and images is impossible because biomedical data are generated from various electronic and electrical resources that can deliver the data with noise. Filtering is widely used for signal and image processing applications in medical, multimedia, communications, biomedical electronics, and computer vision. The biggest problem in biomedical signal and image processing is developing a perfect filter for the system. Digital filters are more advanced in precision and stability than analog filters. Digital filters are getting more attention due to the increasing advancements in digital technologies. Hence, most medical image and signal processing techniques use digital filters for preprocessing tasks. This paper briefly explains various filters used in medical image and signal processing. Matlab is a famous mathematical, analytical software with a platform and built-in tools to design filters and experiment with different inputs. Even though this paper implements filters like, Mean, Median, Weighted Average, Guassian, and Bilateral in Python to verify their performance, a suitable filter can be selected for biomedical applications by comparing their performance. [ABSTRACT FROM AUTHOR]

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Gravity wave (GW) activities play a prominent role in the complex coupling process of wave–wave and wave–background circulation around mid-low-latitude and equatorial areas. The wavelengths are wide, from about 10 m to 100 km, with a period from minutes to hours. However, the oscillations of GW activities are apparently different between the period bands of 0.1 to 1 h (HF) and 1 to 5 h (LF). To further understand the characteristics of GW activities, the neutral winds during 2008–2009 with a resolution of 3 min obtained from a medium-frequency (MF) radar in Kunming (25.6° N, 103.8° E) were analyzed. Using two numerical filters, the HF and LF GWs were estimated. Interestingly, the power spectral density grows larger as the frequency increases. It linearly falls with decreasing frequency when the period is less than 2 h. The seasonal variations in both HF and LF GWs are strongly demonstrated in August–September, November, and February–March with maximum meridional variances of 1100 m2 s−2 and 500 m2 s−2 and maximum zonal variances of 800 m2 s−2 and 350 m2 s−2 in, respectively. The turbulent velocity was also calculated and shows similar oscillations with GW activities. Furthermore, the GW propagation direction exhibits strong seasonal variations, which may be dependent on the location of the motivating source and background wind. [ABSTRACT FROM AUTHOR]

We present relative astrometric and photometric measurements of visual double stars made in 2013–2015, with PISCO2 installed at the 76‐cm refractor of Côte d'Azur Observatory in Nice (France). Our observing list contains orbital couples as well as double stars whose motion is still uncertain. Most of the observations were done in 2015, but some other observations of wide couples were done in 2013–2014 (0.3% of the total of the observations). Three different techniques were used for obtaining measurements: lucky imaging, speckle interferometry and the direct vector autocorrelation method. From our observations of 2837 multiple stars, we obtained 5182 new measurements with angular separations in the range of 0″$$ {}^{{\prime\prime} } $$.1–32″$$ {}^{{\prime\prime} } $$ and an average accuracy of 0″.018$$ {0}^{{\prime\prime} }.018 $$. The mean error on the position angles is 0°.8. Most of the position angles were determined without the usual 180° ambiguity with the application of the direct vector autocorrelation technique and/or by inspection of the Lucky images or the long integration files. We managed to routinely monitor faint systems (mV≈9−11$$ {m}_V\approx 9-11 $$) with large magnitude difference (up to ΔmV≈4.5$$ \Delta {m}_V\approx 4.5 $$). We have thus been able to measure 21 systems containing red dwarf stars that had been poorly monitored since their discovery, from which we estimated the stellar masses thanks to Gaia measurements. We also measured the magnitude difference of the two components of 1079 double stars with an estimated error of 0.2 mag. Except for a few objects that are discussed, our measurements are in good agreement with the ephemerides computed with published orbital elements, even for the double stars whose separation is smaller than the diffraction limit. Thanks to good seeing images and with the use of high‐contrast numerical filters, we have also been able to obtain 196 measurements with an angular separation smaller than the diffraction limit of our instrumentation, and consistent with those obtained with larger telescopes. We also report measurements of the 164 new double stars that we found in the files obtained during the observations. Finally, from a study of the DR3 Gaia release, it is shown that all the objects of the Tycho Double Star Catalog that we did not resolve in 2015 are probably false detections by Tycho. [ABSTRACT FROM AUTHOR]

Numerical simulation of the spatial wind field plays a very important role in the study of wind-induced response law of transmission tower structures. A reasonable construction of a numerical simulation method of the wind field is conducive to the study of wind-induced response law under the action of an actual wind field. Currently, many research studies rely on simulating spatial wind fields as Gaussian wind, often overlooking the basic non-Gaussian characteristics. This paper aims to provide a comprehensive overview of the historical development and current state of spatial wind field simulations, along with a detailed introduction to standard simulation methods. Furthermore, it delves into the composition and unique characteristics of spatial winds. The process of fluctuating wind simulation based on the linear filter AR method is improved by introducing spatial correlation and non-Gaussian distribution characteristics. The numerical simulation method of the wind field is verified by taking the actual transmission tower as a calculation case. The results show that the method summarized in this paper has a broader application range and can effectively simulate the actual spatial wind field under various conditions, which provides a valuable data basis for the subsequent research on the wind-induced response of transmission tower lines. [ABSTRACT FROM AUTHOR]

Efforts to monitor the emissions and absorptions of atmospheric carbon dioxide (CO2) over the globe and to understand their varying regional patterns with greater accuracy have intensified in recent years. This study evaluates the performance of a new model coupling, ICO, built around the Laboratoire de Météorologie Dynamique atmospheric general circulation model (LMDZ) for simulating CO2 transport. ICO utilizes the new icosahedral hydrostatic dynamical core called Dynamico running on an unstructured grid, which enables potential improvements in spatial resolution at the Equator while removing artificial distortions and numerical filters at the poles. Comparisons with a reference configuration using a structured latitude-longitude grid reveal that ICO well captures seasonal variations in CO2 concentrations at surface stations. While not significantly enhancing the capture of complex seasonal patterns, ICO maintains comparable accuracy. Both configurations exhibit similar vertical CO2 concentration profiles and display a consistent bias in the lower stratosphere relative to observational data. ICO demonstrates advantages in computational efficiency and storage, thanks to its reduced cell count per level and a homogeneous grid structure. It holds promise for future developments, including with the LMDZ offline model and associated inversion system, which contribute to the Copernicus Atmosphere Monitoring Service. Overall, the ICO configuration showcases the efficacy of utilizing an unstructured grid for the physics, and the capability of Dynamico in accurately simulating CO2 transport. This study emphasizes the importance of advanced modeling approaches and high-resolution innovative grids in enhancing our understanding of the global carbon cycle and refining climate models. [ABSTRACT FROM AUTHOR]

The Menzel Horr coastal aquifer in Northeastern Tunisia, Mediterranean basin, suffers for decades, from severe water resource depletion and seawater intrusion due to overexploitation. To address this issue, the study focused on using geophysical subsurface imaging techniques to understand seawater flow in porous media and define the geological framework of the study area. Regional ground gravity data supported by a seismic reflection profile was analyzed to highlight the structural control of the Plio-Quaternary to Miocene aquifer system in this region. The study area is located above a hidden folded structure called "the Menzel Horr pop-up" affected by three major faults. In a second step, a time domain electromagnetic (TDEM) survey integrated with an electrical resistivity tomography (ERT) profile was conducted. These surveys were calibrated using detailed well log lithostratigraphy, to dissociate the deep clayey substratum from the sandy shallow aquifer and were able to detect the brackish water signature that impacted the sandy/sandstone aquifer. Finally, at a local scale, a set of nine ERT profiles were performed along the coastline with the Wenner–Schlumberger array to image the porous zone in the first 20 m of depth. The obtained data were smoothed and filtered then inversed. The density transition zone between freshwater/saltwater was imaged as a distinct plume of seawater. Finally, a "Pseudo-3D" electrical resistivity image was built aiming to provide a very useful tool to help hydrogeologists develop effective strategies for managing and conserving water resources and mitigating seawater intrusion in the Menzel Horr coastal aquifer. [ABSTRACT FROM AUTHOR]

In the paper, a tunable split-ring based microstrip band-pass filter is proposed and the corresponding measurement and simulation results are presented. The proposed filter has a band-pass performance around 2.2 GHz. The prototype of the proposed filter is fabricated by using Arlon DiClad 880 substrate with an electrical permittivity of ϵ r = 2.2. There is a quite good agreement between the measurement and simulation results with a slight ( ∼ 0.3 GHz) discrepancies. Also, the frequency tuning performance is achieved by using a simplistic varactor diode model of SMV1247-079LF placed on one gap of the ring. By changing the capacitance on the model, the center resonance frequency of the filter can be adjusted in the range of 2.4–3.4 GHz band. The simulation results demonstrate tunable band-pass characteristics of the proposed design. Modelling and the numerical analysis of the filter design are carried out by using CST Microwave Studio simulator. An equivalent circuit modeling of the proposed filter is presented along with the corresponding simulations. The results of the analysis on the effects of the physical dimensions of the filter on the filter performance are presented. Also, the variation of the resonance frequency with respect to the voltage value on the varactor diode was summarized with cubic spline method and displayed. [ABSTRACT FROM AUTHOR]

Four tropical transition (TT) events in the North Atlantic basin are simulated with the Weather Research and Forecasting (WRF) and the HARMONIE‐AROME (HAR) models to study the main features of the horizontal kinetic energy (HKE) spectra of these kinds of high‐energetic atmospheric system. Though most of the times similar results are obtained with both models, HAR shows a more intense filtering and numerical dissipation, whereas WRF tends to represent overenergized spectra in the synoptic scale and especially at smaller wavelengths. Predictability is dissimilar for the four TTs studied due to the different spectral curve slope obtained for each case, ranging from unlimited to very poor predictability at synoptic scale. Additionally, an increased HKE is presented in the middle–upper troposphere spectra. A deep analysis of the different terms involved in the equation of the spectral energy budget is presented through a detailed study of one of these TTs. The role of all of them is studied, connecting the energy spectra and the meteorological processes involved. The energy budget terms related to the nonlinear spectral transfer, the three‐dimensional divergence, and diabatic process tendencies are identified as the key ones, whereas the potential and kinetic conversion terms and the vertical flux HKE and pressure divergence terms play a secondary role on modulating the spectrum behaviour. The major energetic contributions are found at the synoptic scale, but results show that a two‐dimensional energy cascade does not fully capture the whole spectrum of a TT. The role of convection, latent heat release, and moist convection outbursts is sketched and a link within different vertical levels is found. Results show that a high‐energetic system, such as a TT, can effectively alter the atmospheric energy behaviour. [ABSTRACT FROM AUTHOR]

Flood is one of the most destructive natural hazards, especially in arid and semi-arid areas that experience great of rainfalls. This hazard is caused by the combination of several natural and human factors. Floods occur when the flow of the river increases to a certain extent. that the water crosses the banks of the river and inundates the nearby areas, as a result it affects the communities that live in the vicinity of the rivers. In addition to that, climate change increases the possibility of floods and their magnitude. Annual 75 world floods on average, affects millions of people worldwide. About 20,000 deaths per year, resulting in economic losses of $95 billion. In recent years, the frequency of floods has increased by more than 40%, as climate change significantly affects the intensity, pattern and magnitude of deadly floods. Especially Asian countries are exposed to floods and earthquakes with a relatively high impact. In Asia, more than 90% of human casualties are caused by natural disasters that are due to flooding. Recently, Iran has experienced catastrophic floods. In addition to the impact of climate change. The impact of human activities caused by the poor management of the watershed, including deforestation, excessive livestock grazing, the development of rural and urban settlements, have reduced the hydraulic space of rivers. In all these researches, the study of floodplains, different return periods, and inundation values in each application have been analyzed. Arid and semi-arid areas are definitely more prone to flooding due to heavy rains. In the present study, in order to investigate and distribute large-scale floods in Etrak river basin, the daily inundation data of 35 hydrometer stations were used over a period of 30 years. In order to extract large floods in the region, first, using the statistical method known as the inverse numerical filter method, the base runoff was separated from the total. The results of time series analysis extracted after applying the regression numerical filter method show that in 3 decades, different behavior of floods in the catchment area are observed. In the first decade, the floods had a higher intensity, and this high intensity also had a high frequency. In the second decade, the volume of floods decreased drastically, and in the third decade, the volume of floods was the same as in the first decade, but they occurred at longer intervals. The investigation of most severe floods showed that with the increase in the volume of the total runoff, there was a large difference between it and the base runoff, and the occurrence of this process is sudden and sometimes reaches more than 10 times the base runoff. The high volume of discharge in flood periods and on the other hand, the decade fluctuation in the occurrence of severe floods in the Atrak catchment area showed that for long-term forecasting, the inverse numerical filter method can be used. [ABSTRACT FROM AUTHOR]

A tunnel health monitoring (THM) system ensures safe operations and effective maintenance. However, how to effectively process and denoise several data collected by THM remains to be addressed, as well as safety early warning problems. Thus, an integrated method for Savitzky–Golay smoothing (SGS) and Wavelet Transform Denoising (WTD) was used to smooth data and filter noise, and the coefficient of the non-uniform variation method was proposed for early warning. The THM data, including four types of sensors, were attempted using the proposed method. Firstly, missing values, outliers, and detrend in the data were processed, and then the data were smoothed by SGS. Furthermore, data denoising was carried out by selecting wavelet basis functions, decomposition scales, and reconstruction. Finally, the coefficient of non-uniform variation was employed to calculate the yellow and red thresholds. In data smoothing, it was found that the Signal Noise Ratio (SNR) and Root Mean Square Error (RMSE) of SGS smoothing were superior to those of the moving average smoothing and five-point cubic smoothing by approximately 10% and 30%, respectively. An interesting phenomenon was discovered: the maximum and minimum values of the denoising effects with different wavelet basis functions after selection differed significantly, with the SNR differing by 14%, the RMSE by 8%, and the r by up to 80%. It was found that the wavelet basis functions vary, while the decomposition scales are consistently set at three layers. SGS and WTD can effectively reduce the complexity of the data while preserving its key characteristics, which has a good denoising effect. The yellow and red warning thresholds are categorized into conventional and critical controls, respectively. This early warning method dramatically improves the efficiency of tunnel safety control. [ABSTRACT FROM AUTHOR]

We examine the application of neural network-based methods to improve the accuracy of large eddy simulations of incompressible turbulent flows. The networks are trained to learn a mapping between flow features and the subgrid scales, and applied locally and instantaneously—in the same way as traditional physics-based subgrid closures. Models that use only the local resolved strain rate are poorly correlated with the actual subgrid forces obtained from filtering direct numerical simulation data. We see that highly accurate models in a priori testing are inaccurate in forward calculations, owing to the preponderance of numerical errors in implicitly filtered large eddy simulations. A network that accounts for the discretization errors is trained and found to be unstable in a posteriori testing. We identify a number of challenges that the approach faces, including a distribution shift that affects networks that fail to account for numerical errors. [ABSTRACT FROM AUTHOR]

A human motion pattern recognition algorithm based on Nano-sensor and deep learning is studied to recognize human motion patterns in real time and with high accuracy. First, human motion data are collected by micro electro mechanical system, and the noise in such data is filtered by smoothing filtering method to obtain high-quality motion data. Second, key time-domain features are extracted from high-quality motion data. Finally, after fusing and processing the key time-domain features, it is input into the deep long and short-term memory (LSTM) neural network to build a deep LSTM human motion pattern recognition model and complete human motion pattern recognition. The results show that the proposed algorithm can realize the recognition of various motion patterns with high accuracy of data acquisition, the average recognition accuracy is 94.8%, the average recall reaches 89.7%, and the F1 score of the algorithm are high, and the recognition time consuming is short, which can realize accurate and efficient human motion pattern recognition and provide guarantee for effective monitoring of the target human motion health. [ABSTRACT FROM AUTHOR]

Digital spectroscopy with Silicon Photomultiplier (SiPM) array coupled on CsI(Tl) crystal, needs some consideration to achieve desirable energy resolution. an array of SiPMs must be used with large scintillation crystal, therewithal signal from whole SiPMs is not ideal. Also silicon pixels are arranged in arrays at regular intervals. The distances between the pixels and the interference of the cross-talk of adjacent pixels are undesirable factors for energy resolution when the light is received by the crystal and transmitted to the SiPM array. On the other hand, due to the advantages of the SiPM array, there is a need to improve the energy resolution in order to be able to be applied. In this paper, an attempt is made to obtain the desired energy resolution by using the digital spectroscopy method and digital filters and properly shaping the output pulse of the preamplifier by the trapezoidal filter method. In this case, it will be possible to use it in different applications such as spectroscopy by the detector. [ABSTRACT FROM AUTHOR]

This paper presents the repeated filtering method for finding a smoothed, Bayesian estimate of the path of a stochastic process over a time interval [0, T] when one has used a particle filter to estimate the state of the process. It provides good resolution over [0, T], is easy to implement, and can be used with any sequential importance resampling particle filter regardless of the probabilistic model employed by the stochastic process. Repeated filtering is general, powerful, and simple. It does not require the restrictive assumptions or complex calculations of other methods. It is suitable for real-time operational use in complex situations. We demonstrate the method on two single-target tracking examples. The second of these tracking examples is very difficult to solve by any other method known to us. We then apply repeated filtering to a standard nonlinear time series model that has been used extensively for testing numerical filtering techniques. To further illustrate the power of repeated filtering, we show how adding reflecting boundaries to this time series creates a process that is difficult to smooth with existing techniques but simple with repeated filtering. [ABSTRACT FROM AUTHOR]

GNSS monitoring has become widespread in transport, significantly increasing the efficiency of its use. Moreover, the data received through communication channels, as a rule, is stored by online services and can be further used to solve a variety of tasks. Monitoring service providers provide an opportunity for users to freely upload reports generated on the basis of monitoring. However, the use of this data comes with a number of limitations. First of all, this is due to errors in determining the current speed of the car, which is due to the peculiarities of satellite navigation technology. Therefore, monitoring data are subject to preliminary processing in order to exclude or correct anomalous observations caused by measurement error. The portable application "Stamm" presented in the article contains tools for smoothing incorrect measurements. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China Normal University (Natural Science Edition) / Huanan Shifan Daxue Xuebao (Ziran Kexue Ban) is the property of Journal of South China Normal University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Knowledge of local mechanical behaviour of wood is especially important as silvicultural practices are modified to allow wood to compete as a relevant material in high technology applications. Challenges associated with identification of local mechanical behaviour have resulted in simplified test geometries designed to determine one or two constitutive parameters. The objective of this work was to design and simulate an entire experiment developed to simultaneously identify the earlywood and latewood orthotropic stiffnesses in loblolly pine in a single specimen and load geometry. The virtual experiment was capable of evaluating optimal orthotropy orientation for reduced identification errors and indicating most favourable choices for data smoothing filters and identification methodology. Additionally, certain ring spacing and latewood percentages were shown to produce large errors, but those combinations are unlikely to occur naturally. The simulation was able to identify Q11,Q22, and Q66 with approximately ±10% error; the Q12 error was larger with more scatter. The methodology presented here contributes to the best practices available for heterogeneous stiffness identification. [ABSTRACT FROM AUTHOR]

Faceted macrostep formed at equilibrium is demonstrated based on numerical calculations using the tensor network method and the Monte Carlo method. The adopted model is a lattice model where the surface roughness excitations and the step-step attractive energy (Snt) are taken into consideration. We calculated Snt-T and p-T faceting diagrams for faceted macrosteps where T is the temperature, and p is the surface slope for an inclined surface. Applying the faceting diagrams to Si(113)+(114) surfaces, the step-step attractive energy is approximately 123 meV. We also discuss elementary step attachment/detachment processes to/from the faceted macrostep near equilibrium. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Atmospheric oscillations with daily periodicity are observed in in situ near-surface pressure, temperature, and winds observations and also in remotely sensed temperature and pressure observations of the Martian atmosphere. Such oscillations are interpreted as thermal tides driven by the diurnal cycle of solar radiation and occur at various frequencies, with the most prominent being the diurnal, semidiurnal, terdiurnal, and quadiurnal tides. Mars global circulation models reproduce these tides with varying levels of success. Until recently, both the MarsWRF and newly developed MarsMPAS models were able to produce realistic diurnal and semidiurnal tide amplitudes but predicted higher-order mode amplitudes that were significantly weaker than observed. We use linear wave analysis to show that the divergence damping applied within both MarsWRF and MarsMPAS is responsible for suppressing the amplitude of thermal tides with frequency greater than 2 per sol, despite being designed to suppress only acoustic wave modes. Decreasing the strength of the divergence damping in MarsWRF and MarsMPAS allows for excellent prediction of the higher-order tidal modes. This finding demonstrates that care must be taken when applying numerical dampers and filters that may eliminate some desired dynamical features in planetary atmospheres. [ABSTRACT FROM AUTHOR]

This paper presents efficient and practical implementations of sequential data assimilation methods for the Simplified Parameterizations, primitive‐Equation DYnamics (SPEEDY) Model, a well‐known numerical model, into the data assimilation community for climate prediction. In the SPEEDY model, the time evolution of dynamics is performed via the second‐order Leapfrog integration scheme; this time integrator relies on two steps: the position and the velocity. The computational implementation of SPEEDY blends the time integrator and the spatial discretization of dynamics to accelerate algebraic computations. Thus, there is no access to the right‐hand side function of the ordinary differential equations governing the time evolution of model dynamics. Consequently, the SPEEDY model is often treated as a black box wherein positions and velocities work as inputs and outputs. Since observations in operational data assimilation only match position states, we can exploit augmented vector states to propagate analysis innovations from positions to velocities. For this purpose, we formulate three variants of ensemble‐based filters and perform numerical experiments to assess their accuracies. We consider two scenarios for the experiments: an ideal case wherein positions and velocities can be observed and a more realistic one wherein measurements are only accessible for position states. Besides, we discuss the effects of the ensemble size on the accuracies of our formulations and, even more, the typical case in which velocities are not updated across assimilation steps. The results reveal that all filter formulations' accuracies remain the same in terms of Root‐Mean‐Square‐Error by neglecting observations from velocities (a realistic scenario) even for cases wherein the number of measurements decreases to 6% of model components. Furthermore, for all discussed filter implementations, the propagation of analysis increments from position to velocities improves up to 100% the performance of filter implementations wherein velocities are not updated, a typical operational scenario. [ABSTRACT FROM AUTHOR]

The peculiarities of applying the finite-difference method (FDM) for solving nonlinear dynamic problems of distributed systems (DS) in a flow are considered. The main limitations for using the FDM for numerical modeling of wave propagation and reflection in DS are shown to be the special features of the constitutive quasilinear equations. They necessitate the simultaneous calculation of the variables corresponding to fast and slow wave processes. The term "singularly perturbed system of equations" is used for such systems. These perturbations are the result of a significant difference in the propagation velocities of longitudinal, configurational, bending, and torsional waves in the DS at the physical level. Therefore, it is necessary to use special step-by-step methods of regularization and filtering of the numerical results. It imposes certain contstraints on the ability to model real processes and the accuracy of the results and forces the use of implicit difference schemes and high-frequency filtering. When solving the system of linear algebraic equations, taking into account the poor conditioning of the matrix of convective terms, the method of regularization was chosen by experimental calculation. Calculations according to the Crank–Nicolson difference scheme, even using coarse grids, can give the results with the required degree of accuracy and minimal time cost. Another picture is observed when comparing the results on coarse and fine grids for the Euler difference scheme. Inherent errors brought in by errors in approximating the missing boundary conditions lead to greater differences. [ABSTRACT FROM AUTHOR]

In VLSI, for performing multiplication, we use a*b. We use the booth multiplication mechanism rather than the standard conventional method to improve the speed since it generally requires lower power, area and reduces delay. Some bits overlap with each other during the multiplication process in the convectional multiplication method (whereas booth multipliers consist of shifting process), making the system more complex and decreasing output efficiency. To overcome this, SPST is implemented such that unwanted transactions are neglected and also eliminate the overlapping bits. This affects the carry changes and data generation parts and even reduces the unwanted power flow in adders. It uses Radix 4 multiplier, which requires addition processors. In the previous multipliers usage of Ripple carry adder, Carry save adder, carry ahead adder, which are the basic types of serial adders where they utilize serial adding mechanism where the data is processed bit by bit that make the system more complicated. To reduce these problems, we are using parallel prefix adders that work in a higher speed manner, which uses carry propagation and carry generation internally to perform additional operations and improve speed. So, this project is implemented in VLSI prototype with respect to Xilinx ISE software and Verilog programming language. [ABSTRACT FROM AUTHOR]

Improving biosensor performance which utilize impedance cytometry is a highly interested research topic for many clinical and diagnostic settings. During development, a sensor's design and external factors are rigorously optimized, but improvements in signal quality and interpretation are usually still necessary to produce a sensitive and accurate product. A common solution involves digital signal processing after sample analysis, but these methods frequently fall short in providing meaningful signal outcome changes. This shortcoming may arise from a lack of investigative research into selecting and using signal processing functions, as many choices in current sensors are based on either theoretical results or estimated hypotheses. While a ubiquitous condition set is improbable across diverse impedance cytometry designs, there lies a need for a streamlined and rapid analytical method for discovering those conditions for unique sensors. Herein, we present a comprehensive dissemination of digital filtering parameters applied on experimental impedance cytometry data for determining the limits of signal processing on signal quality improvements. Various filter orders, cutoff frequencies, and filter types are applied after data collection for highest achievable noise reduction. After designing and fabricating a microfluidic impedance cytometer, 9 µm polystyrene particles were measured under flow and signal quality improved by 6.09 dB when implementing digital filtering. This approached was then translated to isolated human neutrophils, where similarly, signal quality improved by 7.50 dB compared to its unfiltered original data. By sweeping all filtering conditions and devising a system to evaluate filtering performance both by signal quality and object counting accuracy, this may serve as a framework for future systems to determine their appropriately optimized filtering configuration. [ABSTRACT FROM AUTHOR]

Boundary integral numerical methods are among the most accurate methods for interfacial Stokes flow, and are widely applied. They have the advantage that only the boundary of the domain must be discretized, which reduces the number of discretization points and allows the treatment of complicated interfaces. Despite their popularity, there is no analysis of the convergence of these methods for interfacial Stokes flow. In practice, the stability of discretizations of the boundary integral formulation can depend sensitively on details of the discretization and on the application of numerical filters. We present a convergence analysis of the boundary integral method for Stokes flow, focusing on a rather general method for computing the evolution of an elastic capsule or viscous drop in 2D strain and shear flows. The analysis clarifies the role of numerical filters in practical computations. [ABSTRACT FROM AUTHOR]

In the energy management of district cooling plants, the thermal energy storage tank is critical. As a result, it is essential to keep track of TES results. The performance of the TES has been measured using a variety of methodologies, both numerical and analytical. In this study, the performance of the TES tank in terms of thermocline thickness is predicted using an artificial neural network, support vector machine, and k-nearest neighbor, which has remained unexplored. One year of data was collected from a district cooling plant. Fourteen sensors were used to measure the temperature at different points. With engineering judgement, 263 rows of data were selected and used to develop the prediction models. A total of 70% of the data were used for training, whereas 30% were used for testing. K-fold cross-validation were used. Sensor temperature data was used as the model input, whereas thermocline thickness was used as the model output. The data were normalized, and in addition to this, moving average filter and median filter data smoothing techniques were applied while developing KNN and SVM prediction models to carry out a comparison. The hyperparameters for the three machine learning models were chosen at optimal condition, and the trial-and-error method was used to select the best hyperparameter value: based on this, the optimum architecture of ANN was 14-10-1, which gives the maximum R-Squared value, i.e., 0.9, and minimum mean square error. Finally, the prediction accuracy of three different techniques and results were compared, and the accuracy of ANN is 0.92%, SVM is 89%, and KNN is 96.3%, concluding that KNN has better performance than others. [ABSTRACT FROM AUTHOR]

The full-cycle discrete Fourier transform (FCDFT) is commonly used to estimate phasors in numerical relays because of its immunity to harmonics and accurate identification of a decaying dc component (DDC) in a fault current. For fast protection of power systems, it is necessary to speed up the phasor estimation by using a data window below one cycle. The conventional least squares (LS) methods below one cycle approximate the DDC as the first several terms of the Taylor series expansion, which degrades the phasor-estimation accuracy. This paper proposes an LS method that uses Fourier analysis for approximation of the DDC to speed up phasor estimation without accuracy degradation. The phasor-estimation accuracy of the proposed method is investigated for the data-window length in simulations using synthetic data of fault currents containing DDC, harmonics, and measurement noise. The phasor-estimation speed is also investigated with respect to the delay of the antialiasing filter in the numerical relay. The proposed method is compared with the conventional LS and FCDFT methods in simulations using synthetic fault-current data. The feasibility of the proposed method is demonstrated through the phasor estimation of simulated transmission-line faults. [ABSTRACT FROM AUTHOR]

Regional seismic networks often observe artificially induced seismic events such as blasting and collapses. Misclassified seismic events in the earthquake catalog can therefore interfere with assessments of natural seismic activity. Traditional methods rely on the period, and phase features of seismic waves to determine the nature of seismic events. We designed three seismic event classifiers with reference to convolutional neural network structures such as VGGnet, ResNet, and Inception. The designed classifiers were tested and compared using three-channel seismic full-waveform time-series data and spectral data. Our classifiers are shown to only require 60 s of full-waveform seismic event data and first-arrival times for alignment; additional phase labeling or numerical filtering is unnecessary. Rapid classification of earthquakes, blasting, and mine collapses can be achieved within approximately 1 min of an event. As a test case, this study uses 6.4 k observations of actual local seismic events with magnitudes ranging from ML 0.6 to ML 4.5 obtained from 47 broadband seismic stations in the Henan Regional Network of the China Seismological Network Center; these observations include natural earthquakes, blasting, and collapse events. The results indicate that our classifiers can reach a lower classification magnitude limit of ML 0.6 and that their recall and accuracy exceed 90%, outperforming manually performed routine classifications and similar approaches. These findings provide an important reference for the rapid classification of small and medium earthquakes. [ABSTRACT FROM AUTHOR]

We present relative astrometric and photometric measurements of visual double stars made in 2013–2014 with PISCO2 installed at the 76‐cm refractor of the Côte d'Azur Observatory in Nice (France). Our observation list contains orbital couples as well as double stars whose motion is still uncertain. Three different techniques were used for obtaining measurements: Lucky Imaging, Speckle Interferometry, and the Direct Vector Autocorrelation method. From our observations of 4,864 multiple stars, we obtained 4,952 new measurements with angular separations in the range 0″.1–14″ and an average accuracy of 0″.015. The mean error on the position angles is 1°.0. Most of the position angles were determined without the usual 180° ambiguity with the application of the direct vector autocorrelation technique and/or by inspection of the Lucky images or the long integration files. We managed to routinely monitor faint systems (mV ≈ 9–11) with large magnitude differences (up to ΔmV ≈ 5). We have thus been able to measure 49 systems containing red dwarf stars that had been poorly monitored since their discovery, from which we estimated the stellar masses thanks to Gaia measurements. We also measured the magnitude difference of the two components of 318 double stars with an estimated error of 0.2 mag. Except for a few objects that have been discussed, our measurements are in good agreement with the ephemerides computed with published orbital elements, even for the double stars whose separation is smaller than the diffraction limit. Thanks to good seeing images and with the use of high‐contrast numerical filters, we have also been able to obtain 455 measurements with an angular separation smaller than the diffraction limit of our instrumentation, and consistent with those obtained with larger telescopes. Finally, we report 378 measurements of the 296 new double stars that we found in the files obtained during the observations. [ABSTRACT FROM AUTHOR]

High-dimensional high-frequency continuous-vibration control problems often have very complex dynamic behaviors. It is difficult for the conventional control methods to obtain appropriate control laws from such complex systems to suppress the vibration. This paper proposes a new vibration controller by using reinforcement learning (RL) and a finite-impulse-response (FIR) filter. First, a simulator with enough physical fidelity was built for the vibration system. Then, the deep deterministic policy gradient (DDPG) algorithm interacted with the simulator to find a near-optimal control policy to meet the specified goals. Finally, the control policy, represented as a neural network, was run directly on a controller in real-world experiments with high-dimensional and high-frequency dynamics. The simulation results show that the maximum peak values of the power-spectrum-density (PSD) curves at specific frequencies can be reduced by over 63%. The experimental results show that the peak values of the PSD curves at specific frequencies were reduced by more than 47% (maximum over 52%). The numerical and experimental results indicate that the proposed controller can significantly attenuate various vibrations within the range from 50 Hz to 60 Hz. [ABSTRACT FROM AUTHOR]

We design moving horizon state estimators for a general model of bioprocesses. The underlying optimization is nonconvex due to the microbial growth kinetics, which are modeled as nonlinear functions. We relax the nonconvex growth constraints so that the optimization becomes a second-order cone program, which can be solved efficiently at large scales. Unfortunately, solutions to the relaxation can be inexact and thus lead to inaccurate state estimates. To recover feasible, albeit potentially locally optimal solutions, we use the concave–convex procedure, which here takes the form of a sequence of second-order cone programs. We find that the moving horizon state estimators outperform the unscented Kalman filter on numerical examples based on the gradostat and anaerobic digestion when there is high process noise or parameter error. • We design a moving horizon estimator for a general bioprocess model. • It is a convex relaxation, which can be solved using second-order cone programming. • When not exact, we use the convex-concave procedure to obtain feasible solutions. [ABSTRACT FROM AUTHOR]

Electrical impedance tomography (EIT) has the potential for monitoring perfusion in addition to respiration on the bedside. Several separation methods were reported, e.g. Filtering, ECG-gating and PCA. However, the separation is not trivial, which is why harmonic analysis was introduced in EIT data analysis. It is based on several assumptions and therefore prove of plausibility is necessary. In this contribution a two-dimensional thorax simulation is introduced, which includes simplified lungs and heart with changing shapes and conductivities due to volume variations and lung perfusion. Harmonic analysis was applied on the simulated data. The separation results are in good agreement with the simulation settings. Further investigations will be required due to limitations of the simulation. Nevertheless, harmonic analysis delivered precise results in ideal and strictly defined conditions. [ABSTRACT FROM AUTHOR]

Capacitive extracellular stimulation is a common method in implanted stimulation electrodes. The basis for investigating the transmission of stimuli from an electrode to adhered cells are in vitro experiments using calcium imaging or patch clamp measurements. Computational spatial models are used to simulate the mechanism of signal transmission at the cell-electrode interface but require high computing power. In this work, the Stern model to characterize the electrochemical double-layer (EDL) formation and a modified two-domain model are combined into a holistic equivalent circuit modelling capacitive cell stimulation. The described parameters can be directly associated with physicochemical effects. A simulation of the involved control and measurement systems allows the validation of the model with in vitro patch clamp recordings. The relationship of the cell's distance to the electrode and efficacy of signal transmission could be observed. With this concept we aim to convert different complex approaches into a simple model and thus give an overview of the mechanisms of stimulation. We want to facilitate the interpretation of measured signals especially in voltage clamp measurements. [ABSTRACT FROM AUTHOR]