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As one of the most complex systems known to science, modeling brain behavior and function is both fascinating and extremely difficult. Empirical data is increasingly available from ex vivo human brain organoids and surgical samples, as well as in vivo animal models, so the problem of modeling the behavior of large-scale neuronal systems is more relevant than ever. The statistical physics concept of a mean-field model offers a tractable way to bridge the gap between single-neuron and population-level descriptions of neuronal activity, by modeling the behavior of a single representative neuron and extending this to the population. However, existing neural mean-field methods typically either take the limit of small interaction sizes, or are applicable only to the specific neuron models for which they were derived. This paper derives a mean-field model by fitting a transfer function called Refractory SoftPlus, which is simple yet applicable to a broad variety of neuron types. The transfer function is fitted numerically to simulated spike time data, and is entirely agnostic to the underlying neuronal dynamics. The resulting mean-field model predicts the response of a network of randomly connected neurons to a time-varying external stimulus with a high degree of accuracy. Furthermore, it enables an accurate approximate bifurcation analysis as a function of the level of recurrent input. This model does not assume large presynaptic rates or small postsynaptic potential size, allowing mean-field models to be developed even for populations with large interaction terms.

This paper explores the importance of the electric eel foraging optimization (EEFO) algorithm in addressing feature selection (FS) problems, with the aim of ameliorating the practical benefit of FS in real-world applications. The use of EEFO to solve FS problems props our goal of providing clean and useful datasets that provide robust effectiveness for use in classification and clustering tasks. High-dimensional feature selection problems (HFSPs) are more common nowadays yet intricate where they contain a large number of features. Hence, the vast number of features in them should be carefully selected in order to determine the optimal subset of features. As the basic EEFO algorithm experiences premature convergence, there is a need to enhance its global and local search capabilities when applied in the field of FS. In order to tackle such issues, a binary augmented EEFO (BAEEFO) algorithm was developed and proposed for HFSPs. The following strategies were integrated into the mathematical model of the original EEFO algorithm to create BAEEFO: (1) resting behavior with nonlinear coefficient; (2) weight coefficient and confidence effect in the hunting process; (3) spiral search strategy; and (4) Gaussian mutation and random perturbations when the algorithm update is stagnant. Experimental findings confirm the effectiveness of the proposed BAEEFO method on 23 HFSPs gathered from the UCI repository, recording up to a 10% accuracy increment over the basic BEEFO algorithm. In most test cases, BAEEFO outperformed its competitors in classification accuracy rates and outperformed BEEFO in 90% of the datasets used. Thereby, BAEEFO has demonstrated strong competitiveness in terms of fitness scores and classification accuracy. When compared to its competitors, BAEEFO produced superior reduction rates with the fewest number of features selected. The findings in this research underscore the critical need for FS to combat the curse of dimensionality concerns and find highly useful features in data mining applications such as classification. The use of a new meta-heuristic algorithm incorporated with efficient search strategies in solving HFSPs represents a step forward in using this algorithm to solve other practical real-world problems in a variety of domains. [ABSTRACT FROM AUTHOR]

Efforts to optimize the design and enhance the efficiency of standing-wave thermoacoustic refrigerators (SWTARs), particularly those with parallel plate stacks, are crucial for achieving rapid and straightforward engineering estimates. This study primarily focused on optimizing the coefficient of performance (COP) by combining linear thermoacoustic theory (LTT) with the design of experiments (DOE) approach. The investigation centered around five key parameters affecting the COP once the working gas had been selected. Then, based on LTT, the COP was estimated numerically over defined intervals of those five parameters. Moreover, through quantitative and qualitative effect analyses, these five parameters and their interactions were determined. Utilizing a transfer function, the study aimed to delineate the best COP value (1.76) over a defined interval of the parameters as well as the contribution of the thermoacoustic main parameters (55.69%) and their interactions (two-way interactions = 33.30%, three-way interactions = 7.36%, and four-way interactions = 3.35%). Furthermore, a comparison between contour and surface responses and several statistical decision approaches applying the full factorial design verified the robustness of the study's findings. Ultimately, the COP results obtained aligned with the existing literature, underscoring the validity and relevance of the study's methodologies and conclusions. [ABSTRACT FROM AUTHOR]

Mathematical tools have been developed that are analogous to the tool that allows one to reduce the description of linear systems in terms of convolution operations to a description in terms of amplitude-frequency characteristics. These tools are intended for use in cases where the system under consideration is described by partial digital convolutions. The basis of the proposed approach is the Fourier–Galois transform using orthogonal bases in corresponding fields. As applied to partial convolutions, the Fourier–Galois transform is decomposed into a set of such transforms, each of which corresponds to operations in a certain Galois field. It is shown that for adequate application of the Fourier–Galois transform to systems described by partial convolutions, it is necessary to ensure the same number of cycles in each of the transforms from the set specified above. To solve this problem, the method of algebraic extensions was used, a special case of which is the transition from real numbers to complex numbers. In this case, the number of cycles varies from p to p n / k , where p is a prime number, n and k are integers, and an arbitrary number divisor of p n can be chosen as k . This allows us to produce partial Fourier–Galois transforms corresponding to different Galois fields, for the same number of cycles. A specific example is presented demonstrating the constructiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Distributed Acoustic Sensing (DAS) is a photonics technology converting seafloor telecommunications and optical fiber cables into dense arrays of strain sensors, allowing to monitor various oceanic physical processes. Yet, several applications are hindered by the limited knowledge of the transfer function between geophysical variables and DAS measurements. This study investigates the quantitative relationship between surface gravity DAS‐recorded wave‐generated strain signals along the seafloor and the pressure at a colocated sensor. A remarkable linear correlation is found over various sea conditions allowing us to reliably determine significant wave heights from DAS data. Utilizing linear wave potential theory, we derive an analytical transfer function linking cable deformation and wave kinematic parameters. This transfer function provides a first quantification of the effects related to surface gravity waves and fiber responses. Our results validate DAS's potential for real‐time reconstruction of the surface gravity wave spectrum over extended coastal areas. It also enables the estimation of waves hydraulic parameters at depth without the need from offshore deployments. Plain Language Summary: Distributed Acoustic Sensing (DAS) technology converts submarine communication cables into real‐time networks of thousands of seismo‐acoustic sensors. The high sensitivity of DAS measurements (nano‐deformation) makes the recorded signals extremely rich in information, and capable of capturing multiple oceanographic processes. Numerous applications can be envisaged such as monitoring turbidity currents, tsunamis, marine renewable energy parks, etc., and some are already in progress, including monitoring surface vessels, marine currents, cetaceans, etc. However, despite all these developments, the relationship between DAS measurements and certain key ocean variables remains poorly understood. In the littoral zone, this study aims to investigate the link between the deformation signals recorded by DAS due to wave passage at the sea surface and the pressure measured by a sensor located nearby, at the bottom. Our findings demonstrate a strong correlation under varying sea conditions. This correlation allows to reliably determine significant wave heights using DAS data. By applying a simplified theory of wave propagation, the linear wave theory, it was possible to develop an analytical transfer function that relates cable deformation to wave movement parameters. The results confirm that DAS has the potential to reconstruct parameters associated with ocean waves, and could ultimately facilitate their real‐time estimation. Key Points: A simple transfer function between Distributed Acoustic Sensing (DAS) and a collocated pressure sensor is retrieved for surface gravity waves heightsOcean waveheights are correctly reconstructed from nearshore DAS measurementsThe signals recorded by DAS can be described from the standpoint of the linear wave theory [ABSTRACT FROM AUTHOR]

Defining the required trackability level of the target condition for the testing facility reconditioning unit represents an unresolved challenge in improving the reproducibility of load-based tests and corresponding performance rating standards development. To enhance the reproducibility of such testing methodology, this paper presents and discusses a new feed-forward compensation technique based on the development of a transfer function model for the delay and offset characteristics of the psychrometric room's air temperature and humidity modulations with reference to the target signal from the room emulator. It is demonstrated that the proposed methodology enables offset and delay reduction in the trackability of the return air condition within 60 s at different testing conditions, enhances the reproducibility of the test results to limit performance deviations to within 2 %, and achieves closely matched controlled parameter modulations during load-based tests. [ABSTRACT FROM AUTHOR]

Understanding responses of stream discharge to precipitation in a watershed is important in gaining insights into watershed hydrology and estimating hydraulic parameters. Transfer functions in the spectral domain are commonly used to quantify the relationship between precipitation and discharge, and estimate watershed hydraulic parameters. However, previous models have not adequately accounted for the impact of the unsaturated zone. To address this, we have developed a novel analytical model that considers the effect of the unsaturated zone to obtain transfer functions within watersheds. These transfer functions are derived by the spectral method and verified through numerical simulations. The results indicate that the transfer functions are influenced significantly by the relative hydraulic conductivity exponent αk in the moisture characteristic curve. A higher αk results in a lower transfer function, indicating more robust filtering of hydrological signals. A thicker unsaturated zone results in lower transfer functions at higher frequencies. The traditional transfer functions, which neglect the retention capacity of the unsaturated zone, tend to overestimate hydrological responses at high frequencies. Our transfer functions agree well with integrated watershed‐scale flow models and are also applied to observed data from four watersheds in Iowa, providing reasonable estimates for the hydraulic parameters. This study contributes to a deeper understanding of watershed behavior and offers an enhanced tool for estimating hydraulic parameters with practical applications. Key Points: Transfer functions are proposed to quantify hydrological signal filtration considering unsaturated flow described by Richards equationThicker unsaturated zones or higher conductivity exponents enhance the filtration of hydrological signalsUnsaturated zone generally possesses a stronger ability to filter watershed hydrological signals than that of saturated zone [ABSTRACT FROM AUTHOR]

Mathematical modeling and numerical simulation have a considerable presence in the vast universe of engineering disciplines, given their usefulness in explaining, comprehending, and simulating phenomena and processes with which engineers are in contact in their daily creative and problem‐solving work. For this reason, engineering study programs have at least one course dedicated to dealing with the mathematical modeling of dynamic systems as an essential complement to subsequent courses such as automatic control, structural dynamics, and mechanical vibrations. Nowadays, many technological tools illustrate the applications of mathematical modeling interactively through experiments that offer an incomparable motivation to the students to corroborate with real‐world examples, the utility and veracity of the theory presented to them in the classroom and that in many occasions seems lacking utility and direct relation with the world in which they develop. Based on those mentioned above, this paper presents an example of applying the Laplace transform in modeling physical systems, using a second‐order circuit attached to an Arduino Due board in conjunction with the Jupyter Notebook environment. The numerical and experimental results can be obtained through three optional kernels: Python, Octave, or MATLAB®. For educational purposes, the resulting computer application was presented to undergraduate students of Mechatronics Engineering as an illustrative complement to two courses entitled Signals and Systems Analysis, part of the second semester, and Mathematical Modeling, part of the fifth semester. [ABSTRACT FROM AUTHOR]

Deepwater subsea wellhead system is subjected to the various cyclic loads transmitted from the drilling riser due to ocean waves, currents and platform motion during drilling operation; therefore, the fatigue damage is accumulated. A fatigue damage assessment methodology and its detailed analysis procedure for subsea wellheads utilising transfer function theory based on monitoring acceleration data at the drilling riser are developed. Studies on an actual monitoring case for a subsea wellhead under drilling operation with 336 h of monitoring events are conducted and a verification method by comparison of the measured fatigue results and predicted fatigue results based on wave and current data are introduced. The results for wave and vortex-induced vibration (VIV) fatigue life based on measured acceleration data are calculated, and the fatigue bias with predicted results are summarised. The developed fatigue damage assessment method and monitoring case studies can be valuable references for deepwater drilling engineering. [ABSTRACT FROM AUTHOR]

In response to improve the quality and level of hydrostatic transmission (HST) products for modern mobile machineries, the closed-loop transfer functions of a HST are established by mathematical modeling, a comprehensive testing equipment is developed employing a proportional relief valve for automatic program loading and fuzzy-PID oil temperature control, the testing equipment is capable of conducting various performance tests for various models of HST integrated pump-motor products, including factory and type tests, especially continuous impact and endurance performance tests under high temperature and high-speed conditions. Comprehensive performance test of the above test sample is carried out using the developed equipment, that the test results agree well with the previous simulation results verifies the correctness of the established theoretical analysis model. The obtained theory and equipment from this work provide theoretical and experimental technical support for improving design and development of high-end fluid components for modern mobile machinery HST. [ABSTRACT FROM AUTHOR]

During the fault period, a phase angle jump may occur at the stator or the point of common coupling, which will deteriorate the low-voltage ride-through (LVRT) characteristics of a doubly fed induction generator (DFIG). The existing LVRT studies focus on the impact of a voltage drop on DFIGs but often ignore that of a phase angle jump. The time-domain simulation is accurate in describing the response of a DFIG during the LVRT process, but it is time-consuming for a DFIG with the full-order model. In this paper, by using the voltage magnitude and phase angle of the stator or the point of common coupling as the inputs, and the state variables as the outputs, the transfer function of a DFIG is derived to analyze its response and find the LVRT measures against the voltage drop and, especially, the phase angle jump. Firstly, the differential-algebraic equations of the DFIG are linearized to propose their transfer function model. Secondly, considering its high-order characteristic, a model reduction method for the transfer function of the DFIG using the Schur decomposition is proposed, and the analytical expression of the output variables of the DFIG with the phase angle jump is derived by the inverse Laplace transformation to judge the necessity of the LVRT measures. Finally, the simulation results of the DFIG are provided to verify the accuracy of the transfer function model and its reduced-order form and validate the feasibility of the LVRT against the phase angle jump with the proposed models. [ABSTRACT FROM AUTHOR]

This work presents an unprecedented resolution for the lack of harmony between wired network TCP, which transacts with stationary routers, and the Ad-hoc, which appoints mobile hosts as routers, causing failure. This is done by modifying the TCP's queue via fuzzy logic as a controller (FLC). The novelty is that when the mobile router changes location, leading to failure, the controller keeps tracking error signals, which become a behavior prediction base for the next error before occurring. The transfer function is designed with respect to the I/O parameters to optimize the system, leading to the steady with free of fluctuation and overshoot. Other than previous techniques, which depend on fixed criteria or proactive strategies such as AQM, the proposed system is distinguished by dynamic adaptation with continuous changes like mobility and signal power. Using FLC, the system response is improved by reducing time delay, packet loss, and enhancing stability and spectral efficiency. A simulation is designed to continuously monitor these parameters' results, represented by the metrics rise time, undershoot, overshoot, and steady time. For comparison purposes, the PID controller is applied, and the results show surpassing this system where the metrics rise time, undershoot, overshoot, and steady time values are for FLC 0.55, 0.017, 0, and 2.5, respectively, with fluctuation-free, while for PID 0.6, 0.526, 9.341, and 3.6533, respectively, with fluctuation. Furthermore, by dealing the FLC with the challenges of high mobility and the signal power changes, it exceeds the traditional PID controller, as the simulation results show that in terms of reducing the delay time by 28.6%, packet loss by 50%. Besides, there is a remarkable increase in the throughput by 20% and spectral efficiency by 16.7%. [ABSTRACT FROM AUTHOR]

Introduction: Central aortic pressure waveform analyses can provide clinically relevant information beyond conventional brachial blood pressure (BP) assessment. This waveform can be reproduced noninvasively through application of a generalized transfer function (GTF) on a peripheral waveform, as conventionally performed by applanation tonometry. Photoplethysmography (PPG) is an alternate approach; however, differences in measurement site and modality demand the use of a transfer function (TF) specific for those differences. This study aimed to compare central aortic waveform features generated from radial tonometry (reference) using a proprietary GTF with a central aortic waveform and its features generated from a simultaneous fingertip PPG measurement using a selective method where one of three different TFs is chosen based on the input signal harmonic profile. Methods: Brachial BP was measured in triplicate under resting conditions and was used for subsequent calibration. Multiple simultaneous radial tonometry (SphygmoCor CVMS) and fingertip PPG measurements were then performed in individual participants (n = 21, 10 females, age: 39 ± 16 years). Measurements were converted into central aortic waveforms with their respective TFs. Twenty central aortic pressure waveform parameters were compared through correlation analysis, Bland-Altman plots, and a repeated measure mixed-effects ANOVA model. Central aortic waveform shape was compared using the root-mean-squared error (RMSE). Results: Correlation (r) of PPG-derived parameters with radially tonometry-derived central aortic parameters was high ranging from 0.79 to 0.99. Mean differences of pressure-related parameters were within 1.3 mm Hg, and differences of time-related parameters ranged from −2.2 to 3.4%. While some parameters were statistically different, these differences are not physiologically meaningful. Central aortic waveform shape had an average RMSE of 1.8 ± 0.9%. Conclusion: Fingertip PPG-derived central aortic waveform parameters using a novel selective TF were comparable to central aortic waveform features derived from radial tonometry using a previously validated GTF.

The continuous enhancement of optimization algorithms and their parameters has spurred the expansion of AI into novel application domains such as image recognition and smart home technology. This paper employs the system response curve (SRC) to the adaptive learning rate optimizer, addressing challenges associated with the establishment of the optimizer control model and parameter adjustments affecting the dynamic performance of the system. These insights offer theoretical support for the optimizer's application in deep learning models. To begin, the adaptive learning rate optimizer is a time‐varying system. Based on the intrinsic relationship between the network optimization and the control system, the time domain expression and approximate transfer function of the adaptive learning rate optimizer are derived, and the system dynamic model is established. Furthermore, based on the system control model of the optimizer, it is proposed to explain the performance impacts of different optimizers and their hyperparameters on the deep learning model through the SRC. Finally, experiments are performed on the MNIST, CIFAR‐10, UTKinect‐Action3D, and Florence3D‐Action datasets to validate the control theory of explaining optimizers through system response curves. The experimental results show that the recognition performance of the Adaptive Moment Estimate (Adam) is better than that of the Adaptive Gradient (AdaGrad) and Root Mean Square Propagation (RMSprop). Additionally, the learning rate affects the model training speed, and the practical application aligns with the theoretical analysis. [ABSTRACT FROM AUTHOR]

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This proposed work investigates the performance of dc voltage of a three-phase Active Front-End (AFE) Sinusoidal Pulse Width Modulated (SPWM) rectifier during load switching. To obtain desired performances, a transfer function is derived in synchronous reference frame (dq), from which the gain of the PI controllers are estimated and observed the performance accordingly. In this research, the load current is feed-forwarded with d-axis current by a suitable choice of gain factor, which results a fractional feedback d-axis current control and improves the transient behaviour simultaneously. This will unalter the other performance parameters of the system such as, UPF operation, less Total Harmonic Distortion (THD) of input current, dc voltage boost etc. All the simulation results exhibits satisfactory performance and validated with a prototype developed using STM32F303RE microcontroller accordingly. [ABSTRACT FROM AUTHOR]

Unwanted signals in electrical systems frequently overpower the desired signal. In an attempt to increase the ratio, the desired signals are amplified proportionately to the undesired ones. Frequency selective circuits, often known as filters, can be used to separate desired signals from undesirable ones as signals can be identified by their frequency characteristics. Analog filters are available in both active and passive varieties, each having unique properties. A first order band pass active filter is developed and characterized in this study. The Operational Amplifier (OP AMP 741) is the active element in this case. With the Band Width (BW), Cut-off Frequencies (FL and FH), Center Frequency (FC), and Quality factor (Q) provided, the Transfer Function (TF) equation is generated and defined. [ABSTRACT FROM AUTHOR]

The current planting of economically important timber species, such as Douglas-fir, mainly relies on genetically improved seeds from seed orchards. However, published research on the effects of climate change has largely focused on natural populations. To bridge this gap, data from 80 cooperative second-cycle coastal Douglas-fir progeny tests across eight breeding zones in western Washington and Oregon were analyzed. Climate transfer functions for age-12 growth were derived, showing significant results for the US Pacific Northwest. Region-specific transfer functions (Coast, Inland, and Cascade) displayed stronger correlations. Mean annual temperature and mean coldest month temperature were the most important climatic variables explaining growth. The study found that populations from slightly warmer areas tended to grow better but moving populations from colder to warmer areas by 2 °C (analogous to projected global warming) would result in an 8% genetic loss in age-12 height and a 25% genetic loss in age-12 volume. However, substantial diversity in climatic response was found among full-sib families within large breeding zones, suggesting that breeding and selecting suitable families for future climatic conditions within breeding zones is feasible. The study discusses potential strategies to adapt current breeding programs to address the impacts of future climate change while maintaining high population growth rates in Douglas-fir breeding programs. [ABSTRACT FROM AUTHOR]

The work is devoted to the helicopter turboshaft engines' gas generator rotor R.P.M. neuro-fuzzy controller development, which improves control accuracy and increases the system's stability to external disturbances and adaptability to changing operating conditions. Methods have been developed, including improvements to the automatic control system structural diagram which made it possible to obtain the system transfer function in the bandpass filter transfer function form. The work also improved the fuzzy rules base and the neuron activation function mathematical model, which significantly accelerated the neuro-fuzzy controller training process. The transfer function frequency and time characteristics analysis showed that the system effectively controlled the engine and reduced vibration. Methods for ensuring a guaranteed stability margin and the synthesis of an adaptive filter were studied, which made it possible to achieve the system's high stability and reliability. The results showed that the developed controller provided high stability with amplitude and phase margins, effectively compensating for changes in external conditions. Experimental studies have demonstrated that the control quality improved by 2.31–2.42 times compared to previous neuro-fuzzy controllers and by 5.13–5.65 times compared to classic PID controllers. Control errors were reduced by 1.84–2.0 times and 5.28–5.97 times, respectively, confirming the developed neuro-fuzzy controller's high efficiency and adaptability. [ABSTRACT FROM AUTHOR]

Summary: Large‐scale linear time‐invariant (LTI) dynamical systems are widely used to characterize complicated physical phenomena. Glover developed the Hankel norm approximation (HNA) algorithm for optimally reducing the system in the Hankel norm, and we study its numerical issues. We provide a remedy for the numerical instabilities of Glover's HNA algorithm caused by clustered singular values. We analyze the effect of our modification on the degree and the Hankel error of the reduced system. Moreover, we propose a two‐stage framework to reduce the order of a large‐scale LTI system given samples of its transfer function for a target degree k$$ k $$ of the reduced system. It combines the adaptive Antoulas–Anderson (AAA) algorithm, modified to produce an intermediate LTI system in a numerically stable way, and the modified HNA algorithm. A carefully computed rational approximation of an adaptively chosen degree d$$ d $$ gives us an algorithm for reducing an LTI system, which achieves a balance between speed and accuracy. [ABSTRACT FROM AUTHOR]

Owing to the significant differences in environmental characteristics and explanatory factors among estuarine and coastal regions, research on diatom transfer functions and database establishment remains incomplete. This study analysed diatoms in surface sediment samples and a sediment core from the Lianjiang coast of the East China Sea, together with environmental variables. Principal component analysis of the environmental variables showed that sea surface salinity (SSS) and sea surface temperature were the most important factors controlling hydrological conditions in the Lianjiang coastal area, whereas canonical correspondence analysis indicated that SSS and pH were the main environmental factors affecting diatom distribution. Based on the modern diatom species-environmental variable database, we developed a diatom-based SSS transfer function to quantitatively reconstruct the variability in SSS between 1984 and 2021 for sediment core HK3 from the Lianjiang coastal area. The agreement between the reconstructed SSS and instrument SSS data from 1984 to 2021 suggests that diatom-based SSS reconstruction is reliable for studying past SSS variability in the Lianjiang coastal area. Three low SSS events in AD 2019, 2013, and 1999, together with an increased relative concentration of freshwater diatom species and coarser sediment grain sizes, corresponded to two super-typhoon events and a catastrophic flooding event in Lianjiang County. Thus, a diatom-based SSS transfer function for reconstructing past SSS variability in the estuarine and coastal areas of the East China Sea can be further used to reflect the paleoenvironmental events in this region. [ABSTRACT FROM AUTHOR]

Feature Selection (FS) is a key pre-processing step in pattern recognition and data mining tasks, which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models. In recent years, meta-heuristic algorithms have been widely used in FS problems, so a Hybrid Binary Chaotic Salp Swarm Dung Beetle Optimization (HBCSSDBO) algorithm is proposed in this paper to improve the effect of FS. In this hybrid algorithm, the original continuous optimization algorithm is converted into binary form by the S-type transfer function and applied to the FS problem. By combining the K nearest neighbor (KNN) classifier, the comparative experiments for FS are carried out between the proposed method and four advanced meta-heuristic algorithms on 16 UCI (University of California, Irvine) datasets. Seven evaluation metrics such as average adaptation, average prediction accuracy, and average running time are chosen to judge and compare the algorithms. The selected dataset is also discussed by categorizing it into three dimensions: high, medium, and low dimensions. Experimental results show that the HBCSSDBO feature selection method has the ability to obtain a good subset of features while maintaining high classification accuracy, shows better optimization performance. In addition, the results of statistical tests confirm the significant validity of the method. [ABSTRACT FROM AUTHOR]

This study presents the electrical modeling and characteristic analyses of energy storage systems (ESSs) based on the internal impedance characteristics of batteries to improve ESS stability. Frequencies ranging from 1 kHz to 0.1 Hz were injected into lithium-ion batteries, and the variation of the internal impedance of the batteries was obtained based on the reflected wave to determine the ESS state of charge (SoC) and temperature. The changes in the basic electrochemical impedance spectroscopy characteristics of the ESSs were observed. Specifically, the voltage, temperature, and SoC of an ESS that could be employed as a renewable ESS were analyzed. The impedance characteristics of the ESS were investigated via experimentation and simulation. The ESS comprised an electrically equivalent circuit of a series inductor ( L S ), series resistor ( R S ), parallel resistor ( R P ), and parallel capacitor ( C P ), as well as a MATLAB program based on its transfer function to generate energy. Furthermore, a method was developed for analyzing the frequency response of ESSs. The feasibility of the proposed electrical modeling was examined for a 58.4 V, 75 Ah, 4.4 kWh ESS. [ABSTRACT FROM AUTHOR]

Vibration is an indispensable part of the tactile perception, which is encoded to oscillatory synaptic currents by receptors and transferred to neurons in the brain. The A2 and B1 neurons in the drosophila brain postsynaptic to the vibration receptors exhibit selective preferences for oscillatory synaptic currents with different frequencies, which is caused by the specific voltage-gated Na+ and K+ currents that both oppose the variations in membrane potential. To understand the peculiar role of the Na+ and K+ currents in shaping the filtering property of A2 and B1 neurons, we develop a linearized modeling framework that allows to systematically change the activation properties of these ionic channels. A data-driven conductance-based biophysical model is used to reproduce the frequency filtering of oscillatory synaptic inputs. Then, this data-driven model is linearized at the resting potential and its frequency response is calculated based on the transfer function, which is described by the magnitude–frequency curve. When we regulate the activation properties of the Na+ and K+ channels by changing the biophysical parameters, the dominant pole of the transfer function is found to be highly correlated with the fluctuation of the active current, which represents the strength of suppression of slow voltage variation. Meanwhile, the dominant pole also shapes the magnitude–frequency curve and further qualitatively determines the filtering property of the model. The transfer function provides a parsimonious description of how the biophysical parameters in Na+ and K+ channels change the inhibition of slow variations in membrane potential by Na+ and K+ currents, and further illustrates the relationship between the filtering properties and the activation properties of Na+ and K+ channels. This computational framework with the data-driven conductance-based biophysical model and its linearized model contributes to understanding the transmission and filtering of vibration stimulus in the tactile system. [ABSTRACT FROM AUTHOR]

The analytical method for the synthesis of a generator of a random process with a given spectrum in the form of a linear system of Itô's equations is proposed. The stationarity of a random process is assumed, the spectral and corresponding transfer functions of which are defined in the form of rational fractions. The coefficients of the system of Itô's equations of the generator are found from recurrent algebraic relations. The method is focused on working with mathematical models of nature random processes, such as the Dryden's wind model. The transformation of the spectra of the wind gust model in three directions is presented in detail and the corresponding stochastic equations are given. [ABSTRACT FROM AUTHOR]

We study L-system realizations generated by the original Weyl–Titchmarsh functions m α (z) . In the case when the minimal symmetric Schrödinger operator is non-negative, we describe Schrödinger L-systems that realize inverse Stieltjes functions (- m α (z)) . This approach allows to derive a necessary and sufficient conditions for the functions (- m α (z)) to be inverse Stieltjes. In particular, the criteria when (- m ∞ (z)) is an inverse Stieltjes function is provided. Moreover, it is shown that the knowledge of the value m ∞ (- 0) and parameter α allows us to describe the geometric structure of the L-system realizing (- m α (z)) . Additionally, we present the conditions in terms of the parameter α when the main and associated operators of a realizing (- m α (z)) L-system have the same or different angle of sectoriality which sets connections with the Kato problem on sectorial extensions of sectorial forms. An example that illustrates the obtained results is presented in the end of the paper. [ABSTRACT FROM AUTHOR]

Abstract Distributed Acoustic Sensing (DAS) is a photonics technology converting seafloor telecommunications and optical fiber cables into dense arrays of strain sensors, allowing to monitor various oceanic physical processes. Yet, several applications are hindered by the limited knowledge of the transfer function between geophysical variables and DAS measurements. This study investigates the quantitative relationship between surface gravity DAS‐recorded wave‐generated strain signals along the seafloor and the pressure at a colocated sensor. A remarkable linear correlation is found over various sea conditions allowing us to reliably determine significant wave heights from DAS data. Utilizing linear wave potential theory, we derive an analytical transfer function linking cable deformation and wave kinematic parameters. This transfer function provides a first quantification of the effects related to surface gravity waves and fiber responses. Our results validate DAS's potential for real‐time reconstruction of the surface gravity wave spectrum over extended coastal areas. It also enables the estimation of waves hydraulic parameters at depth without the need from offshore deployments.

The Three Parallel Rivers of Yunnan Protected Area (TPRYPA) is in the core area of the Hengduan Mountains, characterized by paralleling alternately high mountains and canyons that led to significant vertical temperature variation. Developing a climate transfer function for this region is crucial for both biodiversity protection and enhancing our understanding of feedback mechanisms between low-latitude and high-altitude areas in the context of global climate change. We investigated the chrysophycean stomatocyst assemblages from surface sediments of 46 high mountain lakes (altitude: 3244 to 5043 m) in the TPRYPA to assess the suitability of cysts in prediction of climate variability in high mountains. A total of 194 morphotypes were identified. Gradient forest (GF) model identified that altitude is the key environmental gradient with relation to cyst assemblages and cyst assemblages changed the composition substantially at altitude of 3200 and 4400 m, respectively. Cyst-based inference model for altitude using weighted-averaging method resulted in cross-validated jack-knifed-R2 of 0.60. The cyst-altitude transfer functions suggest that chrysophycean stomatocysts have a great potential as a powerful tool for climate reconstructions in alpine regions of the Hengduan Mountains.

Control of large structures often involves the use of hydraulic equipment due to the large power requirements. In this study, a large telescope is taken as an example of a large structure. A large telescope is equipped with hydraulic support systems to adjust the deformation of the structure, as changes in its pointing direction and ambient temperature deform the structure and reduce the telescope's observational performance. In previous studies, hydraulic support system elements and piping paths have been discussed, but quantitative evaluations and design considerations for the overall system performance of multiple hydraulic support systems and telescope structures have not been discussed. In this paper, simple transfer function models are derived for a coupled system of multiple hydraulic support systems and telescope structures, which allow the static and dynamic transient response characteristics to be evaluated. A design example using these transfer function models is also proposed and it is shown that the selected design parameters enable the large telescope to meet the specifications.