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Purpose – In this research, we explore the dynamics among measures of income inequality in the USA, male and female unemployment rates and growth in government transfer using time series data. Design/methodology/approach – This research adopts a macro-econometric approach to estimate a structural VAR model using time series data. Findings – Our structural impulse responses found that growth in government transfer increases unemployment rates for both males and females. Female income inequality declines with increased government transfer. When the female income ratio rises, we observe that government transfer outlays fall over the forecast horizon. Variance decomposition finds that growth in government transfers is impacted by the male unemployment rate relatively more than the female unemployment rate. This research, therefore, suggests gender-specific government transfers to reduce income inequality. This, in effect, may reduce government transfer outlays over time. Practical implications – This research, therefore, suggests gender-specific government transfers to reduce income inequality. This, in effect, may reduce government transfer outlays over time. Originality/value – This research investigates the dynamics among income inequality, government transfer and unemployment rates. There is a dearth of research articles that adopt a macro-econometric in this area.

This study investigates the complex relationship between oil price fluctuations and economic growth in Algeria, using time series data from 1973 to 2023. Advanced econometric techniques, including Vector Autoregression (VAR), Impulse Response Functions (IRFs), Variance Decomposition (VD), and the Toda-Yamamoto causality test, were employed to examine both the direct and indirect effects of oil price changes on the country’s growth. The results of the VAR model indicate a strong positive relationship between oil prices and economic growth, emphasizing the critical role of hydrocarbon exports in driving Algeria’s economy. However, this connection is influenced by other macroeconomic factors, such as inflation, exchange rates, and money supply. IRF analysis shows that while oil price increases initially stimulate growth, long-term impacts are moderated by adjustments in monetary policy and exchange rates. The variance decomposition analysis further highlights that oil price volatility significantly influences short-term economic performance, but its effect diminishes over time, suggesting progress in economic diversification, policy adaptation, and the effectiveness of managing oil-related volatility. The Toda-Yamamoto causality test provides additional insights, confirming unidirectional causality from oil prices to growth and highlighting indirect effects through exchange rates and money supply. These findings underscore the importance of continued diversification efforts in oil-dependent economies like Algeria, as well as the need for adaptive monetary and exchange rate policies to ensure both short-term stability and long-term growth. This study contributes to the broader literature on oil price-growth dynamics and offers valuable guidance for policymakers in resource-rich countries.

This study analyzes the response of economic fundamentals to a misalignment shock of the real effective exchange rate in Korea. The estimation results of the equilibrium exchange rate determination model and time series model show that there is no significant difference in the direction of the deviation from equilibrium and that the won is significantly undervalued during the period before 1988, or during the currency and global financial crises. The cumulative impulse response analysis of the VAR model over the full period shows that an upward shock to the deviation from the equilibrium exchange rate reduces the GDP gap and inflation rate, while the effect on the call rate is not statistically significant. Furthermore, an upward misalignment shock initially worsens the goods and services balance, but the deficit in the goods and services balance shrinks significantly over time. In rolling regressions analysis, the entire sample is divided into two periods to estimate the impulse response function from the first period, and then the same procedure is repeated by moving the sample forward one by one. The cumulative impulse response results show that, as is the case for the full period, a positive exchange rate misalignment shock initially reduces the GDP gap, inflation, and worsens the goods and services balance, but the impact of this upward shock on these variables becomes increasingly weaker in the more recent sample. It also shows that the negative impact of upward shocks on the current account is smoothed out more recently during periods of undervaluation than during periods of overvaluation.

For continuous wavelet transformation, wavelets based on derivatives of the Gauss function are used, and for multiscale analysis, Daubechies wavelets are used. The development of algorithms for forward and inverse continuous wavelet transform in the frequency domain made it possible in this work to synthesize digital filters with a finite impulse response (FIR) different from existing methods. The quality of the synthesized filters was checked by decomposition and subsequent reconstruction of the signals. To do this, several filters were synthesized that completely cover the frequency range of the signal. Since wavelets are bandpass filters, the authors called the filters wavelets. The more precisely the reconstructed signal repeats the shape of the original signal, the better the wavelet constructed by one method or another. A comparison of the accuracy of signal reconstruction shows that the best conversion result is obtained by using synthesized wavelets. The impulse responses of the FIR filters are synthesized so that their frequency response are similar to the frequency responses of wavelets based on derivatives of the Gaussian function of a large order. The greater the filter order, the closer the frequency response is to a square-wave shape. Algorithms for forward and inverse wavelet transformation of a signal in the frequency domain using wavelets based on derivatives of the Gauss function are proposed. Profiling of the program shows that the time of the wavelet transforms using the fast Fourier transform is 15,000 times less than with the direct numerical integration for sampling the signal of 32,768 samples. These algorithms can be used for wavelets with a square-wave frequency response. At the same time, the numerical calculation time is halved. The accuracy of the reconstruction was compared for wavelets based on second-order derivatives, Daubechies wavelets, and wavelets with a square-wave frequency response. The reconstruction accuracy is highest for the latest wavelets. The use of the wavelet construction method is preferable since this method is relatively simple and it is easy to synthesize multiband filters with any form of frequency response. If, when synthesizing using existing methods, a short transition band can be obtained only for long impulse responses, while the transition band using the method of constructing wavelets is absent even for filters of very small orders. The paper presents the impulse responses of twoband, three-band digital filters and their frequency response. FIR digital filters with a square-wave frequency response have a higher delay-band attenuation coefficient compared to existing filters, do not have transition band, and can be used to process one-dimensional and two-dimensional signals.

Object recognition is an important task for robots to handle different objects. We focus on the differences in vibration propagation through an object in relation to the material characteristics, and develop a novel method to perform object classification using vibration. Object properties such as thickness and hardness are reflected in propagating vibration. In this paper, we introduce an experimental system for the object recognition, and the performance of the impulse response analysis will be described and discussed. In the system, we employ an accelerometer to measure the vibration generated by the collision of two magnets. In a verification experiment, we compared the well-known classification methods using machine learning, and verified that the support vector classifier performed the best recognition with a successful rate of 95% for 8 different objects. The proposed system detects not only the characteristics of the object’s surface but also its internal properties. Thus, it is expected to be applied to discriminate objects with the same surface but different internal structures.

Pulsed radiation field is characterized by short duration and high instantaneous dose rate compared with steady state radiation field, and has been widely used in the development of new detectors, radiological examination, X-ray diagnosis, nuclear accident emergency response and scientific research. Traditional nuclear radiation detectors in the pulsed radiation field often have problems such as signal stacking, ion composite, dead time, etc., which makes it difficult to realize the accurate measurement of pulsed radiation dose. Diamond detectors have the advantages of large bandwidth, high carrier mobility, strong anti-irradiation ability, fast time response, etc. In this paper, we utilize millisecond and nanosecond pulsed X-ray reference radiation field, high dose rate γ irradiation device to carry out the pulsed radiation field response test of single crystal diamond detector TW60019 produced by German PTW company, and we conclude that: the single-pulse response of diamond detector decreases with the increase of pulsed dose, and the single-pulse response of the single-pulse detector decreases with the increase of pulsed dose, and the single-pulse response of the single-pulse detector increases with the increase of pulse dose. It is concluded that the single-pulse response of the diamond detector decreases with the increase of the pulse dose, and similar to the ion complex effect in the ionization chamber in the pulsed radiation field, the diamond detector also suffers from the phenomenon of unsaturated collection in the measurement of large-dose and short-time pulsed radiation, and the detector's collection efficiency decreases with the increase of the single-pulse dose. Based on the experimental phenomenon, the influence of impurities and defects in the synthetic diamond film on the collection efficiency of the detector is analyzed, the impact of back-end electronics systems on collection efficiency during diamond detector signal collection is discussed and it is verified that the diamond detector can meet the requirements of fast response and high instantaneous dose rate of pulsed radiation measurements, which provides a basis for the further realization of the real-time monitoring of the pulsed radiation dose and the solution of the widely existed technical needs of the pulsed radiation dose monitoring in the fields of medical diagnosis and industrial flaw detection. The detector has been used in the following fields: medical diagnosis, industrial flaw detection, etc.

This work discusses an efficient implementation of the kernel regularization method. In particular, this work focuses on the Alternating Direction Method of Multipliers (ADMM) which is one of the convex optimization methods. This work employs two assumptions; (1) the identification input is periodic, and (2) the kernel matrix is tridiagonal. It is shown that ADMM updates can be implemented efficiently under these assumptions. A numerical example is shown to demonstrate the effectiveness of the proposed method. In addition, a practical experiment to estimate the room impulse response of a classroom at the University of Kitakyushu is shown.

This paper is concerned with the $ L_2/L_1 $ induced and Hankel norms of sampled-data systems. In defining the Hankel norm, the $ h $-periodicity of the input-output relation of sampled-data systems is taken into account, where $ h $ denotes the sampling period; past and future are separated by the instant $ \Theta\in[0, h) $, and the norm of the operator describing the mapping from the past input in $ L_1 $ to the future output in $ L_2 $ is called the quasi $ L_2/L_1 $ Hankel norm at $ \Theta $. The $ L_2/L_1 $ Hankel norm is defined as the supremum over $ \Theta\in[0, h) $ of this norm, and if it is actually attained as the maximum, then a maximum-attaining $ \Theta $ is called a critical instant. This paper gives characterization for the $ L_2/L_1 $ induced norm, the quasi $ L_2/L_1 $ Hankel norm at $ \Theta $ and the $ L_2/L_1 $ Hankel norm, and it shows that the first and the third ones coincide with each other and a critical instant always exists. The matrix-valued function $ H(\varphi) $ on $ [0, h) $ plays a key role in the sense that the induced/Hankel norm can be obtained and a critical instant can be detected only through $ H(\varphi) $, even though $ \varphi $ is a variable that is totally irrelevant to $ \Theta $. The relevance of the induced/Hankel norm to the $ H_2 $ norm of sampled-data systems is also discussed.

We studied the characteristics of coupled-core four-core fibers (CC-4CFs) with different core pitches both theoretically and experimentally. The spatial mode dispersions of different fibers were analyzed, and the result agrees well with theoretical ones. In the experiment, we achieved a spatial mode dispersion as low as 3.72 ps/km1/2 for a 4 km-long CC-4CF with core pitch of 19 μm at a bending radius of 8 cm. The impulse response matrices of four core fibers with different core pitches was investigated using swept-wavelength interferometry over 100 nm bandwidth. The impulse responses were Gaussian-shaped and consistent between different inputs and outputs for CC-4CFs. Our results show that the CC-4CFs with core pitches of 17 and 19 μm exhibit narrower pulse broadening. In addition, the mode-dependent losses of different fibers were analyzed. For the fibers with core pitch of 19 μm, the estimated mode-dependent loss was lower than 3 dB for the entire 100 nm bandwidth. Our results are especially helpful for developing coupled-core multicore fibers for long-haul transmission applications.

Abstract Leakage in the wellbore annulus during drilling operations can affect normal production operations, resulting in a severe waste of resources and economic loss, so it is crucial to adopt a fast and effective leak identification method for subsequent plugging operations. For the complex problem of judging the location of the leakage layer, we proposed the method of excitation pressure wave to identify the location of the leakage layer. By analyzing the transfer of pressure waves within the annular pipe system and the pressure head response spectrum, the leak's location is identified based on the location of the resonance point and the change in resonance amplitude. The pressure wave signal contains too much noise. The variational mode decomposition (VMD) algorithm and the Hilbert joint spectrum were used to extract the main frequency components to reconstruct the signal to achieve the denoising effect. On this basis, the reconstructed signal is processed by fast Fourier transform (FFT) to obtain the pressure wave response spectrum, analyze the frequency domain features, and then determine the location of the leakage layer. The experimental results verify that: ① When a leak occurs in the wellbore annulus, the pressure wave will generate additional resonance points in the frequency domain due to the presence of the leak point. ② The combination of the VMD algorithm, FFT, and Hilbert joint spectrum can effectively remove the noise of the pressure wave signal. ③ The method effectively avoids the difficulty of identifying negative pressure waves in the time domain analysis of pressure wave signals. It can effectively locate the leaky layer in the frequency domain analysis. It is concluded that the principle of the method is feasible and has practical significance for field application.

In this paper, new schemes have been proposed for the estimation of the additive white Gaussian noise (AWGN) channel with intersymbol interference (ISI) in an iterative equalization and decoding system using low-density parity check (LDPC) codes. This article explores the use of the least squares algorithm in various scenarios. For example, the impulse response of the AWGN channel h was initially estimated using a training sequence. Subsequently, the impulse response was calculated based on the training sequence and then re-estimated once using the sequence estimated from the output of the LDPC decoder. Lastly, the impulse response was calculated based on the training sequence and re-estimated twice using the sequence estimated from the output of the LDPC decoder. Comparisons were made between the performances of the three mentioned situations, with the situation in which a perfect estimate of the impulse response of the channel is assumed. The performance analysis focused on how the bit error rate changes in relation to the signal-to-noise ratio. The BER performance comes close to the scenario of having a perfect estimate of the impulse response when the estimation is performed based on the training sequence and then re-estimated twice from the sequence obtained from the output of the LDPC decoder.

A characterization of near-field impulse responses based on electromagnetic (EM) near-field data from an EM solver to explore features of the propagation process on a well-known wideband traveling wave antenna—double-slot Vivaldi antenna—is presented in this article. The intensity, propagating time and partitional response characteristics facilitate interpretation of the propagation process and impacts of the antenna partitions on the process. The EM energy flows guided, reoriented and scattered along a sequence of antennas transmitting and radiating segments were recognized. The geometric features of near-field wavefront surfaces supported evaluation of the EM flow proportions and antenna directivity. Impact of the structural section on radiation was also assessed by the partitional far-field response characteristic in frequency and time domains. Supported by many complementary characteristics in the analyses, inherent features of the propagation process were emphasized and false flags were minimized. By this approach, the simplification for the near-field propagation model contributed to enhancing the insight of near-field propagation processes on the double-slot antipodal Vivaldi antennas and enabled optimizing the antenna structure details.

The transmission characteristics of laser in four types of harbor water were studied using Monte Carlo method simulations for the characteristics of strong absorption and high scattering of water in marine harbors. The effects of water quality parameters and transmission distance on the optical signal power, impulse response and spot quality were quantified and analyzed at certain optical wavelengths, optical energies and divergence angles. The analysis shows that the attenuation characteristics of laser remain consistent as the attenuation length increases when the optical signal is transmitted in the four types of harbor waters. The rate of attenuation of the optical power also does not conform to the linear relationship described by Beer's law. Under the same water quality conditions, the width of the optical pulse, the size of the light spot increases with the attenuation length. However, when the attenuation length is transmitted from 20 to 30, the width of the optical pulse is almost unchanged, and the change in the amount of light pulse spreading is less than 1 ns with the size of the light spot increases continuously. In addition, at the same attenuation length, the light pulse and the spreading amount of light spot with the water quality attenuation coefficient increases and decreases.

Typically, background noise of different types and levels is present during the measurement of the impulse response in spaces. The two methods that are, in practice, most frequently used in the measurement of the impulse response, are the exponential sine sweep (ESS), and the maximum length sequence (MLS). This study’s objective was to estimate the impact of background noise (white noise, tonal noise) on the acoustic parameters (T30, EDT, C80, and D50) for ESS and MLS measurements, by introducing artificial background noise, employing an external sound source. For this purpose, measurements were performed with varying levels of external noise (in steps of 2 dB), and the effect was assessed, using the relative error compared to measurements without artificial background noise. According to the findings for white noise (as background noise), in the case of T30 and EDT, the difference between the two methods, as well as the relative error, for the initial levels of added background noise, was small. However, for higher levels of added background noise, there was a sharp increase in the relative error, which was greater for the ESS method, both for T30 and EDT. Regarding C80 and D50, while initially the differences between the ESS and MLS methods were small, cumulatively, as the background noise increased, the relative error increased for both methods, with the ESS method showing the largest error. In the case of tonal noise (as background noise), the results were consistent with those observed in the case of white noise. The study’s findings contribute to a better understanding of the ESS and MLS methods, and suggest the expected relative error of acoustic parameters when various types and levels of background noise are present. Additionally, the study suggests, based on background noise and level, the optimum method to conduct impulse response measurements.

Under the background of “double carbon”, exploring the growth path of green logistics and enhancing the driving force of technological innovation is the urgent need of our country to comply with the green transformation of its economy and realize high-quality economic growth. Taking the panel data of 30 Chinese provinces from 2010 to 2020 as the sample, the green logistics index evaluation system is constructed based on the driver-press-state-impact-response (DPSIR) theoretical framework, and the green economic efficiency of each province in the sample period is measured by using the non-expected output Super- Slacks-based measure (SBM) model, and by constructing the panel vector autoregressive (PVAR) model including technological innovation is used to systematically elaborate the dynamic influence paths among the three. The study found that: China's green economy, technological innovation, and green logistics all have their own mechanisms for growth, which will gradually diminish over time. In the near and long term, green logistics will promote technological innovations and the evolution of a green economy, but there is a lag in the long-term benefits of green logistics on technological progress. In the short term, technological innovation does not lend support to the growth of a green economy, but over time, the impact of technological innovation on the growth of that economy will shift from negative to positive. This shows that improving technological innovation capability is an important path for green logistics to promote green economic efficiency. The findings of the study provide a basis for decision making to achieve the emission reduction target and improve the efficiency of the green economy.

This paper delves into the relationship between the volatility of the capital market and economic growth within the broader framework of the macro capital market. By employing the Heston stochastic volatility model in tandem with macroeconomic theory, we aim to analyze the stochastic control problem between the allocation trajectory of macro-capital and economic fluctuations. Our mathematical analysis reveals that the influence of capital shifts on economic growth's volatility varies across different capital markets due to diverse risk levels inherent within the macro-capital market. To validate these mathematical findings, we embark on an empirical econometric analysis tailored to the nuances of China's capital market and its macroeconomic operations. This econometric exploration yields two primary insights: 1. Distinct components of China's capital market have varying influences on macroeconomic growth. 2. The structure of China's capital market, especially in its impact on macroeconomic development, exhibits imbalances and lacks optimal configuration.

A convenient and efficient rural road network helps to develop the rural economy and promote national rural revitalization. This paper systematically analyzes the role of rural road infrastructure construction on economic growth from a theoretical perspective. Simultaneously, it employs a computer algorithm based on vector autoregression to construct an econometric model. Based on this theoretical analysis empirically examines the relationship between rural infrastructure construction and economic development. It collects data from a city’s rural area from 2000 to 2020 as a research sample and conducts a series of analyses, including VAR model estimation, impulse response, and variance decomposition. The results show that rural road infrastructure construction has a significant impact on economic growth, with coefficients of −0.288 and 0.356 for lags 1 and 2. The construction of rural road infrastructure is responsible for more than 90% of the variation in economic growth, whereas the opposite is about 10%. Therefore, rural road infrastructure development does not have a significant effect on economic growth in the short term but has a significant effect in the medium and long term.

How to create a people-oriented and unique public art space has become an ongoing concern for urban designers and industry scholars. In this paper, based on the artistic design characteristics of the public space environment and its main functions, a questionnaire is designed to obtain the public’s satisfaction with public art space and perception of multidimensional elements in art design. The multidimensional analysis method is proposed based on the VAR base model. The cointegration test examines the stability of the model. Then, the impact of the dimensional elements of art design on public satisfaction of the public space environment is analyzed by the impulse response and variance decomposition methods. The cointegration test shows that there is a correlation between satisfaction, acceptance, and sense perception of public spaces and perception of art design, which supports the subsequent research. Regionality and diversity in art design were found to be Granger’s causes of public satisfaction and acceptance of public art space environments at the 1% confidence level (p

The outbreak of the oil crisis in the 1970s had an extremely profound impact on the world economy. The use of computer simulation is employed in this paper to explore how the oil crisis led to the restructuring of the world economy in the 1970s. Taking the United States as a representative, we study its main economic characteristics, such as price and currency in the 1970s oil crisis, and analyze the changes in economic characteristics before and after the oil crisis to provide data support for this paper to simulate the impact of the oil crisis on the structure of the world economy. Adopt the VAR model for computer simulation research. The GDP and CPI data of the United States, Europe, Japan, and other countries in the 1970s are selected to construct the variables of the world economic structure. After analyzing the processed data in terms of the Granger causality test, impulse response, and variance decomposition, this paper concludes that the oil crisis triggered by the increase of oil prices in the 1970s has had a significant impact on the world economic structure and the long-term oil crisis will force countries around the world to actively adjust their economic structure in order to cope with the downturn of the economy caused by the oil crisis.

The emergence of digital currency has had a significant impact on economic policy while increasingly and profoundly changing the lives of the public. The main research object of this paper is the digital currency that incorporates blockchain and smart contract technology, and it deeply examines how its generation and development impact economic policy and the corresponding regulatory mechanism. The application of blockchain and smart contract technology is used to analyze the characteristics and performance of digital currency, and the advantages and shortcomings of using digital currency are briefly explained. Based on the analysis of related research and practical application requirements, digital currency is being introduced into the DSGE model, which includes households, manufacturers, and other sectors. Impulse response and variance decomposition are used to numerically simulate the impact of digital currency on economic policy and the effectiveness of the regulatory mechanism. The economic policy transmission efficiency is the lowest in the no digital currency scenario, while it is significantly higher in the scenario of full use of digital currency, with interest rate shocks into the maximum degree of change in output and inflation of 0.059% and 0.121%. The variance of each macroeconomic variable in the economic policy regulation scenario shows a significant downward trend, and digital currency contributes to the overall regulation effect of macroeconomic policy.

Artificial Intelligent Reflective Surface (AIRS) is a kind of artificial two-dimensional surface with special electromagnetic properties that can control the absorption, reflection, and refraction characteristics of electromagnetic waves, thus realizing the modulation of the channel. It has a broad application prospect. Based on the fading characteristics of wireless channels, the article explores artificial intelligent, reflective surface-related technology and establishes an IRS-assisted UAV-MIMO channel model in combination with the wireless communication link of the UAV. For the statistical characteristics of the UAV channel, wireless in-site simulation software is used to carry out simulation analysis of its impulse response, space-time frequency correlation function, path fading, Doppler power spectral density, and other statistical characteristics. The peak value of the UAV path loss decreases from 143.8 dB to 129.9 dB when the wind speed increases from 6 m/s to 18 m/s in a fixed-height ground environment, and the communication delay distributions are between [0,450 ns] and [0,1550 ns] when the hovering height of the UAV is 100 m and 500 m, respectively. The Rice factor’s Doppler power spectral density value decreases by 53.48% when it is reduced from 1.2 dB to 0.05 dB. The absolute envelope amplitude of the channel of the UAV fluctuates between [-26.3 dB and 21.8 dB] after the optimal reflective phase design based on IRS adaptation. The use of AI reflective surfaces can facilitate the construction of the UAV channel model, thereby improving the communication quality of the UAV channel.

This paper provides an in-depth analysis of the rapid decline in fertility and population aging. Using LASSO penalized Poisson regression and TVP-VAR model improved by MCMC algorithm based on Bayesian network, the factors affecting the fertility level and their interrelationships are studied. It is found that fertility and education levels affect each other with probabilities of 0.0806 and 0.0825 respectively at 10% significance level. The positive shock of social security level on fertility level increases the response value of fertility level from −1.5 to about 7.5 in 10 lags, showing a positive effect. In contrast, the response of fertility level to house prices gradually decreases from 5 in period 1 to −2.5 in period 10, indicating a negative impact. These analyses provide a scientific basis for constructing a public service fertility support system that can help mitigate the current challenges of low fertility levels.

This paper first establishes a system of international financial risk early warning indicators, constructs a VAR forecasting model for international financial risk, and uses the ADF test to identify the best amount of lags for the model before doing a smoothness test on the variables. The lagged values are used as independent variables to establish equations to describe the dynamic relationship between multiple variables. Then the model is tested for cointegration and Grange causality. The VAR model is then used to estimate the financial risk profile from the standpoint of a global economy and to examine the extent to which the debt crisis has affected international financial risk. The results show that at the 5% significant level, national debt expansion is the cause of asset price volatility, macroeconomic volatility, and debt risk. The forecasted inflation rate for 2023-2025 is 4.18, output gap is 4.94, and the policy interest rate is 4.18 for emerging Asian countries and regions.

This paper first analyzes the current situation and characteristics of English teaching in higher education and explores the current classroom model of English teaching in higher education. Then, it combines the multiple regression model and VAR method to construct an improved multiple autoregressive model of English teaching based on VAR and apply it to higher vocational English teaching. Finally, the improved model was tested for smoothness and co-integration, and the problems of English teaching in higher education were analyzed using impulse responses. The results showed that when the number of teacher-student ratios increased, the teaching quality response was -3, the English teaching time increased, and the English teaching quality response was around 4.5. The best-fit teaching model yielded a positive and stable response of around 4.5.

In this paper, we first establish a VAR model, screen the variables using the CSIS method so as to achieve fast dimensionality reduction, and use the cointegration theory to model non-stationary time variables to determine the pseudo-regression analysis between variables. Impulse response functions and variance decomposition are then used to analyze the dynamic effects of changes in random disturbance terms on the whole system and other variables. Finally, the effects of insider trading on executive compensation are explored using corporate performance and market-based executive pay entitlement as references, respectively. The results show that the regression coefficients of executive compensation level and cumulative excess turnover rate of the event announcement period are 0.805 and 0.018 for corporate performance and market-based compensation, respectively, and the higher the level of executive compensation based on corporate performance and market-based compensation, the lower the impact of insider trading on executive compensation. Equity incentives reduce the impact of insider trading on executive compensation, and the regression coefficient of excess turnover on executive compensation is less than 0 in firms with shareholding levels higher than 10%.

This paper constructs a vector autoregressive (VAR) model between the level of financial management and regional GDP. Through the Granger causality test, the panel data at the financial management level and regional economic phenomena are studied, and the empirical distributions in the stationary situation are given by combining the stochastic simulation method. The auxiliary regression equations are established separately for constrained and unconstrained conditions, and the test statistic is constructed to determine whether there is Granger causality between the variables. Finally, from the actual data, a one standard deviation shock is applied to LATS and LGDP, and a response model with a lag of 100 periods is chosen to obtain the impulse response of the level of financial management and regional economic growth. The results show that the impact of the regional economy on the level of financial management is zero in the first period and then increases continuously, reaching a maximum response of 0.0233 in the 49th period, which indicates that there is a bidirectional promotional effect between the level of financial management and the regional economy and that the effect is not very strong but lasts for a long time.

AbstractThe stable money demand function is a crucial policy tool of the monetary policy of any central bank, which links the monetary sector of an economy to its real sector. Notably, after the global financial crisis of 2007–08, the role of money has come to be envisaged as an essential issue while formulating and conducting the monetary policy, especially at zero lower bound. It is crucial to know whether money demand is stable for inferring a sound monetary policy. To this end, the present study examines the short- and long-run money demand relationship and its stability in India from 2006:Q3 to 2019:Q4. The study has employed a dynamically simulated autoregressive distributed lag (ARDL) cointegration approach, which shows a well-specified and stable money demand in India after incorporating the inflation forecast variable as one of the essential determinants along with other covariates. Furthermore, the dynamically simulated impulse response also supports the economic relationship among variables. The current finding of stable money demand implies a policy implication in terms of focusing on monetary aggregate, in the ongoing flexible inflation targeting framework, as one of the essential information or indicator variables, which acts as a long-term assessment of short-term interest rate setting behaviour to achieve the macroeconomic goals.

The article analyzes a flat circular aperture and proposes to use a new analytical expression that describes the radiation pattern of an elementary radiator of an antenna aperture depending on time and integration angle. The formula presented in the paper can be applied to any flat section of the aperture without taking into account its shape. A new equation for the antiderivative function of the impulse response of a circular aperture is presented in the form of an elliptic integral of the second kind. It is shown that the theoretically calculated results are in good agreement with numerical simulations. In the analysis of the numerical model, the method of finite integration in the time domain (FIT) was used. Due to the requirement of a large computational resource, the numerical model was simplified.

This paper aims to examine the impact of the uncertainty shock on the Tunisian economy and the effectiveness of monetary policy during the Covid-19 pandemic. For this purpose, we employ a Bayesian VAR model considering the world uncertainty index (WUI) as a new proxy for uncertainty measure and the short-term interest rate as the main instrument of monetary policy. The results indicate that the Covid-19 uncertainty shock has a negative consequence on economic activity and on aggregate demand in Tunisia. Moreover, we show that conventional monetary policy is ineffective in times of high uncertainty period.

Gaussian filtering, being a convolution with a Gaussian kernel, is a widespread technique in image analysis and computer vision applications. It is the traditional approach for noise reduction. In some cases, performing the exact convolution can be computationally expensive and time-consuming. To address this problem, approximations of the convolution are often used to achieve a balance between accuracy and computational efficiency, such as with running sums, Bell blur, Deriche approximation, etc. At the same time, modern computing devices support data parallelism (vectorization) via Single Instruction Multiple Data (SIMD) and can process integer numbers faster than floating-point approaches. In this paper, we describe several methods for approximating a Gaussian filter, implement the SIMD and quantized versions, and compare them in terms of speed and accuracy. The experiments were performed on central processing units with a x86_64 architecture using a family of SSE SIMD extensions and an ARMv8 architecture using the NEON SIMD extension. All the optimized approximations demonstrated 10–20× speedup while maintaining the accuracy in the range of 1 × 10−5 or higher. The fastest method is a trivial Stack blur with a relatively high error, so we recommend using the second-order Vliet–Young–Verbeek filter and quantized Bell blur and running sums as more accurate and still computationally efficient alternatives.

This article presents different methods for noninvasive validation and diagnostics of contactless devices. The radio frequency systems studied here operate at 13.56 MHz. When manufacturing these systems in volume, it is essential to separate the fully functional devices from the totally defective ones or even from those communicating but have anomalies that will lead to a significant reduction of their lifetime. This article compares two noninvasive methods, one based on impedance measurements and the other on impulse response measurements. The advantages and drawbacks of these methods are presented and compared to their use in large-scale manufacturing. In addition to the proposed methods, this article describes two decision-making methodologies based on machine learning. This article compares also both measurement methods and machine learning tools. A robustness study shows the limitations of the employed techniques

Abstract This study aims to examine whether life insurance futures can serve as a hedge against the COVID-19 pandemic and whether they have the characteristics of a safe haven under the impact of the health shocks of the COVID-19 pandemic. We chose three life insurance stock futures in India and one in Taiwan as samples, including the market index of the two countries and the number of confirmed COVID-19 cases as sample variables. We used the growth rate of COVID-19 cases as the threshold variable, estimated the asymmetric threshold vector autoregression model, and found that insurance futures in the regime with a significant growth rate of confirmed COVID-19 cases can hedge against COVID-19 risks; therefore, insurance futures are a safe haven for the market. We further estimated the time-varying parameter vector autoregression model, and the impulse response results showed that insurance futures are a safe haven for COVID-19 pandemic risks.

With the development of launch vehicles, the number of satellite-based synthetic aperture radar (SAR) has been increasing. Satellite-based SAR can acquire high-resolution images at day and night regardless of the weather. Since SAR resolution is determined by a chirp bandwidth, corresponding a generation technology of wideband chirp is essentially required for high-resolution SAR images under consideration of relatively small and light weight SAR system. Therefore, an ideal chirp waveform generation, which is challenging task in actual hardware, is proposed by modifying a chirp equation for phase calibration, and is verified based on Sentinel-1 satellite SAR parameters. The proposed method is applied to our SAR hardware and we confirm the calibrated waveform. Evaluation results using SAR observation simulation show that the impulse response with the proposed method has better performances.

Monetary policy transmission mechanism has a complex process since its implementation it involves changes on banking sector, economic actors, the length of time in implementing policies, as well as changes in monetary policy transmission channels according to economic condition of the country. This study aims to examine how consumption and investment respond to monetary shocks that occur in Indonesia. These results indicate that the impact of monetary policy on consumption due to changes in interest rates is larger and more sensitive than investment, according to the impulse response test. The main channel of the effect of policy shocks obtained from consumption is transmitted to output. Therefore, the monetary policy issued by Bank Indonesia will have a greater impact on household spending than corporate spending. This means that channel consumption is the main mechanism of transmission.

The Greening in agro—food sector has become within the last decade a high priority issue given the 17 Sustainable targets set by OECD. More specifically, the Sustainable Development Goals (SDGs) by 2030, intend to promote using environmental resources in close correlation with measures to reduce non-environmental human pressure on the planet as well as in agro-food sector. The present work studies the greening of agro-food sector as synopsized in emissions per capita by agro-food sector for the EU and its relation to economic growth per capita with the assistance of a BVAR framework. Our findings do not validate success in greening of agro—food sector since the emissions reduction is not accompanied by economic growth a result that rejects the hypothesis of eco efficiency. Future research could involve the construction of an index that should incorporate more variables that will reflect more accurately the greening efforts in agro—food sector.

This paper proposes a solution to model fractional behaviours with a convolution model involving non-singular kernels and without using fractional calculus. The non-singular kernels considered are rational functions of time. The interest of this class of kernel is demonstrated with a pure power law function that can be approximated in the time domain by a rational function whose pole and zeros are interlaced and linked by geometric laws. The Laplace transform and frequency response of this class of kernel is given and compared with an approximation found in the literature. The comparison reveals less phase oscillation with the solution proposed by the authors. A parameter estimation method is finally proposed to obtain the rational kernel model for general fractional behaviour. An application performed with this estimation method demonstrates the interest in non-singular rational kernels to model fractional behaviours. Another interest is the physical interpretation fractional behaviours that can be implemented with delay distributions.

Photoacoustic tomography (PAT) is an emerging imaging technique with great potential for a wide range of biomedical imaging applications. The transducers impulse response (TIR) is a key factor affecting the performance of photoacoustic imaging (PAI). It is customary in PAI to assume that TIR is known or obtain it from experiments. In this paper, we investigate the possibility of obtaining TIR in another way. A new method is proposed to extract TIR from observed optoacoustic signal (OPAS) data, without prior knowledge, as a known condition. It is based on the relation between the OPAS data and the photoacoustic pressure signal (PAPS) at transducer positions. The relation can be expressed as a homogeneous linear equation. The TIR is solved by solving the homogeneous equation. The numerical test verifies the effectiveness of the presented method. This article also discusses the effect of calculation parameters on the extracting precision of TIR.

This paper proposes algorithms to model fractional (dynamical) behaviors using non-singular rational kernels whose interest is first demonstrated on a pure power law function. Two algorithms are then proposed to find a non-singular rational kernel that allows the input-output data to be fitted. The first one derives the impulse response of the modeled system from the data. The second one finds the interlaced poles and zeros of the rational function that fits the impulse response found using the first algorithm. Several applications show the efficiency of the proposed work.

Abstract Background The proposal of spiky apertures showed resolution improvement compared with the circular apertures. Three models of corona virus are given. The 1st model consists of uniform circular aperture provided with 8 spikes while the 2nd model has 16 spikes for the same uniform circular aperture. The 3rd model has circular linear distribution with 8 spikes. Results The Normalized Point Spread Function (PSF) or the impulse response is computed for the three models using fast Fourier transform technique. In addition, the autocorrelation function corresponding to these apertures is calculated and compared with that corresponding to the ordinary circular and conic apertures. Coronavirus image is used as an object in the formation of images using confocal scanning laser microscope provided with suggested models. The fabricated MATLAB code is used to compute and plot all images and line plots. Conclusions The PSF plots are computed from Eqs. (8) and (12) using MATLAB code showing narrower cutoff in the PSF for spiky aperture compared with that corresponding to the uniform circular aperture and modulated linear and quadratic apertures. Hence, I reached resolution improvement in the case of spiky aperture.

Purpose – This study investigates the dynamic causality linkages between fiscal deficits and selected macroeconomic indicators in a panel of five East African Community countries. Design/methodology/approach – The research design is based on panel cointegration tests, panel cross-section dependence tests, panel error correction-based Granger causality tests and panel impulse response functions. Findings – Results show that there is long-run feedback causality among fiscal deficits and each of the variables include gross domestic product (GDP) growth, current account balance, interest rates, inflation, grants and debt service. Short-run Granger causality dynamics indicate that there is feedback causality between fiscal deficits and GDP growth; no causality between fiscal deficits and inflation; no causality between fiscal deficits and current account; no causality between fiscal deficits and interest rates; feedback causality between fiscal deficits and grants; and no causality between fiscal deficits and debt service. Impulse response functions show positive and significant impacts of current account balance, inflation and grants; negative and significant impacts of real GDP growth and lending rates; and insignificant effects of debt service. Research limitations/implications – While the study examines the dynamic causality between fiscal deficits and selected macroeconomic indicators in the East African Community, the analysis excludes South Sudan due to significant data limitations. Practical implications – In light of the East African Community's aspirations to achieve convergence on key macroeconomic targets, including the fiscal deficit, this research provides novel insights on fiscal policy determinants and causality dynamics. Social implications – The dynamic relationships between fiscal policy and macroeconomic variables may have social implications for welfare, equitable growth and distribution of resources. Originality/value – With a focus on the East African Community, this paper contributes to the literature on the macroeconomic determinants of fiscal deficits in regional economic communities.

This paper investigates the impulse reaction of five economic sectors to various monetary policy shocks under the currency board regime in Bulgaria. For that purpose, we have estimated five reduced form VAR models with quarterly data over the period 2001Q1 to 2019Q4 for the sectors of agriculture, manufacturing; construction; real estate, and trade, transport and accommodation activities. The relevant impulse response functions represent three monetary transmission channels, in particular via the overnight interbank market rate, the consumer price index and the real effective exchange rate. We found that there is strong heterogeneity between sectors response to the different monetary policy shocks even under a currency board regime. The differences in the reaction are both in terms of depth, direction and duration. The monetary policy has strong positive impact on construction, and to a lesser extent on manufacturing. The response of the agriculture; trade, transport and accommodation activities is rather negative especially in the long term.

This paper is evaluating the impact of external shocks that are affecting the Romanian economy by employing a Bayesian approach. By employing three Bayesian VAR models this paper provides an answer on the importance of external shocks using the impulse response functions, variance and historical decompositions. The relevance of the study is a major one as it quantifies the intensity of external shocks coming from the euro area that have the potential to affect the Romanian economy in terms of external demand, uncertainty and monetary policy, aspects which are of high interest especially in the new economic context of the COVID-19 health crisis.

In this article we present an iterative system of equalization and decoding to manage the intersymbol interference over an additive white Gaussian noise (AWGN) channel. Following the classic turbo equalization scheme, the proposed system consists of low-density parity-check (LDPC) coding at the transmitter side; we applied a Log maximum a posteriori probability (Log-MAP) equalizer and min-sum LDPC decoding at the receiver side. The equalizer and decoder, linked through interleaving and deinterleaving, iteratively update each other’s information. We performed the performance analysis of the proposed system, bit error rate (BER) vs. signal-to-noise ratio (SNR), considering three different impulse responses of the channel (h). Our experimental results indicated that increasing the number of iterations performed by the LDPC decoder from 10 to 20 during the iterative process of equalization and decoding leads to better outcomes. The proposed system was compared with turbo equalization and separate equalization, performed before the decoding process with minimum mean-square error (MMSE) and LDPC decoding, in terms of BER vs. SNR, considering the three different h. Based on the analyzed results, it can be concluded that the equalization performance depends on both the impulse responses of the channel and the chosen decoding and equalization method; therefore, the equalization method does not always offer good results for any h.

Autonomous underwater vehicles require accurate navigation. Techniques such as image registration using consecutive acoustic images from a sonar have shown promising results for this task. The implementation of such techniques using sonar images augmented with deep learning (DL) networks demonstrate high navigation accuracy; this is possible even with highly compressed images. The sonar images are estimates of sampled in time (with a ping period) magnitudes of channel impulse responses representing the underwater acoustic environment. More information about the environment is contained in (almost) continuous in time estimates of the channel impulse responses. Such estimates can be obtained using full-duplex technology. Rather than using sonar images, this paper investigates the use of channel impulse response estimates for underwater platform motion estimation. The proposed system uses a single projector and a small number of receiving transducers installed on the moving platform. A DL network is used to estimate the motion in two degrees of freedom (forward/backward and sideways), using two or more consecutive impulse response estimates as the input. To train the DL network, a specially designed simulator is used to model the underwater acoustic environment, populated with multiple objects spread on the seafloor. The proposed technique can significantly reduce the acoustic hardware and processing complexity of the DL network and obtain a higher accuracy of motion estimation, compared with techniques based on the processing of sonar images, e.g., the error achieved with the technique proposed in this paper is 1.7% of the maximum platform displacement, compared to 4% achieved with a technique using sonar images. The navigation accuracy is further illustrated by examples of estimation of complex trajectories.

In this study, an efficient model of the channel matrix is developed for a $2\times $ 2 wireless body area network multiple input output (WBAN-MIMO) system based on deep learning algorithms. The model is composed of three deep-learning algorithms. Moreover, the model simultaneously predicts channel matrix $H$ in an underground mine and identifies the position of the collected data in both line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios. The model was trained and evaluated using the magnitude and phase of the collected data in an underground mine environment within a frequency range of 2.3 GHz – 2.5 GHz. These measurements, conducted with different antenna configurations in the LoS and NLoS scenarios, constitute an input to the model. The latest predicts the channel matrix ${H}$ with position and identifies whether the channel is a LoS or NLoS. Finally, the path loss and channel impulse response models were compared with measurement-based models. The modeled channel prediction exhibited a lower root mean square error (RMSE) for channel prediction and high classification accuracy for LoS-NLoS and position identification, respectively. The numerical results reveal that deep learning WBAN-MIMO modeling offers a powerful solution for future wireless systems in underground mine environments.

Optoacoustic mesoscopy combines rich optical absorption contrast with high spatial resolution at tissue depths beyond reach for microscopic techniques employing focused light excitation. The mesoscopic imaging performance is commonly hindered by the use of inaccurate delay-and-sum reconstruction approaches and idealized modeling assumptions. In principle, image reconstruction performance could be enhanced by simulating the optoacoustic signal generation, propagation, and detection path. However, for most realistic experimental scenarios, the underlying total impulse response (TIR) cannot be accurately modelled. Here we propose to capture the TIR by scanning of a sub-resolution sized absorber. Significant improvement of spatial resolution and depth uniformity is demonstrated over 3 mm range, outperforming delay-and-sum and model-based reconstruction implementations. Reconstruction performance is validated by imaging subcutaneous murine vasculature and human skin in vivo. The proposed experimental calibration and reconstruction paradigm facilitates quantitative inversions while averting complex physics-based simulations. It can readily be applied to other imaging modalities employing TIR-based reconstructions.

Abstract This paper uses the panel data of 15 countries from 2009 to 2020 to construct the time-varying parameter panel vector error correction model for testing the hypothesis of dynamic hedging characteristics of gold on exchange rate. As the existing literature has never considered that the foreign exchange risk hedged by gold is dynamic, this study can fill the research gap in this area. The empirical results show that: First, gold can partly hedge against the depreciation of the currency in the long run; second, gold is unable to hedge against the risk of the exchange rate when considering dynamic hedging effects in the short run; third, when facing unexpected shocks, the impulse response shows that the gold returns have reversible reactions compared to exchange rate fluctuations; therefore, gold can regard as a safe haven for foreign exchange markets; Finally, the government, as well as investors should always be concerned about these dynamic risks and formulate effective hedging strategies to control the currency uncertainty.

There are numerous studies that examine the impact of social media on the stock market performance but there is a paucity of such evidences from the emerging economies. Today many multinational banks and other financial conglomerates from the developed countries are expanding their operations to the emerging markets, known for their rapid growth. The businesses in developed countries prefer using social media to reach out to their stakeholders. This might be a challenge as emerging markets are very different from the developed markets in terms of infrastructure and stock market development. This study performs the sentiment analysis of the tweets about the Indian companies that are a part of Nifty50 or any sectorial index, for a period of 15 months. The results from the Granger-causalty tests indicate that the Twitter sentiments have a significant relationship with the indices related to the banking and financial sectors of the Indian stock markets. Results from the Impulse Response Function reveal that, on the index returns, the impact of the negative sentiments stays for a longer period of time than the positive sentiments. This study would help businesses use social media effectively for information sharing and dissemination in the new environment.

This study is an attempt to assess the impact of Covid-19 and the lockdown pronounced thereof on the Nifty sectoral indices with specific reference to the financial sector indices owing to their significance in the economy. The OLS regression, Granger Causality and Impulse Response Function were estimated to measure the changes in the future responses of Nifty 50 to the changes in the select sectoral indices, namely, Nifty Bank, Nifty Financial Services and Nifty Private Banks and Nifty PSU Banks for the period consisting two sub-periods, i.e., the first sub-period from April 2019 to March 2020 is assumed as the pre-Covid-19 period and the second sub-period from April 2020 to March 2021 is assumed as the period during COVID-19. The results indicated that the shock of the Covid-19 had an impact on the financial sector indices in India during the Covid-19 period.

In this paper, we propose a super-resolution (pixel grid refinement) method for digital images. It is based on the linear filtering of a zero-padded discrete signal. We introduce a continuous-discrete observation model to create a reconstruction system. The proposed observation model is typical of real-world imaging systems - an initially continuous signal first undergoes linear (dynamic) distortions and then is subjected to sampling and the effect of additive noise. The proposed method is optimal in the sense of mean square recovery error minimization. In the theoretical part of the article, a general scheme of the linear super-resolution of the signal is presented and expressions for the impulse and frequency responses of the optimal reconstruction system are derived. An expression for the error of such restoration is also derived. For the sake of brevity, the entire description is presented for one-dimensional signals, but the obtained results can easily be generalized for the case of two-dimensional images. The experimental section of the paper is devoted to the analysis of the super-resolution reconstruction error depending on the parameters of the observation model. The significant superiority of the proposed method in terms of the reconstruction error is demonstrated in comparison with linear interpolation, which is usually used to refine the grid of image pixels.