Response Function Model Research Articles

Neural encoding of melodic expectations in music across EEG frequency bands.

The human brain tracks regularities in the environment and extrapolates these to predict future events. Prior work on music cognition suggests that low-frequency (1-8Hz) brain activity encodes melodic predictions beyond the stimulus acoustics. Building on this work, we aimed to disentangle the frequency-specific neural dynamics linked to melodic prediction uncertainty (modelled as entropy) and prediction error (modelled as surprisal) for temporal (note onset) and content (note pitch) information. By using multivariate temporal response function (TRF) models, we re-analysed the electroencephalogram (EEG) from 20 subjects (10 musicians) who listened to Western tonal music. Our results show that melodic expectation metrics improve the EEG reconstruction accuracy in all frequency bands below the gamma range (< 30 Hz). Crucially, we found that entropy contributed more strongly to the reconstruction accuracy enhancement compared to surprisal in all frequency bands. Additionally, we found that the encoding of temporal, but not content, information metrics was not limited to low frequencies, rather it extended to higher frequencies (> 8Hz). An analysis of the TRF weights revealed that the temporal predictability of a note (entropy of note onset) may be encoded in the delta- (1-4Hz) and beta-band (12-30 Hz) brain activity prior to the stimulus, suggesting that these frequency bands associate with temporal predictions. Strikingly, we also revealed that melodic expectations selectively enhanced EEG reconstruction accuracy in the beta band for musicians, and in the alpha band (8-12 Hz) for non-musicians, suggesting that musical expertise influences the neural dynamics underlying predictive processing in music cognition.

Assessment of hybrid machine learning models for non-linear system identification of fatigue test rigs

The prediction of system responses for a given fatigue test bench drive signal is a challenging problem, since highly dynamic loads from measurement campaigns must be reproduced accurately. Linear frequency response function models are commonly used for this system identification task, but energy intensive experimental iterations are required to account for system non-linearities. Two novel hybrid modeling strategies are suggested, which augment existing approaches using non-linear Long Short-Term Memory networks. These are trained and deployed on short subsequences of measurement data and recombined using a windowing technique, which enables their application to measurement data with high sampling rates. In addition to fatigue test bench commissioning, these hybrid models can also be employed in the field of virtual sensing. The approach is tested using non-linear experimental data from a servo-hydraulic test rig and this dataset is made publicly available. A variety of metrics in time and frequency domains, as well as fatigue strength under variable amplitudes, are employed in the evaluation. It is shown, that hybrid models can successfully use frequency response function models as a linear baseline estimate, which is further improved by Long Short-Term Memory networks to enable non-linear predictions.

Analyzing the impact of remittance, FDI and inflation rate on GDP: A comparative study of Bangladesh, Pakistan and Sri-Lanka using VAR and BEKK-GARCH approach

This study examines the impact, conditional correlation and volatility spillover effect of remittances, foreign direct investment and inflation rate on GDP in Bangladesh, Pakistan, and Sri-Lanka, three Asian nations that are particularly vulnerable. While numerous studies have examined the relationship between remittances, FDI and IR on GDP but this paper approaches advanced econometric techniques to capture spillover effect and the dynamic interactions between the variables. For estimation purposes the study employs different econometric techniques such as Augmented Dickey-Fuller (ADF) test, VAR model, Granger causality tests, Impulse Response function, Variance Decomposition and BEKK-GARCH model. Bangladesh and Sri Lanka's REM, FDI and IR have no significant effects on GDP according to the VAR model. BEKK-GARCH demonstrates that three countries have both unidirectional and bidirectional transmissions of volatility, with the exception of Pakistan's REM and Bangladesh's FDI. We find that VAR model may not be adequate in capturing the complex dynamics between variables, which can be better captured by BEKK-GARCH model. Our comparison research shows how these variables affect GDP differently and similarly in each of the three nations, giving policymakers information they can use to create customized policies that encourage economic growth.

Projecting non-communicable diseases attributable to air pollution in the climate change era: a systematic review

ObjectivesClimate change is a major global issue with significant consequences, including effects on air quality and human well-being. This review investigated the projection of non-communicable diseases (NCDs) attributable to air...

Modeling Nonlinear Dynamics of Functionalization Layers: Enhancing Gas Sensor Sensitivity for Piezoelectrically Driven Microcantilever.

This article presents a parametrized response model that enhances the limit of detection (LOD) of piezoelectrically driven microcantilever (PD-MC) based gas sensors by accounting for the adsorption-induced variations in elastic properties of the functionalization layer (binder) and the nonlinear motional dynamics of the PD-MC. The developed model is demonstrated for quantifying cadaverine, a volatile biogenic diamine whose concentration is used to assess the freshness of meat. At low concentrations of cadaverine, an increase in the resonance frequency is observed, contrary to the expected reduction due to mass added by adsorption. The study explores the variations in the elastic modulus vis-à-vis the adsorbed mass of cadaverine and derives the resonance frequency to the adsorbed mass response function. We advance a blended technique involving the analysis of atomic force microscopy (AFM) force-distance (f-d) curves and fitting of the quartz crystal microbalance (QCM) impedance response spectrum to deduce the adsorption-induced changes in the viscoelastic properties of the functionalization layer. The findings obtained are subsequently employed in modeling the response function for a structurally nonhomogenous PD-MC, highlighting the significance of the functionalization layer to the global elastic properties. The structural composition of the PD-MC beam adopted herein features a trapezoidal base hosting the actuating piezoelectric stratum and a rectangular free end with a functionalization layer. The Euler-Bernoulli beam theory coupled with Hamilton's principle is used to develop the equation of motion, which is subsequently discretized into a set of nonlinear ordinary differential equations via Galerkin expansion, and the solutions to the first fundamental mode of vibration are determined using the method of multiple scales. The obtained solutions provide a basis for deducing the nonlinear response function model to the adsorbed mass. The derived model is validated by recorded resonance frequency changes resulting from exposure to known concentrations of cadaverine. We demonstrate that the increase in resonance frequency for low concentrations of cadaverine is due to the dominance of the variation of the elastic modulus of the functionalization layer originating from the initial binder-analyte interactions over damping due to added mass. It is concluded that the developed nonlinear response function model can reliably be used to quantify the cadaverine concentration at low concentrations with an elevated Limit of Detection.

Mobile Money Use, Digital Banking Services and Velocity of Money in Ghana

Investigating the correlation between digital financial services, mobile money usage, and money velocity in Ghana, the study analysed time series data spanning from 1992 to 2022. A composite index was constructed by principal component analysis using data extracted from the world development indicators, with the components mobile money usage, digital financial services, and velocity of money. The estimation utilised an impulse response function and vector error correction model; the results indicated that mobile money, digital financial services, and money velocity are related in both the short and long term. Furthermore, the application of a standard deviation innovation to the velocity of money produced increases of both positive and negative magnitude for all the variables. This suggests that mobile money, digital banking services, and velocity of money in Ghana are interdependent in an asymmetric manner. In order to facilitate an increase in the velocity of money, the research concluded that policymakers should guarantee that a greater proportion of the population has access to mobile money and digital banking services. In addition to promoting mobile money, online banking services, and digital payment methods on purpose, the government should reduce reliance on physical currency and expedite the circulation of money. It is recommended that future longitudinal studies involving African nations employ diverse estimation techniques.

Estimating Yield Response Functions to Nitrogen for Annual Crops in Iran

Nitrate is a crucial element for crop growth, and its optimal application is essential for maximizing agricultural yield. In Iranian agriculture, there is a substantial gap between recommended nitrate usage and what farmers actually apply. In this study, our primary objective is to determine the most effective utilization of nitrate for crop cultivation. Simultaneously, we aim to analyze the factors that contribute to the disparity between optimal and current nitrate application practices. Furthermore, our research explores the impact of these differences on regional variations in crop yields. This is achieved using a quadratic yield response function model based on unbalanced panel data spanning the years 2000 to 2016, which includes a total of 14 crop activities and encompasses 31 administrative regions. The results show that rice exhibits the highest nitrogen usage, while rain-fed wheat demonstrates the lowest utilization at the optimal point. Depending on whether random- or fixed-effects estimation is found to be the most suitable specification, average yields corresponding to the optimal level of nitrogen use are calculated by region, or the average across all regions. In Iran, the top-performing regions for cereals like rain-fed wheat and irrigated barley can achieve yields of 1.33 and 3 t/ha, respectively. These yields represent a 31% and a 9% increase from the levels observed in 2016. The outcomes derived from the estimated yield response function will be integrated into comprehensive agricultural, economic, and environmental optimization models. These integrated models will facilitate the assessment of various fertilizer policies on fertilizer use, land allocation, farm-household incomes, and environmental externalities, such as nitrate leaching and nitrate balance. This study holds substantial scientific promise, given its exploration of the policy implications surrounding fertilizer usage, making it crucial not only for Iran, but also for many developing nations grappling with inefficient and unsustainable agricultural practices. It represents the first of its kind in the literature, providing estimations of optimal nitrogen use and crop yield points across all regions in Iran. This is achieved through advanced visualization using GIS maps.

Water hammer in pipelines based on different friction models

Water hammer in pipelines is a difficult problem in fluid transmission field. Especially, there exists some friction items of pipeline transient model such that the simulation model is not consistent to the experimental results. By using the friction model proposed by Kagawa and the model of impulse response function, the pressure transients are calculated with and without cavitation. The corresponding simulation results involving pressure, velocity, steady and dynamic frictions, cavitation volume are analyzed to reveal the effect of friction item on pressure transients. Moreover, the features of steady and dynamic frictions are captured in pipelines with upstream and downstream valves. The comparative simulation results of two friction models have verified that the friction model using an impulse response function has higher consistency between simulation and experimental results of pipeline transients.

한국의 에너지 소비와 경제성장의 탈동조화에 대한 분석

Purpose - This study analyzed the decoupling phenomenon between energy consumption and economic growth in Korea from 1990 to 2021. The main purpose of this study is to suggest policy implications for achieving a low-carbon society and decoupling that Korea must move forward in the face of the climate change crisis.&#x0D; Design/methodology/approach - This study investigated the relationship between energy consumption and economic growth by energy source and sector using the energy-EKC (EEKC) hypothesis which included the energy consumption on the traditional Environmental Kuznets Curve (EKC), and the impulse response function (IRF) model based on Bayesian vector auto-regression (BVAR).&#x0D; Findings - During the analysis period, the trend of decoupling of energy consumption and economic growth in Korea is confirmed starting from 1996. However, the decoupling tendency appeared differently depending on the differences in energy consumption by sources and fields. The results of the IRF model using data on energy consumption by source showed that the impact of GDP and renewable energy consumption resulted in an increase in energy consumption of bio and waste, but a decrease in energy consumption by sources, and the impact of trade dependence was found to increase the consumption of petroleum products.&#x0D; Research implications or Originality - According to the main results, efficient distribution by existing energy source is required through expansion of development of not only renewable energy but also alternative energy. Additionally, in order to increase the effectiveness of existing energy policies to achieve carbon neutrality, more detailed strategies by source and sector of energy consumption are needed.

Optimisation of particle distribution and performance in three-layer tubular particleboards using response surface methodology

To explore the impact of manufacturing parameters on the modulus of rupture (MOR), internal bond strength (IB), and 24-h water absorption thickness swelling (24 h TS) of tubular particleboards, the geometry of the particles and the combination and distribution of the particles were investigated and integrated with response surface modeling (RSM). The results show that coarse particles with high aspect ratios form the network skeleton structure of tubular particleboards and provide the basic mechanical strength of the panels. The addition of an appropriate amount of fine particles can reduce the internal voids of the board and improve its mechanical strength. In addition, the surface fine particles flowed from the gaps of the coarse particles to the interior of the boards, making the boards more compact, which was conducive to improving the modulus of rupture and internal bond strength of the tubular particleboards. It was also found that the tube spacing limited the distribution of particles of different sizes, and regulating the tube spacing could lead to boards with good performance. According to the response surface data, the derived mathematical model of the second-order response function has an R2 of 0.954 for MOR, 0.956 for IB, and 0.923 for 24 h TS. The results predicted by the RSM are close to the experimental results when the fine particle content (FoC) in the core layer is 39.35 %, the surface particle content (Sf) is 37.45 %, the tube spacing (TD) is 18.81 mm, the MOR of the tubular particle board is 17.47 MPa, the IB is 0.70 MPa, and the 24 h Ts is 7.30 %. The optimized tubular particleboard has a thermal conductivity of 38.89 mW m−1K−1, which provides efficient thermal insulation. The RSM is important in the development and structural optimization field.

A role for confidence: volition regimes and news

The economic literature presents a variety of empirical models of the structural impulse response function (SIRF) in real consumption and real output following changes in confidence or sentiment, particularly in the US and EA. This paper replicates them on the orbit of a neokeynesian dynamic stochastic general equilibrium (NK-DSGE) model characterized in particular by macroeconomic agents and derived from start to finish. Trust is specifically modeled as an endogenous variable characterized by a coalescence of three processes governed by a degree of volition, the processes being permanent technology, transient technology, and noise technology. The first two processes affect the actual production technology with a delay of one lag, while the third does not at all. The short-run responses to changes in confidence are shown whenever the degree of willingness allows confidence to change real consumption and aggregate labor, thus being non-negligible. Whenever the degree of volition was, by contrast, negligible, exogenous shocks in noise technology would cause no fluctuation in actual consumption and actual power.

Distinct neural encoding of glimpsed and masked speech in multitalker situations.

Humans can easily tune in to one talker in a multitalker environment while still picking up bits of background speech; however, it remains unclear how we perceive speech that is masked and to what degree non-target speech is processed. Some models suggest that perception can be achieved through glimpses, which are spectrotemporal regions where a talker has more energy than the background. Other models, however, require the recovery of the masked regions. To clarify this issue, we directly recorded from primary and non-primary auditory cortex (AC) in neurosurgical patients as they attended to one talker in multitalker speech and trained temporal response function models to predict high-gamma neural activity from glimpsed and masked stimulus features. We found that glimpsed speech is encoded at the level of phonetic features for target and non-target talkers, with enhanced encoding of target speech in non-primary AC. In contrast, encoding of masked phonetic features was found only for the target, with a greater response latency and distinct anatomical organization compared to glimpsed phonetic features. These findings suggest separate mechanisms for encoding glimpsed and masked speech and provide neural evidence for the glimpsing model of speech perception.

Visuospatial attention revamps cortical processing of sound amid audiovisual uncertainty.

Selective attentional biases arising from one sensory modality manifest in others. The effects of visuospatial attention, important in visual object perception, are unclear in the auditory domain during audiovisual (AV) scene processing. We investigate temporal and spatial factors that underlie such transfer neurally. Auditory encoding of random tone pips in AV scenes was addressed via a temporal response function model (TRF) of participants' electroencephalogram (N = 30). The spatially uninformative pips were associated with spatially distributed visual contrast reversals ("flips"), through asynchronous probabilistic AV temporal onset distributions. Participants deployed visuospatial selection on these AV stimuli to perform a task. A late (~300 ms) cross-modal influence over the neural representation of pips was found in the original and a replication study (N = 21). Transfer depended on selected visual input being (i) presented during or shortly after a related sound, in relatively limited temporal distributions (<165 ms); (ii) positioned across limited (1:4) visual foreground to background ratios. Neural encoding of auditory input, as a function of visual input, was largest at visual foreground quadrant sectors and lowest at locations opposite to the target. The results indicate that ongoing neural representations of sounds incorporate visuospatial attributes for auditory stream segregation, as cross-modal transfer conveys information that specifies the identity of multisensory signals. A potential mechanism is by enhancing or recalibrating the tuning properties of the auditory populations that represent them as objects. The results account for the dynamic evolution under visual attention of multisensory integration, specifying critical latencies at which relevant cortical networks operate.

Localizing Sources of Variability in Crowded TESS Photometry

The Transiting Exoplanet Survey Satellite (TESS) has an exceptionally large plate scale of 21″ px−1, causing most TESS light curves to record the blended light of multiple stars. This creates a danger of misattributing variability observed by TESS to the wrong source, which would invalidate any analysis. We developed a method that can localize the origin of variability on the sky to better than one fifth of a pixel. Given measured frequencies of variability (e.g., from periodogram analysis), we show that the best-fit sinusoid amplitudes to raw light curves extracted from each pixel are distributed in the same way as light from the variable source. The primary assumption of this method is that other nearby stars are not variable at the same frequencies. Essentially, we are using the high frequency resolution of TESS to overcome limitations from its low spatial resolution. We have implemented our method in an open-source Python package, TESS_localize (github.com/Higgins00/TESS-Localize), that determines the location of a variable source on the sky and the most likely Gaia source given TESS pixel data and a set of observed frequencies of variability. Our method utilizes models of the TESS pixel response function, and we characterize systematics in the residuals of fitting these models to data. We find that even stars more than three pixels outside a photometric aperture can produce significant contaminant signals in the extracted light curves. Given the ubiquity of source blending in TESS light curves, verifying the source of observed variability should be a standard step in TESS analyses.

Frequency Response Function-Based Learning Control: Analysis and Design for Finite-Time Convergence

Iterative learning control (ILC) enables substantial performance improvement by using past operational data in combination with approximate plant models. The aim of this article is to develop an ILC framework based on nonparametric frequency response function (FRF) models that requires very limited modeling effort. These FRF models describe the behavior of a system in periodic steady state, yet are employed for the control of arbitrary finite-length tasks. A detailed analysis and design framework is developed to construct noncausal learning filters directly from uncertain FRF models, that achieve ILC convergence for arbitrary tasks. The resulting framework provides a unification between ILC and iterative inversion-based control, where the latter is a learning method specifically developed for periodic tasks.

An unsupervised learning damage diagnosis method based on virtual impulse response function and time series models

As it contains the inherent properties of the structure and is insensitive to variation of the input, the transmissibility function (TF) is widely used for structural damage detection under ambient excitation. However, in TF-based methods, the selection of the optimal damage sensitivity band often requires access to vibration data under different operating conditions, which is not conducive to unsupervised learning. In order to accommodate unsupervised learning and extract more damage information, this paper proposes a data-driven unsupervised damage detection method based on the virtual impulse response function (VIRF) and time series model (TSM). In this method, the VIRF is used as a damage-sensitive feature (DSF), the TSM is used for low-dimensional feature extraction of the VIRF, and the distance metric of the model coefficients is used to detect the occurrence of anomalies. The study analyses the type of TSM applied to VIRF and uses the parameter adjusted cosine similarity (PACS) as a more accurate statistical distance tool for the model coefficients. The application of two well-known benchmark structure experiments verifies the accuracy and practicality of the proposed method in damage detection under minor operational variations and ambient excitations. The comparative analysis and detection results indicate that the proposed method is advantageous in qualitatively quantifying the extent of the damage. Structural anomalies can be detected in both linear and nonlinear damage scenarios.

Analisis Model Mangsa Pemangsa dengan Adanya Penyakit dan Pemanenan pada Pemangsa

The interaction between two populations that are prey and predator can be described in a prey-predator model. In fact, in the interaction of prey and predators it can occur that when the density of prey is low, the effect of predation is also low, but if the size of the prey population increases, predation will be more intense which is stated in the Holling III Type response function model. In addition, it can also be found in an environment where there are populations of sick predator that result in death from the disease. This study aims to analyze the stability of the prey-predator model with the Holling III Type response function which consists of three subpopulations namely prey, healthy predator and sick predator. The analysis was carried out using the linearization method and then the type of stability was determined based on the characteristic eigenvalues obtained using the Routh-Hurwitz criteria. From this research it was found that population extinction is not possible while prey exists, extinction of diseased predator and populations exist is still possible if the required conditions are satisfy. Numerical simulations show that an increased harvesting rate in healthy predator populations results in a decrease in healthy predator populations, an increase in prey populations and a decrease in diseased predator populations. Meanwhile, a reduced harvesting rate for sick predator populations does not have a significant effect on the number of diseased predator populations, but results in an increase in the number of healthy predator populations and a decrease in prey populations

Estimating local-scale forest GPP in Northern Europe using Sentinel-2: Model comparisons with LUE, APAR, the plant phenology index, and a light response function

Northern forest ecosystems make up an important part of the global carbon cycle. Hence, monitoring local-scale gross primary production (GPP) of northern forest is essential for understanding climatic change impacts on terrestrial carbon sequestration and for assessing and planning management practices. Here we evaluate and compare four methods for estimating GPP using Sentinel-2 data in order to improve current available GPP estimates: four empirical regression models based on either the 2-band Enhanced Vegetation Index (EVI2) or the plant phenology index (PPI), an asymptotic light response function (LRF) model, and a light-use efficiency (LUE) model using the MOD17 algorithm. These approaches were based on remote sensing vegetation indices, air temperature (Tair), vapor pressure deficit (VPD), and photosynthetically active radiation (PAR). The models were parametrized and evaluated using in-situ data from eleven forest sites in North Europe, covering two common forest types, evergreen needleleaf forest and deciduous broadleaf forest. Most of the models gave good agreement with eddy covariance-derived GPP. The VI-based regression models performed well in evergreen needleleaf forest (R2 = 0.69–0.78, RMSE = 1.97–2.28 g C m−2 d−1, and NRMSE = 9–11.0%, eight sites), whereas the LRF and MOD17 performed slightly worse (R2 = 0.65 and 0.57, RMSE = 2.49 and 2.72 g C m−2 d−1, NRMSE = 12 and 13.0%, respectively). In deciduous broadleaf forest all models, except the LRF, showed close agreements with the observed GPP (R2 = 0.75–0.80, RMSE = 2.23–2.46 g C m−2 d−1, NRMSE = 11–12%, three sites). For the LRF model, R2 = 0.57, RMSE = 3.21 g C m−2 d−1, NRMSE = 16%. The results highlighted the necessity of improved models in evergreen needleleaf forest where the LUE approach gave poorer results., The simplest regression model using only PPI performed well beside more complex models, suggesting PPI to be a process indicator directly linked with GPP. All models were able to capture the seasonal dynamics of GPP well, but underestimation of the growing season peaks were a common issue. The LRF was the only model tending to overestimate GPP. Estimation of interannual variability in cumulative GPP was less accurate than the single-year models and will need further development. In general, all models performed well on local scale and demonstrated their feasibility for upscaling GPP in northern forest ecosystems using Sentinel-2 data.

International Capital Flows, Dynamic Changes in Cryptocurrency and Noble Metal Markets

In 2022, with the implementation of tightening monetary policies by FOMC, US dollar is experiencing a dramatic appreciation in a very short period. Though numerous studies have demonstrated the connection between the traditional currency market, cryptocurrency market, and precious metal market, rare studies are exploring the relationships between the three markets under a special political environment. This paper selects USDCNY exchange rate, gold and silver, and bitcoin as the representatives of three markets and then tests the volatility response of return on gold &amp; silver and return on bitcoin to the change of return on USDCNY exchange rate. By employing impulse response function and ARMA-GARCHX model, the paper verifies the change of exchange rate will exacerbate the volatility of returns on gold &amp; silver and bitcoin significantly, which suggests high risk and uncertainty of the cryptocurrency market and precious metal market in a complex and extreme political environment. Investors and speculators should take prudent investment strategies in such environment.

온도 챔버 측정시스템의 강건설계 사례연구

A case study is presented applying the signal-response robust design approach to optimize temperature measurement system of a test chamber. Control factors are identified by cause and effect diagram and noises are reflected by a compound noise factor to reduce the size of the experiment.2SUBⅢ/SUBSUP5-2/SUP fractional factorial design is employed with two replications and three signal factor levels. Experimental data are analyzed by the response function modeling, and optimal conditions for the control factors are determined.