Estimation Of Response Function Research Articles

An Economic Study to Estimate Response Functions for Sugar Beet Yield in the Arab Republic of Egypt

An Economic Study to Estimate Response Functions for Sugar Beet Yield in the Arab Republic of Egypt

Digital Twin-Enabled Response Function Analysis: A Synthetic Approach to Ship's Propulsion System Assessment

Analyzing the behavior of vessels in actual sea conditions is crucial for conceptual system design, safety and energy efficiency considerations. However, the essential seakeeping problem is reduced to the analysis of wave-hull interactions often neglecting consideration of the propulsion. But in the meantime, the synthetic consideration of wave–propulsion interactions is a key for safety and energy efficiency. Furthermore, safety evaluation of a ship's design with reduced propulsion power in adverse seas is vital for risk management. Response functions are commonly used to estimate propulsion system responses in incoming seaways. This paper proposes a synthetic approach using digital twin technology for rapid response function estimation. It introduces a companion linearized state-space model linked with the digital twin, enabling immediate retrieval of coefficients for response function analysis at the desired operating point. This integrated methodology provides a comprehensive representation of ship propulsion behavior in wave environments, offering a comprehensive framework for system performance assessment.

Mean Group Distributed Lag Estimation of Impulse Response Functions in Large Panels

Mean Group Distributed Lag Estimation of Impulse Response Functions in Large Panels

An experimental investigation on the fluid–structure coupling in horizontal pipes conveying two-phase intermittent flow

Two-phase flow is typically found in several industrial applications, such as in the production and transportation of oil and gas in the petrochemical industry, in the catalytic cracking and microreactors in the chemical industry, and in nuclear reactor cooling pumps. Measurement of two-phase flow features is usually necessary and has been done in several ways, including pressure probes, resistive sensors, gamma-ray, wire-mesh sensor and many others. However, these are either intrusive or invasive techniques, which might be of challenging application in industrial environments, or rely on a hazardous radioactive source. Vibration-based measurement of two-phase flow in pipes stands out as a non-invasive/non-intrusive approach and, consequently, multiphase-flow induced vibration in pipes has receiving increasing attention in recent years. In this work, the dynamic behaviour of a horizontal tube conveying an intermittent two-phase gas-liquid flow is investigated based on indirect approaches. The phenomenon of fluid–structure coupling is investigated using acceleration and pressure measurement. Moreover, the bubble size distribution is estimated from high-speed camera images and a deep learning model for image segmentation, along with its spectral content and time modulation. Focus is given at frequency bands around the cut-on frequencies of the circumferential wave modes of the pipe. An approach based on the estimation of frequency response function of the pressure and vibration at the liquid slug and elongated bubble is proposed, such that the coherence function can be used as quantitative measure of the coupling. Two experimental conditions with intermittent flow are investigated as representative cases. It is shown that there is a great vibration amplification at the cut-on frequencies of circumferential wave modes in pipes due to the corresponding structural wave and pressure coupling. Consequently, the frequency of passage of bubble can be estimated from the demodulation of vibration response filtered at the cut-on modes. The experimental results pave the way for innovative vibration-based measurement approaches.

Identification of vector autoregressive models with nonlinear contemporaneous structure

We propose a statistical identification procedure for recursive structural vector autoregressive (VAR) models that present a nonlinear dependence (at least) at the contemporaneous level. By applying and adapting results from the literature on causal discovery with continuous additive noise models, we show that, under certain conditions, a large class of structural VAR models is identifiable. We spell out these specific conditions and propose a scheme for the estimation of structural impulse response functions in a nonlinear setting. We assess the performance of this scheme in a simulation experiment. Finally, we apply it in a study on the effects of the macroeconomic shocks that propagate through the economy, allowing for asymmetry between responses from positive and negative impulses.

Ridge regularized estimation of VAR models for inference

Ridge regression is a popular method for dense least squares regularization. In this article, ridge regression is studied in the context of VAR model estimation and inference. The implications of anisotropic penalization are discussed, and a comparison is made with Bayesian ridge‐type estimators. The asymptotic distribution and the properties of cross‐validation techniques are analyzed. Finally, the estimation of impulse response functions is evaluated with Monte Carlo simulations and ridge regression is compared with a number of similar and competing methods.

The role of tweets in agricultural export: an approach from text-mining and time-series analyses

PurposeThis study aims to investigate the relationship between consumer awareness of Zespri International Limited (Zespri™) and its sales promotion in Japan and the recent expansion of New Zealand (NZ) kiwifruit imported into Japan.Design/methodology/approachTweets mentioning Zespri™ were utilised as a proxy of such awareness. They were first summarised using two text-mining techniques: tf-idf scoring and a co-occurrence network graph. Afterwards, the authors estimated a tri-variate vector autoregression (VAR) model consisting of the net imports of NZ kiwifruit in Japan, unit import price and number of tweets. Additionally, the occurrence frequency of tweets with “Kiwi Brothers”, promotional characters for Zespri™’s sales, was added to the model, and a tetra-variate VAR model was estimated. Finally, Granger-causality tests, an estimation of the impulse response function and forecast error variance decomposition was conducted.FindingsAll these variables were found to Granger-cause each other. Furthermore, a shock in the document frequency of “Kiwi Brothers” significantly affected Japan’s kiwifruit imports from NZ, explaining approximately 20% of future imports. Zespri™’s distinctive sales promotion was, thus, found to contribute in part to the recent increase in NZ’s kiwifruit export to Japan.Originality/valueThis paper is the first to apply text-regression methodology to food consumption research; it contributes to food consumption research by proposing a practical way to combine tweets with outcome variables using a time-series analysis.

Contrast response function estimation with nonparametric Bayesian active learning.

Multidimensional psychometric functions can typically be estimated nonparametrically for greater accuracy or parametrically for greater efficiency. By recasting the estimation problem from regression to classification, however, powerful machine learning tools can be leveraged to provide an adjustable balance between accuracy and efficiency. Contrast sensitivity functions (CSFs) are behaviorally estimated curves that provide insight into both peripheral and central visual function. Because estimation can be impractically long, current clinical workflows must make compromises such as limited sampling across spatial frequency or strong assumptions on CSF shape. This article describes the development of the machine learning contrast response function (MLCRF) estimator, which quantifies the expected probability of success in performing a contrast detection or discrimination task. A machine learning CSF can then be derived from the MLCRF. Using simulated eyes created from canonical CSF curves and actual human contrast response data, the accuracy and efficiency of the machine learning contrast sensitivity function (MLCSF) was evaluated to determine its potential utility for research and clinical applications. With stimuli selected randomly, the MLCSF estimator converged slowly toward ground truth. With optimal stimulus selection via Bayesian active learning, convergence was nearly an order of magnitude faster, requiring only tens of stimuli to achieve reasonable estimates. Inclusion of an informative prior provided no consistent advantage to the estimator as configured. MLCSF achieved efficiencies on par with quickCSF, a conventional parametric estimator, but with systematically higher accuracy. Because MLCSF design allows accuracy to be traded off against efficiency, it should be explored further to uncover its full potential.

Estimation of dielectric response functions for NIR-SWIR absorbing dyes by inverse analysis of diffuse reflectance

This study describes the estimation of dielectric response functions for NIR-SWIR absorbing dyes by inverse analysis of diffuse reflectance. The inverse analysis procedure is based on the Kramers–Kronig relations, the Kubelka–Munk model of diffuse reflectance, and the background subtraction of substrate spectra. The calculated response functions provide estimates of the dielectric response characteristics for NIR-SWIR absorbing dyes, as well as their sensitivity to material-background environments. This study demonstrates that Kramers–Kronig analysis can be adopted for calculating estimates of dielectric functions, given any reasonable estimate of absorbance. Specifically, that Kramers–Kronig analysis provides filtering of dielectric response structure with respect to spectrum artifacts due to inverse-analysis and spectroscopic-measurement procedures. This can significantly reduce the experimentation necessary to achieve desired dielectric response characteristics for NIR-SWIR dye and substrate combinations. Furthermore, these calculated response functions can support the construction of approximate effective medium models capable of estimating reflectance features for dyed fabrics.

Endogeneity and other problems in curvilinear income-waste response function estimations

Endogeneity and other problems in curvilinear income-waste response function estimations

Brain perfusion estimation by Tikhonov model-free deconvolution in a long axial field of view PET/CT scanner exploring five different PET tracers

PurposeNew total-body PET scanners with a long axial field of view (LAFOV) allow for higher temporal resolution due to higher sensitivity, which facilitates perfusion estimation by model-free deconvolution. Fundamental tracer kinetic theory predicts that perfusion can be estimated for all tracers despite their different fates given sufficiently high temporal resolution of 1 s or better, bypassing the need for compartment modelling. The aim of this study was to investigate whether brain perfusion could be estimated using model-free Tikhonov generalized deconvolution for five different PET tracers, [15O]H2O, [11C]PIB, [18F]FE-PE2I, [18F]FDG and [18F]FET. To our knowledge, this is the first example of a general model-free approach to estimate cerebral blood flow (CBF) from PET data.MethodsTwenty-five patients underwent dynamic LAFOV PET scanning (Siemens, Quadra). PET images were reconstructed with an isotropic voxel resolution of 1.65 mm3. Time framing was 40 × 1 s during bolus passage followed by increasing framing up to 60 min. AIF was obtained from the descending aorta. Both voxel- and region-based calculations of perfusion in the thalamus were performed using the Tikhonov method. The residue impulse response function was used to estimate the extraction fraction of tracer leakage across the blood–brain barrier.ResultsCBF ranged from 37 to 69 mL blood min−1 100 mL of tissue−1 in the thalamus. Voxelwise calculation of CBF resulted in CBF maps in the physiologically normal range. The extraction fractions of [15O]H2O, [18F]FE-PE2I, [11C]PIB, [18F]FDG and [18F]FET in the thalamus were 0.95, 0.78, 0.62, 0.19 and 0.03, respectively.ConclusionThe high temporal resolution and sensitivity associated with LAFOV PET scanners allow for noninvasive perfusion estimation of multiple tracers. The method provides an estimation of the residue impulse response function, from which the fate of the tracer can be studied, including the extraction fraction, influx constant, volume of distribution and transit time distribution, providing detailed physiological insight into normal and pathologic tissue.

Empirical Transfer Function Estimation With Differential Filtering and Its Application to Fine Positioning Control of Galvano Scanner

Fast and precise point-to-point (PTP) positioning control is a critical technology for improving the throughput and product quality of industrial machines. This study aims to investigate the frequency response function (FRF) estimation method with differential filtering (ETFE-Diff: empirical transfer function estimation with differential filtering) that uses the input and output signals of PTP motion during processing operation and to apply ETFE-Diff to a feedforward (FF) compensation adjustment. In this study, the principle of ETFE-Diff for FRF estimation was clarified, compared to the existing FRF estimation methods, i.e., empirical transfer function estimation and local rational modeling. Moreover, the effectiveness of ETFE-Diff for FF compensation adjustment in improving positioning accuracy of a galvano scanner as an industrial servo system was experimentally demonstrated.

Beyond climate change: Examining the role of environmental justice, agricultural mechanization, and social expenditures in alleviating rural poverty

Extreme weather events and extreme poverty are two sides of the same coin, with far-reaching consequences for emerging nations like Pakistan. Rural people are more likely to experience poverty and inequality as climate change worsens. This research aspires to close the gap between environmental ethics and justice by investigating how climate change issues contribute to poverty in Pakistan. The study used Robust Least Squares (RLS) regression to analyze the impact of water scarcity, extreme temperatures, and excessive rainfall on rural poverty in Pakistan from 1990Q1 to 2022Q4. Further, the study examines the effect of environmental justice interventions, access to healthcare and education, agricultural value-added and agricultural mechanization on the country's rural poverty. Results reveal that climate change contributes to rural poverty in Pakistan, while environmental justice initiatives, healthcare access, and agricultural automation alleviate poverty incidence. The Impulse Response Function (IRF) estimates suggested that rural poverty will be exacerbated over the next decade by water scarcity, high temperatures, and low agricultural value added but alleviated by excessive rainfall, environmental justice intervention, healthcare access, and agricultural mechanization. According to Variance Decomposition Analysis (VDA) projections, agricultural value added will substantially impact rural poverty by 2032, increasing it by 11.431%. Addressing these problems requires policymakers to prioritize the interests of the most marginalized groups by fostering fair results. Policies should cut GHG emissions and encourage sustainable development to combat climate change. Modernizing farming techniques and expanding access to healthcare are also necessary for increasing efficiency and production. It is essential to execute environmental justice interventions so that all communities have access to environmental resources and protections equitably. Promoting equitable outcomes and reducing poverty in Pakistan's climate change context may be achieved by closing the gap between environmental ethics and justice.

Mean Group Distributed Lag Estimation of Impulse Response Functions in Large Panels

Mean Group Distributed Lag Estimation of Impulse Response Functions in Large Panels

Development of automated impact system for modal analysis of micro-end mill

The micro cutting tool used in micromilling process is highly susceptible to instability attributed to its low strength. A catastrophic failure of micro-end mill can occur if the chatter is not controlled during micromilling process, especially for micromilling of difficult-to-machine materials, like Ti and Ni alloys. Consequently, the control of chatter in micromilling process is necessary to manufacture the components with required dimensional and geometrical accuracy. Chatter can be avoided by selecting the stable process parameters from stability lobe diagram or by changing the process parameters during machining process by chatter onset identification. The both ways of chatter avoidance require the reliable identification of the modal parameters of the micro-end mill. Experimental modal analysis is most accurate method for identification of modal parameters owing to its ability of consideration of machine tool compliance. Hence, in the present work, automated impact system has been developed to impact the micro-end mill under both stationary and rotating conditions. The developed impact system has been found to be impacting the micro-end with maximum deviation of 2.6% in force amplitude due to repeated impacts. A maximum variation of 1.7% has been found in the natural frequency due to different impacts of the micro-end mill under both stationary and rotating conditions. The accurate estimation of frequency response function is expected to predict the stability of micromilling process with high reliability.

Low-light image enhancement based on virtual exposure

Under poor illumination, the image information captured by a camera is partially lost, which seriously affects the visual perception of the human. Inspired by the idea that the fusion of multiexposure images can yield one high-quality image, an adaptive enhancement framework for a single low-light image is proposed based on the strategy of virtual exposure. In this framework, the exposure control parameters are adaptively generated through a statistical analysis of the low-light image, and a virtual exposure enhancer constructed by a quadratic function is applied to generate several image frames from a single input image. Then, on the basis of generating weight maps by three factors, i.e., contrast, saturation and saliency, the image sequences and weight images are transformed by a Laplacian pyramid and Gaussian pyramid, respectively, and multiscale fusion is implemented layer by layer. Finally, the enhanced result is obtained by pyramid reconstruction rule. Compared with the experimental results of several state-of-the-art methods on five datasets, the proposed method shows its superiority on several image quality evaluation metrics. This method requires neither image calibration nor camera response function estimation and has a more flexible application range. It can weaken the possibility of overenhancement, effectively avoid the appearance of a halo in the enhancement results, and adaptively improve the visual information fidelity.

A two-stage Polynomial Chaos Expansion application for bound estimation of uncertain FRFs

Polynomial Chaos Expansion (PCE) is a method for analysing uncertain vibratory structures with lower computational effort. It may simply be described as a curve fitting method with orthogonal basis terms, where the polynomial type, dimension and order are predefined for the uncertain responses. However, the polynomial order in PCE must be very high to accurately estimate statistical moments of the frequency response function in resonance regions of lightly damped and uncertain structures. To solve this issue different transformation techniques are reported in the literature, where implementations of PCE produce higher accuracy with a lower order polynomial. However, these transformations lose the attraction for using PCE, since they require some additional mathematical operations and, mostly, they present high accuracy if the higher orders of polynomials are again of interest. In this study, an efficient approach is presented for the upper bound estimation of the uncertain frequency response functions (FRFs) via PCE with lower order terms without performing any transformation. Rather than one-stage application of PCE for the desired response of an uncertain problem, the approach comprises a two-stage application of the classical PCE, i.e. first for the natural frequencies and then for the FRF calculations. As an example application of the approach, a thin beam for two different uncertainty cases is considered, namely local and global uncertainty. The local and global input uncertainties are generated by variability of lumped masses added at the boundary and Young's modulus, respectively. The FRF bounds are compared with extensive experimental and numerical Monte Carlo simulations, showing that low order polynomials are sufficient to calculate the bounds accurately with the technique described.

Functional Data Analysis and Person Response Functions

ABSTRACT The purpose of this study is to illustrate the use of functional data analysis (FDA) as a general methodology for analyzing person response functions (PRFs). Applications of FDA to psychometrics have included the estimation of item response functions and latent distributions, as well as differential item functioning. Although FDA has been suggested for modeling PRFs, there has been relatively little research stressing this application. FDA offers an approach for diagnosing person responses that may be due to guessing and other sources of within-person multidimensionality. PRFs provide graphical displays that can be used to highlight unusual response patterns, and to identify persons that are not responding as expected to a set of test items. In addition to examining individual PRFs, functional clustering techniques can be used to identify subgroups of persons that may be exhibiting categories of misfit such as guessing. A small simulation study is conducted to illustrate how FDA can be used to identify persons exhibiting different levels of guessing behavior (5%, 10%, 15% and 20%). The methodology is also applied to real data from a 3rd grade science assessment used in a southeastern state. FDA offers a promising methodology for evaluating whether or not meaningful scores have been obtained for a person. Typical indices of psychometric quality, such as standard errors of measurement and person fit indices, are not sufficient for representing certain types of aberrance in person response patterns. Nonparametric graphical methods for estimating PRFs that are based FDA provide a rich source of validity evidence regarding the meaning and usefulness of each person’s score.

Capillary electrophoresis-mass spectrometry as a tool for Caenorhabditis elegans metabolomics research

IntroductionPolar metabolites in Caenorhabditis elegans (C. elegans) have predominantly been analyzed using hydrophilic interaction liquid chromatography coupled to mass spectrometry (HILIC-MS). Capillary electrophoresis coupled to mass spectrometry (CE-MS) represents another complementary analytical platform suitable for polar and charged analytes.ObjectiveWe compared CE-MS and HILIC-MS for the analysis of a set of 60 reference standards relevant for C. elegans and specifically investigated the strengths of CE separation. Furthermore, we employed CE-MS as a complementary analytical approach to study polar metabolites in C. elegans samples, particularly in the context of longevity, in order to address a different part of its metabolome.MethodWe analyzed 60 reference standards as well as metabolite extracts from C. elegans daf-2 loss-of-function mutants and wild-type (WT) samples using HILIC-MS and CE-MS employing a Q-ToF-MS instrument.ResultsCE separations showed narrower peak widths and a better linearity of the estimated response function across different concentrations which is linked to less saturation of the MS signals. Additionally, CE exhibited a distinct selectivity in the separation of compounds compared to HILIC-MS, providing complementary information for the analysis of the target compounds. Analysis of C. elegans metabolites of daf-2 mutants and WT samples revealed significant alterations in shared metabolites identified through HILIC-MS, as well as the presence of distinct metabolites.ConclusionCE-MS was successfully applied in C. elegans metabolomics, being able to recover known as well as identify novel putative biomarkers of longevity.

Investigating the Growth Effect of Carbon-Intensive Economic Activities on Economic Growth: Evidence from Angola

Despite its immense natural resources, Angola struggles to significantly improve its economy to reduce poverty. Carbon emissions have been increasing over the years, even though the country plans to reduce them by 35% by 2030. This paper attempts to assess the carbon emissions of several sectors (industries, transport, services, and residences) on economic growth, intending to find a balance between environmental protection that requires carbon emissions reduction and economic development that may add to environmental degradation. The study employed time series data on GDP, CO2, CH4, and N2O covering 1971 to 2021 and ARDL and ECM models. This is the first study at the state level in Angola on the relationship between economic development and environmental sustainability considering methane and nitrous oxide emissions. Additionally, the paper assesses the responses of GDP to deviation shock of GDP, CO2, CH4, and N2O by 2032. Phillip Perron and Augmented Dickey-Fuller tests showed that all the data are stationary at the first difference, favoring the application of the ARDL model to explore the short and long-run relationships. The result reveals that methane from agricultural activities and carbon emissions from the building sector and public services contribute to economic growth, whereas carbon emissions from industrial heat systems, non-renewable electricity production, and manufacturing industries harm economic growth. However, no relationship exists between nitrous oxide emissions and economic development. In addition, impulse response function estimates show that appropriate investments can sustain economic development over the years. Therefore, the country should diversify its economy and avoid polluting fuel sources, such as coal. Raising renewable energy’s proportion in the total energy mix can support growth while considering the environmental quality. Investments in skills training, academic projects in renewable energy technologies development, agriculture mechanization, and sustainable job creation are recommended. Additionally, investing in quality seeds adapted to climate realities might help lessen climate change’s adverse effects and promote growth. Manure manufacturing processes must be improved to reduce agriculture and livestock’s methane and nitrous oxide emissions. The country’s leaders are encouraged to promote raw material processing industries while insisting on reducing carbon emissions.