Recursive Approach Research Articles

Assessing farmers' adaptation responses to water conservation policies through modular recursive hydro-micro-macro-economic modeling

Farmers' adaptation responses to water conservation policies involve a complex decision-making process that depends on a range of criteria, including water availability, profits, and risks, which are in turn dependent on (and might have consequences at) broader scale processes including water systems and the macroeconomy. The non-consideration of the complex interactions between and within natural and human systems often leads to unforeseen consequences and sub-optimal water policy design. There exists a fundamental need to improve our understanding of complex human-water (e.g. hydro-economic) and human-human (e.g. micro-macroeconomic) systems' interactions so to better inform policy-makers. This paper develops an innovative modeling framework for capturing the richness of interactions in complex human-water systems by: i) considering the rationale behind farmers' behavior and responses through microeconomic models; ii) assessing the complex interactions among economic sectors and regions within an economy through macroeconomic models; iii) simulating responses on water cycle dynamics within a river basin by means of hydrologic modeling; and iv) representing the interconnected dynamics and two-way feedback responses between human-water and human-human (through micro-macro-economic) systems. The proposed modeling framework operates through a recursive modular approach built from independent modules which are, in turn, connected through a set of protocols that control the exchange of information. Methods are illustrated considering an incremental agricultural water charging policy in the Spanish part of the Douro River Basin (DRB). Results show that local land use reallocations have an impact on the supply of irrigated (rainfed) agricultural commodities at the macroeconomic level, which further leads to higher (or lower) commodity prices that partially offset changes in crop profitability due to changing water charges and readjustments in the crop portfolio. These, in turn, result in non-linear responses in land and water use with non-trivial impacts on water system's dynamics, where evapotranspiration, surface runoff, and groundwater evapotranspiration are the main hydrologic components affected. We conclude that the integration of hydro-micro-macro-economic modules through a set of protocols can provide crucial information for promoting the efficient design of agricultural water policies.

Efficient assessment of formal concept stability in the Galois lattice

Formal concept analysis (FCA) is a mathematical tool for analyzing data and formally representing conceptual knowledge. Under this formalism, the concept stability metric can be used in many applications: community detection, pattern selection in noisy data, patient care trajectories, and biclusters of similar objects, etc. However, computing the stability is a #P-complete problem. Therefore, most existing studies have focused either on estimating this metric using some heuristics or by proposing recursive approaches to assess the exact value of the stability. In this paper, we introduce an algorithm called the Exact Stability Computing for Galois Lattice Esc-Gl for computing the stability of a formal concept given the underlying partial relation. Based on the lower cover of a given concept, the algorithm can improve scalability and reduce computational complexity. Our experiments performed on large benchmark datasets from the FCA field show that our contributions sharply outperform all the pioneering approaches of the dedicated literature.

Approximated MPC for embedded hardware: Recursive random shooting approach

Advanced, optimization-based control methods are implemented at each level of industrial production. Although the model predictive control (MPC) represents a state-of-the-art control strategy maximizing profit, and, simultaneously, minimizing the energy losses, its industrial implementation is limited by the requirements on the software and hardware resources. This paper proposes an approximated MPC for such cases when the implementation of implicit MPC has a prohibitive effort on industrial hardware and its explicit counterpart is limited by its memory footprint requirements. The recursive random shooting-based approach is introduced to eliminate the conventional optimization and minimize the memory requirements, meanwhile guarantying the asymptotic stability subject to the physical constraints on control inputs and system states. The benefits of the proposed method are significantly reduced computational complexity by 93%, and decreased energy consumption by 93%, compared to non-recursive random shooting, while the performance loss is approximately 8%.

Turbulent flow simulations of the common research model on Cartesian grids using recursive fitting approach

In this study, we propose a new method that uses the recursive fitting method and wall function for the three-dimensional turbulent flow simulation. The recursive fitting method based on the Cartesian grid method is employed for the automatic generation around two- and three-dimensional geometries. The grid approximately represents the concave and convex features, i.e., a wing–fuselage juncture and wing trailing edges. The wall boundary conditions can be imposed on the wall faces; thus, the conservation laws are strictly satisfied. Meanwhile, using the wall function allows turbulent boundary layers to be accurately reproduced. The turbulent flow simulation using the wall function is straightforward because the flow variables at the first cell centroid are employed for friction velocity estimation. However, the distance between the body surface and the first cell centroid varies since cells with different shapes are generated. Therefore, the simulation using the proposed method is validated through turbulent flow simulations in two dimensions. The results show the accurate predictions of the turbulent boundary layers. To investigate the capacity of the proposed method, three-dimensional simulation around aircraft at a transonic cruise condition is conducted. The automatically generated grids by the proposed method approximately represent the geometric features. The results of flow simulation show that the surface pressure and skin friction coefficient distributions agree with those of the reference simulation on a conventional body-fitted grid. Further, predicted pressure and viscous drag coefficients are reasonable compared with those of the reference simulation.

The COVID-19 pandemic and the degree of persistence of US stock prices and bond yields

This paper analyses the possible effects of the Covid-19 pandemic on the degree of persistence of US monthly stock prices and bond yields using fractional integration techniques. The model is estimated first over the period January 1966-December 2020 and then a recursive approach is taken to examine whether or not persistence has changed during the following pandemic period (up to February 2021). We find that the unit root hypothesis cannot be rejected for stock prices while for bond yields the results differ depending on the maturity date and the specification of the error term. In general, bond yields appear to be more persistent, although there is evidence of mean reversion in case of 1-year yields under the assumption of autocorrelated errors. The recursive analysis shows no impact of the Covid-19 pandemic on the persistence of stock prices, whilst there is an increase in the case of both 10- and 1- year bond yields but not of their spread.

Paso a paso se va muy lejos: Traveling the Path with AFS

<i>Paso a paso se va muy lejos</i>: Traveling the Path with AFS

Exploratory Data Analysis and Decision Tree Modeling for Autism Spectrum Disorder: Machine Learning Approach

As the early diagnosis of autism spectrum disorder (ASD) is critical the high accuracy machine learning can be applied to achieve technology based diagnosis. In this context, the present study demonstrates machine learning approach for ASD diagnosis using decision tree (DT) modeling. The dataset employed in the present study comprises two classes of ASD adults with a sample size of 704 instances. The DT model entails a recursive partitioning approach implemented in the “rpart” package of R. The optimum model is derived by tuning parameters such as Min split, Min bucket, Max depth, and complexity. The performance of the model is evaluated in terms of the mean square error estimate of the error rate.

A recursive matched-filter to systematically explore volcanic long-period earthquake swarms

SummaryThe matched-filter technique is an effective way to detect repeats, or near-repeats, of a seismic source, but prior identification of an event from that source to use as a template is required. We propose a recursive matched-filter approach to systematically explore earthquake swarms, here applied to a swarm of volcanic long-period seismicity beneath Mount Sidley in Antarctica. We start with a single visually chosen template event with a high signal-to-noise ratio. We then extend our template database by selecting new templates to use in a subsequent matched-filter search from the newly detected set of events, allowing us to recursively expand the number of templates. We demonstrate that each iteration of the matched-filter search progressively extends the spatial coverage of our set of templates away from the original template event. In such a way, our proposed method overcomes the matched-filter search’s strictest constraint: that an event must already be identified to detect other similar events. Our recursive matched-filtering approach is well suited for the systematic exploration of earthquake swarms in both volcanic and tectonic contexts.

Analytical Solution of Slow Squeeze Flow of Slightly Viscoelastic Fluid Film between Two Circular Disks Using Recursive Approach

This research investigates an analytical solution for the slow squeeze flow of the slightly viscoelastic fluid film between two circular disks in which the upper disk approaches the lower disk with a constant velocity, and the lower disk is kept stationary. The determination of the study is to identify the behavior of the differential type fluid on the steady squeezing flow using Langlois recursive approach. The governing equations for the axisymmetric flow are expressed in cylindrical coordinates and yield the nonlinear system of partial differential equations. The analytical solution of the resulting equations with nonhomogeneous boundary conditions is obtained by the Langlois recursive approach. Flow variables such as stream function, velocity profiles, pressure distribution, shear, normal stresses, and normal force acting on the disk are determined. These flow variables are nondimensionalized by using suitable dimensional quantities. The influence of slightly viscoelastic parameter β , radial distance r , and aspect ratio on velocity components, pressure distribution, and normal squeeze force is examined mathematically and portrayed graphically. The results illustrate that the axial and radial velocities increase at the higher values of the slightly viscoelastic parameter β , which confirms the shear thickening behavior. The obtained solutions of the flow variables satisfied the existing solutions on squeeze flow of viscous fluid upon vanishing the slightly viscoelastic parameters. This solution could elucidate the classical lubrication problems, particularly in load and thrust bearing characteristics of the human body joints, the compression molding process of materials, etc.

Systematic evaluation of recursive approach of EEG‐segment‐based PCA for removal of helium‐pump artefact from MRI

The cryogenic pump is a crucial component of the magnetic resonance imaging (MRI) system for delivering liquid helium to a magnet for superconductivity, thereby generating a mechanical vibration. Thus, the cryogenic pump for liquid helium (helium pump) contaminates the electroencephalography (EEG) simultaneously acquired with functional MRI. The recursive approach of EEG-segment-based principal component analysis (rsPCA) has recently demonstrated its efficacy in removing this helium pump artefact. In the rsPCA, the recursion depth and EEG-segment size are crucial hyperparameters. rsPCA's performance and computational time across recursion depth (1, 2, 3) and segment sizes (165, 220, 265) are systematically evaluated. It is found that the recursion depth of 2 yielded significant reductions in the computational time compared to the depth of 3 across all segment sizes while maintaining the denoising performance. The binary classification performance (left-hand versus right-hand clenching) was also enhanced in this scenario (especially the use of EEG-segment size of 165 and 220) by using EEG gamma-band activity (30–50 Hz), which is predominantly contaminated by the helium-pump artefact.

Simple model with in-parallel elasto-fragile trusses to characterize debonding on FRP-reinforced flat substrates

In many structural applications for masonry and rc strengthening, Fiber Reinforced Polymer (FRP) composites exhibit a typical mode II fracture behavior at the bond between substrate and strip when the latter is subjected to considerable tensile stresses. At structural level, the debonding mechanism is usually considered lumping the non-linearity at the interface. A variety of different models is already available to accurately study the debonding, but their utilization within a standard low-cost FE software is difficult, because of the limited gallery of nonlinear elements at disposal for the user. Among the most common elements available, the simplest one is the so-called cutoff bar, which is a truss behaving in an elastic-perfectly brittle way. The paper presents a recursive analytical approach to study debonding using a device constituted by several in-parallel cutoff bars. To closely fit existing analytical models’ response, it is shown that many in-series cutoff bars should be used. Due to the considerable number of elements required, such procedure is still hardily implementable, albeit theoretically applicable. For this reason, an identification procedure of a layer of trusses representing FRP and connected to the support with a layer constituted by two in-parallel cutoff bars is proposed. To do so, the selection of only two target points located on the actual global debonding curve is needed. Resultant mechanical properties of the cutoff bars allow to fit very closely the global behavior obtained through analytical approaches with an extremely simple discretization. The model is successfully validated against an already presented closed form procedure and applied on a set of experimental data available in the literature.

Robust inference under time‐varying volatility: A real‐time evaluation of professional forecasters

SummaryIn many forecast evaluation applications, standard tests as well as tests allowing for time‐variation in relative forecast ability build on heteroskedasticity‐and‐autocorrelation consistent (HAC) covariance estimators. Yet, the finite‐sample performance of these asymptotics is often poor. “Fixed‐ ” asymptotics, used to account for long‐run variance estimation, improve finite‐sample performance under homoskedasticity, but lose asymptotic pivotality under time‐varying volatility. Moreover, loss of pivotality due to time‐varying volatility is found in the standard HAC framework in certain cases as well. We prove a wild bootstrap implementation to restore asymptotically pivotal inference for the above and new CUSUM‐ and Cramér‐von Mises‐based tests in a fairly general setup, allowing for estimation uncertainty from either a rolling window or a recursive approach when fixed‐ asymptotics are adopted to achieve good finite‐sample performance. We then investigate the (time‐varying) performance of professional forecasters relative to naive no‐change and model‐based predictions in real‐time. We exploit the Survey of Professional Forecasters (SPF) database and analyze nowcasts and forecasts at different horizons for output and inflation. We find that not accounting for time‐varying volatility seriously affects outcomes of tests for equal forecast ability: wild bootstrap inference typically yields convincing evidence for advantages of the SPF, while tests using non‐robust critical values provide remarkably less. Moreover, we find significant evidence for time‐variation of relative forecast ability, the advantages of the SPF weakening considerably after the “Great Moderation.”

The Façade, the Face, and the Sympathies: Opening the Black Box of Symbolic Capital as a Source of Philanthropic Attractiveness

Although most studies on philanthropy have focused on corporate benefactors, limited attention has been given to beneficiaries and their characteristics. The literature thus falls short in explaining how the variance in the perceived value of those characteristics can condition the philanthropic attractiveness of recipients for donors. Drawing from Bourdieu’s concept of symbolic capital and qualitative insights drawn from our empirical context of philanthropy in the field of cultural production in the Russian Federation, we argue that the attractiveness of cultural organizations for potential corporate benefactors depends on a range of determinants, which we classify under the broad umbrellas of respectability and reputability. By using political orientation as a moderator, we show that, although respectability is an indiscriminately attractive part of symbolic capital, reputability, associated with artistic celebrity and renown, can become a deterrent to potential donors when it augments the risk of jeopardizing their relationship with the government. We also demonstrate that a board of trustees, which signals openness to stakeholder involvement, diminishes the returns of reputability for potential donors. We test our hypotheses using original and representative longitudinal data on 449 Russian theaters (2004–2011). Taking an indirect recursive approach to estimate models with high-dimensional fixed effects, we find strong support for our hypotheses across a variety of econometric specifications. Our research offers a unique focus on the beneficiary side of the beneficiary-benefactor relationship, which has both theoretical and practical implications for the literatures on corporate philanthropy, and cultural industries. Supplemental Material: The online appendices are available at https://doi.org/10.1287/orsc.2022.1603 .

Application of Recursive Estimation to Heat Tracing for Groundwater/Surface‐Water Exchange

We present and demonstrate a recursive‐estimation framework to infer groundwater/surface‐water exchange based on temperature time series collected at different vertical depths below the sediment/water interface. We formulate the heat‐transport problem as a state‐space model (SSM), in which the spatial derivatives in the convection/conduction equation are approximated using finite differences. The SSM is calibrated to estimate time‐varying specific discharge using the Extended Kalman Filter (EKF) and Extended Rauch‐Tung‐Striebel Smoother (ERTSS). Whereas the EKF is suited to real‐time (“online”) applications and uses only the past and current measurements for estimation (filtering), the ERTSS is intended for near‐real time or batch‐processing (“offline”) applications and uses a window of data for batch estimation (smoothing). The two algorithms are demonstrated with synthetic and field‐experimental data and are shown to be efficient and rapid for the estimation of time‐varying flux over seasonal periods; further, the recursive approaches are effective in the presence of rapidly changing flux and (or) nonperiodic thermal boundary conditions, both of which are problematic for existing approaches to heat tracing of time‐varying groundwater/surface‐water exchange.

An exact approach for the constrained two-dimensional guillotine cutting problem with defects

This paper studies the constrained two-dimensional guillotine cutting problem with defects, whose objective is to cut a subset of given items from a defective sheet such that the profit of selected items is maximised. The guillotine cut constraint, which requires each cut must go through one side of the sheet to the opposite side, is considered. We solve this problem via a recursive dynamic programming approach. A set of upper bounds is proposed to keep the promising nodes. The normal points and raster points are extended to reduce the number of vertical and horizontal cuts by considering the effect of the defect. The experiment results show that our approach can solve most of the instances in the literature and outperforms existing approaches.

ODYN: AN ONLINE DYNAMIC NETWORK SOLVER FOR PHOTOGRAMMETRY AND LIDAR GEO-REFERENCING

Abstract. In recent years, many methods have been proposed to improve the quality of the estimated trajectories for airborne or terrestrial mapping platforms leveraging multi-sensor fusion. One of the motivating applications is, for example, the use laser scanners on small unmanned aerial vehicles, where the typically employed low-cost MEMS inertial sensors do not allow for satisfactory direct geo-referencing of the laser points. In this work we introduce ODyN, an online Dynamic Network solver that can fuses information from cameras, GNSS and inertial sensors in a single adjustment. It can be employed to estimate a high-frequency trajectory for precise direct geo-referencing, to improve photogrammetric reconstructions in challenging scenarios or to determine several types of system calibration parameters. The presented solver is hosted by the University of Geneva and is free for anybody to use. In this work we present a use case in airborne mapping where the obtained trajectory estimates are improved with respect to the recursive smoothing approaches conventionally used in direct geo-referencing.

Annual Operating Costs Minimization in Electrical Distribution Networks via the Optimal Selection and Location of Fixed-Step Capacitor Banks Using a Hybrid Mathematical Formulation

The minimization of annual operating costs in radial distribution networks with the optimal selection and siting of fixed-step capacitor banks is addressed in this research by means of a two-stage optimization approach. The first stage proposes an approximated mixed-integer quadratic model to select the nodes where the capacitor banks must be installed. In the second stage, a recursive power flow method is employed to make an exhaustive evaluation of the solution space. The main contribution of this research is the use of the expected load curve to estimate the equivalent annual grid operating costs. Numerical simulations in the IEEE 33- and IEEE 69-bus systems demonstrate the effectiveness of the proposed methodology in comparison with the solution of the exact optimization model in the General Algebraic Modeling System software. Reductions of 33.04% and 34.29% with respect to the benchmark case are obtained with the proposed two-stage approach, with minimum investments in capacitor banks. All numerical implementations are performed in the MATLAB software using the convex tool known as CVX and the Gurobi solver. The main advantage of the proposed hybrid optimization method lies in the possibility of dealing with radial and meshed distribution system topologies without any modification on the MIQC model and the recursive power flow approach.

Time-Domain Frequency Estimation With Application to Fault Diagnosis of the Unmanned Aerial Vehicles’ Blade Damage

In this article, a novel time-domain frequency estimation approach is proposed based on the well-known subspace identification method. Differing from the fast Fourier transformation method, the nonlinear identification for frequency estimation is reformulated into the eigenvalue identification problem, which avoids the spectrum peak search. The recursive frequency estimation approach is proposed based on the updating/downdating of the Cholesky decomposition, where the extended observability matrix can be identified from a small matrix with lower computation cost. In addition, a gap metric-oriented performance indicator is proposed as a test statistic for fault detection as well as the evaluation of fault severity. The effectiveness of the proposed methods is verified for frequency estimation and fault diagnosis performance through numerical simulations and the experimental measurements from a laboratory unmanned aerial vehicle rig with partial blade damage.

Performance assessment of Bayesian Causal Modelling for runoff temporal behaviour through a novel stability framework

A strong innovative tendency is nowadays emerging that largely comprises new hydrological modelling approaches, based on Causal Reasoning through Probabilistic Graphical Modelling (PGM), because its ability to support probabilistic reasoning from data with uncertainty. These novel modelling frameworks are quite diverse and disperse not only in terms of techniques but also regarding its aims. It seems necessary to find a general and robust methodology for assessing its performance. This paper aims to provide a novel general methodology for assessing the performance of PGM based on Bayesian Causality for modelling and analysing the riverś runoff behaviour. For it, a structured four-step approach is developed and showed throughout the paper. The proposed methodology begins with the identification of the two main factors that condition the Bayesian Causal (BC) Modelling: the number of synthetic series (data amount) and the number of intervals for probability distributions for training and learning processes. The developed analysis comprises the definition of three levels for the first factor and seven levels for the second one, as well as the design of an innovative stability framework that assesses the level of BC Modelling performance. Furthermore, it has been necessary to create-define two novel indexes, named “Similarity and Stability Indexes” from 21 scenarios arising from the combination of the levels of the both factors. The optimal combination of factors is identified through a bi-objectives recursive approach based on previous indexes. Main results drawn successfully show a high relationship between the level of modelling performance, measured in terms of stability, and the river runoff temporal behaviour, measured in terms of temporal dependence. This research may help water managers and engineers to develop more rigorous and robust hydrological causal modelling implementations.

Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics

Exploring excitation energy transfer (EET) in light-harvesting complexes (LHCs) is essential for understanding the natural processes and design of highly-efficient photovoltaic devices. LHCs are open systems, where quantum effects may play a crucial role for almost perfect utilization of solar energy. Simulation of energy transfer with inclusion of quantum effects can be done within the framework of dissipative quantum dynamics (QD), which are computationally expensive. Thus, artificial intelligence (AI) offers itself as a tool for reducing the computational cost. Here we suggest AI-QD approach using AI to directly predict QD as a function of time and other parameters such as temperature, reorganization energy, etc., completely circumventing the need of recursive step-wise dynamics propagation in contrast to the traditional QD and alternative, recursive AI-based QD approaches. Our trajectory-learning AI-QD approach is able to predict the correct asymptotic behavior of QD at infinite time. We demonstrate AI-QD on seven-sites Fenna–Matthews–Olson (FMO) complex.