First-order Approach Research Articles

Watershed-scale effectiveness of floodplain habitat restoration for juvenile coho salmon in the Chilliwack River, British Columbia

Although billions of dollars have been spent restoring degraded watersheds worldwide, watershed-scale studies evaluating their effectiveness are rare. To mitigate damage from past logging activities, the floodplain of the upper Chilliwack River watershed (∼600 km2) was extensively restored from 1996 to 2000 through off-channel habitat restoration. The contribution of restored habitat to watershed-scale production of wild coho (Oncorhynchus kisutch) smolts was estimated through an extensive mark–recapture program in 2002. 27%–34% of the production of the estimated 247 200 out-migrating coho smolts could be attributed to the 157 000 m2 of newly created habitat. Area-based habitat models from the literature performed reasonably well in predicting smolt production from restored habitat, providing an acceptable first-order approach for evaluating production benefits of restoration. The costs of smolt production integrated over 30 years ranged from US$0.69–US$10.05 per smolt, falling within the range of hatchery production costs reported elsewhere (typical cost of ∼US$1.00 per smolt) at the most cost-effective restoration sites. This study demonstrates that large-scale habitat restoration can effectively enhance fish production at a watershed scale, at a cost that may be comparable to hatchery smolt production.

On the CFO/Channel estimation technique for MIMO-OFDM systems without using a prior knowledge of channel length

In this paper, we propose an estimator which jointly estimates the carrier frequency offset (CFO) and channel state without a prior knowledge of channel length (CL) for a multiple-input multiple-output orthogonal frequency-division multiplexing system. A preliminary CFO/channel estimate is first reached based on the maximum-likelihood criterion. To improve the over-determined problem, a CL estimation technique is further developed. The first-order Taylor series expansion approach is used to determine the trimmed CFO estimate such that the exhaustive point search can be avoided. Simulation results have been presented to demonstrate that the proposed scheme provides a performance comparable to that of the optimal estimation which assumes that CL is known in advance.

Thermoluminescence in LiF crystals and the role of impurities

Eight nominally pure lithium fluoride crystals, obtained from a single LiF crystal containing less than 100 ppm impurities, were irradiated by gamma-rays from a 60Co source at room temperature, with doses from 8.4 to 2.5 × 105 Gy, but one at -60 °C. Optical density measurements were performed to investigate the radiation-induced color centres (CCs) and to evaluate their concentrations. Thermoluminescence (TL) glow curves were collected and simulated by a first-order kinetics approach, and from the best-fit procedure ten glow peaks (GPs) spanning from 100 to 450 °C were highlighted. A comparative analysis of GP intensities and CC concentrations as a function of the irradiation dose has questioned their association as obtained in previous measurements, showing the impurities, less than 1018 cm-3, still playing a predominant role in TL spectra. New measurements on LiF crystals more pure, at least an order of magnitude, are required to establish for sure the association of GPs to CCs.

Quantifying the social welfare loss in moral hazard models

The main aim of this paper is to measure the social welfare loss for a continuous moral hazard model when a set of minimal assumptions are fulfilled. By using a new approach, we are able to reproduce the results of Balmaceda, Balseiro, Correa, and Stier-Moses (2010) pertaining to the social welfare loss for discrete and continuous models respectively. Previous studies rely on the validity of the first-order approach at the expense of strong assumptions, in particular the convexity of the distribution function condition while we do not make such a restrictive assumption in our developments. In addition, we obtain new bounds for the social welfare loss that are both tight and easy to compute.

Prediction of uncertain elastic parameters of a braided composite

Uncertainties are generally included in elastic parameters of the fiber reinforced textile composites. An approach on predicting the statistical characteristics of elastic modulus of braided composites using vibration test data is proposed in this paper. The method consists of two main steps, firstly estimate the initial mean values of the orthotropic elastic parameters by adopting the homogenization method, and then identify the variability of the elastic constants using a stochastic model updating algorithm with the first-order perturbation approach, the uncertain parameters are assumed as the summation of mean value and a deviation term with zero mean. A braided composite panel is employed in numerical simulations to verify the proposed method, the random samples of experimental modal data are simulated with the Latin Hypercube Sampling technique based on a reference finite element model, which is constructed by using deterministic updated elastic parameters. Results show that the mean value and standard deviation of the uncertain effective parameters of braided composite can be identified from modal data using the proposed stochastic identification method.

Analytic perturbative FMSA equation of state and thermodynamic properties from Monte Carlo simulation of the Kihara potential with a spherical core

A new equation of state is found to reproduce analytically the thermodynamic properties of fluids interacting with a spherical Kihara potential formulated for arbitrary values of the inner hard core. The results from the expressions developed are satisfactorily compared with the Monte Carlo simulation data for a wide range of densities and temperatures. The foundation of the new equation is strictly theoretical, based on the first-order mean spherical approximation perturbation approach of Tang and Lu, and provides a good starting point for the description of polyatomic fluids within the benchmark of Wertheim's first-order perturbation theory of associating fluids.

Incremental Learning of Daily Routines as Workflows in a Smart Home Environment

Smart home environments should proactively support users in their activities, anticipating their needs according to their preferences. Understanding what the user is doing in the environment is important for adapting the environment's behavior, as well as for identifying situations that could be problematic for the user. Enabling the environment to exploit models of the user's most common behaviors is an important step toward this objective. In particular, models of the daily routines of a user can be exploited not only for predicting his/her needs, but also for comparing the actual situation at a given moment with the expected one, in order to detect anomalies in his/her behavior. While manually setting up process models in business and factory environments may be cost-effective, building models of the processes involved in people's everyday life is infeasible. This fact fully justifies the interest of the Ambient Intelligence community in automatically learning such models from examples of actual behavior. Incremental adaptation of the models and the ability to express/learn complex conditions on the involved tasks are also desirable. This article describes how process mining can be used for learning users’ daily routines from a dataset of annotated sensor data. The solution that we propose relies on a First-Order Logic learning approach. Indeed, First-Order Logic provides a single, comprehensive and powerful framework for supporting all the previously mentioned features. Our experiments, performed both on a proprietary toy dataset and on publicly available real-world ones, indicate that this approach is efficient and effective for learning and modeling daily routines in Smart Home Environments.

A combined first-order and second-order variation approach for multiplicative noise removal

A combined first-order and second-order variation approach for multiplicative noise removal

First-order irreversible thermodynamic approach to a nonsteady RLC circuit as an energy converter

In this work we show a RLC-circuit as energy converter within the context of first-order irreversible thermodynamics (FOIT). For our analysis, we propose an isothermic model with transient elements and passive elements. With the help of the dynamic equations, the Kirchhoff equations, we found the generalized fluxes and forces of the circuit, the equation system shows symmetry of the cross terms, this property is characteristic of the steady state linear systems, but in this case phenomenological coefficients are function of time. Then, we can use these relations, similar to the linear Onsager relations, to construct the characteristic functions of the RLC energy converter: the power output, efficiency, dissipation and ecological function, and study its energetic performance. The study of performance of the converter is based on two parameters, the coupling parameter and the "forces ratio" parameter, in this case as functions of time. We find that the behavior of the non-steady state converter is similar to the behavior of steady state energy converter. We will explain the linear and symmetric behavior of the converter in the frequencies space rather than in the time space. Finally, we establish optimal operation regimes of economic degree of coupling for this energy converter.

Two Second-Order Nonlinear Extended Kalman Particle Filter Algorithms

In algorithms of nonlinear Kalman filter, the so-called extended Kalman filter algorithm actually uses first-order Taylor expansion approach to transform a nonlinear system into a linear system. It is obvious that this algorithm will bring some systematic deviations because of ignoring nonlinearity of the system. This paper presents two extended Kalman filter algorithms for nonlinear systems, called second-order nonlinear Kalman particle filter algorithms, by means of second-order Taylor expansion and linearization approximation, and correspondingly two recursive formulas are derived. A simulation example is given to illustrate the effectiveness of two algorithms. It is shown that the extended Kalman particle filter algorithm based on second-order Taylor expansion has a more satisfactory performance in reducing systematic deviations and running time in comparison with the extended Kalman filter algorithm and the other second-order nonlinear Kalman particle filter algorithm.

Potentiometric Titration and Out-Of-Equilibrium pH Response of the Biotite-Water System

Minerals in soils contribute significantly to the capacity of soils to buffer pH disturbance. In this paper, we present the pH buffering capacity of a common soil phyllosilicate mineral, biotite. We performed equilibrium potentiometric titrations and we also monitored the pH response kinetics of the mineral-water system in order to develop an out-of-equilibrium model able to capture the interactions between minerals and the surrounding aqueous fluid. During titrations, after each addition of titrant solution from pH 11 to ca. 3, the pH response patterns were monitored over time until reaching a pseudo-equilibrium pH value. Based on the potentiometric dataset Best7, equilibrium calculations were performed to obtain the concentrations and the equilibrium protonation constants of each deprotonable component that fit best our titration curve. In parallel, the out-of-equilibrium pH responses over time at each point of the titration were used in a simple first-order kinetic approach that allow for the determination of “slow” proton exchange [H+ex]t0(mol L-1) and the associated rate constants for the exchange reactions, k (s-1). Our results show a maximum in “slow” proton exchange [H+ex]t0 associated to a minimal value of k at pH < 5 while at neutral and basic pH exhibits the opposite, i.e. , fast rate constant for a minimum value of the “slow” proton exchange. Expressing the observed “slow” proton exchange processes in terms of entropy production, our result demonstrate that the maximum resilience stability of biotite-water system to pH perturbation is in acidic pH, probably due to the consumption of proton associated with biotite dissolution reactions.

A Polynomial Optimization Approach to Principal-Agent Problems

This paper presents a new method for the analysis of moral hazard principal–agent problems. The new approach avoids the stringent assumptions on the distribution of outcomes made by the classical first-order approach and instead only requires the agent's expected utility to be a rational function of the action. This assumption allows for a reformulation of the agent's utility maximization problem as an equivalent system of equations and inequalities. This reformulation in turn transforms the principal's utility maximization problem into a nonlinear program. Under the additional assumptions that the principal's expected utility is a polynomial and the agent's expected utility is rational in the wage, the final nonlinear program can be solved to global optimality. The paper also shows how to first approximate expected utility functions that are not rational by polynomials, so that the polynomial optimization approach can be applied to compute an approximate solution to nonpolynomial problems. Finally, the paper demonstrates that the polynomial optimization approach extends to principal–agent models with multidimensional action sets.

NO3(-) coordination in aqueous solutions by (15)N/(14)N and (18)O/(nat)O isotopic substitution: what can we learn from molecular simulation?

We explore the deconvolution of water-nitrate correlations by the first-order difference approach involving neutron diffraction of heavy- and null-aqueous solutions of KNO3 under (14)N/(15)N and (nat)O(N)/(18)O(N) substitutions to achieve a full characterization of the first water coordination around the nitrate ion. For that purpose we performed isobaric-isothermal simulations of 3.5 m KNO3 aqueous solutions at ambient conditions to generate the relevant radial distribution functions required in the analysis (a) to identify the individual partial contributions to the total neutron-weighted distribution function, (b) to isolate and assess the contribution of NO3(-)···K(+) pair formation, (c) to test the accuracy of the neutron diffraction with isotope substitution based coordination calculations and X-ray diffraction based assumptions, and (d) to describe the water coordination around both the nitrogen and oxygen sites of the nitrate ion.

The Generalized Informativeness Principle

The informativeness principle, as originally formulated by Holmstrom (1979), relies on the first-order approach. This paper extends the informativeness to settings in which the first-order approach cannot be applied. We introduce a generalized informativeness principle that takes into account non-local incentive constraints and holds generically. The provides a sufficient condition for a signal to have value when the first-order approach cannot be applied. This condition is also necessary for a signal to have value in the absence of restrictions on the utility function.

First-Order Reliability Approach to Quantify and Improve Building Portfolio Resilience

AbstractThe concept of disaster-resilient communities has gained considerable acceptance and attention over the past decade, requiring the assessment of not only the monetary losses surrounding a hazard, but also the complex, time-dependent factors that influence community resilience. This paper presents an analytical, reliability-based approach to quantify seismic resilience based on the robustness and restoration rapidity of a portfolio of buildings following an earthquake event. The reliability problem is formulated using random variables to describe the spatially correlated seismic intensity, structural response, and duration of posthazard recovery for predefined building combinations within a portfolio. Based on these random variables, the first-order reliability method (FORM) is used as a basis to develop a new algorithm to evaluate a probability distribution of resilience for a suite of spatially distributed buildings. In addition, sensitivity measures are computed within FORM and used to prioritiz...

Low-energy electron-impact laser-assisted ionization of atomic hydrogen

We have studied the influence of a linearly polarized laser field on the dynamics of low $(e,2e)$ collisions in atomic hydrogen. The influence of the laser on the target states is treated using a first-order perturbation approach. The continuum states of the scattered and ejected electrons are described respectively by Volkov and Coulomb-Volkov wave functions. The second Born approximation is used to calculate triple differential cross sections for laser-assisted ionization by low-energy electron impact. The required scattering amplitudes are evaluated by using the Sturmian basis expansion. The influence of the laser parameters (photon energy, intensity, and direction of polarization) on the triple differential cross sections is analyzed, and several illustrative examples are discussed. Our second Born approximation results agree very well with those obtained in the first-order Born approximation at larger incident energies. The margins between the second and first Born approximation results are large at low incident energies in the vicinity of the recoil peaks.

Predictions of lift-off height of turbulent methane and propane flames issuing in cold surroundings using conditional moment closure coupled with an extinction model

Recently, an extinction model was successfully integrated with a conditional moment closure (CMC) approach to predict lift-off height accurately for the lifted jet diffusion flame discharging to hot surroundings Roy et al. (2014). This method, referred as CMCE, is a low-cost model based on the RANS and first-order CMC approach. The proposed methodology may not be superior compared to involved LES and second-order CMC combination, but enables to simulate industrial problems like gas turbine processes in a reasonable CPU time. Though the credibility of this novel approach was well established by validating its predictions against the experimental data, the applicability of this method and constants involved in this model, for other test cases was questionable. In this brief communication, CMCE model is further validated against lift-off studies of two different fuels issuing in cold surroundings, without changing the model constants used in the earlier work. The results are encouraging and a good match is found for lift-off heights for both the test cases and for varying jet velocities. Large variations are found between the predictions of lift-off heights by CMC and CMCE models.

Application of Path-integral for Studying EXAFS Cumulants

In this work, the path-integral effective potential (PIEP) method has been applied to re-study the temperature dependence of extended X-ray absorption fine structure (EXAFS) cumulants of materials. Using the trial density matrix and effective potential expression, we derived the analytical expressions of the first three EXAFS cumulants in the first shell of materials. The cumulant relation is also calculated to determine the temperature range in which the PIEP method could be applied. Our results are compared with available experimental data as well as with those calculated by the first-order perturbation approach in anharmonic Einstein model and the reasonable agreements are achieved.

OccBin: A toolkit for solving dynamic models with occasionally binding constraints easily

The toolkit adapts a first-order perturbation approach and applies it in a piecewise fashion to solve dynamic models with occasionally binding constraints. Our examples include a real business cycle model with a constraint on the level of investment and a New Keynesian model subject to the zero lower bound on nominal interest rates. Compared with a high-quality numerical solution, the piecewise linear perturbation method can adequately capture key properties of the models we consider. A key advantage of the piecewise linear perturbation method is its applicability to models with a large number of state variables.

Memory in network flows and its effects on spreading dynamics and community detection.

Random walks on networks is the standard tool for modelling spreading processes in social and biological systems. This first-order Markov approach is used in conventional community detection, ranking and spreading analysis, although it ignores a potentially important feature of the dynamics: where flow moves to may depend on where it comes from. Here we analyse pathways from different systems, and although we only observe marginal consequences for disease spreading, we show that ignoring the effects of second-order Markov dynamics has important consequences for community detection, ranking and information spreading. For example, capturing dynamics with a second-order Markov model allows us to reveal actual travel patterns in air traffic and to uncover multidisciplinary journals in scientific communication. These findings were achieved only by using more available data and making no additional assumptions, and therefore suggest that accounting for higher-order memory in network flows can help us better understand how real systems are organized and function.