This paper presents the identification of modal properties of a full-scale five-story reinforced concrete building fully outfitted with nonstructural components and systems (NCSs) tested on the NEES-UCSD shake table. The fixed base building is subjected to a sequence of earthquake motions selected to progressively damage the structure and NCSs. Between seismic tests, ambient vibration response is recorded. Additionally, low-amplitude white noise (WN) base excitation tests are conducted during the test protocol. Using the vibration data recorded, five state-of-the-art system identification (SID) methods are employed, including three output-only and two input-output. These methods are used to estimate the modal properties of an equivalent viscously-damped linear elastic time-invariant model of the building at different levels of damage and their results compared. The results show that modal properties identified from different methods are in good agreement and that the estimated modal parameters are affected by the amplitude of excitation and structural/nonstructural damage. Detailed visual inspections of damage performed between the seismic tests permit correlation of the identified modal parameters with the actual damage. The identified natural frequencies are used to determine the progressive loss of apparent global stiffness of the building, and the state-space models identified using WN test data are employed to investigate the relative modal contributions to the measured building response at different damage states. This research provides a unique opportunity to investigate the performance of different SID methods when applied to vibration data recorded in a real building subjected to progressive damage induced by a realistic source of dynamic excitation. Copyright © 2015 John Wiley & Sons, Ltd.

A full-scale five-story reinforced concrete building fully equipped with nonstructural components and systems (NCSs) was tested to a near collapse condition on the large outdoor shake table at the University of California, San Diego in 2012. This landmark test program was intended to advance the understanding of the seismic behavior of NCSs installed in buildings, and for this reason it was coined the “Building Nonstructural Components and Systems” (BNCS) project. The BNCS test specimen was monitored with digital still cameras, more than 80 video cameras, 500 analog sensors, and a global positioning system (GPS) and subjected to a suite of earthquake input motions of increasing intensity while in a base isolated and fixed-base configuration. The resulting high-quality dataset is now publicly available within the NEES repository (NEEShub). The goal of this paper is to outline the types of data available and provide a roadmap for navigating through it in an effort to support its future use by the community.

Nowadays many devices use a laser beam for diff erent purposes, as it has become a common tool in everyday living. In science, laser has become a top instrument not only in optics but also in metrology. The propagation of laser beams in a turbulent atmosphere has become more and more interesting due to the possibility of using high-data-rate optical transmitters for satellite-communication channels and lasercom systems connecting groundairborne- space or space-airborne-ground data links. This subject also o ffers a wide range of possible applications aside from free-space optical telecommunications (FSO), like remote sensing, target pointing and tracking, etc. But all of these applications have one common enemy, Earth’s atmosphere causes serious degradation of the reliability of such optical communication channels. Moreover, theoretical results are often insu fficient to match experimental data. The main problem is that all theoretical approaches have been based on common assumptions about the atmospheric turbulence through which the optical wave travels. For example, it is usual to suppose that the turbulence is homogeneous and isotropic, with a fixed power law spectrum in the inertial range of scales. Also, there are spectrum cutoff s at small and large scales that are quantitatively represented by numerical values of inner and outer scales.

Based on the previous statements the aim of this project is to develop a new theoretical model, that has a strong connection with phenomenology than the regular ones. We expect that this new model could make a good representation of the turbulence under weak conditions and also, could be extended to the other turbulence regimes such as anisotropic behavior or non-Kolmogorov turbulence.

Hypothetically a region of eddies with random variations of the air refraction index could simulate the atmospheric turbulence if certain scaling conditions are applied. This model is called Quasi-wavelets and was applied previously for sound propagation and temperature fluctuations with good results. Recently we have published an article in which we test this model for the propagation of a laser beam and proved to be valid theoretically in ideal conditions.

As mentioned before, the atmosphere has a random behavior and is clearly not stationary since there are considerable transient events that perturb laser beam propagation, creating wavefront distortions and high intensity deviations. This is due to lateral winds or an increment in the energy budget that generates the perturbation.
Since the Quasi-Wavelet model basically builds the region of turbulence, some hypotheses like uniform distribution of fluctuations and isotropy can be changed to more realistic conditions that could be encountered in the real atmosphere. This is the key to creating a new model that can be either calculated analytically or simulated by computers.

In this paper, we develop a stochastic model for topology optimization. We find robust structures that minimize the compliance for a given main load having a stochastic behavior. We propose a model that takes into account the expected value of the compliance and its variance. We show that, similarly to the case of truss structures, these values can be computed with an equivalent deterministic approach and the stochastic model can be transformed into a nonlinear programming problem, reducing the complexity of this kind of problems. First, we obtain an explicit expression (at the continuous level) of the expected compliance and its variance, then we consider a numerical discretization (by using a finite element method) of this expression and finally we use an optimization algorithm. This approach allows to solve design problems which include point, surface or volume loads with dependent or independent perturbations. We check the capacity of our formulation to generate structures that are robust to main loads and their perturbations by considering several 2D and 3D numerical examples. To this end, we analyze the behavior of our model by studying the impact on the optimized solutions of the expected-compliance and variance weight coefficients, the laws used to describe the random loads, the variance of the perturbations and the dependence/independence of the perturbations. Then, the results are compared with similar ones found in the literature for a different modeling approach.

A set of Fisher information properties are presented in order to draw a parallel with similar properties of Shannon differential entropy. Already known properties are presented together with new ones, which include: (i) a generalization of mutual information for Fisher information; (ii) a new proof that Fisher information increases under conditioning; (iii) showing that Fisher information decreases in Markov chains; and (iv) bound estimation error using Fisher information. This last result is especially important, because it completes Fano’s inequality, i.e., a lower bound for estimation error, showing that Fisher information can be used to define an upper bound for this error. In this way, it is shown that Shannon’s differential entropy, which quantifies the behavior of the random variable, and the Fisher information, which quantifies the internal structure of the density function that defines the random variable, can be used to characterize the estimation error.

This study addresses human health concerns in the city of Temuco that are attributed to wood smoke and related pollutants associated with wood burning activities that are prevalent in Temuco. Polycyclic Aromatic Hydrocarbons (PAHs) were measured in air across urban and rural sites over three seasons in Temuco using polyurethane foam (PUF) disk passive air samplers (PUF-PAS). Concentrations of ΣPAHs (15 congeners) in air ranged from BDL to ∼70 ng m⁻³ and were highest during the winter season, which is attributed to emissions from residential heating by wood combustion. The results for all three seasons showed that the PAH plume was widespread across all sites including rural sites on the outskirts of Temuco. Some interesting variations were observed between seasons in the composition of PAHs, which were attributed to differences in seasonal point sources. A comparison of the PAH composition in the passive samples with active samples (gas + particle phase) from the same site revealed similar congener profiles. Overall, the study demonstrated that the PUF disk passive air sampler provides a simple approach for measuring PAHs in air and for tracking effectiveness of pollution control measures in urban areas in order to improve public health.

This paper develops new methodological insights on Random Regret Minimization (RRM) models. It starts by showing that the classical RRM model is not scale-invariant, and that – as a result – the degree of regret minimization behavior imposed by the classical RRM model depends crucially on the sizes of the estimated taste parameters in combination with the distribution of attribute-values in the data. Motivated by this insight, this paper makes three methodological contributions: (1) it clarifies how the estimated taste parameters and the decision rule are related to one another; (2) it introduces the notion of “profundity of regret”, and presents a formal measure of this concept; and (3) it proposes two new family members of random regret minimization models: the μRRM model, and the Pure-RRM model. These new methodological insights are illustrated by re-analyzing 10 datasets which have been used to compare linear-additive RUM and classical RRM models in recently published papers. Our re-analyses reveal that the degree of regret minimizing behavior imposed by the classical RRM model is generally very limited. This insight explains the small differences in model fit that have previously been reported in the literature between the classical RRM model and the linear-additive RUM model. Furthermore, we find that on 4 out of 10 datasets the μRRM model improves model fit very substantially as compared to the RUM and the classical RRM model.

We investigate the influence of large noise on the formation of localized patterns in the framework of the cubic-quintic complex Ginzburg-Landau equation. The interaction of localization and noise can lead to filling in or noisy localized structures for fixed noise strength. To focus on the interaction between noise and localization we cover a region in parameter space, in particular, subcriticality, for which stationary stable deterministic pulses do not exist. Possible experimental tests of the work presented for autocatalytic chemical reactions and bioinspired systems are outlined.

This article shows for the first time the existence of periodic exploding dissipative solitons. These non-chaotic explosions appear when higher-order non-linear and dispersive effects are added to the complex cubic–quintic Ginzburg–Landau equation modeling soliton transmission lines. This counter-intuitive phenomenon is the result of period-halving bifurcations leading to order (periodic explosions), followed by period-doubling bifurcations leading to chaos (chaotic explosions). This periodic behavior is persistent even when small amounts of noise are added to the system. Since for ultrashort optical pulses it is necessary to include these higher-order effects, it is conjectured that the predictions can be tested in mode-locked lasers.

Simultaneous force and position control of robots interacting with a rigid environment has been broadly studied assuming several contact force models, being the differential algebraic – DAE – model the most complete one, however DAE robots show complex and strong nonlinear couplings that make difficult to achieve tracking, when dynamic model is not available. In this paper, considering the fundamental structural properties of DAE robots, in particular passivity and the orthogonalization of force and velocity vectors, it is proposed a model-free neurofuzzy-based self-tuning robot controller for exponential tracking, which is composed of orthogonalized PID-position plus an I-force (If) control terms, and a feed-forward desired force term. The salient feature of this proposal is a novel neurofuzzy self-tuning scheme aimed at tuning the dissipation rate gain (DRG) so as to enforce dissipativity in closed-loop, rather than the standard scheme of tuning the feedback control gains, or the control structure, which in our case stands for a simple constant feedback gain orthogonalized PID+If controller. In fact, in virtue of such orthogonalization, it emerges a simple and low cost parallel structure of the neuro-fuzzy network that targets solely the DRG so as to drive error dynamics to zero with exponential rate, without any knowledge of robot dynamics or carrying out any approximation of inverse dynamics. Thus, this technique can be applied to other class of systems and controllers that ensure passivity in closed-loop. Simulations show the validity and the feasibility of this new approach.

We present an unsupervised method that selects the most relevant features using an embedded strategy while maintaining the cluster structure found with the initial feature set. It is based on the idea of simultaneously minimizing the violation of the initial cluster structure and penalizing the use of features via scaling factors. As the base method we use Kernel K-means which works similarly to K-means, one of the most popular clustering algorithms, but it provides more flexibility due to the use of kernel functions for distance calculation, thus allowing the detection of more complex cluster structures. We present an algorithm to solve the respective minimization problem iteratively, and perform experiments with several data sets demonstrating the superior performance of the proposed method compared to alternative approaches.

Churn prediction is an important application of classification models that identify those customers most likely to attrite based on their respective characteristics described by e.g. socio-demographic and behavioral variables. Since nowadays more and more of such features are captured and stored in the respective computational systems, an appropriate handling of the resulting information overload becomes a highly relevant issue when it comes to build customer retention systems based on churn prediction models. As a consequence, feature selection is an important step of the classifier construction process. Most feature selection techniques; however, are based on statistically inspired validation criteria, which not necessarily lead to models that optimize goals specified by the respective organization. In this paper we propose a profit-driven approach for classifier construction and simultaneous variable selection based on support vector machines. Experimental results show that our models outperform conventional techniques for feature selection achieving superior performance with respect to business-related goals.

One of the main tasks of conjoint analysis is to identify consumer preferences about potential products or services. Accordingly, different estimation methods have been proposed to determine the corresponding relevant attributes. Most of these approaches rely on the post-processing of the estimated preferences to establish the importance of such variables. This paper presents new techniques that simultaneously identify consumer preferences and the most relevant attributes. The proposed approaches have two appealing characteristics. Firstly, they are grounded on a support vector machine formulation that has proved important predictive ability in operations management and marketing contexts and secondly they obtain a more parsimonious representation of consumer preferences than traditional models. We report the results of an extensive simulation study that shows that unlike existing methods, our approach can accurately recover the model parameters as well as the relevant attributes. Additionally, we use two conjoint choice experiments whose results show that the proposed techniques have better fit and predictive accuracy than traditional methods and that they additionally provide an improved understanding of customer preferences.

An empirical framework for customer churn prediction modeling is presented in this work. This task represents a very interesting business analytics challenge, given its highly class imbalanced nature, and the presence of noisy variables that adversely affect the prediction capabilities of classification models. In this work, two SVM-based techniques are compared: Support Vector Data Description (SVDD), and standard two-class SVMs. The proposed methodology involves the comparison of these two methods under different conditions of class imbalance and using different subsets of variables. Feature ranking is performed via the Fisher Score Criterion, while the class imbalance problem is dealt with through resampling techniques, namely random undersampling and SMOTE oversampling. Experiments on four customer churn prediction datasets show the advantages of SVDD: it outperforms standard SVM in terms of predictive performance, demonstrating the importance of techniques that take the class imbalance problem into account.

This paper proposes a novel framework that combines high-fidelity mechanics-based nonlinear (hysteretic) finite-element (FE) models and a nonlinear stochastic filtering technique, referred to as the unscented Kalman filter, to estimate unknown material parameters in frame-type structures. The proposed identification framework updates nonlinear FE models using spatially limited noisy measurement data, and it can be further used for damage identification purposes. To validate its effectiveness, robustness, and accuracy, this framework is applied to a cantilever steel column representing a bridge pier and two-dimensional steel frame. Both structures are modeled using beam-column elements with distributed plasticity and are subjected to a suite of earthquake ground motions of varying intensity. The results indicate that the material parameters of the nonlinear FE models are accurately estimated provided that the loading intensity is sufficient to exercise the parts (branches) of the nonlinear material model, which are governed by the material parameters to be identified, and the measured response quantities are sufficiently sensitive to the material parameters to be identified, especially when a limited number of measurements are considered.

This paper investigates the dynamic characteristics and seismic behavior of prefabricated steel stairs in a full-scale five-story building shake table test program. The test building was subjected to a suite of earthquake input motions and low-amplitude white noise base excitations first, while the building was isolated at its base, and subsequently while it was fixed to the shake table platen. This paper presents the modal characteristics of the stairs identified using the data recorded from white noise base excitation tests as well as the physical and measured responses of the stairs from the earthquake tests. The observed damage to the stairs is categorized into three distinct damage states and is correlated with the interstory drift demands of the building. These shake table tests highlight the seismic vulnerability of modern designed stair systems and in particular identifies as a key research need the importance of improving the deformability of flight-to-building connections.

The introduction of dynamic spectrum access (DSA) technologies in mobile markets faces technical, economic and regulatory challenges. This paper defines industry openness and spectrum centralization as the two key factors that affect the adoption of DSA technologies. The adoption process is analyzed employing a comprehensive System Dynamics model that considers the network and substitution effects. Two possible scenarios, namely operator-centric and user-centric adoption of DSA technologies are explored in the model. The analysis indicates that operator-centric DSA technologies may be adopted in most countries where spectrum is centralized, while end-user centric DSA technologies may be adopted in countries with decentralized spectrum regime and in niche emerging services. The study highlights the role of standards-based design and concludes by citing case studies that show the practicality of this analysis and associated policy prescriptions

Phenylacetone monooxygenase (PAMO) is an exceptionally robust Baeyer–Villiger monooxygenase, which makes it ideal for potential industrial applications. However, its substrate scope is limited, unreactive cyclohexanone being a prominent example. Such a limitation is unfortunate, because this particular transformation in an ecologically viable manner would be highly desirable, the lactone and the respective lactam being of considerable interest as monomers in polymer science. We have applied directed evolution in search of an active mutant for this valuable C-C activating reaction. Using iterative saturation mutagenesis (ISM), several active mutants were evolved, with only a minimal trade-off in terms of stability. The best mutants allow for quantitative conversion of 2 mM cyclohexanone within 1 h reaction time. In order to circumvent the NADP⁺ regeneration problem, whole E. coli resting cells were successfully applied. Molecular dynamics simulations and induced fit docking throw light on the origin of enhanced PAMO activity. The PAMO mutants constitute ideal starting points for future directed evolution optimization necessary for an industrial process

Optical Flow (OF) approaches for motion estimation calculate vector fields for the apparent velocities of objects in image sequences. In 1981 Horn and Schunck (HS) introduced two basic assumptions: ‘brightness value constancy’ and ‘smooth variation’ to estimate a smooth OF field over the entire image -global approach-. In parallel, Lucas and Kanade (LK) assumed constant motion patterns for image patches, estimating piecewise-homogeneous OF fields -local approach-. Several variations of these approaches exist today. Here we present the combined local-global (CLG) approach by Bruhn et al. which encompasses properties of HS-OF and LK-OF, aiming to improve the OF accuracy for small-scale variations, while delivering the HS-OF dense and smooth fields. A multiscale implementation is provided for 2D images, together with two numerical solvers: Successive Over-Relaxation and the faster Pointwise-Coupled Gauss-Seidel by Bruhn et al.. The algorithm works on gray-scale (single channel) images, with color images being converted prior to the OF computation.

This paper presents two novel second-order cone programming (SOCP) formulations that determine a linear predictor using Support Vector Machines (SVMs). Inspired by the soft-margin SVM formulation, our first approach (ξ-SOCP-SVM) proposes a relaxation of the conic constraints via a slack variable, penalizing it in the objective function. The second formulation (r -SOCP-SVM) is based on the LP-SVM formulation principle: the bound of the VC dimension is loosened properly using the l∞-norm, and the margin is directly maximized. The proposed methods have several advantages: The first approach constructs a flexible classifier, extending the benefits of the soft-margin SVM formulation to second-order cones. The second method obtains comparable results to the SOCP-SVM formulation with less computational effort, since one conic restriction is eliminated. Experiments on well-known benchmark datasets from the UCI Repository demonstrate that our approach accomplishes the best classification performance compared to the traditional SOCP-SVM formulation, LP-SVM, and to standard linear SVM.

The performance of classification methods, such as Support Vector Machines, depends heavily on the proper choice of the feature set used to construct the classifier. Feature selection is an NP-hard problem that has been studied extensively in the literature. Most strategies propose the elimination of features independently of classifier construction by exploiting statistical properties of each of the variables, or via greedy search. All such strategies are heuristic by nature. In this work we propose two different Mixed Integer Linear Programming formulations based on extensions of Support Vector Machines to overcome these shortcomings. The proposed approaches perform variable selection simultaneously with classifier construction using optimization models. We ran experiments on real-world benchmark datasets, comparing our approaches with well-known feature selection techniques and obtained better predictions with consistently fewer relevant features.

We demonstrate the occurrence of anomalous diffusion of dissipative solitons in a “simple” and deterministic prototype model: the cubic-quintic complex Ginzburg-Landau equation in two spatial dimensions. The main features of their dynamics, induced by symmetric-asymmetric explosions, can be modeled by a subdiffusive continuous-time random walk, while in the case dominated by only asymmetric explosions, it becomes characterized by normal diffusion.

The PID controller with constant feedback gains has withstood as the preferred choice for control of linear plants or linearized plants, and under certain conditions for non-linear ones, where the control of robotic arms excels. In this paper a model-free self-tuning PID controller is proposed for tracking tasks. The key idea is to exploit the passivity-based formulation for robotic arms in order to shape the damping injection to enforce dissipativity and to guarantee semiglobal exponential convergence in the sense of Lyapunov. It is shown that a neuro-fuzzy network can be used to tune dissipation rate gain through a self-tuning policy of a single gain. Experimental studies are presented to confirm the viability of the proposed approach.

A prototypical model for a mean field second order transition is presented, which is based on an ensemble of coupled two-states units. This system is used as a basic model to study the effect of memory. To wit, we distinguish two types of memories: weak and strong, depending on the feasibility of linearizing the generalized mean field master equation. For weak memory we find static solutions that behave much like those of the memoryless (Markovian) system. The latter exhibits a pitchfork bifurcation as the control parameter is increased, with two stable and one unstable solution. The former exhibits an imperfect pitchfork bifurcation to states with the same behaviors. In both cases, the stability of the static solutions is analyzed via the usual linearization around the equilibrium solution. For strong memories we again find an imperfect pitchfork bifurcation, with two stable and one unstable branch. However, it is no longer possible to analyze these behaviors via the usual linearization, which is local in time, because a strong memory requires knowledge of the system for its entire past. Finally, we are pleased to dedicate this publication to Helmut Brand on the occasion of his 60th birthday.

Time-domain astronomy (TDA) is facing a paradigm shift caused by the exponential growth of the sample size, data complexity and data generation rates of new astronomical sky surveys. For example, the Large Synoptic Survey Telescope (LSST), which will begin operations in northern Chile in 2022, will generate a nearly 150 Petabyte imaging dataset of the southern hemisphere sky. The LSST will stream data at rates of 2 Terabytes per hour, effectively capturing an unprecedented movie of the sky. The LSST is expected not only to improve our understanding of time-varying astrophysical objects, but also to reveal a plethora of yet unknown faint and fast-varying phenomena. To cope with a change of paradigm to data-driven astronomy, the fields of astroinformatics and astrostatistics have been created recently. The new data-oriented paradigms for astronomy combine statistics, data mining, knowledge discovery, machine learning and computational intelligence, in order to provide the automated and robust methods needed for the rapid detection and classification of known astrophysical objects as well as the unsupervised characterization of novel phenomena. In this article we present an overview of machine learning and computational intelligence applications to TDA. Future big data challenges and new lines of research in TDA, focusing on the LSST, are identified and discussed from the viewpoint of computational intelligence/machine learning. Interdisciplinary collaboration will be required to cope with the challenges posed by the deluge of astronomical data coming from the LSST.

In this work, we study a surface reaction on Pd(111) crystals under ultra-high-vacuum conditions that can be modeled by two coupled reaction-diffusion equations. In the bistable regime, the reaction exhibits travelling fronts that can be observed experimentally using photo electron emission microscopy. The spatial profile of the fronts reveals a coverage-dependent diffusivity for one of the species. We propose a method to solve the nonlinear eigenvalue problem and compute the direction and the speed of the fronts based on a geometrical construction in phase-space. This method successfully captures the dependence of the speed on control parameters and diffusivities.

We consider an array of units each of which can be in one of three states. Unidirectional transitions between these states are governed by Markovian rate processes. The interactions between units occur through a dependence of the transition rates of a unit on the states of the units with which it interacts. This coupling is nonlocal, that is, it is neither an all-to-all interaction (referred to as global coupling), nor is it a nearest neighbor interaction (referred to as local coupling). The coupling is chosen so as to disfavor the crowding of interacting units in the same state. As a result, there is no global synchronization. Instead, the resultant spatiotemporal configuration is one of clusters that move at a constant speed and that can be interpreted as traveling waves. We develop a mean field theory to describe the cluster formation and analyze this model analytically. The predictions of the model are compared favorably with the results obtained by direct numerical simulations.

200 words for Intelligent Data Systems The class imbalance problem is a relatively new challenge that has attracted growing attention from both industry and academia, since it strongly affects classification performance. Research also established that class imbalance is not an issue by itself, but its relationship with class overlapping and noise has an important impact on the prediction performance and stability. This fact has motivated the development of several approaches for classification of imbalanced data see e.g. [29,39]. In this paper, we present credit card customer churn prediction, an important topic in business analytics, using an ensemble of classifiers. Since this problem is considered as highly imbalanced, we employ different techniques for classification, such as Support Vector Data Description SVDD and two-class SVMs. The main idea is to address both class imbalance and class overlapping by stacking different classification approaches, while evaluating the diversity of the individual classifiers considering meta-learning measures. We performed experiments on artificial data sets and one real customer churn prediction problem from a Chilean financial entity, comparing our approach with well-known classification techniques for imbalanced data. The proposed strategy achieves an improvement of 6.1% over the best individual classifier in terms of predictive performance, providing accurate and robust classification models for different levels of balance and noise.

The Coase theorem suggests that a regulatory scheme, which clearly defines spectrum property rights and allows transactions between participants, induces an optimal spectrum assignment. This paper argues that the conditions required by Coase are gradually achieved by the introduction of Dynamic Spectrum Management (DSM), which enables a dynamic reassignment of spectrum bands at different times and places. DSM reduces the costs associated with spectrum transactions and thus provides an opportunity to enhance efficiency through voluntary transactions. This study analyzes the factors affecting the benefits of a regulatory scheme allowing transactions, compares and quantifies the potential gains associated with different spectrum regimes by employing agent-based simulations and suggests policy implications for spectrum regulation.

Conventional acceleration records do not properly account for the observed coseismic ground displacements, thus leading to an inaccurate definition of the seismic demand needed for the design of flexible (long period) structures. Large coseismic displacements observed during the 27 February 2010 Maule earthquake suggest that this effect should be included in the design of flexible structures by modifying the design ground motions and spectra considered. Consequently, Green’s functions are used herein to compute synthetic low‐frequency seismograms that are consistent with the coseismic displacement field obtained from interferometry using synthetic aperture radar (SAR) images. In this case, the coseismic displacement field was determined by interfering twenty SAR images of the Advanced Land Observation Satellite (ALOS)/PALSAR satellite taken between 12 October 2007 and 28 May 2010. These images cover the region affected by the 2010 M_w 8.8 Maule earthquake. Synthetic broadband seismograms are built by superimposing the low‐pass filtered synthetic low‐frequency seismograms with high‐frequency strong‐motion data. The broadband seismograms generated are then consistent with the coseismic displacement field and the high‐frequency content of the earthquake. A sensitivity analysis is performed using three different fault and slip parameters, the rupture velocity, the corner frequency, and the slip rise time. Results show that the optimal corner frequency of the low‐pass filter f_c=1/T_c, leads to a trade‐off between acceleration and displacement accuracy. Furthermore, spectral response for long periods, say T≥8  s, is relatively insensitive to the value of T_c, whereas shorter periods are strongly dependent on both the slip rise time and T_c. In general, larger displacements consistent with coseismic data are obtained using this technique instead of digitally processing the acceleration ground‐motion records.

Within the mining industry, a safe and economical mine ventilation system is an essential component of all underground mines. In recent years, research scientists and engineers have explored operations research methods to assist in the design and safe operation of primary mine ventilation systems. The main objective of these studies is to develop algorithms to identify the primary mine ventilation systems that minimize the fan power costs, including their working performance. The principal task is to identify the number, location, and duty of fans and regulators for installation within a defined ventilation network to distribute the required fresh airflow at minimum cost. The successful implementation of these methods may produce a computational design tool to aid mine planning and ventilation engineers. This paper presents a review of the results of a series of recent research studies that have explored the use of mathematical methods to determine the optimum design of primary mine ventilation systems relative to fan power costs.

The MSK-64 seismic intensities inside the damage area of the 2010 Maule earthquake are estimated. With this purpose, field surveys of damage to typical single-family buildings located in 111 cities of the affected area were used. Cities located close to the north part of the earthquake rupture suffered higher damage, but most of this damage concerned adobe and unreinforced masonry houses. Minor and moderate damage was noted in modern low-rise engineered and nonengineered constructions, especially in confined masonry buildings. Despite the large length of the rupture, which reached more than 450 km, only one intensity value equal to IX was determined, and 21% of the values were greater than VII. The attenuation of seismic intensity was controlled by the distance to the main asperity more than to the hypocenter, which would be an important characteristic of the megathrust earthquakes, and it should therefore be considered in the seismic risk of large subduction environments.

Optical flow approaches calculate vector fields which determine the apparent velocities of objects in time-varying image sequences. They have been analyzed extensively in computer science using both natural and synthetic video sequences. In life sciences, there is an increasing need to extract kinetic information from temporal image sequences which reveals the interplay between form and function of microscopic biological structures. In this work, we test different variational optical flow techniques to quantify the displacements of biological objects in 2D fluorescent image sequences. The accuracy of the vector fields is tested for defined displacements of fluorescent point sources in synthetic image series which mimic protein traffic in neuronal dendrites, and for GABA_BR1 receptor subunits in dendrites of hippocampal neurons. Our results reveal that optical flow fields predict the movement of fluorescent point sources within an error of 3% for a maximum displacement of 160 nm. Displacement of agglomerated GABA_BR1 receptor subunits can be predicted correctly for maximum displacements of 640 nm. Based on these results, we introduce a criteria to derive the optimum parameter combinations for the calculation of the optical flow fields in experimental images. From these results, temporal sampling frequencies for image acquisition can be derived to guarantee correct motion estimation for biological objects.

A multi-period stochastic model and an algorithmic approach to location of prison facilities under uncertainty are presented and applied to the Chilean prison system. The problem consists of finding locations and sizes of a preset number of new jails and determining where and when to increase the capacity of both new and existing facilities over a time horizon, while minimizing the expected costs of the prison system. Constraints include maximum inmate transfer distances, upper and lower bounds for facility capacities, and scheduling of facility openings and expansion, among others. The uncertainty lies in the future demand for capacity, because of the long time horizon under study and because of the changes in criminal laws, which could strongly modify the historical tendencies of penal population growth. Uncertainty comes from the effects of penal reform in the capacity demand. It is represented in the model through probabilistic scenarios, and the large-scale model is solved via a heuristic mixture of branch-and-fix coordination and branch-and-bound schemes to satisfy the constraints in all scenarios, the so-called branch-and-cluster coordination scheme. We discuss computational experience and compare the results obtained for the minimum expected cost and average scenario strategies. Our results demonstrate that the minimum expected cost solution leads to better solutions than does the average scenario approach. Additionally, the results show that the stochastic algorithmic approach that we propose outperforms the plain use of a state-of-the-art optimization engine, at least for the three versions of the real-life case that have been tested by us.

Tall buildings and flexible structures require a better characterization of long period ground motion spectra than the one provided by current seismic building codes. Motivated by that, a methodology is proposed and tested to improve recorded and synthetic ground motions which are consistent with the observed co-seismic displacement field obtained from interferometric synthetic aperture radar (InSAR) analysis of image data for the Tocopilla 2007 earthquake (M_w=7.7) in Northern Chile. A methodology is proposed to correct the observed motions such that, after double integration, they are coherent with the local value of the residual displacement. Synthetic records are generated by using a stochastic finite-fault model coupled with a long period pulse to capture the long period fling effect.

It is observed that the proposed co-seismic correction yields records with more accurate long-period spectral components as compared with regular correction schemes such as acausal filtering. These signals provide an estimate for the velocity and displacement spectra, which are essential for tall-building design. Furthermore, hints are provided as to the shape of long-period spectra for seismic zones prone to large co-seismic displacements such as the Nazca-South American zone.