Investigations of masonry churches seismic performance with numerical models: application to a case study

Recovering and preserving ancient churches is necessary to ensure the transmission of this cultural heritage to the future generations. To this scope, it is necessary to evaluate their performance in seismic prone areas, to design interventions capable of reducing their vulnerability ensuring also their safety use for the faithful. In this paper, investigations on seismic performance of masonry churches are illustrated by applying two different numerical methods on a case study, an existing brick masonry church. The seismic assessment is conducted by applying two simplified methods proposed by the current Italian Directive containing the Guidelines for assessment and reduction of cultural heritage seismic risk. Moreover, linear kinematic analysis is used also for investigating the influence of main parameters governing to the main façade simple overturning and narthex longitudinal response. The investigations performed highlight that the activation multiplier of macro-element response mechanism may significantly vary according to the assumptions made and that also, as narthex longitudinal response, a minimization procedure of the activation multiplier is required.

Fuzzy Bit-Plane-Dependence Region Competition

This paper presents a novel variational model based on fuzzy region competition and statistical image variation modeling for image segmentation. In the energy functional of the proposed model, each region is characterized by the pixel-level color feature and region-level spatial/frequency information extracted from various image domains, which are modeled by the windowed bit-plane-dependence probability models. To efficiently minimize the energy functional, we apply an alternating minimization procedure with the use of Chambolle’s fast duality projection algorithm, where the closed-form solutions of the energy functional are obtained. Our method gives soft segmentation result via the fuzzy membership function, and moreover, the use of multi-domain statistical region characterization provides additional information that can enhance the segmentation accuracy. Experimental results indicate that the proposed method has a superior performance and outperforms the current state-of-the-art superpixel-based and deep-learning-based approaches.

Acoustic Characterization of Some Steel Industry Waste Materials

From a circular economy perspective, the acoustic characterization of steelwork by-products is a topic worth investigating, especially because little or no literature can be found on this subject. The possibility to reuse and add value to a large amount of this kind of waste material can lead to significant economic and environmental benefits. Once properly analyzed and optimized, these by-products can become a valuable alternative to conventional materials for noise control applications. The main acoustic properties of these materials can be investigated by means of a four-microphone impedance tube. Through an inverse technique, it is then possible to derive some non-acoustic properties of interest, useful to physically characterize the structure of the materials. The inverse method adopted in this paper is founded on the Johnson–Champoux–Allard model and uses a standard minimization procedure based on the difference between the sound absorption coefficients obtained experimentally and predicted by the Johnson–Champoux–Allard model. The results obtained are consistent with other literature data for similar materials. The knowledge of the physical parameters retrieved applying this technique (porosity, airflow resistivity, tortuosity, viscous and thermal characteristic length) is fundamental for the acoustic optimization of the porous materials in the case of future applications.

Estimating the Quadratic Form xTA−mx for Symmetric Matrices: Further Progress and Numerical Computations

In this paper, we study estimates for quadratic forms of the type xTA−mx, m∈N, for symmetric matrices. We derive a general approach for estimating this type of quadratic form and we present some upper bounds for the corresponding absolute error. Specifically, we consider three different approaches for estimating the quadratic form xTA−mx. The first approach is based on a projection method, the second is a minimization procedure, and the last approach is heuristic. Numerical examples showing the effectiveness of the estimates are presented. Furthermore, we compare the behavior of the proposed estimates with other methods that are derived in the literature.

Alternating minimization methods for strongly convex optimization

We consider alternating minimization procedures for convex and non-convex optimization problems with the vector of variables divided into several blocks, each block being amenable for minimization with respect to its variables while maintaining other variables blocks constant. In the case of two blocks, we prove a linear convergence rate for an alternating minimization procedure under the Polyak–Łojasiewicz (PL) condition, which can be seen as a relaxation of the strong convexity assumption. Under the strong convexity assumption in the many-blocks setting, we provide an accelerated alternating minimization procedure with linear convergence rate depending on the square root of the condition number as opposed to just the condition number for the non-accelerated method. We also consider the problem of finding an approximate non-negative solution to a linear system of equations A ⁢ x = y {Ax=y} with alternating minimization of Kullback–Leibler (KL) divergence between Ax and y.

Inferring functional units in ion channel pores via relative entropy

Coarse-grained protein models approximate the first-principle physical potentials. Among those modeling approaches, the relative entropy framework yields promising and physically sound results, in which a mapping from the target protein structure and dynamics to a model is defined and subsequently adjusted by an entropy minimization of the model parameters. Minimization of the relative entropy is equivalent to maximization of the likelihood of reproduction of (configurational ensemble) observations by the model. In this study, we extend the relative entropy minimization procedure beyond parameter fitting by a second optimization level, which identifies the optimal mapping to a (dimension-reduced) topology. We consider anisotropic network models of a diverse set of ion channels and assess our findings by comparison to experimental results.

Correcting the effect of stellar spots on ARIEL transmission spectra

ABSTRACT The goal of this study is to assess the impact of the stellar spots on the extraction of the planetary transmission spectra observed by ARIEL. We develop a method to model the stellar spectrum of a star in the presence of spots by using the out-of-transit observations. It is based on a chi squared minimization procedure of the out-of-transit spectrum on a grid of stellar spectra with different sizes and temperatures of the spots. The approach allows us also to study the temporal evolution of the spots when comparing stellar spectra observed at different epochs. We also present a method to correct the transit depth variations due to non-occulted stellar spots and estimate the error we introduce if we apply the same correction to crossings over the stellar spots. The method is tested on three types of stellar targets that ARIEL will observe in its 4-yr mission lifetime. In all the explored cases, the approach allows us to reliably recover the spot parameters (size and temperature) from out-of-transit observations and, for non-occulted spots, to confidently recover the planetary atmosphere transmission spectrum within the noise level (with average uncertainty of at most $3.3{{\ \rm per\ cent}}$ of the planetary signal). Conversely, we find systematic biases in the inferred planetary spectra due to the occulted spots, with measurable effects for the brightest targets especially for more contrasted spots.

Optimal Control Implementation with Terminal Penalty Using Metaheuristic Algorithms

Optimal control problems can be solved by a metaheuristic based algorithm (MbA) that yields an open-loop solution. The receding horizon control mechanism can integrate an MbA to produce a closed-loop solution. When the performance index includes a term depending on the final state (terminal penalty), the prediction’s time possibly surpasses a sampling period. This paper aims to avoid predicting the terminal penalty. The sequence of the best solution’s state variables becomes a reference trajectory; this one is used by a tracking structure that includes the real process, a process model (PM) and a tracking controller (TC). The reference trajectory must be followed up as much as possible by the real trajectory. The TC makes a one-step-ahead prediction and calculates the control inputs through a minimization procedure. Therefore the terminal penalty’s calculation is avoided. An example of a tracking structure is presented. The TC may also use an MbA for its minimization procedure. The implementation is presented in two versions: using a simulated annealing algorithm and an evolutionary algorithm. The simulations have proved that the proposed approach is realistic. The tracking structure does or does not work well, depending on the PM’s accuracy in reproducing the real process.

A mathematical analysis for constructal design of tree flow networks under unsteady flow

Tree flow networks play an important role in both natural and man-made systems. In an effort to develop a deeper understanding of the optimal design of these networks, we have developed a simple analytical approach to deal with steady and unsteady flows. As a result, optimal relations for the homothetic ratio of tube sizes and optimal angles between daughter tubes are obtained. The obtained optimum homothetic ratios satisfy the criterion of the minimization procedure of flow impedance based on geometry and the svelteness ratio. The robustness, accuracy and convergence of model are also proved mathematically in order to validate the results.