A comparison between first-order microscopic and macroscopic differential models of crowd dynamics is established for an increasing number N of pedestrians. The novelty is the fact of considering massive agents, namely, particles whose individual mass does not become infinitesimal when N grows. This implies that the total mass of the system is not constant but grows with N. The main result is that the two types of models approach one another in the limit N -> ?, provided the strength and/or the domain of pedestrian interactions are properly modulated by N at either scale. This is consistent with the idea that pedestrians may adapt their interpersonal attitudes according to the overall level of congestion.

We briefly review some aspects of the anomalous diffusion, and its relevance in reactive systems. In particular we consider strong anomalous diffusion characterized by the moment behaviour <(x(t)(q)> similar to t(qv)(q), where v(q) is a non constant function, and we discuss its consequences. Even in the apparently simple case v(2) = 1/2, strong anomalous diffusion may correspond to non trivial features, such as non Gaussian probability distribution and peculiar scaling of large order moments.

HVAC systems are the largest energy consumers in a building and a clean HVAC system can get about 11% in energy saving. Moreover, particulate pollution represents one of the main causes of cancer death and several health damages. This paper presents an innovative and not invasive procedure for the automatic indoor air quality assessment that depends on HVAC cleaning conditions. It is based on a mathematical algorithm that processes a few on-site physical measurements that are acquired by dedicated sensors at suitable locations with a specif-ic time table. The output of the algorithm is a set of indexes that provide a snapshot of the sys-tem with separated zoom on filters and ducts. The proposed methodology contributes to opti-mize both HVAC maintenance procedures and air quality preservation. Robustness, portability and low implementation costs allow to plan maintenance intervention, limiting it only when standard HVAC working conditions need to be restored.

The paper presents a model for assessing image quality from a subset of pixels. It is based on the fact that human beings do not explore the whole image information for quantifying its degree of distortion. Hence, the vision process can be seen in agreement with the Asymptotic Equipartition Property. The latter assures the existence of a subset of sequences of image blocks able to describe the whole image source with a prefixed and small error. Specifically, the well known Structural SIMilarity index (SSIM) has been considered. Its entropy has been used for defining a method for the selection of those image pixels that enable SSIM estimation with enough precision. Experimental results show that the proposed selection method is able to reduce the number of operations required by SSIM of about 200 times, with an estimation error less than 8%.

Unlike for systems in equilibrium, a straightforward definition of a metastable set in the non-stationary, non-equilibrium case may only be given case-by-case-and therefore it is not directly useful any more, in particular in cases where the slowest relaxation time scales are comparable to the time scales at which the external field driving the system varies. We generalize the concept of metastability by relying on the theory of coherent sets. A pair of sets A and B is called coherent with respect to the time interval [t(1),t(2)] if (a) most of the trajectories starting in A at t(1) end up in B at t(2) and (b) most of the trajectories arriving in B at t(2) actually started from A at t(1). Based on this definition, we can show how to compute coherent sets and then derive finite-time non-stationary Markov state models. We illustrate this concept and its main differences to equilibrium Markov state modeling on simple, one-dimensional examples.

The establishment of thermal diffusion in an Ar-Kr Lennard-Jones mixture is investigated via dynamical non equilibrium molecular dynamics [G. Ciccotti, G. Jacucci, Phys. Rev. Lett. 35, 789 (1975)]. We observe, in particular, the evolution of the density and temperature fields of the system following the onset of the thermal gradient. In stationary conditions, we also compute the Soret coefficient of the mixture. This study confirms that dynamical non equilibrium molecular dynamics is an effective tool to gather information on transient phenomena, even though the full evolution of the mass and energy fluxes associated to the temperature and density fields requires, in this case, a more substantial numerical effort than the one employed here.

We study sparse high dimensional additive model fitting via penalization with sparsity-smoothness penalties. We review several existing algorithms that have been developed for this problem in the recent literature, highlighting the connections between them, and present some computationally efficient algorithms for fitting such models. Furthermore, using reasonable assumptions and exploiting recent results on group LASSO-like procedures, we take advantage of several oracle results which yield asymptotic optimality of estimators for high-dimensional but sparse additive models. Finally, variable selection procedures are compared with some high-dimensional testing procedures available in the literature for testing the presence of additive components.

This paper focuses on the use the Jensen Shannon divergence for guiding denoising. In particular, it aims at detecting those image regions where noise is masked; denoising is then inhibited where it is useless from the visual point of view. To this aim a reduced reference version of the Jensen Shannon divergence is introduced and it is used for determining a denoising map. The latter separates those image pixels that require to be denoised from those that have to be leaved unaltered. Experimental results show that the proposed method allows to improve denoising performance of some simple and conventional denoisers, in terms of both peak signal to noise ratio (PSNR) and structural similarity index (SSIM). In addition, it can contribute to reduce the computational effort of some performing denoisers, while preserving the visual quality of denoised images.

This paper presents a methodology for assessing and monitoring the cleaning state of a heating, ventilation, and air conditioning (HVAC) system of a building. It consists of a noninvasive method for measuring the amount of dust in the whole ventilation system, that is, the set of filters and air ducts. Specifically, it defines the minimum amount of measurements, their time table, locations, and acquisition conditions. The proposed method promotes early intervention on the system and it guarantees high indoor air quality and proper HVAC working conditions. The effectiveness of the method is proved by some experimental results on different study cases.

Time-scale transforms play a fundamental role in the compact representation of signals and images [1]. Non linear time representation provided a significant contribution to the definition of more flexible and adaptive transforms. However, in many applications signals are better characterized in the frequency domain. In particular, frequency distribution in the frequency axis is strictly dependent on the signal under study. On the contrary, frequency axis partition provided by conventional transforms obeys more rigid rules. It would be then desirable to have a transform able to adapt to the frequency content of the signal under study, i.e. having a changing Q factor. The rational dilation wavelet transform [2, 3] (RDWT) is a flexible tool that allows to change the dilation factor at each step of the transformaswell as the analyzingwindowfunction, by maintaining the structure and properties of the classical wavelet transform, which is implemented through perfect reconstruction filter banks. Some examples concerning the way of selecting significant scales, i.e. central frequencies and bandwidths of the filter bank, in different applications, including image denoising, deblurring and fusion, will be shown. The properties of the corresponding adaptive transformwill be also discussed.

In the present paper, we constructed an estimator of a delta contaminated mixing density function $g(\lam)$ of an intensity $\lambda$ of the Poisson distribution. The estimator is based on an expansion of the continuous portion $g_0(\lambda)$ of the unknown pdf over an overcomplete dictionary with the recovery of the coefficients obtained as the solution of an optimization problem with Lasso penalty. In order to apply Lasso technique in the, so called, prediction setting where it requires virtually no assumptions on the dictionary and, moreover, to ensure fast convergence of Lasso estimator, we use a novel formulation of the optimization problem based on the inversion of the dictionary elements. We formulate conditions on the dictionary and the unknown mixing density that yield a sharp oracle inequality for the norm of the difference between $g_0 (\lambda)$ and its estimator and, thus, obtain a smaller error than in a minimax setting. Numerical simulations and comparisons with the Laguerre functions based estimator recently constructed by \cite{Comte} also show advantages of our procedure. At last, we apply the technique developed in the paper to estimation of a delta contaminated mixing density of the Poisson intensity of the Saturn's rings data.

We correct an error in the proof of Theorem 4.1 of the paper "Boundedness of solutions to anisotropic variational problems" [Comm. Part. Diff. Eq. 36 (2011); 470-486].

Vesicles are involved in a vast variety of transport processes in living organisms. Additionally, they serve as a model for the dynamics of cell suspensions. Predicting the rheological properties of their suspensions is still an open question, as even the interaction of pairs is yet to be fully understood. Here we analyse the effect of a single vesicle, undergoing tank-treading motion, on its surrounding shear flow by studying the induced disturbance field $\delta \vec{V}$, the difference between the velocity field in its presence and absence. The comparison between experiments and numerical simulations reveals an impressive agreement. Tracking ridges in the disturbance field magnitude landscape, we identify the principal directions along which the velocity difference field is analysed in the vesicle vicinity. The disturbance magnitude is found to be significant up to about 4 vesicles radii and can be described by a power law decay with the distance $d$ from the vesicle $ \| \delta \vec{V} \| \propto d^{-3/2}$. This is consistent with previous experimental results on the separation distance between two interacting vesicles under similar conditions, for which their dynamics is altered. This is an indication of vesicles long-range effect via the disturbance field and calls for the proper incorporation of long-range hydrodynamic interactions when attempting to derive rheological properties of vesicle suspensions.

The obstacles to the E-Mobility (EM)'s development are widely discussed both in scientific and in industrial fields. Approaches to overcome these obstacles are still not consolidate. At the same time, it is not so clear, what Business Models (BM) are more sustainable for the owners of Charging Infrastructure (CI). With the aim to support the development of charging network (CN), the authors propose a new BM based on intelligent, collaborative and digital services for all actors of the value chain. The implementation of this BM starts with the development of a decisional structure (DS) and the sharing of data and information among all operators that are involved into the charging process. The main elements of the model are explained and the first results of implementation are given. Future development are discussed to enrich the research and to supply at industrial field a useful tool to face decisions in the real context of CNs.

The authors propose a Multi Attributes approach to meet the demand of personalized tourist tours into cultural cities. Respecting to others works present into the literature, in this paper the decisional process includes two phases and a high number of variables that don't increase the complexity of the problem. A real application in an Italian city, Florence, is presented to demonstrate the great potential of this system into real context. The first phase of optimization is solved applying an innovative Genetic Algorithm, the second one a Multi Criteria Method, Analytic Hierarchy Process (AHP). The combination of these two approach gives flexibility to the system with respect to number of variables and allow to return a good solution for tourist in few second of computational time.

A new algorithm for the solution of free surface flows with large front deformation and fragmentation is presented. The algorithm is obtained by coupling a classical Finite Volume (FV) approach, that discretizes the Navier-Stokes equations on a block structured Eulerian grid, with an approach based on the Smoothed Particle Hydrodynamics (SPH) method, implemented in a Lagrangian framework. The coupling procedure is formulated in such a way that each solver is applied in the region where its intrinsic characteristics can be exploited in the most efficient and accurate way: the FV solver is used to resolve the bulk flow and the wall regions, whereas the SPH solver is implemented in the free surface region to capture details of the front evolution. The reported results clearly prove that the combined use of the two solvers is convenient from the point of view of both accuracy and computing time.

The aim of the present paper is the analysis of simplified boundary conditions to be used in numerical simulations, to take into account blockage effects for wind tunnel experiments of large scale wind tur- bines. The goal is the development of an efficient and reliable tool to be used to correct data obtained from experiments where the blockage coefficient is high and/or the turbine is highly loaded, for which traditional correction coefficients (derived from the Glauert theory or its more recent versions) fail. Numerical simulations of the flow around a three-bladed model-scale wind turbine with horizontal axis are reported; in all test cases, the turbine diameter is comparable with test section dimensions, and therefore blockage effects are significant. The actual experiments were approximated numerically with a simplified wind tunnel geometry, that retains the symmetries of the isolated turbine simulation in a rotating frame and therefore allows steady state computations. To this end, two circular wind tunnel were tested: for the first, the radius was chosen to retain the same cross-section as the actual wind tunnel; in the second, its was set to be equal to half of the smallest cross-section dimension. The aerodynamic performances of the turbine, in terms of power and thrust coefficients, are analyzed and compared with available experimental data. Detailed analysis of the flow in the wake is also reported. Analogous simulations in an unbounded domain are also reported.

Conformational heterogeneity is key to the function of many biomacromolecules, but only a few groups have tried to characterize it until recently. Now, thanks to the increased throughput of experimental data and the increased computational power, the problem of the characterization of protein structural variability has become more and more popular. Several groups have devoted their efforts in trying to create quantitative, reliable and accurate protocols for extracting such information from averaged data. We analyze here different approaches, discussing strengths and weaknesses of each. All approaches can roughly be clustered into two groups: Those satisfying the maximum entropy principle and those recovering ensembles composed of a restricted number of molecular conformations. In the first case, the solution focuses on the features that are common to all the infinite solutions satisfying the experimental data; in the second case, the reconstructed ensemble shows the conformational regions where a large probability can be placed. The upper limits for conformational probabilities (MaxOcc) can also be calculated. We also give an overview of the mainstream experimental observables, with considerations on the assumptions underlying their usage.

The problem is addressed of the maximal integrability of the gradient of solutions to quasilinear elliptic equations, with merely measurable coefficients, in two variables. Optimal results are obtained in the framework of Orlicz spaces, and in the more general setting of all rearrangement-invariant spaces. Applications to special instances are exhibited, which provide new gradient bounds, or improve certain results available in the literature.