Effectively dealing with invasive species is a pervasive problem in environmental management. The damages that stem from invasive species are well known. However, controlling them cost-effectively is an ongoing challenge, and mathematical modeling and optimization are becoming increasingly popular as a tool to assist management. In this paper we investigate problems where optimal control theory has been implemented. We show that transforming these problems from state-costate systems to state-control systems provides the complete qualitative description of the optimal solution and leads to its theoretical expression for free terminal time problems. We apply these techniques to two case studies: one of feral cats in Australia, where we use logistic growth; and the other of wild-boars in Italy, where we include an Allee effect. (C) 2019 The Authors. Published by Elsevier Ltd.

In this paper, we deploy the hybrid Lattice Boltzmann - Particle Dynamics (LBPD) method to investigate the transport properties of blood flow within arterioles and venules. The numerical approach is applied to study the transport of Red Blood Cells (RBC) through plasma, highlighting significant agreement with the experimental data in the seminal work by Fahraeus and Lindqvist. Moreover, the results provide evidence of an interesting hand-shaking between the range of validity of the proposed hybrid approach and the domain of viability of particle methods. A joint inspection of accuracy and computational cost, indicate that LBPD offers an appealing multiscale strategy for the study of blood transport across scales of motion, from macroscopic vessels, down to arterioles and venules, where particle methods can eventually take over.

The dihydrogen complex Ru(H2)2H2(P(C5H9)3)2 has been investigated, via ab initio accelerated molecular dynamics, to elucidate the H ligands dynamics and possible reaction paths for H2/H exchange. We have characterized the free energy landscape associated with the H atoms positional exchange around the Ru centre. From the free energy landscape, we have been able to estimate a barrier of 6 kcal mol-1 for the H2/H exchange process. We have also observed a trihydrogen intermediate as a passing state along some of the possible reaction pathways.

Network-based ranking methods (e.g. centrality analysis) have found extensive use in systems medicine for the prediction of essential proteins, for the prioritization of drug targets candidates in the treatment of several pathologies and in biomarker discovery, and for human disease genes identification. Here we propose to use critical nodes as defined by the Critical Node Problem for the analysis of key physiological and pathophysiological signaling pathways, as target candidates for treatment and management of several cancer types, neurologic and inflammatory dysfunctions, among others. We show how critical nodes allow to rank the importance of proteins in the pathways in a non-trivial way, substantially different from classical centrality measures. Such ranking takes into account the extent to which the network depends on its key players to maintain its cohesiveness and consistency, and coherently maps biologically relevant characteristics that can be critical in disease onset and treatments.

The computation of the eigenvalue decomposition of symmetric matrices is one of the most investigated problems in numerical linear algebra. For a matrix of moderate size, the customary procedure is to reduce it to a symmetric tridiagonal one by means of an orthogonal similarity transformation and then compute the eigendecomposition of the tridiagonal matrix. Recently, Malyshev and Dhillon have proposed an algorithm for deflating the tridiagonal matrix, once an eigenvalue has been computed. Starting from the aforementioned algorithm, in this manuscript we develop a procedure for computing an eigenvector of a symmetric tridiagonal matrix, once its associate eigenvalue is known. We illustrate the behavior of the proposed method with a number of numerical examples.

A new technique for nonparametric regression of multichannel signals is presented. The technique is based on the use of the Rational-Dilation Wavelet Transform (RADWT), equipped with a tunable Q-factor able to provide sparse representations of functions with different oscillations persistence. In particular, two different frames are obtained by two RADWT with different Q-factors that give sparse representations of functions with low and high resonance. It is assumed that the signals are measured simultaneously on several independent channels and that they share the low resonance component and the spectral characteristics of the high resonance component. Then, a regression analysis is performed by means of the grouped lasso penalty. Furthermore, a result of asymptotic optimality of the estimator is presented using reasonable assumptions and exploiting recent results on group-lasso like procedures. Numerical experiments show the performance of the proposed method in different synthetic scenarios as well as in a real case example for the analysis and joint detection of sleep spindles and K-complex events for multiple electroencephalogram (EEG) signals. (C) 2018 Elsevier B.V. All rights reserved.

In this paper, we present a 2-Dimensional (2D) Optimal Interpolation (OI) technique for spatially scattered infrared satellite observations, from which level 2 products have been obtained, in order to yield level 3, regularly gridded, data. The scheme derives from a Bayesian predictor-corrector scheme used in data assimilation and is based on the Kalman Filter estimation. It has been applied to 15-minutes temporal resolution Spinning Enhanced Visible and Infrared Imager (SEVIRI) emissivity and temperature products and to Infrared Atmospheric Sounding Interferometer (IASI) atmospheric ammonia (NH3) retrievals, a gas affecting the air quality. Results have been exemplified for target areas over Italy. In particular, temperature retrievals have been compared with gridded data from MODIS (Moderate-resolution Imaging Spectroradiometer) observations. Our findings show that the proposed strategy is quite effective to ll gaps because of data voids due, e.g., to clouds, gains more efficiency in capturing the daily cycle for surface parameters and provides valuable information on NH3 concentration and variability in regions not yet covered by ground-based instruments.

Dimensionality reduction is a hot research topic in data analysis today.Thanks to the advances in high-performance computing technologies andin the engineering eld, we entered in the so-called big-data era and an enormous quantity of data is available in every scientificc area, rangingfrom social networking, economy and politics to e-health and life sciences.However, much of the data is highly redundant and can be efficientlybrought down to a much smaller number of variables without a significantloss of information using didifferent strategies.

The morphology of a mixture made of a polar active gel immersed in an isotropic passive fluid is studied numerically. Lattice Boltzmann method is adopted to solve the Navier-Stokes equation and coupled to a finite-difference scheme used to integrate the dynamic equations of the concentration and of the polarization of the active component. By varying the relative amounts of the mixture phases, different structures can be observed. In the contractile case, at moderate values of activity, elongated structures are formed when the active component is less abundant, while a dynamic emulsion of passive droplets in an active matrix is obtained for symmetric composition. When the active component is extensile, aster-like rotating droplets and a phase-separated pattern appear for asymmetric and symmetric mixtures, respectively. The relevance of space dimensions in the overall morphology is shown by studying the system in three dimensions in the case of extensile asymmetric mixtures where interconnected tube-like structures span the whole system.

We present here a comparison between collision-streaming and finite-difference lattice Boltzmann (LB) models. This study provides a derivation of useful formulae which help one to properly compare the simulation results obtained with both LB models. We consider three physical problems: the shock wave propagation, the damping of shear waves, and the decay of Taylor-Green vortices, often used as benchmark tests. Despite the different mathematical and computational complexity of the two methods, we show how the physical results can be related to obtain relevant quantities.

We investigate nonlinear elliptic Dirichlet problems whose growth is driven by a general anisotropic N-function, which is not necessarily of power-type and need not satisfy the ? nor the ? -condition. Fully anisotropic, non-reflexive Orlicz-Sobolev spaces provide a natural functional framework associated with these problems. Minimal integrability assumptions are detected on the datum on the right-hand side of the equation ensuring existence and uniqueness of weak solutions. When merely integrable, or even measure, data are allowed, existence of suitably further generalized solutions--in the approximable sense--is established. Their maximal regularity in Marcinkiewicz-type spaces is exhibited as well. Uniqueness of approximable solutions is also proved in case of L-data.

This review discusses the lattice Boltzmann-particle dynamics (LBPD) multiscale paradigm for the simulation of complex states of flowing matter at the interface between physics, chemistry, and biology. In particular, current large-scale LBPD simulations of biopolymer translocation across cellular membranes, molecular transport in ion channels, and amyloid aggregation in cells are described. Prospects are provided for future LBPD explorations in the direction of cellular organization, the direct simulation of full biological organelles, all the way up to physiological scales of potential relevance to future precision-medicine applications, such as the accurate description of homeostatic processes. It is argued that. with the advent of Exascale computing, the mesoscale physics approach advocated in this review may come to age in the next decade and open up new exciting perspectives for physics-based computational medicine.

Process mining is the set of techniques to retrieve a process model starting from available logging data. The discovered process model has to be analyzed to verify whether it respects the defined properties, i.e., the so-called compliance checking. Our aim is to use a model checking based approach to verify compliance. First, we propose an integrated-tool approach using existing tools as ProM (a framework supporting process mining techniques) and CADP (a formal verification environment). More precisely, the execution traces from a software system are extracted. Then, using the "Mine Transition System" plugin in ProM, we obtain a labelled transition system, that can be easily used to verify formal properties through CADP. However, this choice presents the "state explosion" problem, i.e., models discovered through the classical process mining techniques tend to be large and complex. In order to solve this problem, another custom-made approach is shown, which accomplishes a pre-processing on the traces to obtain abstract traces, where abstraction is based on the set of temporal logic formulae specifying the system properties. Then, from the set of abstracted traces, we discover a system described in Lotos, a process algebra specification language; in this way we do not build an operational model for the system, but we produce only a language description from which a model checking environment will automatically obtain the reduced corresponding transition system. Real systems have been used as case studies to evaluate the proposed methodologies.

Measures and methods used in financial sector to quantify risk, have been recently applied to cyber world. The aim is to help organizations to improve risk management strategies and to wisely plan investments in cyber security. On the other hand, they are useful instruments for insurance companies in pricing cyber insurance contracts and setting the minimum capital requirements defined by the regulators. In this paper we propose an estimation of Value at Risk (VaR), referred to as Cyber Value at Risk in cyber security domain, and Tail Value at risk (TVaR). The data breach information we use is obtained from the 'Chronology of data breaches' compiled by the Privacy Rights Clearinghouse.

In the last decades companies worldwide are facing a new kind of risk, namely cyber risk, that has emerged as one of the top challenges in risk management. Insurance was only recently applied to cyber world and it is increasingly becoming part of the risk management process, posing many challenges to actuaries. One of the main issues is the lack of data, in particular nancial ones. The aim of the paper is to point out the peculiarities of cyber insurance contracts with respect to the classical non life insurance ones both from the insurer and the insured's perspective. Therefore, the main actuarial principles that are fundamental to any valu- ation in cyber context are discussed. An illustrative example is proposed where the Chronology of Data Breaches provided by the Privacy Rights Clearing House is deeply analyzed. The most suitable distributions to represent the frequency and the severity of the reported cyber incidents are examined and the value at risk measure is estimated. Then, two ex- emplifying cases oer the assessment of both the premium required by the insurer and the indierence premium that the insured is willing to pay. Even though this research is still preliminary and shows some limits highlighted by the authors, it could offer useful information to better un- derstand this peculiar kind of insurance policies.

Cloud detection is a critical issue for satellite optical remote sensing, since potential errors in cloud masking can be translated directly into significant uncertainty in the retrieved downstream geophysical products. The problem is particularly challenging when only of a limited number of spectral bands is available, and thermal infrared bands are lacking. This is the case of Proba-V instrument, for which the European Space Agency (ESA) carried out a dedicated Round Robin exercise, aimed at intercomparing several cloud detection algorithms to better understand their advantages and drawbacks for various clouds and surface conditions, and to learn lessons on cloud detection in the VNIR and SWIR domain for land and coastal water remote sensing. The present contribution is aimed at a thorough quality assessment of the results of the cloud detection approach we proposed, based on Cumulative Discriminant Analysis. Such a statistical method relies on the empirical cumulative distribution function of the measured reflectance in clear and cloudy conditions to produce a decision rule. It can be adapted to the user's requirements in terms of preferred levels for both type I and type II errors. In order to obtain a fully automatic procedure, we choose as a training dataset a subset of the full Proba-V scenes for which a cloud mask is estimated by a consolidated algorithm (silver standard), that is from either SEVIRI, MODIS or both sensors. Within this training set, different subsets have been setup according to the different types of surface underlying scenes (water, vegetation, bare land, urban, and snow/ice). We present the analysis of the cloud classification errors for a range of such test scenes to yield important inferences on the efficiency and accuracy of the proposed methodology when applied to different types of surfaces.

Background. Type 2 diabetes (T2D) is a chronic metabolic disease potentially leading to serious widespread tissue damage. Human organism develops T2D when the glucose-insulin control is broken for reasons that are not fully understood but have been demonstrated to be linked to the emergence of a chronic inflammation. Indeed such low-level chronic inflammation affects the pancreatic production of insulin and triggers the development of insulin resistance, eventually leading to an impaired control of the blood glucose concentration. On the contrary, it is well-known that obesity and inflammation are strongly correlated. Aim. In this study, we investigate in silico the effect of overfeeding on the adipose tissue and the consequent set up of an inflammatory state. We model the emergence of the inflammation as the result of adipose mass increase which, in turn, is a direct consequence of a prolonged excess of high calorie intake. Results. The model reproduces the fat accumulation due to excessive caloric intake observed in two clinical studies. Moreover, while showing consistent weight gains over long periods of time, it reveals a drift of the macrophage population toward the proinflammatory phenotype, thus confirming its association with fatness.

Sex dimorphism in cell response to stress has previously been investigated by different research groups. This dimorphism could be at least in part accounted for by sex-biased expression of regulatory elements such as microRNAs (miRs). In order to spot previously unknown miR expression differences we took advantage of prior knowledge on specialized databases to identify X chromosome-encoded miRs potentially escaping X chromosome inactivation (XCI). MiR-548am-5p emerged as potentially XCI escaper and was experimentally verified to be significantly up-regulated in human XX primary dermal fibroblasts (DFs) compared to XY ones. Accordingly, miR-548am-5p target mRNAs, e.g. the transcript for Bax, was differently modulated in XX and XY DFs. Functional analyses indicated that XY DFs were more prone to mitochondria-mediated apoptosis than XX ones. Experimentally induced overexpression of miR548am-5p in XY cells by lentivirus vector transduction decreased apoptosis susceptibility, whereas its down-regulation in XX cells enhanced apoptosis susceptibility. These data indicate that this approach could be used to identify previously unreported sex-biased differences in miR expression and that a miR identified with this approach, miR548am-5p, can account for sex-dependent differences observed in the susceptibility to mitochondrial apoptosis of human DFs.