Breast cancer is one of the most common invasive tumors causing high mortality among women. It is characterized by high heterogeneity regarding its biological and clinical characteristics. Several high-throughput assays have been used to collect genome-wide information for many patients in large collaborative studies. This knowledge has improved our understanding of its biology and led to new methods of diagnosing and treating the disease. In particular, system biology has become a valid approach to obtain better insights into breast cancer biological mechanisms. A crucial component of current research lies in identifying novel biomarkers that can be predictive for breast cancer patient prognosis on the basis of the molecular signature of the tumor sample. However, the high dimension and low sample size of data greatly increase the difficulty of cancer survival analysis demanding for the development of ad-hoc statistical methods. In this work, we propose novel screening-network methods that predict patient survival outcome by screening key survival-related genes and we assess the capability of the proposed approaches using METABRIC dataset. In particular, we first identify a subset of genes by using variable screening techniques on gene expression data. Then, we perform Cox regression analysis by incorporating network information associated with the selected subset of genes. The novelty of this work consists in the improved prediction of survival responses due to the different types of screenings (i.e., a biomedical-driven, data-driven and a combination of the two) before building the network-penalized model. Indeed, the combination of the two screening approaches allows us to use the available biological knowledge on breast cancer and complement it with additional information emerging from the data used for the analysis. Moreover, we also illustrate how to extend the proposed approaches to integrate an additional omic layer, such as copy number aberrations, and we show that such strategies can further improve our prediction capabilities. In conclusion, our approaches allow to discriminate patients in high-and low-risk groups using few potential biomarkers and therefore, can help clinicians to provide more precise prognoses and to facilitate the subsequent clinical management of patients at risk of disease.

Hypothesis testing is a statistical decisional process that allows one to choose between two complementary possibilities on the basis of samples drawn from the population(s) of interest. The two possibilities are called the null and alternative hypothesis, respectively. For each decision, two types of errors might occur, i.e., rejecting the null hypothesis when it is true (Type I error) or accepting the null hypothesis when it is false (Type II error). The decision is taken by compromising the two error types. When multiple hypotheses are compared one also has to define and control the overall decisional error.

Ever more organizations, both private and public, are placing a greater importance on employee engagement as a means of generating better organizational climate and higher levels of performance. Actually, employee engagement is part of the strategic management of high performance organization, which pay always more attention to human resource initiatives. Moreover, forms of involvement in the decision processes make more motivating and more satisfying the activity for employees, as they create the conditions for greater inspiration and, in turn, contribute to their well-being. Besides, several studies show that when employees believe they have opportunities for voice in decision-making, such awarenesscanpositively affectthe organisational commitment.Based on the foregoing premise, this study proposes a new "employee voice framework"for stimulating employee voice andemployee participation in strategic decision-making. The first step of the framework prescribes to organizea number of "World Cafè"events dedicated toa specific subject of the strategic decision-making. The World Cafèmethodis a structured conversational process for knowledge sharing in which an informal climate allows groups of voluntary participants (in this case employees) to discuss a specific topic, enhancing creativity and cross-pollination of ideas. In the second step, the proposals emerged from the World Cafèevents are included in a questionnaire to be submitted to all employeesthat should be involved in the decision-making process. Each proposal is evaluated on the basis oftwo variables: "importance" and "feasibility". The top-management has to answer the same questions to which employees respond. The third step of the frameworkprescribesthe creation of "importance/feasibility matrices"that allowscomparing employee and top-management viewpoints on the proposals. The matrices offer an opportunity for employees and managers to exchange views. Therefore, the matrices give insightinto which proposals should be implementedas they result the most important for employees but also feasible for the top-management.The paper concludes with a real case study application to the Italian National Research Council (CNR), the largest research organization in Italy. The application of the "employee voice framework"involved all CNR employees and concluded with the formulation of various proposals for the design of a new performance evaluation and incentive system.

The knowledge production provided by universities is essential to sustaining a country's long-term economic growth and international competitiveness. Many nations are thus driving to create sustainable and effective funding environments. The evaluation of university knowledge, productivity and research quality becomes critical, with ever increasing share of public funding allocated on the basis of research assessment exercises. Nevertheless, the existing methods to assess the universities' knowledge production are often affected by limits and biases, extensively discussed in the scientific literature. In this paper we study how to reduce the effect of size-related bias due to university size on the indicators of knowledge performance used in evaluation exercises. We propose an innovative utilization of the scale-free property of the power laws as a scaling relationship, to normalize research productivity indicators, and provide results independent by the university size. Our method has evident policy implications and gives a contribution for the future design of assessment exercises. We apply our findings in a recent Italian research assessment exercise.

E-commerce is a sector in continual growth in all countries and, in particular, the increase in B2C (Business to Consumer) e-commerce market has important effects on last-mile deliveries in city areas. The delivery of a parcel to a consumer's address involves not only high costs for both couriers (extended car routes) and consumers (high prices) and also greater environmental pollution. The growing demand for deliveries in urban areas involves increases in traffic and congestion problems and, consequently, environmental issues. In recent years, many studies have focused on alternative measures to reduce the negative aspects and impact of last-mile deliveries. Good practice to rationalize last-mile delivery should involve the use of various systems, such as reception boxes, delivery boxes, controlled access systems, collection points and lockers. This paper compares two alternative options to home delivery. In particular, it makes comparisons between point-to-point and lockers, states the pro and cons of both, and defines the best positions to locate lockers to reduce consumers' deviations. The proposed method is applied to a real case: the Italian municipality of Dolo (near Venice).

Linear regression models a dependent variable Y in terms of a linear combination of p independent variables X=[X1|...|Xp] and estimates the coefficients of the combination using independent observations (x_i,Y_i ),i=1,...,n. The Gauss-Markov conditions guarantees that the least squares estimate of the regression coefficients constitutes the best linear estimator. Under the assumption of white noise, it is possible to test the significance of each regression coefficient, evaluate the uncertainty/goodness of fit, and use the fitted model for predicting novel outcomes. When p>n, classical linear regression cannot be applied, and penalized approaches such as ridge regression, lasso or elastic net have to be used.

Network flows and specifically water flow in open canals can be modeled by systems of balance laws defined on graphs. The shallow water or Saint-Venant system of balance laws is one of the most used model and present two phases: fluvial or sub-critical and torrential or super-critical. Phase transitions may occur within the same canal but transitions related to networks are less investigated. In this paper we provide a complete characterization of possible phase transitions for a case study of a simple scenario with two canals and one junction. However, our analysis allows the study of more complicate networks. Moreover, we provide some numerical simulations to show the theory at work.

A growing amount of evidences indicates that inflammaging - the chronic, low grade inflammation state characteristic of the elderly - is the result of genetic as well as environmental or stochastic factors. Some of these, such as the accumulation of senescent cells that are persistent during aging or accompany its progression, seem to be sufficient to initiate the aging process and to fuel it. Others, like exposure to environmental compounds or infections, are temporary and resolve within a (relatively) short time. In both cases, however, a cellular memory of the event can be established by means of epigenetic modulation of the genome. In this review we will specifically discuss the relationship between epigenetics and inflammaging. In particular, we will show how age-associated epigenetic modifications concerned with heterochromatin loss and gene-specific remodelling, can promote inflammaging. Furthermore, we will recall how the exposure to specific nutritional, environmental and microbial stimuli can affect the rate of inflammaging through epigenetic mechanisms, touching also on the recent insight given by the concept of trained immunity.

The generalized Schur algorithm is a powerful tool allowing to compute classical decompositions of matrices, such as the QR and LU factorizations. When applied to matrices with particular structures, the generalized Schur algorithm computes these factorizations with a complexity of one order of magnitude less than that of classical algorithms based on Householder or elementary transformations. In this manuscript, we describe the main features of the generalized Schur algorithm. We show that it helps to prove some theoretical properties of the R factor of the QR factorization of some structured matrices, such as symmetric positive definite Toeplitz and Sylvester matrices, that can hardly be proven using classical linear algebra tools. Moreover, we propose a fast implementation of the generalized Schur algorithm for computing the rank of Sylvester matrices, arising in a number of applications. Finally, we propose a generalized Schur based algorithm for computing the null-space of polynomial matrices.

Many studies have shown that Physarum polycephalum slime mold is able to find the shortest path in a maze. In this paper we study this behavior in a network, using a hyperbolic model of chemotaxis. Suitable transmission and boundary conditions at each node are considered to mimic the behavior of such an organism in the feeding process. Several numerical tests are presented for special network geometries to show the qualitative agreement between our model and the observed behavior of the mold.

Kinetic BGK numerical schemes for the approximation of incompressible Navier-Stokes equations are derived via classical discrete velocity vector BGK approximations, but applied to an inviscid compressible gas dynamics system with small Mach number parameter, according to the approach of Carfora and Natalini (2008). As the Mach number, the grid size and the timestep tend to zero, the low Mach number limit and the time-space convergence of the scheme are achieved simultaneously, and the numerical viscosity tends to the physical viscosity of the Navier-Stokes system. The method is analyzed and formulated as an explicit finite volume/difference flux vector splitting (FVS) scheme over a Cartesian mesh. It is close in spirit to lattice Boltzmann schemes, but it has several advantages. The first is that the scheme is expressed only in terms of momentum and mass compressible variables. It is therefore very easy to implement, and several types of boundary conditions are straightforward to apply. The second advantage is that the scheme satisfies a discrete entropy inequality, under a CFL condition of parabolic type and a subcharacteristic stability condition involving a cell Reynolds number that ensures that diffusion dominates advection at the level of the grid size. This ensures the robustness of the method, with explicit uniform bounds on the approximate solution. Moreover the scheme is proved to be second-order accurate in space if the parameters are well chosen, this is the case in particular for the Lax-Friedrichs scheme with Mach number proportional to the grid size. The scheme falls then into the class of artificial compressibility methods, the novelty being its exceptionally good theoretical properties. We show the efficiency of the method in terms of accuracy and robustness on a variety of classical two-dimensional benchmark tests. The method is finally applied in three dimensions to compute the permeability of a porous medium defined by a complex idealized Kelvin-like cell. Relations between our scheme and compressible low Mach number schemes are discussed.

Chapter in book "High Performance Computing in Science and Engineering ' 17"

As discussed in the recent literature, several innovative car insurance concepts are proposed in order to gain advantages both for insurance companies and for drivers. In this context, the "pay-how-you-drive" paradigm is emerging, but it is not thoroughly discussed and much less implemented. In this paper, we propose an approach in order to identify the driver behavior exploring the usage of unsupervised machine learning techniques. A real-world case study is performed to evaluate the effectiveness of the proposed solution. Furthermore, we discuss how the proposed model can be adopted as risk indicator for car insurance companies.

In the last years, several safety automotive concepts have been proposed, for instance the cruise control and the automatic brakes systems. The proposed systems are able to take the control of the vehicle when a dangerous situation is detected. Less effort was produced in driver aggressiveness in order to mitigate the dangerous situation. In this paper we propose an approach in order to identify the driver aggressiveness exploring the usage of unsupervised machine learning techniques. A real world case study is performed to evaluate the effectiveness of the proposed method.

Several techniques have been developed in last years by automotive industry in order to protect drivers and car passengers. These methods, for instance the automatic brake systems and the cruise control, are able to intervene when there is a dangerous situation. With the aim to minimize these risks, in this paper we propose a method able to suggest to the driver the driving style to adopt in order to avoid dangerous situations. Our method is basically a two-level fuzzy systems: the first one is related to the driver under analysis, while the second one is a centralized server with the responsibility to send suggestions to drivers in order to prevent traffic incidents. We carried out a preliminary evaluation to demonstrate the effectiveness of the proposed method: we obtain of percentage variation ranging from 85.48% to 88.99% in the number of traffic incidents between the scenarios we considered using the proposed method and the scenario without the proposed method applied.

A specific kind of insurance that is emerging within the domain of cyber-systems is that of cyber-insurance. Cyber-insurance is the transfer of financial risk associated with network and computer incidents to a third party. Insurance companies are increasingly offering such policies, in particular in the USA, but also in Europe. The emerging trends in cyber insurance raise a number of unique challenges and force actuaries to reconsider how to think about underwriting, pricing and aggregation risk. Aim of this contribution is to offer a review of the recent literature on cyber risk management in the actuarial field. Moreover, basing on the most significant results in IT domain, we outline possible synergies between the two lines of research.

We develop a modelling approach for the optimal spatiotemporal control of invasive species in natural protected areas of high conservation value. The proposed approach, based on diusion equations, is spatially explicit, and includes a functional response (Holling type II) which models the control rate as a function of the invasive species density. We apply a budget constraint to the control program and search for the optimal eort allocation for the minimization of the invasive species density. Both the initial density map and the land cover map used to estimate the habitat suitability to the species diusion, have been generated by using very high resolution satellite images and validated by means of ground truth data. The approach has been applied to the Alta Murgia National Park, one of the study site of the on-going H2020 project ECOPOTENTIAL: Improving Future Ecosystem Benets Through Earth Observations' (http://www.ecopotential-project.eu) which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 641762. All the ground data regarding Ailanthus altissima (Mill.) Swingle presence and distribution are from the EU LIFE Alta Murgia Project (LIFE12 BIO/IT/000213) titled Eradication of the invasive exotic plant species Ailanthus altissima from the Alta Murgia National Park funded by the LIFE+ nancial instrument of the European Commission.

We analyze the stability of convolution quadrature methods for weakly singular Volterra integral equations with respect to a linear test equation. We prove that the asymptotic behavior of the numerical solution replicates the one of the continuous problem under some restriction on the stepsize. Numerical examples illustrate the theoretical results.