In recent papers, new sets of Sheffer and Brenke polynomials based on higher order Bell numbers have been studied, and several integer sequences related to them have been introduced. In the article other types of Sheffer polynomials are considered, by introducing two sets of Euler-type polynomials.

In this review we analyse the impact of age-associated factors such as inflammaging, immunosenescence and immunobiography on immune response to vaccination in the elderly, and we consider systems biology approaches as a mean for integrating a multitude of data in order to rationally design vaccination approaches specifically tailored for the elderly. The unprecedented increase of life expectancy challenges society to protect the elderly from morbidity and mortality making vaccination a crucial mean to safeguard this population. Indeed, infectious diseases, such as influenza and pneumonia, are among the top killers of elderly people in the world. Elderly individuals are more prone to severe infections and less responsive to vaccination prevention, due to immunosenescence combined with the progressive increase of a proinflammatory status characteristic of the aging process (inflammaging). These factors are responsible for most age-related diseases and correlate with poor response to vaccination. Therefore, it is of utmost interest to deepen the knowledge regarding the role of inflammaging in vaccination responsiveness to support the development of effective vaccination strategies designed for elderly.

The dynamics of deformable liquid-filled bodies (e.g., droplets, capsules, lipid vesicles) suspended in a fluid flow is a fascinating fundamental problem with increasing relevance for technological applications, for example, in drug delivery, or designing lab-on-chip devices. In the present chapter we review two main families of lattice Boltzmann models for multicomponent flows, their mechanical properties, and transport phenomena, with special focus on their application to biofluidic problems, such as the dynamics, merging, and breakup of microfluidic droplets and the motion of deformable membranes and vesicles under geometrical confinement.

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.

Despite the development of new technologies, in order to prevent the stealing of cars, the number of car thefts is sharply increasing. With the advent of electronics, new ways to steal cars were found. To avoid auto-theft attacks, in this paper we propose a machine leaning based method to silently e continuously profile the driver by analyzing built-in vehicle sensors. We evaluate the efficiency of the proposed method in driver identification using 10 different drivers. Results are promising, as a matter of fact we obtain a high precision and a recall evaluating a dataset containing data extracted from real vehicle.

Macrophages derived from monocyte precursors undergo specific polarization processes being influenced by the local tissue environment: classically-activated (M1) macrophages, showing a pro-inflammatory activity affecting effector cells in Th1 cellular immune responses; and alternatively-activated (M2) macrophages, with anti-inflammatory functions, involved in immunosuppression and tissue repair. At least three distinctive subsets of M2 macrophages, i.e. M2a, M2b and M2c, are characterized in the literature based on their eliciting molecular signals. The triggering and polarization of macrophages is attained through numerous, interweaved signaling pathways. To depict the logical relations among the genes involved in macrophage polarization, we utilized a computational modeling methodology, viz. Boolean modeling of gene regulation. We combined experimental data/knowledge from the literature to build a logical gene regulation network model driving macrophage polarization to M1, M2a, M2b and M2c phenotypes. Exploiting the GINsim software we studied the network dynamics under different settings and perturbations to comprehend how they affect cell polarization. Simulations of the network model, enacting the most significant biological conditions, showed consistency with the experimentally observed behaviour of in vivo macrophages. The model could properly replicate the polarization toward the four main phenotypes as well as to numerous hybrid phenotypes, known to be experimentally associated to physiological and pathological conditions. We speculate that shifts among different phenotypes in our model mimic the hypothetical continuum of macrophage polarization, with M1 and M2 being the poles of a continuous succession of states. Our simulations also suggest that anti-inflammatory macrophages are more resilient to shift to the pro-inflammatory phenotype.

The amount of traffic data collected by automatic number plate reading systems constantly incrseases. It is therefore important, for law enforcement agencies, to find convenient techniques and tools to analyze such data. In this paper we propose a scalable and fully automated procedure leveraging the Apache Accumulo technology that allows an effective importing and processing of traffic data. We discuss preliminary results obtained by using our application for the analysis of a dataset containing real traffic data provided by the Italian National Police. We believe the results described here can pave the way to further interesting research on the matter.

This paper describes the efforts, pitfalls, and successes of applying unsupervised classification techniques to analyze the Trap-2017 dataset. Guided by the informative perspective on the nature of the dataset obtained through a set of specifically-written perl/bash scripts, we devised an automated clustering tool implemented in python upon openly-available scientific libraries. By applying our tool on the original raw data it is possibile to infer a set of trending behaviors for vehicles travelling over a route, yielding an instrument to classify both routes and plates. Our results show that addressing the main goal of the Trap-2017 initiative (``to identify itineraries that could imply a criminal intent'') is feasible even in the presence of an unlabelled and noisy dataset, provided that the unique characteristics of the problem are carefully considered. Albeit several optimizations for the tool are still under investigation, we believe that it may already pave the way to further research on the extraction of high-level travelling behaviors from gates transit records.

A novel approach for extracting gauge-invariant information about spin-orbit coupling in gravitationally interacting binary systems is introduced. This approach is based on the "scattering holonomy", i.e. the integration (from the infinite past to the infinite future) of the differential spin evolution along the two worldlines of a binary system in hyperboliclike motion. We apply this approach to the computation, at the first post-Minkowskian approximation (i.e. first order in G and all orders in v/c), of the values of the two gyrogravitomagnetic ratios describing spin-orbit coupling in the effective one-body formalism. These gyrogravitomagnetic ratios are found to tend to zero in the ultrarelativistic limit.

In this paper, we consider the isoperimetric problem in the space R with a density. Our result states that, if the density f is lower semi-continuous and converges to a limit a> 0 at infinity, with f<= a far from the origin, then isoperimetric sets exist for all volumes. Several known results or counterexamples show that the present result is essentially sharp. The special case of our result for radial and increasing densities positively answers a conjecture of Morgan and Pratelli (Ann Glob Anal Geom 43(4):331-365, 2013.

We describe the basic properties and consequences of introducing active stresses, with principal direction along the local director, in cholesteric liquid crystals. The helical ground state is found to be linearly unstable to extensile stresses, without threshold in the limit of infinite system size, whereas contractile stresses are hydrodynamically screened by the cholesteric elasticity to give a finite threshold. This is confirmed numerically and the non-linear consequences of instability, in both extensile and contractile cases, are studied. We also consider the stresses associated to defects in the cholesteric pitch (? lines) and show how the geometry near to the defect generates threshold-less flows reminiscent of those for defects in active nematics. At large extensile activity ? lines are spontaneously created and can form steady-state patterns sustained by constant active flows.

In this work we numerically study the switching dynamics of a 2D cholesteric emulsion droplet immersed in an isotropic fluid under an electric field, which is either uniform or rotating with constant speed. The overall dynamics depend strongly on the magnitude and on the direction (with respect to the cholesteric axis) of the applied field, on the anchoring of the director at the droplet surface and on the elasticity. If the surface anchoring is homeotropic and a uniform field is parallel to the cholesteric axis, the director undergoes deep elastic deformations and the droplet typically gets stuck into metastable states which are rich in topological defects. When the surface anchoring is tangential, the effects due to the electric field are overall less dramatic, as a small number of topological defects form at equilibrium. The application of the field perpendicular to the cholesteric axis usually has negligible effects on the defect dynamics. The presence of a rotating electric field of varying frequency fosters the rotation of the defects and of the droplet as well, typically at a lower speed than that of the field, due to the inertia of the liquid crystal. If the surface anchoring is homeotropic, a periodic motion is found. Our results represent a first step to understand the dynamical response of a cholesteric droplet under an electric field and its possible application in designing novel liquid crystal-based devices.

La proprietà di conservazione della positività dei metodi numerici applicati ai sistemi differenziali di tipo ODE e PDE a valori iniziali e/o ai bordi, è un argomento di ricerca di notevole interesse. La positività del flusso numerico è un aspetto fondamentale in numerose applicazioni che vanno dalla biologia computazionale, alla dinamica molecolare, alla modellistica in ambito ecologico, dovunque risulti fondamentale che le grandezze in gioco (popolazioni, densità, concentrazioni) non assumano valori negativi. Tale condizione, generalmente, non è verificata dai metodi standard (Runge-Kutta o multistep), a meno di imporre restrizioni sul passo di integrazione talvolta molto significative. Anche nell'ambito della integrazione geometrica, le proprietà di conservazione di cui godono i flussi numerici, quali ad esempio l'energia del sistema e la simpletticità, non garantiscono automaticamente la positività delle soluzioni. In [5] vengono individuate, usando anche le tecniche di backward analysis, le condizioni che garantiscono la positività del metodo di Eulero simlettico e della sua variante esplicita, quando applicati all'equazione di Lotka-Volterra. Tuttavia, le restrizioni sul passo di integrazione diminuiscono sensibilmente l'efficienza dei metodi numerici a tal punto da renderli di fatto inutilizzabili nelle applicazioni reali. La letteratura più recente si è quindi focalizzata sulla costruzione di integratori numerici che garantiscono la positività del flusso numerico per costruzione. Tra i lavori su questo argomento, citiamo [8, 6] in cui vengono proposte tecniche di splitting and composition per la soluzione di modelli differenziali. Gli autori in [8] si preoccupano di dimostrare la positività di uno schema del secondo ordine applicato ad un generico problema parabolico semilineare in uno spazio di Banach. Poichè la positività delle tecniche di splitting è garantita da semiflussi numerici positivi, in [6] gli autori propongono una procedura di splitting applicata al sistema dinamico trasformato mediante una trasformazione logaritmica. Infine, nel più ampio ambito dell'integrazione non standard, possiamo trovare in letteratura integratori simplettici e positivi in grado di preservare anche la stabilità locale della soluzione. Si vedano in particolare il metodo di Mickens ed i metodi di Mounim applicati al sistema Lotka-Volterra [11]. Un ulteriore approccio alla conservazione de facto della positività, si può avvalere del calcolo non Newtoniano. Negli anni '70-'80 Michael Grossman e Robert Katz hanno introdotto metodi di calcolo basati sulla generalizzazione delle operazioni di derivazione e integrazione. A seconda della scelta di opportuni parametri si possono costruire infinite varianti di calcolo non Newtoniano: tra queste, l'approccio basato sugli operatori di derivata e integrale di tipo moltiplicativo è alla base del calcolo moltiplicativo. Tale strumento, è stato riscoperto e utilizzato negli ultimi anni in diversi campi delle scienze applicate (si veda ad esempio [7]), per via della sua caratteristica di preservare, per costruzione, la positività. Alcuni autori si sono già preoccupati di generalizzare la classe dei metodi Runge-Kutta nell'ambito del calcolo moltiplicativo [1] o, più in generale, non Newtoniano [9]. Rimane tuttavia ancora inesplorata la potenzialità del calcolo non Newtoniano nella derivazione e nell'analisi di metodi numerici simplettici e positivi.

Unattended Wireless Sensor Networks (UWSNs), characterized by the intermittent presence of the sink, are exposed to attacks aiming at tampering with the sensors and the data they store. In order to prevent an adversary from erasing any sensed data before the sink collects them, it is common practice to rely on data replication. However, identifying the most suitable replication rate is challenging: data should be redundant enough to avoid data loss, but not so much as to pose an excessive burden on the limited resources of the sensors. As noted before in the literature, this problem is similar to finding the minimum infection rate that makes a disease endemic in a population. Yet, unlike previous attempts to leverage on this parallelism, we argue that model and system parameters must be carefully bound according to conservative and realistic assumptions on the behavior of the network, further taking into account possible statistical fluctuations. In this paper, we therefore refine the connection between the Susceptible, Infected, Susceptible (SIS) epidemic model and the survivability of sensed data in UWSNs. In particular, based on probabilistic data replication and deletion rates, we identify proper conditions to guarantee that sensed information become endemic. In both the full visibility model (i.e. unlimited transmission range) and the geometric one (i.e. limited transmission range), the proposed approach achieves: (i) data survivability, (ii) optimal usage of sensors resources, and (iii) fast collecting time. Building on advanced probabilistic tools, we provide theoretically sound results, that are further supported by an extensive experimental campaign performed on synthetically generated networks. Obtained results show the quality of our model and viability of the proposed solution.

The cube attack is a flexible cryptanalysis technique, with a simple and fascinating theoretical implant. It combines offline exhaustive searches over selected tweakable public/IV bits (the sides of the "cube"), with an online key-recovery phase. Although virtually applicable to any cipher, and generally praised by the research community, the real potential of the attack is still in question, and no implementation so far succeeded in breaking a real-world strong cipher. In this paper, we present, validate and analyze the first thorough implementation of the cube attack on a GPU cluster. The framework is conceived so as to be usable out-of-the-box for any cipher featuring up to 128-bit key and IV, and easily adaptable to larger key/IV, at just the cost of some fine (performance) tuning, mostly related to memory allocation. As a test case, we consider previous state-of-the-art results against a reduced-round version of a well-known cipher (Trivium). We evaluate the computational speedup with respect to a CPU-parallel benchmark, the performance dependence on system parameters and GPU architectures (Nvidia Kepler vs Nvidia Pascal), and the scalability of our solution on multi-GPU systems. All design choices are carefully described, and their respective advantages and drawbacks are discussed. By exhibiting the benefits of a complete GPU-tailored implementation of the cube attack, we provide novel and strong elements in support of the general feasibility of the attack, thus paving the way for future work in the area.

Motivated by the upcoming Internet of Things, designing light-weight authentication protocols for resource constrained devices is among the main research directions of the last decade. Current solutions in the literature attempt either to improve the computational efficiency of cryptographic authentication schemes, or to build a provably-secure scheme relying on the hardness of a specific mathematical problem. In line with the principles of information-theoretic security, in this paper we present a novel challenge-response protocol, named SLAP, whose authentication tokens only leak limited information about the secret key, while being very efficient to be generated. We do support our proposal with formal combinatorial arguments, further sustained by numeric evaluations, that clarify the impact of system parameters on the security of the protocol, yielding evidence that SLAP allows performing a reasonable number of secure authentication rounds with the same secret key.

In this work, we face a variant of the capacitated lot sizing problem. This is a classical problem addressing the issue of aggregating lot sizes for a finite number of discrete periodic demands that need to be satisfied, thus setting up production resources and eventually creating inventories, while minimizing the overall cost. In the proposed variant we take into account lifetime constraints, which model products with maximum fixed shelflives due to several possible reasons, including regulations or technical obsolescence. We propose four formulations, derived from the literature on the classical version of the problem and adapted to the proposed variant. An extensive experimental phase on two datasets from the literature is used to test and compare the performance of the proposed formulations.

We face the problem of scheduling optimally the activities in a wireless sensor network in order to ensure that, in each instant of time, the activated sensors can monitor all points of interest (targets) and route the collected information to a processing facility. Each sensor is allocated to a role, depending on whether it is actually used to monitor the targets, to forward information or kept idle, leading to different battery consumption ratios. We propose a column generation algorithm that embeds a highly efficient genetic metaheuristic for the subproblem. Moreover, to optimally solve the subproblem, we introduce a new formulation with fewer integer variables than a previous one proposed in the literature. Finally, we propose a stopping criterion to interrupt the optimal resolution of the subproblem as soon as a favorable solution is found. The results of our computational tests show that our algorithm consistently outperforms previous approaches in the literature, and also improves the best results known to date on some benchmark instances.

The aim of the Connected Maximum Lifetime Problem is to define a schedule for the activation intervals of the sensors deployed inside a region of interest, such that at all times the activated sensors can monitor a set of interesting target locations and route the collected information to a central base station, while maximizing the total amount of time over which the sensor network can be operational. Complete or partial coverage of the targets are taken into account. To optimally solve the problem, we propose a column generation approach which makes use of an appropriately designed genetic algorithm to overcome the difficulty of solving the subproblem to optimality in each iteration. Moreover, we also devise a heuristic by stopping the column generation procedure as soon as the columns found by the genetic algorithm do not improve the incumbent solution. Comparisons with previous approaches proposed in the literature show our algorithms to be highly competitive, both in terms of solution quality and computational time.

We aim to maximize the operational time of a network of sensors, which are used to monitor a predefined set of target locations. The classical approach proposed in the literature consists in individuating subsets of sensors (covers) that can individually monitor the targets, and in assigning appropriate activation times to each cover. Indeed, since sensors may belong to multiple covers, it is important to make sure that their overall battery capacities are not violated. We consider additional constraints that prohibit certain sensors to appear in the same cover, since they would interfere with each other. We propose a Column Generation approach, in which the pricing subproblem is solved either exactly or heuristically by means of a recently introduced technique to enhance basic greedy algorithms, known as Carousel Greedy. Our experiments show the effectiveness of this approach.