A DISTANCE MEASURE FOR CLASSIFYING ARIMA MODELS

Abstract. In a number of practical problems where clustering or choosing from a set of dynamic structures is needed, the introduction of a distance between the data is an early step in the application of multivariate statistical methods. In this paper a parametric approach is proposed in order to introduce a well-defined metric on the class of autoregressive integrated moving-average (ARIMA) invertible models as the Euclidean distance between their autoregressive expansions. Two case studies for clustering economic time series and for assessing the consistency of seasonal adjustment procedures are discussed. Finally, some related proposals are surveyed and some suggestions for further research are made.     

3DStreaming: an open-source flexible framework for real-time 3D streaming services

The progress of three-dimensional 3D technologies, together with the wide diffusion of both Internet and broadband technologies, is paving the way to emerging live streaming services which have been conceived for delivering 3D video contents in real-time fashion to end users. Nowadays, the only available tools supporting stereoscopic 3D video services cannot be freely downloaded and require the adoption of owner stereoscopic players. Motivated by the lack of an effective solution, we developed a freeware and open source 3D live streaming framework, namely 3DStreaming. It provides stereoscopic 3D live streaming services over the Internet. In particular, it realizes a complete server implementation, offering the support for any transmission protocol and encoding scheme, as well as the full compatibility with any network architecture (i.e., LAN, MAN, Internet, and so on). At the same time, it allows users to use the preferable stereoscopic player and to render the video through any technique available for the chosen player. The overall performances of the proposed tool have been presented by testing its behavior in several network configurations (i.e., by varying network topology, coding technique, 3D representation format, and average encoding rate). All the measured metrics, which include the number of RTP segments that are transmitted and received, the frame loss ratio, and the PSNR, fully demonstrate the right behavior of the implemented tool in all the considered scenarios. We believe that, thanks to its high flexibility, this tool can be exploited by researchers working on stereoscopic-3D related issues to design, test, and evaluate novel and innovative algorithms, protocols, and network architectures.     

Time series clustering and classification by the autoregressive metric

The statistical properties of the autoregressive (AR) distance between ARIMA processes are investigated. In particular, the asymptotic distribution of the squared AR distance and an approximation which is computationally efficient are derived. Moreover, the problem of time series clustering and classification is discussed and the performance of the AR distance is illustrated by means of some empirical applications.     

Redox proteomics identification of 4‐hydroxynonenal‐modified brain proteins in Alzheimer's disease: Role of lipid peroxidation in Alzheimer's disease pathogenesis

Numerous studies have shown that neuronal lipids are highly susceptible to oxidative stress including in those brain areas directly involved in the neurodegenerative process of Alzheimer's disease (AD). Lipid peroxidation directly damages membranes and also generates a number of secondary biologically active products (toxic aldehydes)that are capable of easily attacking lipids, proteins, and DNA. Accumulating evidence has demonstrated regionally increased brain lipid peroxidation in patients with AD; however, extensive studies on specific targets of lipid peroxidation‐induced damage are still missing. The present study represents a further step in understanding the relationship between oxidative modification of protein and neuronal death associated with AD. We used a proteomics approach to determine specific targets of lipid peroxidation in AD brain, both in hippocampus and inferior parietal lobule, by coupling immunochemical detection of 4‐hydroxynonenal‐bound proteins with 2‐D polyacrylamide gel electrophoresis and MS analysis. We identified 4‐hydroxynonenal‐bound proteins in the hippocampus and inferior parietal lobule brain regions of subjects with AD. The identified proteins play different biological functions including energy metabolism, antioxidant system, and structural proteins, thus impairing multiple molecular pathways. Our results provide further evidence for the role of lipid peroxidation in the pathogenesis of AD.     

Age is more than just a number: implications for an aging workforce in the US transportation sector

The US workforce is aging. At the same time, there are a record number of open positions in the transportation sector, which has traditionally been a well-paying, but stressful and schedule-dependent, occupation. Due to increasing longevity, need, and ability to work, a possible solution to the transportation workforce shortfall may lie within the retention and recruitment of older workers. This paper uses a socio-technical framework to examine the pertinent, though scant, literature and data related to older workforce demographics and operational needs, the regulatory environment, requisite knowledge, skills and abilities, and application of support technology and training. Although there is evidence of age-related changes in physiology and cognition, the current science remains unable to resolve how an older workforce may most appropriately be applied to transportation to maximize system safety and minimize negative impact to worker well-being.     

A new approach to the cheap LQ regulator exploiting the geometric properties of the Hamiltonian system

The cheap LQ regulator is reinterpreted as an output nulling problem which is a basic problem of the geometric control theory. In fact, solving the LQ regulator problem is equivalent to keep the output of the related Hamiltonian system identically zero. The solution lies on a controlled invariant subspace whose dimension is characterized in terms of the minimal conditioned invariant of the original system, and the optimal feedback gain is computed as the friend matrix of the resolving subspace. This study yields a new computational framework for the cheap LQ regulator, relying only on the very basic and simple tools of the geometric approach, namely the algorithms for controlled and conditioned invariant subspaces and invariant zeros.     

Inverse analysis procedure to determine flow stress and friction data for finite element modeling of machining

This paper describes an inverse procedure to determine the constitutive constants and the friction conditions in the machining processes using Finite Elements (FE) simulations. In general, the FE modeling of machining processes is an effective tool to analyze the materials machinability under different cutting conditions. However, the use of reliable rheological and friction models represents the basis of a correct numerical investigation. The presented inverse procedure was based on the numerical results obtained using a commercial FE code and was developed considering a specific optimization problem, in which the objective functions that have to be minimized is the experimental/numerical error. This problem was performed by a routine developed in a commercial optimization software. In order to verify the goodness and the robustness of the methodology, it was applied on a Super Duplex Stainless Steel (SDSS) and on an Austenitic Stainless Steel (AUSS) orthogonal machining processes. This work, then, was focused on the identification of the Johnson-Cook (JC) coefficients (A,B,C, n and m) and on the calibration of a Coulomb friction model, in the specific cases of the SAF2507 SDSS and of an AISI 316 Based AUSS Alloy (AISI 316 ASBA). The identification phases were performed considering forces and temperatures experimental data, collected in two specific experimental tasks in which different orthogonal cutting tests were carried out under different cutting parameters conditions.     

A new multi‐scale dispersive gradient elasticity model with micro‐inertia: Formulation and ‐finite element implementation

Motivated by nano‐scale experimental evidence on the dispersion characteristics of materials with a lattice structure, a new multi‐scale gradient elasticity model is developed. In the framework of gradient elasticity, the simultaneous presence of acceleration and strain gradients has been denoted as dynamic consistency. This model represents an extension of an earlier dynamically consistent model with an additional micro‐inertia contribution to improve the dispersion behaviour. The model can therefore be seen as an enhanced dynamic extension of the Aifantis' 1992 strain‐gradient theory for statics obtained by including two acceleration gradients in addition to the strain gradient. Compared with the previous dynamically consistent model, the additional micro‐inertia term is found to improve the prediction of wave dispersion significantly and, more importantly, requires no extra computational cost. The fourth‐order equations are rewritten in two sets of symmetric second‐order equations so that ‐continuity is sufficient in the finite element implementation. Two sets of unknowns are identified as the microstructural and macrostructural displacements, thus highlighting the multi‐scale nature of the present formulation. The associated energy functionals and variationally consistent boundary conditions are presented, after which the finite element equations are derived. Considerable improvements over previous gradient models are observed as confirmed by two numerical examples. Copyright © 2016 John Wiley & Sons, Ltd.     

Cell-protection mechanism through autophagy in HGFs/<Emphasis Type="Italic">S. mitis</Emphasis> co-culture treated with Chitlac-nAg

Silver-based products have been proven to be effective in retarding and preventing bacterial growth since ancient times. In the field of restorative dentistry, the use of silver ions/nanoparticles has been explored to counteract bacterial infections, as silver can destroy bacterial cell walls by reacting with membrane proteins. However, it is also cytotoxic towards eukaryotic cells, which are capable of internalizing nanoparticles. In this work, we investigated the biological effects of Chitlac-nAg, a colloidal system based on a modified chitosan (Chitlac), administered for 24–48?h to a co-culture of primary human gingival fibroblasts and Streptococcus mitis in the presence of saliva, developed to mimic the microenvironment of the oral cavity. We sought to determine its efficiency to combat oral hygiene-related diseases without affecting eukaryotic cells. Cytotoxicity, reactive oxygen species production, apoptosis induction, nanoparticles uptake, and lysosome and autophagosome metabolism were evaluated. In vitro results show that Chitlac-nAg does not exert cytotoxic effects on human gingival fibroblasts, which seem to survive through a homoeostasis mechanism involving autophagy. That suggests that the novel biomaterial Chitlac-nAg could be a promising tool in the field of dentistry.     

Two is better than one: A diploid genotype for neural networks

In nature the genotype of many organisms exhibits diploidy, i.e., it includes two copies of every gene. In this paper we describe the results of simulations comparing the behavior of haploid and diploid populations of ecological neural networks living in both fixed and changing environments. We show that diploid genotypes create more variability in fitness in the population than haploid genotypes and buffer better environmental change; as a consequence, if one wants to obtain good results for both average and peak fitness in a single population one should choose a diploid population with an appropriate mutation rate. Some results of our simulations parallel biological findings.