Spatial behaviour in dynamical thermoelasticity backward in time for porous media

The aim of this paper is to study the spatial behaviour of the solutions to the boundary-final value problems associated with the linear theory of elastic materials with voids. More precisely the present study is devoted to porous materials with a memory effect for the intrinsic equilibrated body forces. An appropriate time-weighted volume measure is associated with the backward in time thermoelastic processes. Then, a first-order partial differential inequality in terms of such measure is established and further is shown how it implies the spatial exponential decay of the thermoelastic process in question.Comment: 12 pages, accepted by Journal of Thermal Stresse

Some Theorems in Thermoelasticity for Micropolar Porous Media

Within the context of a linear theory of heat-flux dependent thermoelasticity for micropolar porous media some variational principles and a reciprocal relation are derived.Comment: 15 pages, accepted by Rev.Roum.Sci.Tech.-Mec.App

Uniqueness of Solutions in Thermopiezoelectricity of Nonsimple Materials

This article presents the theory of thermopiezoelectricity in which the second displacement gradient and the second electric potential gradient are included in the set of independent constitutive variables. This is achieved by using the entropy production inequality proposed by Green and Laws. At first, appropriate thermodynamic restrictions and constitutive equations are obtained, using the well-established Coleman and Noll procedure. Then, the balance equations and the constitutive equations of linear theory are derived, and the mixed initial-boundary value problem is set. For this problem a uniqueness result is established. Next, the basic equations for the isotropic case are derived. Finally, a set of inequalities is obtained for the constant constitutive coefficients of the isotropic case that, on the basis on the previous theorem, ensure the uniqueness of the solution of the mixed initial-boundary value problem

Control of locomotion systems and dynamics in relative periodic orbits

The connection between the dynamics in relative periodic orbits of vector fields with noncompact symmetry groups and periodic control for the class of control systems on Lie groups known as `(robotic) locomotion systems' is well known, and has led to the identification of (geometric) phases. We take an approach which is complementary to the existing ones, advocating the relevance|for trajectory generation in these control systems|of the quali-tative properties of the dynamics in relative periodic orbits. There are two particularly important features. One is that motions in relative periodic orbits of noncompact groups can only be of two types: Either they are quasi-periodic, or they leave any compact set as t →±∞ (`drifting motions'). Moreover, in a given group, one of the two behaviours may be predominant. The second is that motions in a relative periodic orbit exhibit `spiralling', `meandering' behaviours, which are routinely detected in numerical integrations. Since a quantitative description of meandering behaviours for drifting motions appears to be missing, we provide it here for a class of Lie groups that includes those of interest in locomotion (semidirect products of a compact group and a normal vector space). We illustrate these ideas on some examples (a kinematic car robot, a planar swimmer)

Molecular Docking on Azepine Derivatives as Potential Inhibitors for H1N1-A Computational Approach

Azepine are an important class of organic compounds. They are effective in a wide range of biological activity such as antifeedants, antidepressants, CNS stimulants, calcium channel blocker, antimicrobial and antifungal properties. In our continue efforts to search for a potent inhibitor for H1N1 virus using molecular docking. In this study, 15 azepine (ligands) derivatives were docked to the neuraminidase of A/Breving Mission/1/1918 H1N1 strain in complex with zanamivir (protein). The Cdocker energy was then calculated for these complexes (protein-ligand). Based on the calculation, the lowest Cdocker interaction energy was selected and potential inhibitors can be identified. Compounds MA4, MA7, MA8, MA10, MA11 and MA12 with promising Cdocker energy was expected to be very effective against the neuraminidase H1N1

Extending OpenStack Monasca for Predictive Elasticity Control

Traditional auto-scaling approaches are conceived as reactive automations, typically triggered when predefined thresholds are breached by resource consumption metrics. Managing such rules at scale is cumbersome, especially when resources require non-negligible time to be instantiated. This paper introduces an architecture for predictive cloud operations, which enables orchestrators to apply time-series forecasting techniques to estimate the evolution of relevant metrics and take decisions based on the predicted state of the system. In this way, they can anticipate load peaks and trigger appropriate scaling actions in advance, such that new resources are available when needed. The proposed architecture is implemented in OpenStack, extending the monitoring capabilities of Monasca by injecting short-term forecasts of standard metrics. We use our architecture to implement predictive scaling policies leveraging on linear regression, autoregressive integrated moving average, feed-forward, and recurrent neural networks (RNN). Then, we evaluate their performance on a synthetic workload, comparing them to those of a traditional policy. To assess the ability of the different models to generalize to unseen patterns, we also evaluate them on traces from a real content delivery network (CDN) workload. In particular, the RNN model exhibites the best overall performance in terms of prediction error, observed client-side response latency, and forecasting overhead. The implementation of our architecture is open-source

Predictive auto-scaling with OpenStack Monasca

Cloud auto-scaling mechanisms are typically based on reactive automation rules that scale a cluster whenever some metric, e.g., the average CPU usage among instances, exceeds a predefined threshold. Tuning these rules becomes particularly cumbersome when scaling-up a cluster involves non-negligible times to bootstrap new instances, as it happens frequently in production cloud services. To deal with this problem, we propose an architecture for auto-scaling cloud services based on the status in which the system is expected to evolve in the near future. Our approach leverages on time-series forecasting techniques, like those based on machine learning and artificial neural networks, to predict the future dynamics of key metrics, e.g., resource consumption metrics, and apply a threshold-based scaling policy on them. The result is a predictive automation policy that is able, for instance, to automatically anticipate peaks in the load of a cloud application and trigger ahead of time appropriate scaling actions to accommodate the expected increase in traffic. We prototyped our approach as an open-source OpenStack component, which relies on, and extends, the monitoring capabilities offered by Monasca, resulting in the addition of predictive metrics that can be leveraged by orchestration components like Heat or Senlin. We show experimental results using a recurrent neural network and a multi-layer perceptron as predictor, which are compared with a simple linear regression and a traditional non-predictive auto-scaling policy. However, the proposed framework allows for the easy customization of the prediction policy as needed

Design and Synthesis of Hsp90 Inhibitors with B-Raf and PDHK1 Multi-Target Activity

5noopenThe design of multi-target ligands has become an innovative approach for the identification of effective therapeutic treatments against complex diseases, such as cancer. Recent studies have demonstrated that the combined inhibition of Hsp90 and B-Raf provides synergistic effects against several types of cancers. Moreover, it has been reported that PDHK1, which presents an ATP-binding pocket similar to that of Hsp90, plays an important role in tumor initiation, maintenance and progression, participating also to the senescence process induced by B-Raf oncogenic proteins. Based on these premises, the simultaneous inhibition of these targets may provide several benefits for the treatment of cancer. In this work, we set up a design strategy including the assembly and integration of molecular fragments known to be important for binding to the Hsp90, PDHK1 and B-Raf targets, aided by molecular docking for the selection of a set of compounds potentially able to exert Hsp90-B-Raf-PDHK1 multi-target activities. The designed compounds were synthesized and experimentally validated in vitro. According to the in vitro assays, compounds 4 a, 4 d and 4 e potently inhibited Hsp90 and moderately inhibited the PDHK1 kinase. Finally, molecular dynamics simulations were performed to provide further insights into the structural basis of their multi-target activity.openPinzi L.; Foschi F.; Christodoulou M.S.; Passarella D.; Rastelli G.Pinzi, L.; Foschi, F.; Christodoulou, M. S.; Passarella, D.; Rastelli, G

A model for the generation of social network graphs

In this paper we present and evaluate a social network model which exploits fundamental results coming from the social anthropology literature. Specifically, our model focuses on ego networks, i.e., the set of active social relationships for a given individual. The model is based on a function that correlates the level of emotional closeness of a social relationship to the time invested in it. The size of the social network is limited by the time budget a person invests in socializing. We exploit the model to define a constructive algorithm to generate synthetic social networks. Experimental results show that our model satisfies, on average, known properties of ego networks such as the size, the composition and the hierarchical structure

Histone Deacetylase and Microtubules as Targets for the Synthesis of Releasable Conjugate Compounds

Design and synthesis of an HDAC inhibitor and its merger with three tubulin binders to create releasable conjugate compounds is described. The biological evaluation includes: a) in vitro reactivity with glutathione, b) antiproliferative activity, c) cell cycle analysis and d) quantification of protein acetylation. The cellular pharmacology study indicated that the HDAC-inhibitor-drug conjugates retained antimitotic and proapoptotic activity with a reduced potenc