In-field comparison among polarization-mode-dispersion measurementtechniques

We report polarization mode dispersion measurements on a 168 km long undersea optical cable, performed with two techniques. From experimental results, statistical behavior of polarization parameters and their time fluctuations have been extracted. An average differential group delay of about 13 ps shows that dispersion-shifted fibers can be affected by a polarization dispersion significantly larger than standard step-index fibers. The correlation length of the polarization-coupling perturbation in the tested fibers is not negligible with respect to the link length     

Component-Based is Architecture

Advances in distributed architectures and the Internet have had a strong influence on the use of component technologies. The authors discuss the organizational and technical requirements for an effective and economically viable component-based approach.     

Anti-BlUFf: towards counterfeit mitigation in IC supply chains using blockchain and PUF

International Journal of Information Security - The complexity of today’s integrated circuit (IC) supply chain, organised in several tiers and including many companies located in different...     

On the detection of card-sharing traffic through wavelet analysis and Support Vector Machines

In the last years, the interest in methods and techniques for circumventing the security of the available digital video broadcasting systems is continuously increasing. Digital TV providers are struggling to restrict access to their video contents only to authorized users, by deploying more and more sophisticated conditional access systems. At the state-of-the-art, the most significant menace is the card-sharing activity which exploits a known weakness allowing an authorized subscriber to provide access to digital contents to a potentially large group of unauthorized ones connected over a communication network. This is usually realized by using ad hoc customized devices. Detecting the presence of these illegal systems on a network, by recognizing their related traffic is an issue of primary importance. Unfortunately, to avoid the identification of such traffic, payload obfuscation strategies based on encryption are often used, hindering packet inspection techniques.This paper presents a strategy for the detection of card-sharing traffic, empowered by machine-learning-driven traffic classification techniques and based on the natural capability of wavelet analysis to decompose a traffic time series into several component series associated with particular time and frequency scales and hence allowing its observation at different frequency component levels and with different resolutions. These ideas have been used for the proof-of-concept implementation of an SVM-based binary classification scheme that relies only on time regularities of the traffic and not on the packet contents and hence is immune to payload obfuscation techniques.     

Finite Element Analysis of the Stability (Buckling and Post-Buckling) of Composite Laminated Structures: Well Established Procedures and Challenges

Finite element procedures for the analysis of composite structures under compressive loads (buckling and post-buckling) generally are not deployed in books because they are still considered object of research whereas they are deemed as assessed by researchers that focus their papers on restricted audience topics. Therefore, regarding these procedures, a gap exists in literature between what researchers consider as well established and what has been already published. The aim of this paper is to contribute to close this gap by providing an insight in linear and non-linear buckling analyses and in their use with composite structures. Both modelling and analysis considerations are presented and discussed, focusing on what can be considered as best practice when dealing with this kind of problems. Applications (to a stiffened panel and to a wing box) are provided for demonstrating the effectiveness of the procedures presented.     

Computational intelligence modelling of pharmaceutical tabletting processes using bio-inspired optimization algorithms

In pharmaceutical development, it is very useful to exploit the knowledge of the causal relationship between product quality and critical material attributes (CMA) in developing new formulations and products, and optimizing manufacturing processes. With the big data captured in the pharmaceutical industry, computational intelligence (CI) models could potentially be used to identify critical quality attributes (CQA), CMA and critical process parameters (CPP). The objective of this study was to develop computational intelligence models for pharmaceutical tabletting processes, for which bio-inspired feature selection algorithms were developed and implemented for optimisation while artificial neural network (ANN) was employed to predict the tablet characteristics such as porosity and tensile strength. Various pharmaceutical excipients (MCC PH 101, MCC PH 102, MCC DG, Mannitol Pearlitol 200SD, Lactose, and binary mixtures) were considered. Granules were also produced with dry granulation using roll compaction. The feed powders and granules were then compressed at various compression pressures to produce tablets with different porosities, and the corresponding tensile strengths were measured. For the CI modelling, the efficiency of seven bio-inspired optimization algorithms were explored: grey wolf optimization (GWO), bat optimization (BAT), cuckoo search (CS), flower pollination algorithm (FPA), genetic algorithm (GA), particle swarm optimization (PSO), and social spider optimization (SSO). Two-thirds of the experimental dataset was randomly chosen as the training set, and the remaining was used to validate the model prediction. The model efficiency was evaluated in terms of the average reduction (representing the fraction of selected input variables) and the mean square error (MSE). It was found that the CI models can well predict the tablet characteristics (i.e. porosity and tensile strength). It was also shown that the GWO algorithm was the most accurate in predicting porosity. While the most accurate prediction for the tensile strength was achieved using the SSO algorithm. In terms of the average reduction, the GA algorithm resulted in the highest reduction of inputs (i.e. 60%) for predicting both the porosity and the tensile strength.     

Analogues of perillaketone as highly potent agonists of TRPA1 channel

Transient receptor potential (TRP) channels represent interesting molecular target structures involved in a number of different physiological and pathophysiological systems. In particular, TRPA1 channel is involved in nociception and in sensory perception of many pungent chemesthetic compounds, which are widespread in spices and food plants, including Perilla frutescens. A natural compound from P. frutescens (isoegomaketone) and 16 synthetic derivatives of perillaketone have been prepared and tested in vitro on rTRPA1 expressed in HEK293 cells and their potency, efficacy and desensibilisation activity measured. Most derivatives proved to be high potency agonists of TRPA1, with a potency higher than most natural agonists reported in the literature. These furylketones derivatives, represent a new class of chemical structures active on TRPA1 with many potential applications in the agrifood and pharmaceutical industry.     

Quantum transport including nonparabolicity and phonon scattering: application to silicon nanowires

In this work, the particular and combined influence of nonparabolicity and phonon scattering on the device characteristics of a triple-gate silicon nanowire is investigated. In addition, different approximations of the retarded self-energy for electron-phonon scattering are analyzed in terms of the electrostatics, current and computational cost.