Influence of disbond on notch crack behaviour in single bonded lap joints

The behaviour of the adhesive bonded joints due to the imposed eccentric loading generates a very complex distribution of the stress in the structure. Good adhesion between substrate and adhesive ensures a successful and lasting assembly. In this study the finite element method is used to analyze the behaviour of a bonded lap joint of dissimilar materials. The effects of the mechanical properties of the joints on the shear stress variation with and without presence of a circular notch are investigated. The results show that the maximum shear stresses are located at a distance of about 18% that of the lap length whatever the type of material used. In addition, the stress intensity factor is amplified by the presence of the negative effect of disband whose increase is linearly proportional to the square of the stress intensity factor. It reached its maximum value for a crack length equal to two-fifths of the notch radius.     

Hydrodynamic lubrication of porous journal bearings using a modified Brinkman-extended Darcy model

A numerical solution for the hydrodynamic lubrication of finite porous journal bearings considering the flexibility of the liner is introduced. The Brinkman-extended Darcy equations and the Stokes' equations are utilized to model the flow in the porous region and fluid film region, respectively. A stress jump boundary condition at the porous media/fluid film interface and effects of viscous shear are included into the lubrication analysis. Elrod's cavitation algorithm, which automatically predicts film rupture and reformation in the bearing, is implemented in the solution scheme. The present analysis predictions for pressure distributions, load carrying capacity, and friction factor are in good agreement with three different sets of experimental results available in the literature. Furthermore, the effects of dimensionless permeability parameter, and stress jump parameter on performance parameters such as load carrying capacity, side leakage, friction factor, and attitude angle, are presented and discussed.     

Spatial analysis of intracerebral electroencephalographic signals in the time and frequency domain: identification of epileptogenic networks in partial epilepsy

Electroencephalography (EEG) occupies an important place for studying human brain activity in general, and epileptic processes in particular, with appropriate time resolution. Scalp EEG or intracerebral EEG signals recorded in patients with drug-resistant partial epilepsy convey important information about epileptogenic networks that must be localized and understood prior to subsequent therapeutic procedures. However, this information, often subtle, is 'hidden' in the signals. It is precisely the role of signal processing to extract this information and to put it into a 'coherent and interpretable picture' that can participate in the therapeutic strategy. Nowadays, the panel of available methods is very wide depending on the objectives such as, for instance, the detection of transient epileptiform events, the detection and/or prediction of seizures, the recognition and/or the classification of EEG patterns, the localization of epileptic neuronal sources, the characterization of neural synchrony, the determination of functional connectivity, among others. The intent of this paper is to focus on a specific category of methods providing relevant information about epileptogenic networks from the analysis of spatial properties of EEG signals in the time and frequency domain. These methods apply to either interictal or ictal recordings and share the common objective of localizing the subsets of brain structures involved in both types of paroxysmal activity. Most of these methods were developed by our group and are routinely used during pre-surgical evaluation. Examples are detailed. Results, as well as limitations of the methods, are also discussed.     

A comparative study between three sensorless control strategies for PMSG in wind energy conversion system

This paper presents a comparative study of sliding mode, artificial neural network and model reference adaptive speed observers for a speed sensorless permanent magnet synchronous generator (PMSG) in wind energy conversion system (WECS). Wind velocity and position sensorless operating methods for wind generation system using observer are proposed only by measuring phase voltages and currents. Maximum wind energy extraction is achieved by running the wind turbine generator in variable-speed mode. In addition the three speed observers are compared to verify the robustness against parameter variations.     

Analysis of the distribution of thermal residual stresses in bonded composite repair of metallic aircraft structures

In this study, the distribution of the thermal residual stresses due to the adhesive curing in bonded composite repair is analysed using the finite element method. The computation of these stresses comprises all components of the structures: cracked plate, composite patch and adhesive layer. In addition, the influence of these residual stresses on the repair performance is highlighted by analysing their effect on the stress intensity factor at the crack tip. The obtained results show that the normal thermal stresses in the plate and the patch are important and the shear stresses are less significant. The level of the adhesive thermal stresses is relatively high. The presence of the thermal stresses increases the stress intensity factor at the crack tip, what reduce the repair performance.     

Modeling Framework and Decomposition Scheme for On‐Demand Mobility Services with Ridesharing and Transfer

This article presents a modeling framework for the operations of on‐demand mobility services (ODMS) in urban areas. The framework provides the capabilities to analyze ODMS operations while representing emerging services such as ridesharing and transfer. The problem is formulated as a mixed integer program and an efficient decomposition‐based methodology is developed for its solution. The methodology adopts a modified version of the column generation algorithm, which implements iterative decomposition and network augmentation techniques to allow studying networks of moderate size. The results of a set of experiments considering grid and real‐world networks are presented. The results show that increasing the number of passengers willing to rideshare and/or transfer improves the overall performance of ODMS as it increases number of served passengers and associated profit and reduces the number of used vehicles. Although presented as an offline planning tool, the methodology could be adopted for real‐time applications as adequate computational resources become available.     

Hybrid Deep Neural Network-Based Text Representation Model to Improve Microblog Retrieval

Abstract

Retrieving relevant information from Twitter is always a challenging task given its vocabulary mismatch, sheer volume and noise. Representing the content of text tweets is a critical part of any microblog retrieval model. For this reason, deep neural networks can be used for learning good representations of text data and then conduct to a better matching. In this paper, we are interested in improving both representation and retrieval effectiveness in microblogs. For that, a Hybrid-Deep Neural-Network-based text representation model is proposed to extract effective features’ representations for clustering oriented microblog retrieval. HDNN combines recurrent neural network and feedforward neural network architectures. Specifically, using a bi-directional LSTM, we first generate a deep contextualized word representation which incorporates character n-grams form FasText. However, these contextual embedded existing in a high-dimensional space are not all important. Some of them are redundant, correlated and sometimes noisy making the learning models over-fitting, complex and less interpretable. To deal with these problems, we proposed a Hybrid-Regularized-Autoencoder-based method which combines autoencoder with Elastic Net regularization for an effective unsupervised feature selection and extraction. Our experimental results show that the performance of clustering and especially information retrieval in microblogs depend heavily on features’ representation.     

Synchronised interrelationship between lipid/protein oxidation analysis and sensory attributes in refrigerated minced beef meat formulated with Punica granatum peel extract

Microbiological, chemical and sensory effects of pomegranate peel ethanol extract (EE) at 0.1, 0.5 and 1% (w w⁻¹) on refrigerated minced beef meat were undertaken. Chemical analyses revealed that the treated meat underwent significant decrease (P < 0.05) of primary and secondary lipid oxidation. By the end of storage, EE at 1% reduced the MetMb, carbonyl group accumulation and the disappearance of sulphydryl proteins by 56.68, 65.71 and 59.69% respectively, compared with control samples. EE at 1% showed also the highest scores for colour, appearance, odour and overall acceptability (OA) attributes using a 9-point hedonic scale. Chemometric approaches showed that lipid/protein oxidation analysis, sensory attributes and storage time allowed the samples discrimination. Oxidative stability parameters were equally used to predict OA by linear regression analysis. The reported data provide an approach to strengthen the interpretation of sensory quality and how they are affected by chemical changes in newly formulated meat products.     

Influence of polarization electric field on the dielectric properties of BaTiO3-based ceramics

The titanate of barium (BaTiO₃) is a ferroelectric material with perovskite structure. The electric properties (ferroelectricity, positive temperature coefficient, piezoelectricity, etc.) of BaTiO₃ were largely studied. Usually, addition of small quantities of impurity can modify the dielectric properties of BaTiO₃ and widen its number of applications. In this investigation different additions of sodium niobate (NaNbO₃), according to the composition, (1−x) BaTiO₃+x NaNbO₃ with x=1, 3, 5, 7 and 10 mol%, were considered. Additionally, the effect of simultaneous addition of 3 mol% NaNbO₃ and 3 mol% SiO₂ on the dielectric properties we studied as function of temperature, frequency and the applied bias. The obtained results reveal a net evolution of permittivity with addition. Furthermore, the obtained results show that the permittivity is independent of the applied electric field at temperature lower than Curie point (T_c), but presents relatively high values at low frequencies. Beyond this temperature, the permittivity considerably increases (about 25×10³) and largely depends both on frequency and applied voltage.