Modeling inertia causatives:validating in the password manager adoption context

Cyber criminals are benefiting from the fact that people do not take the required precautions to protect their devices and communications. It is the equivalent of leaving their home’s front door unlocked and unguarded, something no one would do. Many efforts are made by governments and other bodies to raise awareness, but this often seems to fall on deaf ears. People seem to resist changing their existing cyber security practices: they demonstrate inertia. Here, we propose a model and instrument for investigating the factors that contribute towards this phenomenon

Improvements on design and analysis of deep beams based on the strut-and-tie method

La adaptación del método de las bielas y tirantes para el análisis de estructuras tridimensionales es un problema complejo. Los recientes análisis realizados por muchos investigadores desde una perspectiva parcial hacen que muchas veces se pierda la visión global del problema. El trabajo trata de hacer un análisis crítico de las últimas adaptaciones publicados tratando de despejar un camino válido para futuras investigaciones.The adaptation o the strut-and-tie method for the 3D structures is a complex problem. Lastest published findings from many researchers have pointed an specific side of the problem, sometimes loosing an overall perspective. This work is a critical analysis of all these advances, trying to give a guidance to the next point to improve the knowledge of the method.Vancoillie, T. (2018). Improvements on design and analysis of deep beams based on the strut-and-tie method. http://hdl.handle.net/10251/106242TFG

Novel 2D strain-rate-dependent lamina-based and RVE/phase-based progressive fatigue damage criteria for randomly loaded multi-layer fiber-reinforced composites

Two implicit progressive fatigue damage models that rely on new equivalent-damage and equivalent-stress criteria are presented for the prediction of various failure modes of the composites. The criteria are coupled with lamina-based and representative-volume-element-based damage progression approaches. The common concepts of residual strength and residual stiffness are revisited and modified. A fatigue life assessment algorithm that incorporates the strain-rate-dependence of the fatigue strengths and stiffnesses, and random and asynchronous changes of the stress components, distinct mean values, and phase shifts of the stress components is employed. New ideas and new post-processing procedures are employed in the current research. It is the first time that the significant impacts of the strain-rate-dependence of the properties of the composites on stress and fatigue life analyses are investigated. Results of the proposed fatigue criteria are first implemented to a composite plate with a complex lamination scheme under a random transverse load and the predicted fatigue lives are verified by the experimental results. Then, these criteria are implemented to a composite chassis frame of an SUV car under realistic random road inputs and the theoretical results are verified by the experimental results. Results confirm the significant role of the strain-rate-dependence effects on the fatigue lives

Workshop 2 Expanding Horizons: New strategies for multifield fracture problems across scales in heterogeneous systems for energy, health and transport

NewFrac Workshop-2 is especially focused on Phase Field and Finite Fracture Mechanics. It is open to senior researchers and PhD students in fracture mechanics.Horizonte 2020 (Unión Europea) 86106

Automatic crack detection on road pavements using encoder-decoder architecture

Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low-level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can extract and fuse different context sizes and different levels of feature maps than other algorithms