Corrosion and mechanical properties of plasma electrolytic oxidation‐coated AZ80 magnesium alloy

Plasma electrolytic oxidation (PEO) coatings were produced on AZ80 magnesium alloy in a solution containing silicates and phosphates and working at high current densities with short treatment times. The effect of a sealing treatment in boiling water on corrosion and mechanical properties of the coatings were investigated. Moreover, the corrosion mechanism of the samples with and without the sealing treatment was evaluated. The microstructure of the coatings was characterized with scanning electron microscope observation and X‐ray diffraction analysis. The mechanical properties were evaluated with nanoindentation tests and the corrosion resistance was studied by potentiodynamic polarization, electrochemical impedance spectroscopy, and scanning vibrating electrode technique. The results showed that the sealing did not influence the microstructure and the mechanical properties of the samples and instead produced a remarkable increase in the corrosion resistance. The crevice corrosion, present in the sample without the sealing, was avoided with the treatment in boiling water.     

Subspace Clustering for Situation Assessment in Aquatic Drones: A Sensitivity Analysis for State-Model Improvement

Abstract

In this paper, we propose the use of subspace clustering to detect the states of dynamical systems from sequences of observations. In particular, we generate sparse and interpretable models that relate the states of aquatic drones involved in autonomous water monitoring to the properties (e.g., statistical distribution) of data collected by drone sensors. The subspace clustering algorithm used is called SubCMedians. A quantitative experimental analysis is performed to investigate the connections between i) learning parameters and performance, ii) noise in the data and performance. The clustering obtained with this analysis outperforms those generated by previous approaches.     

Soft clustering using weighted one-class support vector machines

This paper describes a new soft clustering algorithm in which each cluster is modelled by a one-class support vector machine (OC-SVM). The proposed algorithm extends a previously proposed hard clustering algorithm, also based on OC-SVM representation of clusters. The key building block of our method is the weighted OC-SVM (WOC-SVM), a novel tool introduced in this paper, based on which an expectation-maximization-type soft clustering algorithm is defined. A deterministic annealing version of the algorithm is also introduced, and shown to improve the robustness with respect to initialization. Experimental results show that the proposed soft clustering algorithm outperforms its hard clustering counterpart, namely in terms of robustness with respect to initialization, as well as several other state-of-the-art methods.     

Corrosion behavior of a superduplex stainless steel in chloride aqueous solution

Super duplex stainless steels (SDSS) have been widely used as structural materials for chemical plants (especially in those engaged in phosphoric acid production), in the hydrometallurgy industries, and as materials for offshore applications due to their excellent corrosion resistance in chloride environments, compared with other commercial types of ferritic stainless steels. These alloys also possess superior weldability and better mechanical properties than austenitic stainless steels. However, due to their two-phase structure, the nature of which is very dependent on their composition and thermal history, the behavior of SDSS regarding localized corrosion appears difficult to predict, especially in chloride environments. To improve their final properties, the effect of the partition of the alloying elements between the two phases, and the composition and microstructure of each phase are the key to understanding the localized corrosion phenomena of SDSS. This paper concerns the effects of the SDSS microstructure and heat treatment on the SDSS corrosion resistance in aqueous solutions, containing different amounts of NaCl at room temperature.     

DILIGENT: integrating digital library and Grid technologies for a new Earth observation research infrastructure

This paper introduces DILIGENT, a digital library infrastructure built by integrating digital library and Grid technologies and resources. This infrastructure allows different communities to dynamically build specialised digital libraries capable to support the entire e-Science knowledge production and consumption life-cycle by using shared computing, storage, content, and application resources. The paper presents some of the main software services that implement the DILIGENT system. Moreover, it exemplifies the provided features by presenting how the DILIGENT infrastructure is being exploited in supporting the activity of user communities working in the Earth Science Environmental sector. This work is partially funded by the European Commission in the context of the DILIGENT project, under the 2nd call of FP6 IST priority.     

TAPISTRY—A Software Process Improvement Approach Tailored for Small Enterprises

TAPISTRY is a tutored process improvement approach tailored for small enterprises. The approach was developed, used and validated in an ongoing ESSI Esprit project (No 24238), called “TAPISTRY.” The TAPISTRY project adopted a downscaled assessment model of the BOOTSTRAP assessment methodology, called BootCheck, and developed a workshop-based assessment and improvement method, to form together a process improvement approach for small-to-medium-sized enterprises. In TAPISTRY workshops the participants are tutored in self-assessment and improvement planning by software process improvement experts. The resulting TAPISTRY approach was validated through the experiments performed during the TAPISTRY project. This revised version was published online in August 2006 with corrections to the Cover Date.     

Morphological evaluation of bioartificial hydrogels as potential tissue engineering scaffolds

Poly(vinyl alcohol) hydrogels prepared by freeze-thawing procedure represent synthetic systems widely investigated as non-biodegradable scaffolds for tissue regeneration. In order to improve the biocompatibility properties of pure poly(vinyl alcohol) (PVA) hydrogels, blends of PVA with different biological macromolecules, such hyaluronic acid, dextran, and gelatin were prepared and used to produce bioartificial hydrogels. The porosity characteristics of these hydrogels were investigated by scanning electron microscopy and mercury intrusion porosimetry. The morphology of bioartificial hydrogels was evaluated and compared with that of pure PVA hydrogels. In particular the effect exerted by each biological component on pore size and distribution was investigated. The obtained results indicate that when a natural macromolecule is added to PVA the internal structure of the material changes. A small amount of biopolymer induces the structural elements of PVA matrix to take on a well evident lamellar appearance and an apparent preferential orientation. Comparing the results of SEM and mercury intrusion porosimetry it was concluded that hydrogels containing 20% of biological component have the most regular structure and at the same time the lowest total porosity. On the contrary samples with the highest content of natural polymer (40%) show the less regular structure and the highest total porosity.     

High Dynamic Range Point Clouds for Real‐Time Relighting

Acquired 3D point clouds make possible quick modeling of virtual scenes from the real world. With modern 3D capture pipelines, each point sample often comes with additional attributes such as normal vector and color response. Although rendering and processing such data has been extensively studied, little attention has been devoted using the light transport hidden in the recorded per‐sample color response to relight virtual objects in visual effects (VFX) look‐dev or augmented reality (AR) scenarios. Typically, standard relighting environment exploits global environment maps together with a collection of local light probes to reflect the light mood of the real scene on the virtual object. We propose instead a unified spatial approximation of the radiance and visibility relationships present in the scene, in the form of a colored point cloud. To do so, our method relies on two core components: High Dynamic Range (HDR) expansion and real‐time Point‐Based Global Illumination (PBGI). First, since an acquired color point cloud typically comes in Low Dynamic Range (LDR) format, we boost it using a single HDR photo exemplar of the captured scene that can cover part of it. We perform this expansion efficiently by first expanding the dynamic range of a set of renderings of the point cloud and then projecting these renderings on the original cloud. At this stage, we propagate the expansion to the regions not covered by the renderings or with low‐quality dynamic range by solving a Poisson system. Then, at rendering time, we use the resulting HDR point cloud to relight virtual objects, providing a diffuse model of the indirect illumination propagated by the environment. To do so, we design a PBGI algorithm that exploits the GPU's geometry shader stage as well as a new mipmapping operator, tailored for G‐buffers, to achieve real‐time performances. As a result, our method can effectively relight virtual objects exhibiting diffuse and glossy physically‐based materials in real time. Furthermore, it accounts for the spatial embedding of the object within the 3D environment. We evaluate our approach on manufactured scenes to assess the error introduced at every step from the perfect ground truth. We also report experiments with real captured data, covering a range of capture technologies, from active scanning to multiview stereo reconstruction.