Watermarked 3-D Mesh Quality Assessment

This paper addresses the problem of assessing distortions produced by watermarking 3D meshes. In particular, a new methodology for subjective evaluation of the quality of 3D objects is proposed and implemented. Two objective metrics derived from measures of surface roughness are then proposed and their efficiency to predict the perceptual impact of 3D watermarking is assessed and compared with the state of the art. Results obtained show good correlations between the proposed objective metrics and subjective assessments by human observers     

Preparation and flame resistance properties of revolutionary self-extinguishing epoxy nanocomposites based on layered double hydroxides

Layered double hydroxides/epoxy (LDHs/EP) nanocomposites were prepared from organo-modified LDHs, a diglycidyl ether of bisphenol A monomer (DGEBA) and amine curing agents. The organo-modified LDHs were obtained by ionic exchange of a magnesium-aluminum carbonate LDH in an acid medium. X-ray diffraction and transmission electron microscopy showed a dispersion of the layers at a nanometer scale, indicating the formation of LDH/EP nanocomposites. The thermal degradation and flame resistance properties of LDH/EP nanocomposites, montmorillonite-epoxy (MMT/EP) nanocomposites, LDH/EP microcomposites and aluminum hydroxide-epoxy microcomposites were compared by thermogravimetrical analyses, simultaneous thermal analyses, UL94 and cone calorimeter tests. Only LDH/EP nanocomposites showed self-extinguishing behavior in the horizontal UL94 test; LDH/EP microcomposites and MMT/EP nanocomposites samples burned completely showing that the unique flame resistance of LDH/EP nanocomposites is related to both the level of dispersion and the intrinsic properties of LDH clay. Furthermore, cone calorimeter revealed intumescent behavior for LDH/EP nanocomposites and a higher reduction in the peak heat release rate compared to MMT/EP nanocomposites.     

Fluoroelastomers-dependence of relaxation phenomena on composition

The dynamic-mechanical properties of some fluoroelastomers were determined as a function of composition at low frequency (≈ 1 Hz), by means of a free oscillation torsion pendulum, between −180°C and the softening point. Vinylidenefluoride (VDF)-hexafluoropropene (HFP) copolymers of molar composition 0–39% HFP and terpolymers of VDF and HFP with up to 30mol% tetrafluoroethylene (TFE) and a constant VDF to HFP molar ratio of 3.4 were considered. Two relaxation processes typical of the amorphous phase were found. The first, located at about −87°C, is related to local motions and the transition temperature was found to be independent of composition for copolymers, while it depends on TFE molar content for terpolymers. The second is related to the glass transition and the transition temperature depends on the composition. However, for semicrystalline copolymers the double glass transition phenomenon was observed. When crystallinity goes to zero at about 20 mol% HFP, only one transition is observed. It was also found that ordered structures can take place for terpolymers when TFE molar concentration exceeds 20%. The crystal disorder transition of pure PVDF (75°C) is observed also for low HFP concentrations but the transition temperature is strongly reduced. Analogies between the VDF-HFP and E-P systems are also discussed.     

A scalable algorithm for extraction and clustering of event-related pictures

The event detection problem, which is closely related to clustering, has gained a lot of attentions within event detection for textual documents. However, although image clustering is a problem that has been treated extensively in both Content-Based Image Retrieval (CBIR) and Text-Based Image Retrieval (TBIR) systems, event detection within image management is a relatively new area. Having this in mind, we propose a novel approach for event extraction and clustering of images, taking into account textual annotations, time and geographical positions. Our goal is to develop a clustering method based on the fact that an image may belong to an event cluster. Here, we stress the necessity of having an event clustering and cluster extraction algorithm that are both scalable and allow online applications. To achieve this, we extend a well-known clustering algorithm called Suffix Tree Clustering (STC), originally developed to cluster text documents using document snippets. The idea is that we consider an image along with its annotation as a document. Further, we extend it to also include time and geographical position so that we can capture the contextual information from each image during the clustering process. This has appeared to be particularly useful on images gathered from online photo-sharing applications such as Flickr. Hence, our STC-based approach is aimed at dealing with the challenges induced by capturing contextual information from Flickr images and extracting related events. We evaluate our algorithm using different annotated datasets mainly gathered from Flickr. As part of this evaluation we investigate the effects of using different parameters, such as time and space granularities, and compare these effects. In addition, we evaluate the performance of our algorithm with respect to mining events from image collections. Our experimental results clearly demonstrate the effectiveness of our STC-based algorithm in extracting and clustering events.     

Scientific workflows for process mining: building blocks,scenarios, and implementation

Over the past decade process mining has emerged as a new analytical discipline able to answer a variety of questions based on event data. Event logs have a very particular structure; events have timestamps, refer to activities and resources, and need to be correlated to form process instances. Process mining results tend to be very different from classical data mining results, e.g., process discovery may yield end-to-end process models capturing different perspectives rather than decision trees or frequent patterns. A process-mining tool like ProM provides hundreds of different process mining techniques ranging from discovery and conformance checking to filtering and prediction. Typically, a combination of techniques is needed and, for every step, there are different techniques that may be very sensitive to parameter settings. Moreover, event logs may be huge and may need to be decomposed and distributed for analysis. These aspects make it very cumbersome to analyze event logs manually. Process mining should be repeatable and automated. Therefore, we propose a framework to support the analysis of process mining workflows. Existing scientific workflow systems and data mining tools are not tailored towards process mining and the artifacts used for analysis (process models and event logs). This paper structures the basic building blocks needed for process mining and describes various analysis scenarios. Based on these requirements we implemented RapidProM, a tool supporting scientific workflows for process mining. Examples illustrating the different scenarios are provided to show the feasibility of the approach.     

Non-encapsulated thermo-liquid crystals for digital particle tracking thermography/velocimetry in microfluidics

The ever accelerating state of technology has powered an increasing interest in heat transfer solutions and process engineering innovations in the microfluidics domain. In order to carry out such developments, reliable heat transfer diagnostic techniques are necessary. Thermo-liquid crystal (TLC) thermography, in combination with particle image velocimetry, has been a widely accepted and commonly used technique for the simultaneous measurement and characterization of temperature and velocity fields in macroscopic fluid flows for several decades. However, low seeding density, volume illumination, and low TLC particle image quality at high magnifications present unsurpassed challenges to its application to three-dimensional flows with microscopic dimensions. In this work, a measurement technique to evaluate the color response of individual non-encapsulated TLC particles is presented. A Shirasu porous glass membrane emulsification approach was used to produce the non-encapsulated TLC particles with a narrow size distribution and a multi-variable calibration procedure, making use of all three RGB and HSI color components, as well as the proper orthogonally decomposed RGB components, was used to achieve unprecedented low uncertainty levels in the temperature estimation of individual particles, opening the door to simultaneous temperature and velocity tracking using 3D velocimetry techniques.     

CAD based shape optimization for gas turbine component design

In order to improve product characteristics, engineering design makes increasing use of Robust Design and Multidisciplinary Design Optimisation. Common to both methodologies is the need to vary the object’s shape and to assess the resulting change in performance, both executed within an automatic loop. This shape change can be realised by modifying the parameter values of a suitably parameterised Computer Aided Design (CAD) model. This paper presents the adopted methodology and the achieved results when performing optimisation of a gas turbine disk. Our approach to hierarchical modelling employing design tables is presented, with methods to ensure satisfactory geometry variation by commercial CAD systems. The conducted studies included stochastic and probabilistic design optimisation. To solve the multi-objective optimisation problem, a Pareto optimum criterion was used. The results demonstrate that CAD centric approach enables significant progress towards automating the entire process while achieving a higher quality product with the reduced susceptibility to manufacturing imperfections.     

An Integrated Assessment of the Impacts of Changing Climate Variability on Agricultural Productivity and Profitability in an Irrigated Mediterranean Catchment

Climate change is likely to have a profound effect on many agricultural variables, although the extent of its influence will vary over the course of the annual farm management cycle. Consequently, the effect of different and interconnected physical, technical and economic factors must be modeled in order to estimate the effects of climate change on agricultural productivity. Such modeling commonly makes use of indicators that summarize the among environmental factors that are considered when farmers plan their activities. This study uses net evapotranspiration (ET_N), estimated using EPIC, as a proxy index for the physical factors considered by farmers when managing irrigation. Recent trends suggest that the probability distribution function of ET_N may continue to change in the near future due to changes in the irrigation needs of crops. Also, water availability may continue to vary due to changes in the rainfall regime. The impacts of the uncertainties related to these changes on costs are evaluated using a Discrete Stochastic Programming model representing an irrigable Mediterranean area where limited water is supplied from a reservoir. In this context, adaptation to climate change can be best supported by improvements to the collective irrigation systems, rather than by measures aimed at individual farms such as those contained within the rural development policy.     

A Low‐Memory,Straightforward and Fast Bilateral Filter Through Subsampling in Spatial Domain

In this work we present a new algorithm for accelerating the colour bilateral filter based on a subsampling strategy working in the spatial domain. The base idea is to use a suitable subset of samples of the entire kernel in order to obtain a good estimation of the exact filter values. The main advantages of the proposed approach are that it has an excellent trade‐off between visual quality and speed‐up, a very low memory overhead is required and it is straightforward to implement on the GPU allowing real‐time filtering. We show different applications of the proposed filter, in particular efficient cross‐bilateral filtering, real‐time edge‐aware image editing and fast video denoising. We compare our method against the state of the art in terms of image quality, time performance and memory usage.     

Gut Microbiota,Metabolic Disorders and Breast Cancer: Could Berberine Turn Out to Be a Transversal Nutraceutical Tool? A Narrative Analysis

Metabolic disorders, mainly characterized as the marked alteration of the lipid and carbohydrate profile, in addition to the clinical presence of the direct consequences of these alterations, are pathological conditions that have considerably increased in prevalence in recent years. They are directly linked to the onset of various pathologies, including cancer, particularly breast cancer, and are hormone-responsive. Alongside the known conditions responsible for this scenario, such as nutrition and lifestyle in general, the importance of both the colonic microbiota and the various organs and systems is becoming increasingly evident. In fact, it is now evident that microbial dysbiosis plays a fundamental role in the onset of these metabolic disorders, and therefore how these conditions are indirectly responsible for the onset and progression of neoplasms. Indirect mechanisms such as an altered Firmicutes/Bacteroidetes ratio; the formation of metabolites such as short-chain fatty acids (SCFAs), in particular, butyrate, which is capable of acting as a tumor suppressor; and the glucuronidase activity of estroboloma (bacteria responsible for estrogen metabolism) are just some of the most important mechanisms that contribute to the history of breast cancer. It is therefore understandable that in clinical terms, it is essential to associate the modulation of metabolic disorders and the microbial conditions that contribute to generating them with common therapies, preferably using compounds and solutions that are effective and acceptable for the patient without side effects. Nutraceuticals such as berberine (active both in metabolic scenarios and in the microbiota) and interventions modulating the microbial structure such as the use of probiotics and prebiotics seem to be ideal solutions for these preventive and no-longer-ignorable strategies in the light of numerous data now present in the literature.