Melt processing of nanocomposites of cellulose nanocrystals with biobased thermoplastic polyurethane

Nanocomposites of thermoplastic polyurethane (TPU) with cellulose nanocrystals (CNC) without and with surface treatment are obtained by melt processing. Nanocomposites are obtained with nanofiller weight content near of the theoretical percolation threshold (3.9 wt%). Visual observation of CNC agglomerates is sufficient to prove the inefficiency of the mixing in systems with untreated CNC. The crystallization kinetics of the TPU changes with the addition of CNC and this is confirmed by differential scanning calorimetry analysis. Thermogravimetric analysis prove that the addition of CNC increases the thermal stability of the TPU. From the rheological analysis it is possible to verify the absence of percolation and an intermediate state of sol–gel transition in the nanocomposites. CNC/TPU nanocomposites with 5 wt% of treated CNC present better mechanical performance than de neat TPU and the other processed nanocomposites and display around 130% increase in Young's modulus while retaining significant values of toughness, tensile strength and elongation at break.     

High-velocity oxyfuel thermal spray coatings for biomedical applications

Plasma spraying is used to produce most commercially available bioceramic coatings for dental implants; however, these coatings still contain some inadequacies. Two types of coatings produced by the high- velocity oxyfuel (HVOF) combustion spray process using commercially available hydroxyapatite (HA) and fluorapatite (FA) powders sprayed onto titanium were characterized to determine whether this relatively new coating process can be applied to bioceramic coatings. Diffuse reflectance Fourier transform infrared (FTIR) spectroscopy, x- ray diffraction (XRD), and scanning electron microscopy (SEM) were used to characterize the composition, microstructure, and morphology of the coatings. The XRD and FTIR techniques revealed an apatitic structure for both HA and FA coatings. However, XRD patterns indicated some loss in crystallinity of the coatings due to the spraying process. Results from FTIR showed a loss in the intensity of the OH^∼ and F^∼ groups due to HVOF spraying; the phosphate groups, however, were still present. Analysis by SEM showed a coating morphology similar to that obtained with plasma spraying, with complete coverage of the titanium substrate. Interfacial SEM studies revealed an excellent coating-to-substrate apposition. These results indicate that with further optimization the HVOF thermal spray process may offer another method for producing bioceramic coatings.     

XPS spectra of the AFe4Al8 compounds with A=Y, Sc, U and Th

The XPS electronic structures of the AFe₄Al₈ (A=Sc, Y, U, Th) single crystals were measured. The valence band of UFe₄Al₈ exhibits domination of the U 5f states at the Fermi level, while for the other AFe₄Al₈ compounds the valence states are shifted by 0.5 eV toward higher binding energy. The multiplet structure of the U 4f and Th 4f states was analyzed in comparison with the U 4f states of U₃Ge₅ and UNiSb₂.     

Impact of virtual atmospherics and functional qualities on the online gambler's experience

This study empirically tests a stimulus–organism–response (S–O–R) structural model that proposes that an online casino's atmospheric cues and functional qualities influence individuals' affective and cognitive responses, which in turn impact consumer behavioural intentions. Using self-reported data from a primarily US-based sample, analysed elements of the online gambling site stimulus included high and low task-relevant cues, financial trust, and gambling value. Demographic characteristics were found to moderate the relationship between the stimulus and internal satisfaction, with demographic group differences found among all stimulus constructs. Results emphasise the significant role that atmospherics and functional qualities play in generating positive behaviours from online gamblers, with additional implications in marketplace competitiveness and in responsible gambling practices.     

Assessment of crowdsourcing and gamification loss in user-assisted object segmentation

There has been a growing interest in applying human computation – particularly crowdsourcing techniques – to assist in the solution of multimedia, image processing, and computer vision problems which are still too difficult to solve using fully automatic algorithms, and yet relatively easy for humans. In this paper we focus on a specific problem – object segmentation within color images – and compare different solutions which combine color image segmentation algorithms with human efforts, either in the form of an explicit interactive segmentation task or through an implicit collection of valuable human traces with a game. We use Click’n’Cut, a friendly, web-based, interactive segmentation tool that allows segmentation tasks to be assigned to many users, and Ask’nSeek, a game with a purpose designed for object detection and segmentation. The two main contributions of this paper are: (i) We use the results of Click’n’Cut campaigns with different groups of users to examine and quantify the crowdsourcing loss incurred when an interactive segmentation task is assigned to paid crowd-workers, comparing their results to the ones obtained when computer vision experts are asked to perform the same tasks. (ii) Since interactive segmentation tasks are inherently tedious and prone to fatigue, we compare the quality of the results obtained with Click’n’Cut with the ones obtained using a (fun, interactive, and potentially less tedious) game designed for the same purpose. We call this contribution the assessment of the gamification loss, since it refers to how much quality of segmentation results may be lost when we switch to a game-based approach to the same task. We demonstrate that the crowdsourcing loss is significant when using all the data points from workers, but decreases substantially (and becomes comparable to the quality of expert users performing similar tasks) after performing a modest amount of data analysis and filtering out of users whose data are clearly not useful. We also show that – on the other hand – the gamification loss is significantly more severe: the quality of the results drops roughly by half when switching from a focused (yet tedious) task to a more fun and relaxed game environment.     

Landmark-based music recognition system optimisation using genetic algorithms

Audio fingerprinting allows us to label an unidentified music fragment within a previously generated database. The use of spectral landmarks aims to obtain a robustness that lets a certain level of noise be present in the audio query. This group of audio identification algorithms holds several configuration parameters whose values are usually chosen based upon the researcher’s knowledge, previous published experimentation or just trial and error methods. In this paper we describe the whole optimisation process of a Landmark-based Music Recognition System using genetic algorithms. We define the actual structure of the algorithm as a chromosome by transforming its high relevant parameters into various genes and building up an appropriate fitness evaluation method. The optimised output parameters are used to set up a complete system that is compared with a non-optimised one by designing an unbiased evaluation model.     

Efficiently finding the optimum number of clusters in a dataset with a new hybrid differential evolution algorithm: DELA

Clustering algorithms, a fundamental base for data mining procedures and learning techniques, suffer from the lack of efficient methods for determining the optimal number of clusters to be found in an arbitrary dataset. The few methods existing in the literature always use some sort of evolutionary algorithm having a cluster validation index as its objective function. In this article, a new evolutionary algorithm, based on a hybrid model of global and local heuristic search, is proposed for the same task, and some experimentation is done with different datasets and indexes. Due to its design, independent of any clustering procedure, it is applicable to virtually any clustering method like the widely used \(k\)-means algorithm. Moreover, the use of non-parametric statistical tests over the experimental results, clearly show the proposed algorithm to be more efficient than other evolutionary algorithms currently used for the same task.     

Solving the electricity production planning problem by a column generation based heuristic

This paper presents a heuristic method based on column generation for the EDF (Electricité De France) long-term electricity production planning problem proposed as subject of the ROADEF/EURO 2010 Challenge. This is to our knowledge the first-ranked method among those methods based on mathematical programming, and was ranked fourth overall. The problem consists in determining a production plan over the whole time horizon for each thermal power plant of the French electricity company, and for nuclear plants, a schedule of plant outages which are necessary for refueling and maintenance operations. The average cost of the overall outage and production planning, computed over a set of demand scenarios, is to be minimized. The method proceeds in two stages. In the first stage, dates for outages are fixed once for all for each nuclear plant. Data are aggregated with a single average scenario and reduced time steps, and a set-partitioning reformulation of this aggregated problem is solved for fixing outage dates with a heuristic based on column generation. The pricing problem associated with each nuclear plant is a shortest path problem in an appropriately constructed graph. In the second stage, the reload level is determined at each date of an outage, considering now all scenarios. Finally, the production quantities between two outages are optimized for each plant and each scenario by solving independent linear programming problems.     

Evolving Takagi–Sugeno fuzzy model based on switching to neighboring models

In this paper, we propose a new online identification approach for evolving Takagi–Sugeno (TS) fuzzy models. Here, for a TS model, a certain number of models as neighboring models are defined and then the TS model switches to one of them at each stage of evolving. We define neighboring models for an in-progress (current) TS model as its fairly evolved versions, which are different with it just in two fuzzy rules. To generate neighboring models for the current model, we apply specially designed split and merge operations. By each split operation, a fuzzy rule is replaced with two rules; while by each merge operation, two fuzzy rules combine to one rule. Among neighboring models, the one with the minimum sum of squared errors – on certain time intervals – replaces the current model.To reduce the computational load of the proposed evolving TS model, straightforward relations between outputs of neighboring models and that of current model are established. Also, to reduce the number of rules, we define and use first-order TS fuzzy models whose generated local linear models can be localized in flexible fuzzy subspaces. To demonstrate the improved performance of the proposed identification approach, the efficiency of the evolving TS model is studied in prediction of monthly sunspot number and forecast of daily electrical power consumption. The prediction and modeling results are compared with that of some important existing evolving fuzzy systems.