Effect of Fermentation and Drying on Procyanidins, Antiradical Activity and Reducing Properties of Cocoa Beans

This work was aimed to determine the effect of fermentation and drying on the content and profile of procyanidins (from monomers P1 to polymers P10) as well as on the antiradical and scavenging properties of cocoa beans. To this purpose, three experiments were carried out: a traditional fermentation process followed by air drying and two pilot-scale fermentation processes by either natural microbiota or starter followed by sun drying. Procyanidins were evaluated by HPLC analysis, while the total polyphenol index (TPI), the antiradical activity as well as the reducing power were determined by means of the reaction with the Folin–Ciocalteu reagent, the decolorization assays of the ABTS radical (TEAC) and the Ferric Reducing Antioxidant Power (FRAP) methods, respectively. Both the traditional and pilot-scale processes resulted to affect the profile and content of the procyanidins fractions as well as the antiradical and reducing power functionality. Drying caused a severe reduction of compounds and thus resulted to be the critical step for the loss of procyanidins and monomers in particular. The indices of functionality generally showed a decreasing trend as a consequence of processing, and their evolution was similar to that observed in procyanidins content. To study the relationship between the individual procyanidins and the antioxidant activity expressed as TEAC, FRAP and TPI, the data set were processed by modified partial least squares regression. The obtained models presented a good predictive ability. Normalised regression coefficients showed that the relative contribution of each single class of compounds to total antioxidant activity resulted as follows: P1?>?P2?>?P3?>?P4?>?P6?>?P8?>?P5?>?P7?>?P9?>>?P10.     

An association analysis between OXT genotype and milk yield and flow in Italian Mediterranean river buffalo

The aim of this study was to evaluate possible associations between three SNPs at the oxytocin locus (AM234538: g.28C>T; g.204A>G and g.1627G>T) and two productive traits, milk yield and milkability, in Italian Mediterranean river buffaloes. Effects of parity, calving season and month of production were also evaluated. A total of 41 980 test-day records belonging to 219 lactations of 163 buffalo cows were investigated. The allele call rate was 98·8% and the major allele frequency for all the investigated loci was 0·76. The OXT genotype was significantly associated with milk yield (P=0·029). The TT genotype showed an average daily milk yield approximately 1·7 kg higher than GT buffaloes. Such a difference represents about 23% more milk/d. A large dominance effect (-1·17±0·43 kg) was estimated, whereas the contribution of OXT genotype (r(2)(OXT)) to the total phenotypic variance in milk yield was equal to 0·06. The TT genotype showed higher values also for the milk flow, even though the estimated difference did not reach a level of statistical significance (P=0·07). Such an association, among the first reported for the oxytocin locus in ruminants, should be tested on a population scale and possible effects on milk composition traits should be evaluated in order to supply useful indications for the application of marker-assisted selection programmes in river buffaloes.     

Water Network Protection from Intentional Contamination by Sectorization

Each single phase of a water supply network, from water adduction to distribution to end-users, is exposed to many diverse potential sources of intentional contamination (or malicious attacks). One of the most dangerous threats is a backflow attack that occurs when a pump system, easily available on the market, is utilized to overcome the pressure gradient of network pipes. In this work, a simple backflow attack with cyanide being introduced into a real-water system is modeled and the most dangerous introduction points for a contaminant incident are defined. Moreover, the network vulnerability has been analyzed by computing the lethal dose of cyanide ingested by users and the total length of the contaminated water system. Eventually the effects of network partitioning and district isolation to protect water supply systems have been investigated. The results show how district closing - by network sectorization techniques used to improve leakage search and reduction - can significantly decrease contaminant diffusion and protect part of the users from cyanide uptake. Network sectorization can also reduce the risk of simple malicious attacks because several introduction points are necessary to have a massive negative impact on the network. Simulation results also show that in some cases water network partitioning may worsen water network protection and further studies are necessary to design water districts for network security and safety.     

Parameter-less co-clustering for star-structured heterogeneous data

The availability of data represented with multiple features coming from heterogeneous domains is getting more and more common in real world applications. Such data represent objects of a certain type, connected to other types of data, the features, so that the overall data schema forms a star structure of inter-relationships. Co-clustering these data involves the specification of many parameters, such as the number of clusters for the object dimension and for all the features domains. In this paper we present a novel co-clustering algorithm for heterogeneous star-structured data that is parameter-less. This means that it does not require either the number of row clusters or the number of column clusters for the given feature spaces. Our approach optimizes the Goodman–Kruskal’s τ, a measure for cross-association in contingency tables that evaluates the strength of the relationship between two categorical variables. We extend τ to evaluate co-clustering solutions and in particular we apply it in a higher dimensional setting. We propose the algorithm CoStar which optimizes τ by a local search approach. We assess the performance of CoStar on publicly available datasets from the textual and image domains using objective external criteria. The results show that our approach outperforms state-of-the-art methods for the co-clustering of heterogeneous data, while it remains computationally efficient.     

Unveiling the complexity of human mobility by querying and mining massive trajectory data

The technologies of mobile communications pervade our society and wireless networks sense the movement of people, generating large volumes of mobility data, such as mobile phone call records and Global Positioning System (GPS) tracks. In this work, we illustrate the striking analytical power of massive collections of trajectory data in unveiling the complexity of human mobility. We present the results of a large-scale experiment, based on the detailed trajectories of tens of thousands private cars with on-board GPS receivers, tracked during weeks of ordinary mobile activity. We illustrate the knowledge discovery process that, based on these data, addresses some fundamental questions of mobility analysts: what are the frequent patterns of people’s travels? How big attractors and extraordinary events influence mobility? How to predict areas of dense traffic in the near future? How to characterize traffic jams and congestions? We also describe M-Atlas, the querying and mining language and system that makes this analytical process possible, providing the mechanisms to master the complexity of transforming raw GPS tracks into mobility knowledge. M-Atlas is centered onto the concept of a trajectory, and the mobility knowledge discovery process can be specified by M-Atlas queries that realize data transformations, data-driven estimation of the parameters of the mining methods, the quality assessment of the obtained results, the quantitative and visual exploration of the discovered behavioral patterns and models, the composition of mined patterns, models and data with further analyses and mining, and the incremental mining strategies to address scalability.     

Hierarchical co-clustering: off-line and incremental approaches

Clustering data is challenging especially for two reasons. The dimensionality of the data is often very high which makes the cluster interpretation hard. Moreover, with high-dimensional data the classic metrics fail in identifying the real similarities between objects. The second challenge is the evolving nature of the observed phenomena which makes the datasets accumulating over time. In this paper we show how we propose to solve these problems. To tackle the high-dimensionality problem, we propose to apply a co-clustering approach on the dataset that stores the occurrence of features in the observed objects. Co-clustering computes a partition of objects and a partition of features simultaneously. The novelty of our co-clustering solution is that it arranges the clusters in a hierarchical fashion, and it consists of two hierarchies: one on the objects and one on the features. The two hierarchies are coupled because the clusters at a certain level in one hierarchy are coupled with the clusters at the same level of the other hierarchy and form the co-clusters. Each cluster of one of the two hierarchies thus provides insights on the clusters of the other hierarchy. Another novelty of the proposed solution is that the number of clusters is possibly unlimited. Nevertheless, the produced hierarchies are still compact and therefore more readable because our method allows multiple splits of a cluster at the lower level. As regards the second challenge, the accumulating nature of the data makes the datasets intractably huge over time. In this case, an incremental solution relieves the issue because it partitions the problem. In this paper we introduce an incremental version of our algorithm of hierarchical co-clustering. It starts from an intermediate solution computed on the previous version of the data and it updates the co-clustering results considering only the added block of data. This solution has the merit of speeding up the computation with respect to the original approach that would recompute the result on the overall dataset. In addition, the incremental algorithm guarantees approximately the same answer than the original version, but it saves much computational load. We validate the incremental approach on several high-dimensional datasets and perform an accurate comparison with both the original version of our algorithm and with the state of the art competitors as well. The obtained results open the way to a novel usage of the co-clustering algorithms in which it is advantageous to partition the data into several blocks and process them incrementally thus “incorporating” data gradually into an on-going co-clustering solution.     

Distance estimation of near‐field visual objects in stereoscopic displays

Stereoscopic displays have a promising future because of recent advancements and popularity of handheld devices and maturing head mounted displays. Gesture interaction such as pointing, selection, pinching, and manipulation are now possible in the current virtual environments, where accurate distance judgment is required. In this paper, we address the perception of exocentric distance in stereoscopic displays under two target orientations: horizontal and vertical. Three parallax conditions (on screen, 5 cm from screen, and 10 cm from screen) were considered, where the screen was fixed at a distance of 100 cm from the observer. Four levels of center‐to‐center distance between 10 and 50 cm were employed. The perceptual matching task revealed underestimation in all conditions. The overall judgment of exocentric distance was only about 80% of the actual. We also found a main effect of distance and interaction between layout and distance to be significant. The two important findings of this study are that underestimation of exocentric distance increases as the separation between virtual targets increases and that in vertical orientation, accuracy increases with closer targets. However, the main effects of layout and parallax on accuracy of judgment were not significant. Engineering implications of the results are also discussed in this paper.     

PIPELINE DEFECT PREDICTION USING SUPPORT VECTOR MACHINES

Oil and gas pipeline condition monitoring is a potentially challenging process due to varying temperature conditions, harshness of the flowing commodity and unpredictable terrains. Pipeline breakdown can potentially cost millions of dollars worth of loss, not to mention the serious environmental damage caused by the leaking commodity. The proposed techniques, although implemented on a lab scale experimental rig, ultimately aim at providing a continuous monitoring system using an array of different sensors strategically positioned on the surface of the pipeline. The sensors used are piezoelectric ultrasonic sensors. The raw sensor signal will be first processed using the discrete wavelet transform (DWT) as a feature extractor and then classified using the powerful learning machine called the support vector machine (SVM). Preliminary tests show that the sensors can detect the presence of wall thinning in a steel pipe by classifying the attenuation and frequency changes of the propagating lamb waves. The SVM algorithm was able to classify the signals as abnormal in the presence of wall thinning.     

Computer Imagery and Visualization in Civil Engineering Education

Higher education institutions in the United Kingdom have invested significantly in the implementation of communication and information technology in teaching, learning, and assessment of civil and building engineering—with mixed results. This paper focuses on the use of digital imagery and visualization materials to improve student understanding. It describes ways in which these materials are being used in the civil and building engineering curriculum, and, in particular, how distributed performance support systems (DPSS) can be applied to make more effective use of digital imagery and visualization material. This paper centers on the extent to which DPSS can be used in a civil and building vocational and continuing professional development context by tutors in the form of an electronic course delivery tool and by students in the form of an open-access student information system. This paper then describes how a DPSS approach to education is being adopted at Loughborough University as part of the CAL-Visual project. After highlighting the main aims and objectives of the project and describing the system, this paper discusses some of the issues encountered during the design and implementation of a DPSS and presents some preliminary results from initial trials.