LDPE/PET laminated films modified with FeO(OH) × H2O,Fe2O3, and ascorbic acid to develop oxygen scavenging system for food packaging

The iron compounds (iron(III) oxide‐hydroxide monohydrate FeO(OH) × H₂O, iron(III) oxide Fe₂O₃, and ascorbic acid) were used as oxygen scavengers modifiers in laminating of polymer films. This oxygen‐scavenging system was coated on preselected films (low density polyethylene [LDPE] and polyethylene terephthalate [PET]) from which the laminates were formed. It presents the new form of composite material packaging that has the function of oxygen scavenging, which could be suitable for food packaging. The scope of the research included studies of morphology of oxygen scavengers by scanning electron microscope and their average particle size distribution measure by particle size analyzer, the effect of type, and concentration of these substances on viscosity of adhesive and seal strength of laminates. The Fourier‐transform infrared spectroscopy (FTIR) of laminates was also performed to observe the potential interaction of functional groups of polyurethane adhesives with oxygen scavenger components. The most important ability of the developed system for oxygen scavenging was confirmed by measuring oxygen concentration (% vol) in a headspace with the prepared laminates. The concentrations of selected oxygen scavengers (4‐6 wt%) and their combinations were studied. The most effective oxygen scavenger system integrated within the PE/PET composite film consists of 6 wt% ascorbic acid and 1 wt% FeO(OH) × H₂O, where the oxygen concentration of 1.0 vol% (±0.20 vol%) was obtained after 15 days of storage. It was found that in this system the oxygen scavenging reaction occurs through ascorbate oxidation to dehydroascorbic acid, which is catalyzed by reduction of Fe³⁺ to Fe2⁺ ions.     

Representing Hyper-arithmetical Sets by Equations over Sets of Integers

Systems of equations with sets of integers as unknowns are considered. It is shown that the class of sets representable by unique solutions of equations using the operations of union and addition, defined as S+T={m+n?Om??S,n??T}, and with ultimately periodic constants is exactly the class of hyper-arithmetical sets. Equations using addition only can represent every hyper-arithmetical set under a simple encoding. All hyper-arithmetical sets can also be represented by equations over sets of natural numbers equipped with union, addition and subtraction $S \mathop {\mbox {$-^{\hspace {-.5em}\cdot }\,\,$}}T=\{m-n \mid m \in S, n \in T, m \geq n\}$ . Testing whether a given system has a solution is $\varSigma ^{1}_{1}$ -complete for each model. These results, in particular, settle the expressive power of the most general types of language equations, as well as equations over subsets of free groups.     

An unsupervised approach to feature discretization and selection

Many learning problems require handling high dimensional datasets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevant (or even detrimental) for the learning tasks. It is thus clear that there is a need for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for medium and high-dimensional datasets. The experimental results on several standard datasets, with both sparse and dense features, show the efficiency of the proposed techniques as well as improvements over previous related techniques.     

Time–HOBI: Index for optimizing star queries

One of the important research and technological problems in data warehouse query optimization concerns star queries. So far, most of the research focused on optimizing such queries by means of join indexes, bitmap join indexes, or various multidimensional indexes. These structures neither support navigation well along dimension hierarchies nor optimize joins with the Time dimension, which in practice is used in most of the star queries. In this paper we propose an index, called Time–HOBI, for optimizing the star queries that compute aggregates along dimension hierarchies. Time–HOBI, created on a dimension hierarchy, is composed of (1) a Hierarchically Organized Bitmap Index (HOBI), where one bitmap index is maintained for one dimension level, and (2) a Time Index (TI) that implicitly encodes time in every dimension. HOBI allows to quickly search for fact rows satisfying predicates defined on different levels of dimension hierarchies. With the support of TI joining a fact table with the Time dimension is avoided. Thus, Time–HOBI supports a broad class of star queries. In this paper we explain how query execution plans for star queries can profit from Time–HOBI. We show, based on experiments, the efficiency of Time–HOBI for different classes of queries, as compared to HOBI and a traditional bitmap index. Based on the experiments, we also demonstrate how sensitive Time–HOBI is to variable selectivity of queries. We also analyze the maintenance time of Time–HOBI as compared to HOBI and a traditional bitmap index. The experiments used in the paper have been conducted on a real dataset, coming from the biggest East-European Internet auction platform Allegro.pl. The experiments show that Time–HOBI can be successfully applied to the optimization of star queries as it offers promising performance improvement.     

Automatic contrast enhancement of low-light images based on local statistics of wavelet coefficients

This paper describes a new method for contrast enhancement in images and image sequences of low-light or unevenly illuminated scenes based on statistical modelling of wavelet coefficients of the image. A non-linear enhancement function has been designed based on the local dispersion of the wavelet coefficients modelled as a bivariate Cauchy distribution. Within the same statistical framework, a simultaneous noise reduction in the image is performed by means of a shrinkage function, thus preventing noise amplification. The proposed enhancement method has been shown to perform very well with insufficiently illuminated and noisy imagery, outperforming other conventional methods, in terms of contrast enhancement and noise reduction in the output data.     

Ambient bloom: new business, content, design and models to increase the semantic ambient media experience

Semantic ambient media are the novel trend in the world of media reaching from the pioneering subareas such as ambient advertising to the new and emerging subareas such as ambient assisted living. They will likely shape the upcoming years in terms of modeling smart environments and also media consumption and interaction. This work analyzes semantic ambient media by considering business models, content and media, interaction design and consumer experience, and methods and techniques that are important to create this new form of media. Discussion is led using the state-of-the-art event of the semantic ambient media, which is the annual international workshop on Semantic Ambient Media Experience (SAME). The study also creates a vision for how ambient media will evolve and how they will look like in the future decade.     

Serious storytelling – a first definition and review

Multimedia Tools and Applications - In human culture, storytelling is a long-established tradition. The reasons people tell stories are manifold: to entertain, to transfer knowledge between...     

On Metrics to Distinguish Skype Flows from HTTP Traffic

Skype is a Voice over IP (VoIP) Internet application that is gaining huge popularity in recent years. A key point to Skype popularity is its capability to dynamically adapt itself to operate behind firewalls or network proxies. A common way adopted by Skype to delude these network devices is to use port 80, normally expected to comprise HTTP traffic. In this paper, we propose metrics and investigate statistical tests intended to clearly distinguish Skype flows from HTTP traffic. We validate our study using real-world experimental datasets gathered at a commercial Internet Service Provider (ISP). Our experimental results suggest that the proposed methodology may be seen as a promising building block towards a system to detect general protocol anomalies in HTTP traffic.     

Scalable plasma simulation with ELMFIRE using efficient data structures for process communication

We describe the parallel full-f gyrokinetic particle-in-cell plasma simulation code ELMFIRE and the issue of solving an electrostatic potential from particle data distributed across several MPI (Message Passing Interface) processes. The potential is solved through a linear system with a strongly sparse matrix and ELMFIRE stores data of the estimated non-zero diagonals of the whole matrix in every MPI process. We present and compare several memory efficient structures for gathering the matrix data while keeping only a local part of the matrix in each process. We also demonstrate that these alternative structures improve scalability, thus enabling ELMFIRE to use more MPI processes and a finer time and space scale than before without sacrificing performance.