Meat microflora is mainly composed of Acinetobacter, Moraxella, Brochothrix termosphacta, Lactobacillus, Pseudomonas and Enterobacteriaceae family genera, such as Klebsiella sp. and E. coli. In natural conditions meat pH can range from about 6.0 (being close to the optimum level for most pathogenic and alteration-causing
bacteria) to values close to 5.5, at which microbial growth rate decreases significantly. Combining low pH with other factors
such as low temperatures can almost completely prevent microbial growth from occurring. Muscle pH variation is highly dependent
on the tissue glycogen level at the time of slaughter.

Abstract  With the explosive growth of the Word Wide Web, information overload became a crucial concern. In a data-rich information-poor
environment like the Web, the discrimination of useful or desirable information out of tons of mostly worthless data became
a tedious task. The role of Machine Learning in tackling this problem is thoroughly discussed in the literature, but few systems
are available for public use. In this work, we bridge theory to practice, by implementing a web-based news reader enhanced
with a specifically designed machine learning framework for dynamic content personalization. This way, we get the chance to
examine applicability and implementation issues and discuss the effectiveness of machine learning methods for the classification
of real-world text streams. The main features of our system named PersoNews are: (a) the aggregation of many different news
sources that offer an RSS version of their content, (b) incremental filtering, offering dynamic personalization of the content
not only per user but also per each feed a user is subscribed to, and (c) the ability for every user to watch a more abstracted
topic of interest by filtering through a taxonomy of topics. PersoNews is freely available for public use on the WWW (http://news.csd.auth.gr).

In this contribution we consider the inversion of quasi-linear models by means of Monte-Carlo methods. Quasi-linear models
are a special class of non-linear models, which can be formally written in matrix-vector formulation but whose design matrix
depends on a subset of the unknown parameters. A large class of geodetic problems can be recast as quasi-linear models. As
there exist no general analytical solutions for the quasi-linear model, Monte Carlo optimization techniques in the context
of a Bayesian approach are investigated here. In order to accelerate the Monte Carlo method we utilize the analytical solution
of the linear model under the condition that the unknown parameters in the design matrix are considered as constant. Thereby
the sampling dimension in the Monte Carlo approach can be reduced. The estimators for expectation and covariance of the parameters
that we derive turn out as weighted means of the individual sample least-squares solutions. We develop an efficient set of
algorithms for the solution of quasi-linear models using Monte Carlo techniques and demonstrate the efficiency of the method
in a numerical example which is taken from satellite geodesy and gravity field recovery. Two groups of unknown parameters
are relevant in this example: the spherical harmonic coefficients of a gravity field model, and the state vectors of the satellite(s)
which affect the observation model through the design matrix.

Abstract  Recommender systems enable merchants to assist customers in finding products that best satisfy their needs. Unfortunately,
current recommender systems suffer from various privacy-protection vulnerabilities. Customers should be able to keep private
their personal information, including their buying preferences, and they should not be tracked against their will. The commercial
interests of merchants should also be protected by allowing them to make accurate recommendations without revealing legitimately
compiled valuable information to third parties. We introduce a theoretical approach for a system called Alambic, which achieves
the above privacy-protection objectives in a hybrid recommender system that combines content-based, demographic and collaborative
filtering techniques. Our system splits customer data between the merchant and a semi-trusted third party, so that neither
can derive sensitive information from their share alone. Therefore, the system could only be subverted by a coalition between
these two parties.

Abstract  A variable selection method using global score estimation is proposed, which is applicable as a selection criterion in any
multivariate method without external variables such as principal component analysis, factor analysis and correspondence analysis.
This method selects a subset of variables by which we approximate the original global scores as much as possible in the context
of least squares, where the global scores, e.g. principal component scores, factor scores and individual scores, are computed
based on the selected variables. Global scores are usually orthogonal. Therefore, the estimated global scores should be restricted
to being mutually orthogonal. According to how to satisfy that restriction, we propose three computational steps to estimate
the scores. Example data is analyzed to demonstrate the performance and usefulness of the proposed method, in which the proposed
algorithm is evaluated and the results obtained using four cost-saving selection procedures are compared. This example shows
that combining these steps and procedures yields more accurate results quickly.

Abstract  Recently, new network topologies including relays and meshed networks have emerged as a key technology receiving a lot of
attention due to the increasing need for additional resources such as spectrum, and required high data-rates specified by
the International Mobile Telecommunication-Advanced (IMT-Advanced) systems (Report ITU-R M.1645, Framework and overall objectives
of the future development of IMT-2000 and systems beyond IMT-2000). These systems are designed to provide diverse services
sustaining low to high mobility applications and yielding a significant improvement in terms of Quality-of-Service (QoS).
In this paper, we identify and describe the novel network topologies that will be able to accommodate the high data rates
demanded within the spectrum scarcity paradigm. By efficiently integrating mesh networks and flexible resource sharing, the
overall network efficiency is shown to increase. In addition, direct terminal-to-terminal (T2T) communication both with and
without the use of relays is presented, where terminals communicate without the use of base stations (BSs) as it is done in
conventional systems. The results show significant gains in terms of throughput, average frame delays and power consumption
compared to conventional networks. Finally, open issues and potential challenges are addressed.

Abstract  The earthworm fauna was recorded from a former open clay pit five and seven years after the area had been recultivated with
various tree species. The highest abundances (200–245 specimens/m²) and biomasses (46.8 to 48.4 g/m²) were found in the following stand types: mixed woodland of balsam poplar (various sorts) and grey alder (Alnus incana (L.)Moench), mixed woodland of balsam poplar (Andoscoggin) and black alder (Alnus glutinosa (L.)Gaertn.), and a pure stand of black alder. Clearly lower values were found in a plot with common oaks (Quercus robur L.) under a dense shelterwood of naturally regenerated willows (Salix spec.), and a plot exclusively stocked with naturally regenerated willows. The rapid resettlement of the backfilled, compacted
clay soil is above all explained by the high pH-values found in the surface soil. The differences between the studied plots
regarding total abundance and biomass might be explained by the different qualities and amounts of litter. The highest species
diversity — expressed by the Shannon-index — and the highest proportion of endogeic species, particularlyAporrectodea caliginosa Savigny 1826, were calculated for the two stands with a mixture of alder and balsam poplar. This result could have been caused
by the lack of parent soil bed and the particularly high pH-value of the soils found there.

Abstract  LetM be the moduli space of generalized parabolic bundles (GPBs) of rankr and degree dona smooth curveX. LetM

_−L
be the closure of its subset consisting of GPBs with fixed determinant− L. We define a moduli functor for whichM

_−L
is the coarse moduli scheme. Using the correspondence between GPBs onX and torsion-free sheaves on a nodal curveY of whichX is a desingularization, we show thatM

_−L
can be regarded as the compactified moduli scheme of vector bundles onY with fixed determinant. We get a natural scheme structure on the closure of the subset consisting of torsion-free sheaves
with a fixed determinant in the moduli space of torsion-free sheaves onY. The relation to Seshadri-Nagaraj conjecture is studied.

Even though they’re based on one page, Flex applications can become very large. If you were to concentrate all the code in
the main application file, the resulting application would be difficult to maintain, and it would be impossible to reuse the
code and the debugging constructs. Fortunately, Flex supports a component-based development model, which enables the developer
to structure the code of the application in a logical way, subdividing it into different files. These external files can be
MXML or ActionScript files. When you develop an application with Flex, you use the MXML tag or ActionScript code to declare
the objects in the project. These objects are called components, so it is considered to be a componentbased framework.

Security is a fundamental aspect in the development of an application, and different levels of security can be guaranteed
to each project—taking into consideration the interaction with data and remote calls.