Hermetic sealing of food packaging trays

Suojakaasupakkaaminen on lisääntynyt voimakkaasti viime vuosina elintarvikkeiden pakkaamisessa sillä pakkaamalla elintarvike suojakaasuun voidaan sen hyllyikää pidentää ilman säilöntäaineita. Tällainen pakkaaminen vaatii kuitenkin täysin kaasutiiviin pakkauksen, jonka kaasunläpäisevyys on myös alhainen. Yleisimmin käytetyt pakkausmateriaalit suojakaasupakkaamisessa ovat monikerroksiset muovimateriaalit, joissa yhdistyy monen eri muovin parhaimmat ominaisuudet. Yleisimmin käytettyjä muovilaatuja näissä monikerrosrakenteissa ovat PE, PET, PA ja EVOH polymeerit. Myös muita perinteisiä polymeerejä käytetään jonkin verran näissä rakenteissa. Uudemmat muovilaadut, kuten biohajoavat muovit, eivät ole vielä yleistyneet kaupallisessa käytössä pääasiallisesti niiden korkean hinnan vuoksi. Muovisten pakkausten korvaamista esimerkiksi muovipäällystetyillä kartonkipakkauksilla on viime vuosien aikana tutkittu enenevissä määrin. Muovipakkausten korvaamista helpommin kierrätettävillä ja mahdollisesti biohajoavilla materiaaleilla edistävät EU:n direktiivit, jotka käsittelevät pakkausjätteen käsittelyä. Kartonkivuokien saumaamista kaasutiiviisti tutkittiin myös tässä työssä. Tavoitteena oli löytää pakkaus, joka soveltuisi kanasuikaleiden pakkaamiseen suojakaasuun. Kana on herkkä mikrobiologiselle hajoamiselle, minkä johdosta se tulee pakata suojakaasuun jossa happipitoisuuden tulee olla alle 1 % pakkauspäivästä viimeiseen käyttöpäivään saakka. Suorittamalla erilaisia tiiveystutkimuksia voitiin osoittaa, että kartonkivuoka on mahdollista saumata kaasutiiviisti luotettavalla tavalla. Tämä vaatii kuitenkin kartonkivuokien valmistuksen optimoimista päällystemuovikerroksen ja kartongin paksuuden mukaan sekä kannen saumaamista optimoiduilla saumausparametreilla. Tiivein vuoka saavutettiin muovifilmikannella, jonka saumaus perustui samaan muoviin kuin vuoan saumaus. Polyeteenillä saavutettiin tiivein ja kestävin saumaustulos.

Expokit: A software package for computing matrix exponentials

Expokit provides a set of routines aimed at computing matrix exponentials. More precisely, it computes either a small matrix exponential in full, the action of a large sparse matrix exponential on an operand vector, or the solution of a system of linear ODEs with constant inhomogeneity. The backbone of the sparse routines consists of matrix-free Krylov subspace projection methods (Arnoldi and Lanczos processes), and that is why the toolkit is capable of coping with sparse matrices of large dimension. The software handles real and complex matrices and provides specific routines for symmetric and Hermitian matrices. The computation of matrix exponentials is a numerical issue of critical importance in the area of Markov chains and furthermore, the computed solution is subject to probabilistic constraints. In addition to addressing general matrix exponentials, a distinct attention is assigned to the computation of transient states of Markov chains.

Pharmacokinetic software for the health sciences - Choosing the right package for teaching purposes

Computer assisted learning has an important role in the teaching of pharmacokinetics to health sciences students because it transfers the emphasis from the purely mathematical domain to an 'experiential' domain in which graphical and symbolic representations of actions and their consequences form the major focus for learning. Basic pharmacokinetic concepts can be taught by experimenting with the interplay between dose and dosage interval with drug absorption (e.g. absorption rate, bioavailability), drug distribution (e.g. volume of distribution, protein binding) and drug elimination (e.g. clearance) on drug concentrations using library ('canned') pharmacokinetic models. Such 'what if' approaches are found in calculator-simulators such as PharmaCalc, Practical Pharmacokinetics and PK Solutions. Others such as SAAM II, ModelMaker, and Stella represent the 'systems dynamics' genre, which requires the user to conceptualise a problem and formulate the model on-screen using symbols, icons, and directional arrows. The choice of software should be determined by the aims of the subject/course, the experience and background of the students in pharmacokinetics, and institutional factors including price and networking capabilities of the package(s). Enhanced learning may result if the computer teaching of pharmacokinetics is supported by tutorials, especially where the techniques are applied to solving problems in which the link with healthcare practices is clearly established.

Prediction of coal ash fusion temperatures with the F*A*C*T thermodynamic computer package

This paper outlines research on the processes taking place within the coal mineral matter at high temperatures and development of the relationship between ash fusion temperatures (AFT) and phase equilibria of the coal ash slags. A new thermodynamic database for the Al-Ca-Fe-O-Si system developed by the author was used in conjunction with the thermodynamic computer package F*A*C*T for these purposes. In addition, high temperature experimental studies were undertaken that involved heat treatment and quenching of the ash cones followed by the analyses using different techniques. The study provided new information on the processes taking place during AFT test and demonstrated the validity of the AFTs predictions with F*A*C*T. Examples of practical applications of the AFT prediction method are given in the paper. The results of this study are important not only for the AFT predictions, but also in general for the application of phase equilibrium science to the characterisation of the coal mineral matter interactions at high temperature. (C) 2002 Elsevier Science Ltd. All rights reserved.

Readability of medicinal package leaflets: a systematic review

OBJECTIVE To review studies on the readability of package leaflets of medicinal products for human use.METHODS We conducted a systematic literature review between 2008 and 2013 using the keywords “Readability and Package Leaflet” and “Readability and Package Insert” in the academic search engine Biblioteca do Conhecimento Online,comprising different bibliographic resources/databases. The preferred reporting items for systematic reviews and meta-analyses criteria were applied to prepare the draft of the report. Quantitative and qualitative original studies were included. Opinion or review studies not written in English, Portuguese, Italian, French, or Spanish were excluded.RESULTS We identified 202 studies, of which 180 were excluded and 22 were enrolled [two enrolling healthcare professionals, 10 enrolling other type of participants (including patients), three focused on adverse reactions, and 7 descriptive studies]. The package leaflets presented various readability problems, such as complex and difficult to understand texts, small font size, or few illustrations. The main methods to assess the readability of the package leaflet were usability tests or legibility formulae. Limitations with these methods included reduced number of participants; lack of readability formulas specifically validated for specific languages (e.g., Portuguese); and absence of an assessment on patients literacy, health knowledge, cognitive skills, levels of satisfaction, and opinions.CONCLUSIONS Overall, the package leaflets presented various readability problems. In this review, some methodological limitations were identified, including the participation of a limited number of patients and healthcare professionals, the absence of prior assessments of participant literacy, humor or sense of satisfaction, or the predominance of studies not based on role-plays about the use of medicines. These limitations should be avoided in future studies and be considered when interpreting the results.

SDAR: a package for plotting and analyzing stratigraphy data in R

Stratigraphic Columns (SC) are the most useful and common ways to represent the eld descriptions (e.g., grain size, thickness of rock packages, and fossil and lithological components) of rock sequences and well logs. In these representations the width of SC vary according to the grain size (i.e., the wider the strata, the coarser the rocks (Miall 1990; Tucker 2011)), and the thickness of each layer is represented at the vertical axis of the diagram. Typically these representations are drawn 'manually' using vector graphic editors (e.g., Adobe Illustrator®, CorelDRAW®, Inskape). Nowadays there are various software which automatically plot SCs, but there are not versatile open-source tools and it is very di cult to both store and analyse stratigraphic information. This document presents Stratigraphic Data Analysis in R (SDAR), an analytical package1 designed for both plotting and facilitate the analysis of Stratigraphic Data in R (R Core Team 2014). SDAR, uses simple stratigraphic data and takes advantage of the exible plotting tools available in R to produce detailed SCs. The main bene ts of SDAR are: (i) used to generate accurate and complete SC plot including multiple features (e.g., sedimentary structures, samples, fossil content, color, structural data, contacts between beds), (ii) developed in a free software environment for statistical computing and graphics, (iii) run on a wide variety of platforms (i.e., UNIX, Windows, and MacOS), (iv) both plotting and analysing functions can be executed directly on R's command-line interface (CLI), consequently this feature enables users to integrate SDAR's functions with several others add-on packages available for R from The Comprehensive R Archive Network (CRAN).

An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.

Estudi de la configuració magnètica de l'experiment LTP (LISA Technology Package)

La Teoria de la Relativitat General preveu que quan un objecte massiu és sotmès a una certa acceleració en certes condicions ha d’emetre ones gravitacionals. Es tracta d’un tipus d’on altament energètica però que interacciona amb la matèria de manera molt feble i el seu punt d’emissió és força llunyà. Per la qual cosa la seva detecció és una tasca extraordinàriament complicada. Conseqüentment, la detecció d’aquestes ones es creu molt més factible utilitzant instruments situats a l’espai. Amb aquest objectiu, neis la missió LISA (Laser Interferometer Space Antenna). Es tracta aquesta d’una missió conjunta entre la NASA i l’ESA amb llançament previst per 2020-2025. Per reduir els riscs que comporta una primera utilització de tecnologia no testejada, unit a l’alt cost econòmic de la missió LISA. Aquesta missió contindrà instruments molt avançats: el LTP (LISA Technoplogy Package), desenvolupat per la Unió Europea, que provarà la tecnologia de LISA i el Drag Free flying system, que s’encarregarà de provar una sèrie de propulsors (thrusters) utilitzats per al control d’actitud i posició de satèl•lit amb precisió de nanòmetres. Particularment, el LTP, està composat per dues masses de prova separades per 35 centímetres, i d’un interferòmetre làser que mesura la variació de la distància relativa entre elles. D’aquesta manera, el LTP mesurarà les prestacions dels equips i les possibles interferències que afecten a la mesura. Entre les fonts de soroll es troben, entre d’altres, el vent i pressió de radiació solar, les càrregues electrostàtiques, el gradient tèrmic, les fluctuacions de voltatge o les forces internes. Una de les possibles causes de soroll és aquella que serà l’objecte d’estudi en aquest projecte de tesi doctoral: la presència dintre del LTP de camps magnètics, que exerceixen una força sobre les masses de prova, la seva estimació i el seu control, prenent en compte les caracterírstiques magnètiques de l’experiment i la dinàmica del satèl•lit.

Local Distance-Based Generalized Linear Models using the dbstats package for R

This paper introduces local distance-based generalized linear models. These models extend (weighted) distance-based linear models firstly with the generalized linear model concept, then by localizing. Distances between individuals are the only predictor information needed to fit these models. Therefore they are applicable to mixed (qualitative and quantitative) explanatory variables or when the regressor is of functional type. Models can be fitted and analysed with the R package dbstats, which implements several distancebased prediction methods.

The MIGCLIM R package - seamless integration of dispersal constraints into projections of species distribution models

The MIGCLIM R package is a function library for the open source R software that enables the implementation of species-specific dispersal constraints into projections of species distribution models under environmental change and/or landscape fragmentation scenarios. The model is based on a cellular automaton and the basic modeling unit is a cell that is inhabited or not. Model parameters include dispersal distance and kernel, long distance dispersal, barriers to dispersal, propagule production potential and habitat invasibility. The MIGCLIM R package has been designed to be highly flexible in the parameter values it accepts, and to offer good compatibility with existing species distribution modeling software. Possible applications include the projection of future species distributions under environmental change conditions and modeling the spread of invasive species.

A compositional data analysis package for R providing multiple approaches

”compositions” is a new R-package for the analysis of compositional and positive data.It contains four classes corresponding to the four different types of compositional andpositive geometry (including the Aitchison geometry). It provides means for computation,plotting and high-level multivariate statistical analysis in all four geometries.These geometries are treated in an fully analogous way, based on the principle of workingin coordinates, and the object-oriented programming paradigm of R. In this way,called functions automatically select the most appropriate type of analysis as a functionof the geometry. The graphical capabilities include ternary diagrams and tetrahedrons,various compositional plots (boxplots, barplots, piecharts) and extensive graphical toolsfor principal components. Afterwards, ortion and proportion lines, straight lines andellipses in all geometries can be added to plots. The package is accompanied by ahands-on-introduction, documentation for every function, demos of the graphical capabilitiesand plenty of usage examples. It allows direct and parallel computation inall four vector spaces and provides the beginner with a copy-and-paste style of dataanalysis, while letting advanced users keep the functionality and customizability theydemand of R, as well as all necessary tools to add own analysis routines. A completeexample is included in the appendix