Del Big Bang a l'Homo Sapiens Sapiens. Una visió evolutiva global de la Història de l'Univers

Crec que un resum lleugerament informal pot animar al lector a endinsar-se en el seriós contingut del llibre. S'aixeca el teló (fa uns tretze mil set cents milions d'anys) amb un "chupinazo" que deixa en ridícul als dels "sanfermines". L' entropia comença a augmentar ... i ja no pararà! L'empat inicial entre la matèria i l' antimateria es decanta a favor de la matèria, encara que per molt poc! Matèria i antimatèria s'anihilen, però el lleuger excés de matèria fa que en l'anihilació en quedin suficients traces per a que la funció pugui continuar. Existeix un antim��n d'antimatèria que transcorre en un antitemps i en el qual un antiCastells format de D-aminoàcids i L-carbohidrats ha escrit un antillibre? L'univers ja s'ha refredat suficientment per a que el deuteri pugui perviure un xic: via lliure a la formació de l'heli-4 (etapa còsmica). La matèria s'organitza ostensiblement: neix el Sistema de Períodes (etapa estel·lar). Els estels exploten i la pols formada embruta tot l'espai interestel·lar (etapa interestel·lar). Visita al "zoo molecular": molt carboni i molt enllaç triple. Espècies molt reactives que no reaccionen ... per no trobar amb què! El futbol, va també envair l'espai? En el tebi oceà, la matèria continua la seva escalada de la complexitat (etapa planetària). Apareix la vida: però, què és la vida? Fi de l'evolució determinista i començament de l'evolució contingent (etapa biològica). Justificada alarma entre els anaerobis: qui ha deixat escapar aquest gas (oxigen) tan verinós? El miracle de la vida en un univers hostil. L'arsenal d'armes preventives i defensives: vitamines C i E, catalasa, peroxidases, dismutases superoxídiques, etc. La meravella del transport electrònic. Com es possible tanta perfecció? L'escalada de la complexitat prossegueix acceleradament. Finalment, s'arriba a l'Homo sapiens sapiens: podria no ser com és? Millor canviar l'entorn que canviar l'Homo: l'evolució biològica s'ha acabat. Comença l'evolució cultural o social. La possible existència de vida i de vida intel·ligent extra-terrestres. La inevitable protagonisme de la Química dels derivats del carboni. El Principi antropo-cosmològic: realment, estem sols?

Predicting random level and seasonality of hotel prices. A structural equation growth curve approach

This article examines the effect on price of different characteristics of holiday hotels in the sun-and-beach segment, under the hedonic function perspective. Monthly prices of the majority of hotels in the Spanish continental Mediterranean coast are gathered from May to October 1999 from the tour operator catalogues. Hedonic functions are specified as random-effect models and parametrized as structural equation models with two latent variables, a random peak season price and a random width of seasonal fluctuations. Characteristics of the hotel and the region where they are located are used as predictors of both latent variables. Besides hotel category, region, distance to the beach, availability of parking place and room equipment have an effect on peak price and also on seasonality. 3- star hotels have the highest seasonality and hotels located in the southern regions the lowest, which could be explained by a warmer climate in autumn

La Física. Fites en l'aplicació del seu m��tode: Newton

Anàlisi del llegat de Newton

Tirant lo blanc, Tirante el blanco

Crítica a la pel·lícula "Tirant lo Blanc" de Vicente Aranda

Quaranta anys de docència

Article sobre els 45 anys d'experiència docent de l'autor

Algunes reflexions al voltant del procés transcendent de l'educació

Breu estudi sobre l'estat de l'educació avui en dia en l'àmbit escolar i familiar

La Passió de Verges : anàlisi filològica dels textos

Anàlisi de textos que tracten el tema de La Passió de Verges

El Paisatge des de la perspectiva geogràfica

El paisatge és un dels objectes d'estudi m��s antics de la geografia i té les seves arrels científiqe s en els geògrafs i naturalistes de finals del selge XIX (...)

The single principle of compositional data analysis, continuing fallacies, confusions and misunderstandings and some suggested remedies

In any discipline, where uncertainty and variability are present, it is important to have principles which are accepted as inviolate and which should therefore drive statistical modelling, statistical analysis of data and any inferences from such an analysis. Despite the fact that two such principles have existed over the last two decades and from these a sensible, meaningful methodology has been developed for the statistical analysis of compositional data, the application of inappropriate and/or meaningless methods persists in many areas of application. This paper identifies at least ten common fallacies and confusions in compositional data analysis with illustrative examples and provides readers with necessary, and hopefully sufficient, arguments to persuade the culprits why and how they should amend their ways

Inference of distributional parameters from compositional samples containing nondetects

Low concentrations of elements in geochemical analyses have the peculiarity of being compositional data and, for a given level of significance, are likely to be beyond the capabilities of laboratories to distinguish between minute concentrations and complete absence, thus preventing laboratories from reporting extremely low concentrations of the analyte. Instead, what is reported is the detection limit, which is the minimum concentration that conclusively differentiates between presence and absence of the element. A spatially distributed exhaustive sample is employed in this study to generate unbiased sub-samples, which are further censored to observe the effect that different detection limits and sample sizes have on the inference of population distributions starting from geochemical analyses having specimens below detection limit (nondetects). The isometric logratio transformation is used to convert the compositional data in the simplex to samples in real space, thus allowing the practitioner to properly borrow from the large source of statistical techniques valid only in real space. The bootstrap method is used to numerically investigate the reliability of inferring several distributional parameters employing different forms of imputation for the censored data. The case study illustrates that, in general, best results are obtained when imputations are made using the distribution best fitting the readings above detection limit and exposes the problems of other more widely used practices. When the sample is spatially correlated, it is necessary to combine the bootstrap with stochastic simulation

When zero doesn't mean it and other geomathematical mischief

There is almost not a case in exploration geology, where the studied data doesn’t includes below detection limits and/or zero values, and since most of the geological data responds to lognormal distributions, these “zero data” represent a mathematical challenge for the interpretation. We need to start by recognizing that there are zero values in geology. For example the amount of quartz in a foyaite (nepheline syenite) is zero, since quartz cannot co-exists with nepheline. Another common essential zero is a North azimuth, however we can always change that zero for the value of 360°. These are known as “Essential zeros”, but what can we do with “Rounded zeros” that are the result of below the detection limit of the equipment? Amalgamation, e.g. adding Na2O and K2O, as total alkalis is a solution, but sometimes we need to differentiate between a sodic and a potassic alteration. Pre-classification into groups requires a good knowledge of the distribution of the data and the geochemical characteristics of the groups which is not always available. Considering the zero values equal to the limit of detection of the used equipment will generate spurious distributions, especially in ternary diagrams. Same situation will occur if we replace the zero values by a small amount using non-parametric or parametric techniques (imputation). The method that we are proposing takes into consideration the well known relationships between some elements. For example, in copper porphyry deposits, there is always a good direct correlation between the copper values and the molybdenum ones, but while copper will always be above the limit of detection, many of the molybdenum values will be “rounded zeros”. So, we will take the lower quartile of the real molybdenum values and establish a regression equation with copper, and then we will estimate the “rounded” zero values of molybdenum by their corresponding copper values. The method could be applied to any type of data, provided we establish first their correlation dependency. One of the main advantages of this method is that we do not obtain a fixed value for the “rounded zeros”, but one that depends on the value of the other variable. Key words: compositional data analysis, treatment of zeros, essential zeros, rounded zeros, correlation dependency

Discrete and continuous compositions

This paper examines a dataset which is modeled well by the Poisson-Log Normal process and by this process mixed with Log Normal data, which are both turned into compositions. This generates compositional data that has zeros without any need for conditional models or assuming that there is missing or censored data that needs adjustment. It also enables us to model dependence on covariates and within the composition

Bayesian tools for zero counts in compositional data

The log-ratio methodology makes available powerful tools for analyzing compositional data. Nevertheless, the use of this methodology is only possible for those data sets without null values. Consequently, in those data sets where the zeros are present, a previous treatment becomes necessary. Last advances in the treatment of compositional zeros have been centered especially in the zeros of structural nature and in the rounded zeros. These tools do not contemplate the particular case of count compositional data sets with null values. In this work we deal with \count zeros" and we introduce a treatment based on a mixed Bayesian-multiplicative estimation. We use the Dirichlet probability distribution as a prior and we estimate the posterior probabilities. Then we apply a multiplicative modi¯cation for the non-zero values. We present a case study where this new methodology is applied. Key words: count data, multiplicative replacement, composition, log-ratio analysis

CODAPACK3D. A new version of Compositional Data Package

The statistical analysis of compositional data should be treated using logratios of parts, which are difficult to use correctly in standard statistical packages. For this reason a freeware package, named CoDaPack was created. This software implements most of the basic statistical methods suitable for compositional data. In this paper we describe the new version of the package that now is called CoDaPack3D. It is developed in Visual Basic for applications (associated with Excel©), Visual Basic and Open GL, and it is oriented towards users with a minimum knowledge of computers with the aim at being simple and easy to use. This new version includes new graphical output in 2D and 3D. These outputs could be zoomed and, in 3D, rotated. Also a customization menu is included and outputs could be saved in jpeg format. Also this new version includes an interactive help and all dialog windows have been improved in order to facilitate its use. To use CoDaPack one has to access Excel© and introduce the data in a standard spreadsheet. These should be organized as a matrix where Excel© rows correspond to the observations and columns to the parts. The user executes macros that return numerical or graphical results. There are two kinds of numerical results: new variables and descriptive statistics, and both appear on the same sheet. Graphical output appears in independent windows. In the present version there are 8 menus, with a total of 38 submenus which, after some dialogue, directly call the corresponding macro. The dialogues ask the user to input variables and further parameters needed, as well as where to put these results. The web site http://ima.udg.es/CoDaPack contains this freeware package and only Microsoft Excel© under Microsoft Windows© is required to run the software. Kew words: Compositional data Analysis, Software