El acceso al agua de los agricultores familiares de la región pampeana : Un análisis multidimensional

Este artículo presenta, en su primera parte, el enfoque conceptual y metodológico de un proyecto de investigación-acción participativa abordado de forma interdisciplinaria. La investigación refiere a las diferentes dimensiones que condicionan el acceso al agua de los agricultores familiares en distintos sitios de la región pampeana. La problemática se define en la dinámica con los actores involucrados, considerando que el agua mediatiza las relaciones sociales -de apropiación para el riego y la producción agrícola y de otros sectores, el consumo y la eliminación de residuos- entre diferentes actores. En las últimas secciones se presentan algunas de las observaciones y reflexiones del equipo de investigación en base a los primeros avances del trabajo de campo

Towards a model-driven engineering approach of data mining

Nowadays, data mining is based on low-level specications of the employed techniques typically bounded to a specic analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Here, we propose a model-driven approach based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (via data-warehousing technology) and the analysis models for data mining (tailored to a specic platform). Thus, analysts can concentrate on the analysis problem via conceptual data-mining models instead of low-level programming tasks related to the underlying-platform technical details. These tasks are now entrusted to the model-transformations scaffolding.

A Bayesian modeling approach to multi-dimensional sentiment distributions prediction

Sentiment analysis has long focused on binary classification of text as either positive or negative. There has been few work on mapping sentiments or emotions into multiple dimensions. This paper studies a Bayesian modeling approach to multi-class sentiment classification and multidimensional sentiment distributions prediction. It proposes effective mechanisms to incorporate supervised information such as labeled feature constraints and document-level sentiment distributions derived from the training data into model learning. We have evaluated our approach on the datasets collected from the confession section of the Experience Project website where people share their life experiences and personal stories. Our results show that using the latent representation of the training documents derived from our approach as features to build a maximum entropy classifier outperforms other approaches on multi-class sentiment classification. In the more difficult task of multi-dimensional sentiment distributions prediction, our approach gives superior performance compared to a few competitive baselines. © 2012 ACM.

Some clarifications on the DEA clustering approach

This paper clarifies the role of alternative optimal solutions in the clustering of multidimensional observations using data envelopment analysis (DEA). The paper shows that alternative optimal solutions corresponding to several units produce different groups with different sizes and different decision making units (DMUs) at each class. This implies that a specific DMU may be grouped into different clusters when the corresponding DEA model has multiple optimal solutions. © 2011 Elsevier B.V. All rights reserved.

Multidimensional Heterogeneous Variable Prediction Based on Experts’ Statements

* The work was supported by the RFBR under Grant N07-01-00331a.

MPGN – An Approach for Discovering Class Association Rules

his article presents some of the results of the Ph.D. thesis Class Association Rule Mining Using MultiDimensional Numbered Information Spaces by Iliya Mitov (Institute of Mathematics and Informatics, BAS), successfully defended at Hasselt University, Faculty of Science on 15 November 2011 in Belgium

Developing multidimensional metrics for evaluating paediatric neurodevelopmental disorders

Healthy brain functioning depends on efficient communication of information between brain regions, forming complex networks. By quantifying synchronisation between brain regions, a functionally connected brain network can be articulated. In neurodevelopmental disorders, where diagnosis is based on measures of behaviour and tasks, a measure of the underlying biological mechanisms holds promise as a potential clinical tool. Graph theory provides a tool for investigating the neural correlates of neuropsychiatric disorders, where there is disruption of efficient communication within and between brain networks. This research aimed to use recent conceptualisation of graph theory, along with measures of behaviour and cognitive functioning, to increase understanding of the neurobiological risk factors of atypical development. Using magnetoencephalography to investigate frequency-specific temporal dynamics at rest, the research aimed to identify potential biological markers derived from sensor-level whole-brain functional connectivity. Whilst graph theory has proved valuable for insight into network efficiency, its application is hampered by two limitations. First, its measures have hardly been validated in MEG studies, and second, graph measures have been shown to depend on methodological assumptions that restrict direct network comparisons. The first experimental study (Chapter 3) addressed the first limitation by examining the reproducibility of graph-based functional connectivity and network parameters in healthy adult volunteers. Subsequent chapters addressed the second limitation through adapted minimum spanning tree (a network analysis approach that allows for unbiased group comparisons) along with graph network tools that had been shown in Chapter 3 to be highly reproducible. Network topologies were modelled in healthy development (Chapter 4), and atypical neurodevelopment (Chapters 5 and 6). The results provided support to the proposition that measures of network organisation, derived from sensor-space MEG data, offer insights helping to unravel the biological basis of typical brain maturation and neurodevelopmental conditions, with the possibility of future clinical utility.

A csomagoláson található információtartalom újragondolása sokdimenziós skálázással az élelmiszeripar példáján (Rethinking of information content on food packaging using multidimensional scaling)

A csomagolás részét képezi a jelölés – vagy más néven címke, label –, aminek elsődleges funkciója a termék tulajdonságairól való tájékoztatás, amellett, hogy a vállalat és a fogyasztó egyik legfontosabb találkozási pontja. Kiemelt szerepe van a marketing és a vállalati menedzsment eszköztárában, hiszen a fogyasztói döntéshozatal meghatározó forrása. A szerző írásában a jelölések definícióját, fajtáit és csoportosítását tárja az olvasó elé, majd ismerteti jelentőségét, fontosságát és szerepét az élelmiszer-ipari termékek segítségével. Ezután egy 630 fős megkérdezés eredményeképp a sokdimenziós skálázás (MDS) módszerével a jelölések új értelmezését mutatja be: a jelöléseket három dimenzió mentén lehet elhelyezni (előzetes tudás, érdek, megbízhatóság), valamint ezenkívül a jelölések öt homogén csoportot alkotnak (klasszikus, diétás, funkcionális, tudatos, előállítási). A téma jelentőségét az egészség és a környezet iránti növekvő érdeklődés, valamint a változó jogszabályi környezet is alátámasztja. / === / Signs, labels, claims are to inform consumers of product attributes, and are part of the packaging. Labeling is one of the most important marketing and management tool, while purchase decision is made at the point of purchase. The aim of this paper is to present the basic definitions and elements of information content on food packaging. The author developed a new approach to examine labeling using multidimensional scaling as a result of a pilot study. Labels are to distinguish through three dimensions: precognition, interest and reliability. Beyond that labels can be sorted to five homogeneous clusters based on classic, dietary, functional, conscious and production attributes. The relevancy of labeling is supported by growing interest of health and environmental issues and changing law environment.

Multidimensional Intrusion Detection System for IEC 61850 based SCADA Networks

Emerging cybersecurity vulnerabilities in supervisory control and data acquisition (SCADA) systems are becoming urgent engineering issues for modern substations. This paper proposes a novel intrusion detection system (IDS) tailored for cybersecurity of IEC 61850 based substations. The proposed IDS integrates physical knowledge, protocol specifications and logical behaviours to provide a comprehensive and effective solution that is able to mitigate various cyberattacks. The proposed approach comprises access control detection, protocol whitelisting, model-based detection, and multi-parameter based detection. This SCADA-specific IDS is implemented and validated using a comprehensive and realistic cyber-physical test-bed and data from a real 500kV smart substation.

Multiple Imputation of missing values in exploratory factor analysis of multidimensional scales: estimating latent trait scores

ABSTRACT Researchers frequently have to analyze scales in which some participants have failed to respond to some items. In this paper we focus on the exploratory factor analysis of multidimensional scales (i.e., scales that consist of a number of subscales) where each subscale is made up of a number of Likert-type items, and the aim of the analysis is to estimate participants' scores on the corresponding latent traits. We propose a new approach to deal with missing responses in such a situation that is based on (1) multiple imputation of non-responses and (2) simultaneous rotation of the imputed datasets. We applied the approach in a real dataset where missing responses were artificially introduced following a real pattern of non-responses, and a simulation study based on artificial datasets. The results show that our approach (specifically, Hot-Deck multiple imputation followed of Consensus Promin rotation) was able to successfully compute factor score estimates even for participants that have missing data.

Intelligent outcome measures in liaison psychiatry:essential even if not desirable. Commentary on... a multidimensional Framework for Routine Outcome Measurement in Liaison Psychiatry (FROM-LP)

Service development is guided by outcome measures that inform service commissioners and providers. Those in liaison psychiatry should be encouraged to develop a positive approach that integrates the collection of outcome measures into everyday clinical practice. The Framework for Routine Outcome Measurement in Liaison Psychiatry (FROM-LP) is a very useful tool to measure service quality and clinical effectiveness, using a combination of clinician-rated and patient-rated outcome measures and patient-rated experience measures. However, it does not include measures of cost-effectiveness or training activities. The FROM-LP is a significant step towards developing nationally unified outcome measures.

Trammel net catch species composition, catch rates and metiers in southern European waters: A multivariate approach

We identified and quantified the effect of season, depth, and inner and outer panel mesh size on the trammel net catch species composition and catch rates in four southern European areas (Northeast Atlantic: Basque Country, Spain; Algarve, Portugal; Gulf of Cadiz, Spain; Mediterranean: Cyclades, Greece), all of which are characterised by important trammel net fisheries. In each area, we conducted, in 1999-2000, seasonal, experimental fishing trials at various depths with trammel nets of six different inner/outer panel mesh combinations (i.e., two large outer panel meshes and three small inner panel meshes). Overall, our study covered some of the most commonly used inner panel mesh sizes, ranging from 40 to 140 mm (stretched). We analysed the species composition and catch rates of the different inner/outer panel combinations with regression, multivariate analysis (cluster analysis and multidimensional scaling) and other 'community' techniques (number of species, dominance curves). All our analyses indicated that the outer panel mesh sizes used in the present study did not significantly affect the catch characteristics in terms of number of species, catch rates and species composition. Multivariate analyses and seasonal dominance plots indicated that in Basque, Algarve and Cyclades waters, where sampling covered wide depth ranges, both season and depth strongly affected catch species compositions. For the Gulf of Cadiz, where sampling was restricted to depths 10-30 m, season was the only factor affecting catch species composition and thus group formation. In contrast, the inner panel mesh size did not generally affect multidimensional group formation in all areas but affected the dominance of the species caught in the Algarve and the Gulf of Cadiz. Multivariate analyses also revealed 11 different metiers (i.e., season-depth-species-inner panel mesh size combinations) in the four areas. This clearly indicated the existence of trammel net 'hot spots', which represent essential habitats (e.g., spawning, nursery or wintering grounds) of the life history of the targeted and associated species. The number of specimens caught declined significantly with inner panel mesh size in all areas. We attributed this to the exponential decline in abundance with size, both within- and between-species. In contrast, the number of species caught in each area was not related to the inner mesh size. This was unexpected and might be a consequence of the wide size-selective range of trammel nets. (c) 2006 Elsevier B.V All rights reserved.