Occupational health and safety and older workers: are they in need of a special legal framework?

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Assessing the Performance of the Gsimcli Homogenisation Method with Precipitation Monthly Data from the COST-HOME Benchmark

Ribeiro, S., Caineta, J., & Costa, A. C. (2017). Assessing the Performance of the Gsimcli Homogenisation Method with Precipitation Monthly Data from the COST-HOME Benchmark. In J. J. Gómez-Hernández, J. Rodrigo-Ilarri, E. Cassiraga, M. E. Rodrigo-Clavero, & J. A. Vargas-Guzmán (Eds.), Geostatistics Valencia 2016 (pp. 909-918). (Quantitative Geology and Geostatistics; Vol. 16). Springer. DOI: 10.1007/978-3-319-46819-8_63 ----------------------------- The authors gratefully acknowledge the financial support of Fundação para a Ciência e Tecnologia (FCT), Portugal, through the research project PTDC/GEO-MET/4026/2012 (“GSIMCLI – Geostatistical simulation with local distributions for the homogenization and interpolation of climate data”).Nowadays, climate data series are used in so many different studies that their importance implies the essential need of good data quality. For this reason, the process of homogenisation became a hot topic in the last decades, and many researchers have focused on developing efficient methods for the detection and correction of inhomogeneities in climate data series. This study evaluates the efficiency of the gsimcli homogenisation method, which is based on a geostatistical simulation approach. For each instant in time, gsimcli uses the direct sequential simulation algorithm to generate several equally probable realisations of the climate variable at the candidate station’s location, disregarding its values. The probability density function estimated at the candidate station’s location (local probability density functions (PDF)), for each instant in time, is then used to verify the existence of inhomogeneities in the candidate time series. When an inhomogeneity is detected, that value is replaced by a statistical value (correction parameter) derived from the estimated local PDF. In order to assess the gsimcli efficiency with different implementation strategies, we homogenised monthly precipitation data from an Austrian network of the COST-HOME benchmark data set (COST Action ES0601, Advances in homogenization methods of climate series: an integrated approach – HOME). The following parameters were tested: grid cell size, candidate order in the homogenisation process, local radius parameter, detection parameter and correction parameter. Performance metrics were computed to assess the efficiency of gsimcli. The results show the high influence of the grid cell size and of the correction parameter in the method’s performance.authorsversionpublishe

Notas sobre o ónus da prova e danos morais no assédio: caminhos a desbravar

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Análise ao artigo 22.º da CSE(R) Art. 22º da CSE ‐ Direito de tomar parte na determinação e na melhoria das condições de trabalho e do meio de trabalho

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The european committee of social rights’ audacity in protecting occupational health and safety

Occupational health and safety is directly foreseen by the art. 3 of the European Social Charter (ESC), even though it is also indirectly covered by other norms that regulate the right to just working conditions, protect children and young persons’ work, maternity of employed women and health, and assure the workers’ involvement in the fixing and improving of the working conditions and environment, and determine the right to dignity at work – articles 2 (4), 7, 8, 11, 22 (b) and 26 of the ESC. These norms have been interpreted in a constructive and dignifying way by the European Committee of Social Rights, through the issuing of conclusions subsequent to the reports presented by the States that signed the Charter, as well as through the decisions reported under the collective complaints’ system.info:eu-repo/semantics/publishedVersio

Reflexões sobre o ónus da prova e danos morais no tratamento jurídico do assédio em Portugal

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Riscos psicossociais e Covid-19: o renascimento da saúde e segurança no trabalho

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A utilização do RECOVSAT na análise de reclamações e recuperações de serviço

Dissertação para obtenção do Grau de Mestre em Engenharia e Gestão IndustrialNo âmbito dos Serviços, nomeadamente na área da Qualidade em serviços, o presente trabalho procurou aplicar uma metodologia, baseada no RECOVSAT de Boshoff et al., (2005), instrumento de medição da satisfação na recuperação de serviço. O instrumento de recolha de dados utilizado foi o inquérito por questionário, realizado através de entrevistas individuais e por divulgação on-line. Os serviços são fundamentais para uma sociedade e parte integrante desta, e as organizações (empresas que prestam serviços), sabem que é impossível manter uma relação com os clientes se estes estiverem insatisfeitos. Desta forma, a análise de reclamações e recuperações de serviço poderão auxiliar a identificar problemas que dificultam a subsistência destas relações. O desenvolvimento prático deste trabalho assentou sobre análises a reclamações reais e respectivas recuperações de serviço, sendo este realizado em duas fases. Inicialmente, o objectivo passou por perceber quais os tipos de serviço mais reclamados, a respectiva razão da reclamação e o tempo médio da sua resolução. Posteriormente, o estudo foi conduzido de modo a perceber quais os factores mais importantes na recuperação de serviço, e finalmente, foi analisado o peso de tais factores na Satisfação global, Intenção de recompra e Recomendação. É importante esclarecer que a adaptação do RECOVSAT não foi utilizada para medir a satisfação com a recuperação de serviços entre os consumidores que apresentaram uma queixa a um prestador de serviços específico, tal como aconteceu noutros estudos, mas sim, questionar o maior número de pessoas relativamente a situações de comportamento reclamante de um modo geral

Analysing the detection and correction parameters in the homogenisation of climate data series using gsimcli

Ribeiro, S., Caineta, J., Costa, A. C., & Henriques, R. (2015). Analysing the detection and correction parameters in the homogenisation of climate data series using gsimcli. In M. Painho, & F. Bação (Eds.), 18th AGILE International Conference on Geographic Information ScienceHomogenisation of climate data series is the process of detection and correction of non-natural irregularities present in the data. Such process is extremely important due to the use of climate data in many hydrological and environmental projects. Several homogenisation methods have been developed in the last decades. In the geostatistical field, studies already showed an approach based on the direct sequential simulation algorithm as a very promising technique for the detection and correction of irregularities. This approach, called gsimcli, uses the probability distribution function (estimated from simulated values) to identify the presence of irregularities, with a specific probability p. The correction of the identified irregularity can be done through the replacement of that value by a given percentile value of the probability distribution function. The present work depicts an analysis undertaken in order to assess two parameters, the probability p of detection and the percentile for correction in the homogenisation using gsimcli. Two networks of the HOME benchmark data set were used and the performance metrics were calculated to compare this analysis with other homogenisation methods. Results show gsimcli as a favourable homogenisation method for monthly precipitation data, and reveal the most efficient detection and correction parameters for the homogenisation procedure.publishersversionpublishe

Establishment of Detection and Correction Parameters for a Geostatistical Homogenisation Approach

Ribeiro, S., Caineta, J., Costa, A. C., & Soares, A. (2015). Establishment of Detection and Correction Parameters for a Geostatistical Homogenisation Approach. Procedia Environmental Sciences, 27, 83-88. https://doi.org/10.1016/j.proenv.2015.07.115Abstract Non-natural irregularities are an inevitable part of long-time climate records. They are originated during the process of measuring and collecting data from weather stations. In order to use those records as an input for environmental projects or climate studies, it is essential to detect and correct the irregularities through the process of homogenisation. The use of geostatistical approaches as homogenisation techniques has already been proven to be successful. The gsimcli homogenisation process is based on a geostatistical simulation method, the direct sequential simulation. This method generates a set of equally probable and independent realisations, and calculates a probability distribution function at the candidate station's location. This probability distribution function is then used in the identification and correction of irregularities. Currently, gsimcli is being developed into an open source software package. During the homogenisation process, gsimcli requires the selection of several parameters in the detection and correction of irregularities. The candidate stations’ order to be homogenised, the value of the probability used in the detection of irregularities, and the statistic value to be used in the correction of the irregularity or in the replacement of missing data, are examples of parameters to be chosen for the homogenisation with gsimcli. This work presents a sensitivity analysis of those parameters, in order to find the most suitable set of values for the homogenisation of monthly precipitation data. A benchmark data set, comprising climate records from an Austrian precipitation network, will be used in this analysis. Performance metrics are calculated to evaluate the efficiency of the homogenisation process. The set of parameters providing the best values of performance metrics will be defined as the default set of homogenisation parameters for precipitation data.publishersversionpublishe