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

Avaliação de variáveis que influenciam o processo de aglomeração de rolhas técnicas de cortiça

O sector da cortiça tem uma grande importância na indústria portuguesa. A Corticeira Amorim é a líder mundial deste sector, apostando de forma contínua no melhoramento dos seus processos e produtos. A cortiça apresenta diversas aplicações, das quais a mais rentável é a rolha natural, seguida da rolha técnica. A presente dissertação foi realizada na Unidade Industrial Equipar em Coruche, pertencente à Amorim&Irmãos e foca-se na avaliação de variáveis que possam influenciar o processo de aglomeração de rolhas técnicas de cortiça. Uma rolha mal aglomerada poderá não cumprir com o seu papel fundamental enquanto vedante, pôr em caus um lote, a empresa e inclusive o sector que depende deste tipo de produtos técnicos para sobreviver. Numa primeira fase efetuou-se uma familiarização com o processo de aglomeração por forma a identificar os fatores críticos que resultam no problema/efeito de má aglomeração em rolhas técnicas de cortiça com o auxílio de uma ferramenta da qualidade, a Análise dos Modos de Falha e Efeitos (AMFE). Numa segunda fase desenvolveram-se dois Desenhos de Experiências (DOE). Foram testadas condições extremas de humidade superficial do granulado e de temperatura, condições essas que podem ser atingidas no processo de extrusão ao longo do ano. As variáveis que influenciam o processo de aglomeração são a temperatura de extrusão, a humidade do granulado, a quantidade de parafina e a idade da mistura (fatores controláveis que são potencialmente influentes para a má aglomeração). O primeiro DOE permitiu perceber que o efeito de má aglomeração está relacionado com a combinação das variáveis do processo: temperatura de extrusão e humidade do granulado. Após realização do segundo DOE a solução encontrada para que não ocorra má aglomeração passa por não utilizar granulado com humidade abaixo de 6% e acima de 11%, e utilizar temperaturas entre 110ºC-130ºC. O trabalho apresentado é fruto de muitas horas de esforço, dedicação e entrega. Para aqui chegar foi necessário abdicar, muitas vezes, da vida pessoal, tempo de descanso, férias, entre outros. Mas sempre soube que no final iria valer a pena

Improving quality of electronic health records with SNOMED

The use of classifications, standards and terminology proves to be of particular importance to classify therapist techniques, clinical and nursing procedures and formulate diagnoses. This work shows a different implementation than usual, the application developed doesn’t depend of platform of medical record. The Department of pathological anatomy was chosen as a pilot service to enter the SNOMED system, to produce reports and to evaluate the benefits of its use in a real context. The successful implementation is directly related to the success of interoperability between information and the use of electronic health record systems. It is the first step to extend the system to the whole hospital and to the success of clinical research, to provide alerts and prevention systems and reduce medical errors

Prediction of nosocomial infections associated with surgical interventions

Nosocomial infections represent an ongoing challenge to healthcare quality and patient safety, negatively impacting clinical outcomes and increasing the burden on healthcare systems. Thus, controlling this type of infection plays a very important role in ensuring a better quality of life for patients. Although the control and prevention measures for these infections are well defined, their signaling and detection is carried out manually and sometimes late, which compromises the health status of patients and everyone around them. In this context, this study emerged with the aim of exploring the potential of data mining techniques to predict the occurrence of nosocomial infections, with a specific focus on infections associated with surgical interventions. Using datasets for the period between 2018 and 2022, sourced from a Portuguese hospital and duly anonymized to protect patient privacy, several classification algorithms and data balancing techniques were analyzed to deal with the uneven nature of the data and the presence of minority classes. Among the algorithms and balancing techniques used, it was found that the Random Forest algorithm combined with the Oversampling technique showed superior performance in identifying cases of nosocomial infections associated with surgical interventions. The results of this study highlight the importance of collaboration between medicine and technology, indicating that the integration of data mining techniques can prove to be valuable tools to improve clinical decision-making and infection management in surgical context.This research was funded by Fundação para a Ciência e Tecnologia, within the Project Scope: UIDB/00319/2020

Nasal Skin Necrosis: A Very Rare Manifestation of Antiphospholipid Syndrome

A 23-year-old woman, a smoker and oral oestrogen user, presented with nasal necrosis. No other symptoms or local trauma were described. Relevant laboratory findings included complement consumption, positive lupus-anticoagulant assay, increased rheumatoid factor and positive cryoglobulins. Screening for autoimmune conditions, haematological malignancies and infectious diseases was negative. Histological examination of the nasal skin showed small vessel occlusion without vasculitis. Later, a second positive lupus-anticoagulant assay supported the diagnosis of antiphospholipid syndrome. The patient improved with glucocorticoids and anticoagulation. This case report describes an unusual manifestation of antiphospholipid syndrome in a patient with cryoglobulinaemia contributing to the thrombotic event and highlights the importance of recognizing these overlapping disorders

Semi-invasive aspergillosis in an immunocompetent patient with Swyer-James-MacLeod Syndrome: a case report

<p>Abstract</p> <p>Introduction</p> <p>Invasive and semi-invasive pulmonary aspergillosis usually occurs in immunocompromised patients. It has been described occasionally in patients with normal immunity and previous lung disease such as chronic obstructive pulmonary disease.</p> <p>Swyer-James-MacLeod Syndrome is a rare condition characterized by hyperlucency of one lung, lobe or part of a lobe due to decreased vascularity and air trapping.</p> <p>Case presentation</p> <p>We report a case of semi-invasive pulmonary aspergillosis in a 38-year-old Portuguese, Caucasian man who is immunocompetent, with a pre-existing Swyer-James-McLeod Syndrome, a structural lung disease.</p> <p>Conclusions</p> <p>To the best of our knowledge, this is the first reported case in the literature on the relationship between these two diseases. Although rare, aspergillosis can occur in immunocompetent adults with a pre-existing lung disease other than chronic obstructive pulmonary disorder.</p

Doenças neuromusculares: rediscutindo o overtraining

Centro Universitário Augusto MottaUniversidade de São Paulo School of Medicine Department of Physical TherapyUniversidade Federal FluminenseUniversidade Federal de São Paulo (UNIFESP) Neurology DepartmentUNIFESP, Neurology DepartmentSciEL