Analyses based on precipitation data may be limited by the quality of the data, the size of the available historical series and the efficiency of the adopted methodologies; these factors are especially limiting when conducting analyses at the daily scale. Thus, methodologies are sought to overcome these barriers. The objective of this work is to develop a hybrid model through the maximum overlap discrete wavelet transform (MODWT) to estimate daily rainfall in homogeneous regions of the Tocantins-Araguaia Hydrographic Region (TAHR) in the Amazon (Brazil). Data series from the Climate Prediction Center morphing (CMORPH) satellite products and rainfall data from the National Water Agency (ANA) were divided into seasonal periods (dry and rainy), which were adopted to train the model and for model forecasting. The results show that the hybrid model had a good performance when forecasting daily rainfall using both databases, indicated by the Nash–Sutcliffe efficiency coefficients (0.81–0.95), thus, the hybrid model is considered to be potentially useful for modelling daily rainfall.
Ďalšie vzdelávanie zdravotníckych pracovníkov je potrebné prakticky vo všetkých vedných odboroch zdravotníctva, nevynímajúc verejné zdravotníctvo. Na zabezpečenie kontinuity ďalšieho vzdelávania bolo potrebné stanoviť podmienky pre ďalšie vzdelávanie zdravotníckych pracovníkov v špecializačných odboroch v oblasti verejného zdravotníctva. Vzhľadom na danú skutočnosť bolo nevyhnutné pristúpiť k vypracovaniu a následnému schváleniu právnych predpisov a ďalších dokumentov týkajúcich sa ďalšieho vzdelávania zdravotníckych pracovníkov. Tieto právne predpisy a ďalšie dokumenty sa počas rokov postupne dopĺňali a novelizovali. Ďalšie vzdelávanie zdravotníckych pracovníkov na Slovensku v súčasnosti upravuje Nariadenie vlády SR č. 296/2010 Z. z. o odbornej spôsobilosti na výkon zdravotníckeho povolania, spôsobe ďalšieho vzdelávania zdravotníckych pracovníkov, sústave špecializačných odborov a sústave certifikovaných pracovných činností s účinnosťou od 1. júla 2010 a odborne a metodicky ho riadi Ministerstvo zdravotníctva SR. Toto vzdelávanie zahŕňa špecializačné štúdium, prípravu na výkon práce v zdravotníctve a sústavné vzdelávanie., Ongoing education of health professionals is needed in all health disciplines, including public health. To ensure continuity of further education in health professionals it was necessary to establish several conditions for the further education of health professionals in specialized areas of public health. In view of all that, it was necessary to elaborate and approve legislation and other documents relating to the continuing education of health professionals. That legislation and other documents have been complemented and revised over the years. Currently, further education of health professionals in Slovakia is governed by Government Regulation No. 296/2010 Coll. on professional competence to perform the medical profession, on the method of further education of health professionals, on the system of specialized disciplines and on the system of certifited work activities with effectiveness from 1 July 2010, and is professionally and methodically managed by the Ministry of Health. This additional training in the field of public health includes specialization studies, preparation for work in health care and continuing education., Daniela Mihinová, Mário Ležovič, Jana Babjaková, Andrej Kováč Jana Boledovičová, and Literatura 10
We present DaMuEL, a large Multilingual Dataset for Entity Linking containing data in 53 languages. DaMuEL consists of two components: a knowledge base that contains language-agnostic information about entities, including their claims from Wikidata and named entity types (PER, ORG, LOC, EVENT, BRAND, WORK_OF_ART, MANUFACTURED); and Wikipedia texts with entity mentions linked to the knowledge base, along with language-specific text from Wikidata such as labels, aliases, and descriptions, stored separately for each language. The Wikidata QID is used as a persistent, language-agnostic identifier, enabling the combination of the knowledge base with language-specific texts and information for each entity. Wikipedia documents deliberately annotate only a single mention for every entity present; we further automatically detect all mentions of named entities linked from each document. The dataset contains 27.9M named entities in the knowledge base and 12.3G tokens from Wikipedia texts. The dataset is published under the CC BY-SA licence.