Artículo de publicación ISIIn this article, a new bidirectional tuned liquid column damper (BTLCD) is proposed for controlling the seismic response of structures. The device acts as two independent and orthogonal tuned liquid column dampers (TLCDs), but due to its configuration, it requires less liquid than two equivalent independent TLCDs. The equations of motion of the system formed by the BTLCD and the primary structure to be controlled are obtained by means of Lagrangian dynamics explicitly considering the non-symetrical action of the damping forces. First, the primary structure was assumed to have two degrees of freedom (DOFs). Assuming that the system is excited by a base acceleration that can be considered to be a white noise random process, the optimum design parameters of the device were obtained to minimise the response of the primary structure. The optimum design parameters are presented as expressions covering a wide range of possible configurations for the device in a controlled structure.The use of a BTLCD to control the seismic response of several DOF structures was also studied, showing that if the structural response occurs mainly in two perpendicular modes, then the optimum design parameters for two DOF structures can be used. Experimental analyses of the BTLCD are developed in order to verify its dynamical properties. Finally, the device is designed for controlling the seismic response of a sixDOF scalemodel.Numerical analyses are developed in order to verify the effectiveness and accuracy of the equations and design procedures proposed herein

In this work we describe the Waiting List Corpus consisting of de-identified referrals for several specialty consultations from the waiting list in Chilean public hospitals. A subset of 900 referrals was manually annotated with 9,029 entities, 385 attributes, and 284 pairs of relations with clinical relevance. A trained medical doctor annotated these referrals, and then together with other three researchers, consolidated each of the annotations. The annotated corpus has nested entities, with 32.2% of entities embedded in other entities. We use this annotated corpus to obtain preliminary results for Named Entity Recognition (NER). The best results were achieved by using a biLSTM-CRF architecture using word embeddings trained over Spanish Wikipedia together with clinical embeddings computed by the group. NER models applied to this corpus can leverage statistics of diseases and pending procedures within this waiting list. This work constitutes the first annotated corpus using clinical narratives from Chile, and one of the few for the Spanish language. The annotated corpus, the clinical word embeddings, and the annotation guidelines are freely released to the research community.

Here we describe a new clinical corpus rich in nested entities and a series of neural models to identify them. The corpus comprises de-identified referrals from the waiting list in Chilean public hospitals. A subset of 5,000 referrals (58.6% medical and 41.4% dental) was manually annotated with 10 types of entities, six attributes, and pairs of relations with clinical relevance. In total, there are 110,771 annotated tokens. A trained medical doctor or dentist annotated these referrals, and then, together with three other researchers, consolidated each of the annotations. The annotated corpus has 48.17% of entities embedded in other entities or containing another one. We use this corpus to build models for Named Entity Recognition (NER). The best results were achieved using a Multiple Single-entity architecture with clinical word embeddings stacked with character and Flair contextual embeddings. The entity with the best performance is abbreviation , and the hardest to recognize is finding . NER models applied to this corpus can leverage statistics of diseases and pending procedures. This work constitutes the first annotated corpus using clinical narratives from Chile and one of the few in Spanish. The annotated corpus, clinical word embeddings, annotation guidelines, and neural models are freely released to the community.

Word embeddings have been widely used in Natural Language Processing (NLP) tasks. Although these representations can capture the semantic information of words, they cannot learn the sequence-level semantics. This problem can be handled using contextual word embeddings derived from pre-trained language models, which have contributed to significant improvements in several NLP tasks. Further improvements are achieved when pretraining these models on domain-specific corpora. In this paper, we introduce Clinical Flair, a domain-specific language model trained on Spanish clinical narratives. To validate the quality of the contextual representations retrieved from our model, we tested them on four named entity recognition datasets belonging to the clinical and biomedical domains. Our experiments confirm that incorporating domain-specific embeddings into classical sequence labeling architectures improves model performance dramatically compared to general-domain embeddings, demonstrating the importance of having these resources available.

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