Strength training (ST) has been studied for acute and chronic effects on blood pressure (BP) and heart rate variability (HRV). These effects have never been reviewed collectively concerning the variables that comprise a ST program. Therefore, this review aims to examine the manipulation of ST variables (i.e., load intensity, number of sets and repetitions, exercise order, and rest interval length) on BP and HRV after a session and a long-term program. The BP reduced significantly after an ST session independently of the load intensity, the number of sets and repetitions, the rest interval length, the mode, and the participant characteristic (healthy patients or patients with chronic disease). However, a high number of sets and repetitions, prioritizing multijoint exercises, with longer rest interval lengths between sets and exercises may potentiate these effects. In the HRV analyses, most of the trials showed a sympathetic predominance after an ST session. Hence, it is reasonable to confirm that central adjustments are responsible to control hemodynamics after an ST session.

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Abstract. In the context of the Competition on Legal Information Extraction/Entailment (COLIEE), we propose a method comprising the necessary steps for finding relevant documents to a legal question and deciding on textual entailment evidence to provide a correct answer. The proposed method is based on the combination of several lexical and morphological characteristics, to build a language model and a set of features for Machine Learning algorithms. We provide a detailed study on the proposed method performance and failure cases, indicating that it is competitive with state-of-the-art approaches on Legal Information Retrieval and Question Answering, while not needing extensive training data nor depending on expert produced knowledge. The proposed method achieved significant results in the competition, indicating a substantial level of adequacy for the tasks addressed.

Abstract. Natural language descriptors used for categorizations are present from folksonomies to ontologies. While some descriptors are composed of simple expressions, other descriptors have complex compositional patterns (e.g. 'French Senators Of The Second Empire', 'Churches Destroyed In The Great Fire Of London And Not Rebuilt'). As conceptual models get more complex and decentralized, more content is transferred to unstructured natural language descriptors, increasing the terminological variation, reducing the conceptual integration and the structure level of the model. This work describes a representation for complex natural language category descriptors (NLCDs). In the representation, complex categories are decomposed into a graph of primitive concepts, supporting their interlinking and semantic interpretation. A category extractor is built and the quality of its extraction under the proposed representation model is evaluated.

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