Publications by authors named "L Ortega"
The stable isotope composition of meteoric water has been widely used to understand hydrological processes worldwide. We present a unique dataset, with the isotopic composition (δO and δH) of meteoric waters, derived from a nationwide study in Cuba. It includes monthly composite and event-based precipitations, from January 2017 to December 2021 (N = 526 and N = 111 respectively).
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- * The ECRB was surgically tenodesed (attached) to the extensor carpi radialis longus to address the rupture.
- * Post-surgery, the patient was able to return to gardening just two weeks later and maintained good function and pain relief at a 13-month follow-up, highlighting a successful surgical outcome for a rare complication.
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- The study evaluated how effective a screening program for liver fibrosis is in reducing alcohol consumption among individuals with alcohol use disorder (AUD) at the Hospital Clinic of Barcelona.
- Out of 334 individuals screened, 45% were abstinent from alcohol after 6 months, significantly higher than the 29% abstinence rate in a control group.
- Key factors promoting abstinence included receiving medication for AUD, no illicit drug use, and participation in the screening program, highlighting the benefits of early diagnosis and lifestyle counseling.
Background: Chronotropic incompetence (ChI) is linked with diminished exercise capacity in heart failure with preserved ejection fraction (HFpEF). Although exercise training has shown potential for improving functional capacity, the exercise modality associated with greater functional and chronotropic response (ChR) is not well-known. Additionally, how the ChR from different exercise modalities mediates functional improvement remains to be determined.
View Article and Find Full Text PDFThis article presents a comprehensive collection of formulas and calculations for hand-crafted feature extraction of condition monitoring signals. The documented features include 123 for the time domain and 46 for the frequency domain. Furthermore, a machine learning-based methodology is presented to evaluate the performance of features in fault classification tasks using seven data sets of different rotating machines.
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