Predicting Inflow Flow in Hydraulic Dams Using Artificial Neural Networks

Cursos e Congresos, C-155[Abstract] Accurate prediction of inflow in dams plays a crucial role in water resource management Kim et al. (2019); Vargas-Garay et al. (2018); Zhong et al. (2018) and risk mitigation Costabile et al. (2020); Rabuñal et al. (2007). This study focuses on the Portodemouros dam (located between the provinces of A Coruña and Pontevedra), where a model based on a Long Short-Term Memory (LSTM) artificial neural network has been implemented to predict dam inflow. The results demonstrate the well-established effectiveness of the LSTM network in flow prediction Dongkyuna and Seokkoob (2021); Jo and Jung (2023); Li et al. (2020) applied to the Portodemouros dam compared to other models. This comparison has already been performed in other studies with both mathematical models Amirreza et al. (2022); Ansori and Anwar (2022); A.R1 et al. (2018); Beck et al. (2017); Ciabatta et al. (2016); Costabile et al. (2020); Fan et al. (2013); Hermanovsky et al. (2017); Kim et al. (2019); Vargas-Garay et al. (2018); Zhong et al. (2018), genetic programming Aytek et al. (2008); Havl´ıˇcek et al. (2013); Heˇrmanovsk´y et al. (2017); Rabuñal et al. (2007) and other machine learning algorithms Jo and Jung (2023). Combining precipitation data from multiple regions and meteorological forecasts significantly enhances the model’s ability to anticipate variations in dam inflow. This improved accuracy is essential for early flood detection and informed decision-making in dam operation. This study forms part of the Marine Science programme (ThinkInAzul) supported by Ministerio de Ciencia e Innovación and Xunta de Galicia with funding from European Union NextGenerationEU (PRTR-C17.I1) and European Maritime and Fisheries Fun

GA-Based Data Mining Applied to Genetic Data for the Diagnosis of Complex Diseases

Nature provides inspiration and guidance in the creation of techniques, applications and new technologies in the fields of Artificial Intelligence and Soft Computing. Soft Computing Methods for Practical Environment Solutions: Techniques and Studies presents various practical applications of Soft Computing techniques in real-world situations and problems, aiming to show the enormous potential of such techniques in solving all kinds of problems, and thus, providing the latest advances in these techniques in an extensive state-of-the-art and a vast theoretical study. Ideal for students studying AI and researchers familiarizing themselves with such techniques, so to offer recent and novel applications, helping expand and explore new areas of research. Nature provides inspiration and guidance in the creation of techniques, applications and new technologies in the fields of Artificial Intelligence and Soft Computing. Soft Computing Methods for Practical Environment Solutions: Techniques and Studies presents various practical applications of Soft Computing techniques in real-world situations and problems, aiming to show the enormous potential of such techniques in solving all kinds of problems, and thus, providing the latest advances in these techniques in an extensive state-of-the-art and a vast theoretical study. Ideal for students studying AI and researchers familiarizing themselves with such techniques, so to offer recent and novel applications, helping expand and explore new areas of research

COVID-19 in hospitalized HIV-positive and HIV-negative patients : A matched study

CatedresObjectives: We compared the characteristics and clinical outcomes of hospitalized individuals with COVID-19 with [people with HIV (PWH)] and without (non-PWH) HIV co-infection in Spain during the first wave of the pandemic. Methods: This was a retrospective matched cohort study. People with HIV were identified by reviewing clinical records and laboratory registries of 10 922 patients in active-follow-up within the Spanish HIV Research Network (CoRIS) up to 30 June 2020. Each hospitalized PWH was matched with five non-PWH of the same age and sex randomly selected from COVID-19@Spain, a multicentre cohort of 4035 patients hospitalized with confirmed COVID-19. The main outcome was all-cause in-hospital mortality. Results: Forty-five PWH with PCR-confirmed COVID-19 were identified in CoRIS, 21 of whom were hospitalized. A total of 105 age/sex-matched controls were selected from the COVID-19@Spain cohort. The median age in both groups was 53 (Q1-Q3, 46-56) years, and 90.5% were men. In PWH, 19.1% were injecting drug users, 95.2% were on antiretroviral therapy, 94.4% had HIV-RNA < 50 copies/mL, and the median (Q1-Q3) CD4 count was 595 (349-798) cells/μL. No statistically significant differences were found between PWH and non-PWH in number of comorbidities, presenting signs and symptoms, laboratory parameters, radiology findings and severity scores on admission. Corticosteroids were administered to 33.3% and 27.4% of PWH and non-PWH, respectively (P = 0.580). Deaths during admission were documented in two (9.5%) PWH and 12 (11.4%) non-PWH (P = 0.800). Conclusions: Our findings suggest that well-controlled HIV infection does not modify the clinical presentation or worsen clinical outcomes of COVID-19 hospitalization

How do women living with HIV experience menopause? Menopausal symptoms, anxiety and depression according to reproductive age in a multicenter cohort

CatedresBackground: To estimate the prevalence and severity of menopausal symptoms and anxiety/depression and to assess the differences according to menopausal status among women living with HIV aged 45-60 years from the cohort of Spanish HIV/AIDS Research Network (CoRIS). Methods: Women were interviewed by phone between September 2017 and December 2018 to determine whether they had experienced menopausal symptoms and anxiety/depression. The Menopause Rating Scale was used to evaluate the prevalence and severity of symptoms related to menopause in three subscales: somatic, psychologic and urogenital; and the 4-item Patient Health Questionnaire was used for anxiety/depression. Logistic regression models were used to estimate odds ratios (ORs) of association between menopausal status, and other potential risk factors, the presence and severity of somatic, psychological and urogenital symptoms and of anxiety/depression. Results: Of 251 women included, 137 (54.6%) were post-, 70 (27.9%) peri- and 44 (17.5%) pre-menopausal, respectively. Median age of onset menopause was 48 years (IQR 45-50). The proportions of pre-, peri- and post-menopausal women who had experienced any menopausal symptoms were 45.5%, 60.0% and 66.4%, respectively. Both peri- and post-menopause were associated with a higher likelihood of having somatic symptoms (aOR 3.01; 95% CI 1.38-6.55 and 2.63; 1.44-4.81, respectively), while post-menopause increased the likelihood of having psychological (2.16; 1.13-4.14) and urogenital symptoms (2.54; 1.42-4.85). By other hand, post-menopausal women had a statistically significant five-fold increase in the likelihood of presenting severe urogenital symptoms than pre-menopausal women (4.90; 1.74-13.84). No significant differences by menopausal status were found for anxiety/depression. Joint/muscle problems, exhaustion and sleeping disorders were the most commonly reported symptoms among all women. Differences in the prevalences of vaginal dryness (p = 0.002), joint/muscle complaints (p = 0.032), and sweating/flush (p = 0.032) were found among the three groups. Conclusions: Women living with HIV experienced a wide variety of menopausal symptoms, some of them initiated before women had any menstrual irregularity. We found a higher likelihood of somatic symptoms in peri- and post-menopausal women, while a higher likelihood of psychological and urogenital symptoms was found in post-menopausal women. Most somatic symptoms were of low or moderate severity, probably due to the good clinical and immunological situation of these women